2022
Greetings, 

On behalf of the Director's Office, it is my pleasure to welcome you to the 2022 SURF Colloquium. For the second 

year in a row, colloquiums are 100% virtual. 

Founded by scientist in the Physics Laboratory (PL) with a passion for STEM outreach, the SURF Program

has grown immensely since its establishment in 1993. The first cohort of the SURF Program consisted of

20 participants from 8 universities primarily conducting hands-on research in the physics lab.

Representing all STEM disciplines, this summer's cohort of the SURF Program includes 156 participants

from 100 universities engaging in research projects remotely sponsored by the Boulder, CO and Gaithersburg, MD 

sites. This the first time that participants from both campuses have participated in the all activities collectively 

including the Colloquium. It's expected that the program will continue to grow in the future to include a virtual 

component while maintaining the in-person component. The nation’s workforce is changing, and we must adapt.

During your attendance at the SURF Colloquium, I encourage you to interact with the SURF participants.

In a virtual format, you may think the interactions are limited. Like the in-person program, there will be a Q & A 

period after each presentation. During this time, you’re encouraged to ask questions using the chat feature, raise 

hand feature, or simply unmute yourself and verbally asking the question. 

The colloquium is the perfect venue to exchange findings and new ideas from the most recent and rigorous 

research in all STEM fields.

Furthermore, I suggest chatting with NIST staff and scientist after the colloquium. Don't be afraid to ask

questions about the on-going research in a specific NIST laboratory. Most staff and scientist love to talk

about their role or research at NIST.

Moreover, I invite you to share your colloquium experience on the National Institute of

Standards and Technology (NIST) Facebook page using the hashtag, #2022SURFProgram. 

Lastly, I could not conclude this letter without mentioning the individuals which make the SURF Program

possible. Thank you to the Lab SURF Directors, the SURF mentors, administrative staff, OISM, and all the staff who 

play an integral role in making the SURF participants experience valuable. Also, thank you to the participants, their 

families and friends, and the ambassadors who spread the word about SURF. Your hard work and support are

greatly appreciated.

Again, welcome to the Colloquium. I'm glad that you are spending your time with us and I hope you learn 

something new about the nation’s standards laboratory.

Best regards,

Brandi Toliver, Ph.D.

NIST SURF Program Managing Director

2022 SURF Colloquium 1
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2022 SURF Colloquium 2
NIST SURF Program Team

Organizational Unit (OU) Name

Director's Office Brandi Toliver

Director's Office Kara Robinson

Communications Technology Lab Linda Derr

Communications Technology Lab Wesley Garey

Communications Technology Lab David Griffith

Engineering Lab Cartier Murrill

Engineering Lab Shonali Nazare

Information Technology Lab Lotfi Benmohamed

Information Technology Lab Timothy Burns

Information Technology Lab Yolanda Bursie

Information Technology Lab Michaela Iorga

Information Technology Lab Derek Juba

Information Technology Lab Annie Sokol

Material Measurement Lab Ilse Bercik

Material Measurement Lab Tom Forbes

Material Measurement Lab Katherine Gettings

Material Measurement Lab Nathan Mahynski

Material Measurement Lab Jessica Staymates

NIST Center for Neutron Research Julie A. Borchers

NIST Center for Neutron Research Joseph Dura

NIST Center for Neutron Research Susana Teixeira

Physical Measurement Lab Uwe Arp

Physical Measurement Lab Michael Berilla

Physical Measurement Lab Maritoni Litorja

Physical Measurement Lab Matthew Pufall

Physical Measurement Lab Richard Steiner

Physical Measurement Lab Thomas “Mitch” Wallis

Advance Manufacturing Lisa Fronczek

2022 SURF Colloquium 3
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2022 SURF Colloquium 4
Table of Contents

SURF Colloquium: Tuesday, August 2, 2022 (Morning) .................................................................................6 

Plenary Session: https://primetime.bluejeans.com/a2m/live-event/sgsskrrx 

SURF Colloquium: Tuesday, August 2, 2021 (Afternoon) ..............................................................................7 

Parallel Sessions: Engineering Laboratory https://bluejeans.com/351211267/0730 

Parallel Sessions: Information Technology Laboratory https://bluejeans.com/946424079/6073 

Parallel Sessions: Material Measurement Laboratory https://bluejeans.com/544223532/3099 

SURF Colloquium: Wednesday, August 3, 2021 (Morning) ........................................................................10

Parallel Sessions: Engineering Laboratory https://bluejeans.com/351211267/0730 

Parallel Sessions: Information Technology Laboratory https://bluejeans.com/946424079/6073

Parallel Sessions: Material Measurement Laboratory https://bluejeans.com/544223532/3099

SURF Colloquium: Wednesday, August 3, 2021 (Afternoon) .....................................................................11

Parallel Sessions: Physical Measurement Laboratory https://bluejeans.com/961384113/4619 

Parallel Sessions: Communication Technology Laboratory https://bluejeans.com/435534290/0420 

Parallel Sessions: NIST Center for Neutron Research https://bluejeans.com/964520725/2477 

SURF Colloquium: Thursday, August 4, 2021 (Morning) ............................................................................13

Parallel Sessions: Physical Measurement Laboratory https://bluejeans.com/961384113/4619

Student Abstracts by OU (Alphabetical, Student Last Name) ....................................................................15

2022 SURF Colloquium 5
Summer Undergraduate Research Fellowship (SURF) ColloquiumPlenary Session

Tuesday, August 2, 2022 

Session Link: https://primetime.bluejeans.com/a2m/live-event/sgsskrrx 

Time (EDT) Session Information

10:00 AM Welcome (Jim Olthoff)

Session Moderator

10: 15 AM Communication Technology Laboratory (CTL)

Ovidio Castillo

Simulating the Impact of Manufacturing System Configurations on 

Production & Quality

10:30 AM Engineering Laboratory (EL)

Catherine McCarthy

Ion specificity and the Hofmeister series in charged colloidal systems

10:50 AM Information Technology Laboratory (ITL)

Ivy Liang

Interactive Online Histogram-Based Visualization of AI Model Fingerprints

11:10 AM Material Measurement Laboratory (MML)

Yemi (Christine)Ogunsula

Building and Expanding the Drug DART-MS Forensics Database with 

Automated Tools

11:30 AM NIST Center for Neutron Research (NCNR)

Jack Rooks

Particle orientation in soft materials from Small Angle Neutron Scattering

11:50 AM Physical Measurement Laboratory (PML)

Lucy Yagodich

Interconnecting and Controlling In-Operando Spectroscopic 

Measurements

2022 SURF Colloquium 6
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Tuesday, August 2, 2022 (1:00 to 2:30 PM (EDT)) 

Engineering Laboratory (EL) Information Technology Laboratory (ITL) Material Measurement Laboratory (MML)

Moderator: Helen Qiao Moderator: Tim Burns Moderator: Kal Migler

Time Virtual Link: https://bluejeans.com/ 351211267/ 0730 Virtual Link: https://bluejeans.com/ 946424079/ 6073 Virtual Link: https://bluejeans.com/ 544223532/ 3099

1:00 PM Ainsley Rexford

Smarter Manufacturing: How images can 

help monitor thermal drift of machine tools

Mitchell Campbell

Enhanced Viewing of 3D Objects Scanned using 

Photogrammetry

Ryan Puthumana 

Optimization of Clustering Algorithms for Small 

Angle Scattering of Polymer Formulations

1:15 PM Alex Dai

Integration of Neural Networks to Aid in 

Identifying Flame Temperatures 

Jason Eveleth

High-dimensional consensus mass spectra 

comparison

Dennis Zhao

Analyzing Ternary Phase Diagrams Using Machine 

Learning

1:30 PM Arin Zeng

Optimizing Functionality of the Smart Target 

Data Collection System

Ayush Varshney

Optimizing Data Communication for Low Latency 

Quantum Network Metrology

Richard Ma

Improving robustness of active learning to 

accelerate XRD measurements of metal alloys 

using NMF

1:45 PM Diego Pantelis 

Semantic Models for Building Systems

Mikhail Krepets

Creating an Algorithm for Searching RNGs to Link 

with Test Results

Maggie Hampson

Finite Element Modeling For Hydrogen 

Transportation & Storage

2:00 PM Dominique Paige 

Development & Analysis of a Database of 

Tornado Impacts on Fire & Rescue Stations

Miguel Lopez

Translating Mathematica Source Code to a 

Presentable LaTeX Format

Aidan Knab

Machine Learning Automation of Charpy Impact 

Verification

2:15 PM Emily Liu 

Premise Plumbing: Pressure Loss in Pipe 

Fittings and Water Quality Effect on 

Microbial Growth

Brooke Calvo

Assessment of Shear Fracture Appearance in 

Charpy Specimens of Modern Steels

2:30 PM Break

2022 SURF Colloquium 7
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Tuesday, August 2, 2022 (2:40 to 4:10 PM (EDT)) 

Engineering Laboratory (EL) Information Technology Laboratory (ITL) Material Measurement Laboratory (MML)

Moderator: Amanda Pertzborn Moderator: Ian Soboroff Moderator: Becky Steffen

Time Virtual Link: https://bluejeans.com/ 351211267/ 0730 Virtual Link: https://bluejeans.com/ 946424079/ 6073 Virtual Link: https://bluejeans.com/ 544223532/ 3099

2:40 PM Emmy Smith

Additive Manufacturing Data Registration 

Software Development

Samuel Rennich

Making TRECVID Results More Accessible and 

Coherent

Charlotte Dohne

Creating Reference Libraries and STRBase Topic 

Pages on Emerging Areas in Forensic DNA

2:55 PM Eric Matthew Fagan

Structural Hardening for WUI Fire - 

Illustration Development

Joshua Zarb

Understanding Neural Search Algorithms

Jennifer Li

Characterization of Micrometer-Scale Particle 

Motion through Optimization of Analysis 

Parameters

3:10 PM Ethan Sundel

Using Standard Tests to Create a Public Drone 

Park

Vaibhav Sanjay

Term and Relation Extraction in Mathematical Texts

Grace Finch

Exploring Neutron Activation Analysis at the 

University of Maryland

3:25 PM James Timothy Allen

A Procedure for Implementing a Digital Twin

Abishay Reddy

Multimodal Fusion with Modality-Specific Factors for 

IEMOCAP dataset

Jacqueline Maloney

Evaluation of the Microscopy NexusLIMS Search 

Functionalities

3:40 PM John Gallo

Estimating Steady State Conditions of HVAC 

Systems

Yuvan Sundrani

Artificial Intelligence-based texture analysis

Terence Murphy

A Versatile Simulator for Cotranscriptionally 

Encoded RNA Strand Displacement Circuits

3:55 PM John Paul Alecia

Coordinated work as an aspect of Robotic 

Agility

Robert Bao

Multimodal image registration for fluorescence guided 

surgery

Nitin Elavarasu

Separation of composition from structure in MicroX-ray fluorescence measurements

4:10 PM Break

2022 SURF Colloquium 8
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Tuesday, August 2, 2022 (4:20 to 6:05 PM (EDT)) 

Engineering Laboratory (EL) Information Technology Laboratory (ITL) Material Measurement Laboratory (MML)

Moderator: Stephanie Watson Moderator: Derek Juba Moderator: Ashlee Aiello

Time Virtual Link: https://bluejeans.com/ 351211267/ 0730 Virtual Link: https://bluejeans.com/ 946424079/ 6073 Virtual Link: https://bluejeans.com/ 544223532/ 3099

4:20 PM Jonathan Smilovich

Automation of Data Analysis for Rapid 

Characterization of Spectroscopic Imaging 

Maps

Samuel Galita

Exploring Graph Analytics on Nisaba GPU cluster with 

cuGraph

Bintou Koroma

The effect of randomness in branch spacing on 

LLDPE dilute solution properties

4:35 PM Joseph Fernandez

Graphic User Interface for ARIAC Robotics 

Competition

Jaxon Ko

Benchmarking Queries from Zeno against FCPW

Emily Frashure

Polymer Database Pipeline for the Circular 

Economy

4:50 PM Khoa Huynh

Object Recognition Framework for AR 

Interfaces

Johannes Losert

Scientific Reproducibility of AI Trojan Detector Results

Hrishikesh Ram

Interaction Energies of Zwitterionic Polybetaines 

with NaCl

5:05 PM Sin Lin

Developing Python script to facilitate ASTM 

C1556 Analysis of Bulk Concrete diffusion 

Coefficient

Jane Liu

Implementing Real Time Constraints in Hedgehog API

Marquesa Calderon

Analysis of existing NIST SRM for microplastics

5:20 PM Lindon Luu 

Literature Review on Recreational Vehicle 

Fires

5:35 PM Linsen Liu

Using Image Registration to Examine Melt 

Pool Monitoring Images.

5:50 PM Mary Ruxsarash 

Characterization of Components for a 

Pyrrhotite Reference Material

6:05 PM End of Day

2022 SURF Colloquium 9
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Wednesday, August 3, 2022 (10:00 AM – 12:00 PM (EDT))

Engineering Laboratory (EL) Information Technology Laboratory (ITL) Material Measurement Laboratory (MML)

Moderator: Xiaohong Gu Moderator: Yolanda Bursie Moderator: Ilse Bercik

Time Virtual Link: https://bluejeans.com/ 351211267/ 0730 Virtual Link: https://bluejeans.com/ 946424079/6073Virtual Link: https://bluejeans.com/ 544223532/ 3099

10:00 AM Noelle Crump

Validation Tests for the NIST Fire Dynamics 

Simulator

Kyle Truong

Evaluating the Implementation of NIST SP 800-

181 in Cybersecurity-Related Job Descriptions

Leilani Meyers

Assessing Ambiguities in Metabolite Identification 

when Analyzed by LC-MS/MS

10:15 AM Oren Minsk

Measuring and improving the performance of 

industrial synergistic network

Francis Durso

Addressing the Causes and Consequences of AI 

Failures

Alexandra Terres

COMSOL Simulations of Contact Line Propagation 

in Additive Manufacturing with Ceramics

10:30 AM Owen Hammer 

Mesh Optimization for AM Defect Detection Study 

using XCT Simulation

Evelyn Hu

Dynamic Access Review and Control 

Implementation and Enforcement (DARCIE)

Ross Gunther

Investigating Filament Fusion in Embedded 3D 

Printing

10:45 AM Robert Bennett

Deep Learning Applied to Industrial Robotics 

Simulation Environments

Maggie Kristina Cavitt

Visualizing Cybersecurity Vulnerabilities and 

their Role in Recent Cyber Attacks

Caressia Edwards

Atomistic Molecular Dynamics Analysis of a Model 

Polycarbonate/Silica Composite System

11:00 AM Robert Pang

A Biofidelic Impact Dummy to Evaluate the Safety of 

Collaborative Robot Applications

Sheldon Douglas

Geometric augmentations to file identifiers in 

file system forensics

Melissa A. Flores-Rivera

Structural Characterization of Extracellular SignalRegulated Kinase 2

11:15 AM Sahil Kochar

Development of Web-based Service Life Prediction 

Tool for Photovoltaics and Building Materials

11:30 AM Savannah Moldvay

Investigating Building Performance Using the 

Commissioning Software HVAC-Cx

11:45 AM Tejas Nazare

Understanding the Evolution of “Sustainability” and 

“Resilience” in Literature

12:00 PM Matthew Pham

Structural Stability Analysis: Application to 3D 

Printing of Cement-Based-Materials

12:15 PM Break

2022 SURF Colloquium 10
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Wednesday, August 3, 2022 (1:00 to 2:30 PM (EDT)) 

Communication Technology Laboratory (CTL) Physical Measurement Lab

Moderator: Wesley Garey Moderator: Richard Steiner

Time Virtual Link: https://bluejeans.com/ 435534290/ 0420 Virtual Link: https://bluejeans.com/ 961384113/ 4619

1:00 PM Megan Lizambri

NetSimulyzer - A 3D Network Simulation Analyzer for 

ns-3

Evan McClintock 

Automating instrumentation for the calibration of DC power 

meters

1:15 PM Hannah Covington

Generating Realistic Residential Household Models 

of Varying Income Level for Simulations

Samir Kulkarni

Simulating electron beam behavior in a miniature electron 

beam ion trap

1:30 PM Michael Belluscio

Applying Network-Enhanced GFCM to Characterize Grid 

Resilience

Satvik Manjigani

Characterizing DNA Structures with Machine Learning

1:45 PM Aleysha Varghese

Assessment of Interoperability of Phaser Measurement 

Unit-Based Smart Sensors

Shawn Meyer

Analysis of Dopant Quantum Dots in Silicon

2:00 PM Benjamin Philipose

Virtual Testing of ADS Equipped Vehicles

Connor Lewis

Diffusion and Kurtosis in Brain Tissue Mimics

2:15 PM Prasun Guragain

Web API for visualizing data transference and 

vulnerabilities in systems

Michael Gutowski

Computer Vision Identification and Analysis of Interferometric 

Fringes

2:30 Break

2022 SURF Colloquium 11
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Wednesday, August 3, 2022 (2:40 to 4:10 PM (EDT))

Communication Technology Laboratory (CTL) NIST Center for Neutron Research (NCNR) Physical Measurement Lab (PML)

Moderator: Wesley Garey Moderator: Dr. Guebre Tessema, National Science FoundationModerator: Uwe Arp

Time Virtual Link: https://bluejeans.com/ 435534290/0420Virtual Link: https://bluejeans.com/ 964520725/ 2477 Virtual Link: https://bluejeans.com/ 961384113/ 4619

2:40 PM Stephanie Estrella

Development and versions of standards 

repositories

John Nunez

Revealing the magnetism of Weyl semimetal CeAlGe

2:55 PM Shriya Haravu

Automatic Identification of Regions of Neutron 

Diffraction Patterns Changing During Phase Transition

Michael Gabe

Nonperturbative nonlinear optics in a rubidium vapor 

cell

3:10 PM Benen Crombie

Automating the Fuel Element Visual Inspection Process 

at the NCNR Reactor

Abrar Sheikh

Progress Towards Validation of the Cold-Atom 

Vacuum Standard

3:25 PM Washat Ware

Determining the validity of Guinier analysis in slitsmeared Small Angle Scattering data

Nicolas Aguilar

Testing Calibration Uncertainty with Low-Cost Inertial 

Measurement Units with a Pendulum Method

3:40 PM Christopher Stallard

Developing a User-Friendly Framework for Stopped 

Flow Small Angle Neutron Scattering at the NCNR

Joshua Young

Development of an Open-Source Workflow for Endto-End Simulation of an Ultrasonic Calorimeter for 

Imaging of Radiation Dose

3:55 PM Kim Taylor

Surfactant-Free Oil-in-Water Emulsion Stabilized by 

Chitin Nanocrystals: A Green Recipe

Travis White

Metric Program Resource Development

4:10 PM Amy Musser

Multi-Scale Structures of Starch as Revealed by 

Scattering Techniques: From Unit Cell to 

Nanostructure

4:25 PM End of Day

2022 SURF Colloquium 12
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Thursday, August 4, 2022 (10:00 to 12:00 PM (EDT)) 

Physical Measurement Lab (PML)

 Moderator: Uwe Arp

Time Virtual Link: https://bluejeans.com/ 961384113/ 4619

10:00 AM

10:15 AM Faith Makumbi

Examining the Thermal Properties of an Artificial Star for Flux 

Calibration

10:30 AM Hunter Staiger

An automatic calibration algorithm and the efficiency 

determination of the NIST spectrometer

10:45 AM William Dienstfrey

Monitoring Atmospheric Stability for Plume Emission Flux 

Measurements

11:00 AM Aagam Dalal

Testing and demonstrating a new toolkit for public randomness 

beacons

11:15 AM Briana Chen

Developing resources for understanding an enhanced 

randomness beacon

11:30 AM Hayden Craun

Design of Multiresonant Plasmonic Nanocavities for Label-Free 

Nonlinear Optical Voltage Sensing

11:45 AM Gabriel Lewis

Normal Modes of Nanoscopic Ferromagnets

12:00 PM Break

2022 SURF Colloquium 13
Summer Undergraduate Research Fellowship (SURF) Colloquium Parallel Session

Thursday, August 4, 2022 (1:00 to 3:00 PM (EDT))

Physical Measurement Lab

Moderator: Michael Berilla

Time Virtual Link: https://bluejeans.com/ 961384113/ 4619

1:00 PM Helen Zhang

Hyperspectral Imaging for Tumor Margin Analysis 

in Breast Tissue Resection

1:15 PM Grace Tang

Evaluating Organic Thin-Film transistors and 

Processing Methods

1:30 PM Daniel Quinter

Quantum Random Number Generation in TrappedIon Computing with Error Mitigation

1:45 PM Max Buskirk

Analysis of Solder Joint Failures using Resistance 

Measurements

2:00 PM End of Colloquium

2022 SURF Colloquium 14
2022

2022 SURF Colloquium 15
Summer Undergraduate Research Fellowship (SURF) - 2022 Participants

Communication Technology Laboratory (CTL)

Michael Belluscio

Ovidio Castillo

Hannah Covington

Stephanie Estrella

Prasun Guragain

Megan Lizambri

Benjamin Philipose

Aleysha Varghese

2022 SURF Colloquium 16
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Michael Belluscio

Purdue University Mathematics

Dean's List

To grad school for PhD in Mathematics, then to academia to research and teach.

CTL, Smart-Grid Group

Cheyney O'Fallon

Applying Network-Enhanced GFCM to Characterize Grid Resilience

Using the Generator Fleet Characteristics Model (GFCM) and only public data sources, we seek to 

characterize the resilience of large segments of the electric grid during major disturbance events that can 

lead to customer service interruptions. The network structure of the grid contributes to its resilience or 

lack thereof. We improve the GFCM through the development of a network module. We have created and 

implemented an algorithm we call “Prioritized Pathway” which creates synthetic connections between 

communities served by the electric grid based on spatio-temporal data of population and electric system 

operations. The algorithm creates synthetic networks of communities to lower the computational burden 

of generating more granular models, while also avoiding the need for sensitive and proprietary data on 

real world physical networks. We use this new module to construct network-derived cost adders that are 

incorporated into the existing model to deduce plausible hourly generator allocations. Furthermore, the 

summary statistics of the county level networks and data from the DOE-417, which details major electric 

system disturbances, are used to characterize a balancing authority’s resilience for every hour in a given 

year.

2022 SURF Colloquium 17
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

 Ovidio Castillo

George Mason University Systems Engineering

Senior

Continue to Master's program

CTL - Communications Technology Laboratory

Michael Sharp

Simulating the Impact of Manufacturing System Configurations on Production & Quality

 Optimization of production lines impact manufacturing quality and speed, and seeing configuration 

options in action provide manufacturers the ability to make informed, strategic decisions about their lines. 

Simulation tools are necessary to perform effective analysis of situations and configurations to better 

understand the systems and make educated decisions for positive impacts. The National Institute of 

Standards and Technology (NIST) maintains a simulation tool, Sim-PROCESD (Simulated-Production 

Resource for Operations and Conditions Evaluation to Support Decision-making), a PYTHON based 

software, to simulate different scenarios for manufacturing networks and help manufacturers make 

educated decisions to optimize their processes. The tool takes different production process configurations 

and simulates product quality and throughput as functions of increasing wear and degradation to the 

manufacturing equipment involved in the processes. It provides traceability of production systems and 

makes analysis faster and effective. The more elaborate the design becomes, there is longer production 

time, more issues arise and changes in quality occur. Research conducted shows there are limited amount 

of tools that can be used to automatically make random configurations of production networks. If the tool 

can make automation of configurations happen, in turn, it will give manufacturers more configurations to 

test and decrease planning time. There are situations where the manufacturer must test a configuration a 

certain way and there is no need for randomness or alternative designs. With sim-PROCESD, we can 

trace the paths and pin point defected machines and/or materials within the process to target the source of 

issues for decision making. With that, the tool helps information discovery capacity along with support in 

decision making. In the future, we want to be able to design configurations and help decrease production 

time by finding the best possible manufacturing line designs.

2022 SURF Colloquium 18
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Hannah Covington

Santa Clara University Mechanical Engineering

Graduate Student

I plan to pursue an M.S.M.E. degree at Santa Clara University

CTL, Smart Connected Systems Division, Smart Grid Group

Cuong Nguyen

Generating Realistic Residential Household Models of Varying Income Level for Simulations

Energy costs make up a significant portion of residential household expenditures. Developing innovations 

to decrease this cost involves rigorous testing. Testing using digital simulation is generally cheaper and 

faster than implementing products into actual homes. To support these simulations, it is crucial to create a 

method for quickly generating a multitude of realistic digital household models, each with varying house 

structure, occupant behaviors, and income level. Distinguishing between household income level allows 

innovators to ensure that their product promotes economic fairness across all users. 

In order to inform the models, data had to be gathered about house characteristics, appliance usage, and 

occupancy patterns. Relevant data was gathered from the Residential Energy Consumption Survey 

(RECS), the American Time Use Survey (ATUS), and the U.S. Census. Surveyed households were then 

classified into one of five income levels based on their annual income, location, and number of household 

members. The data from surveyed households in each income level was then used to create probability 

distribution information for each of the model variables. For example, it is important to know the 

likelihood of an appliance being run a certain amount of times per week. Random number generation and 

the Monte Carlo method was employed alongside the probability distributions to determine the variables 

for each model. This information was inputted into the building energy simulator EnergyPlus to create the 

model and run simulations. The accuracy of the models was verified by running multiple baseline 

simulations and comparing the average energy consumption of the models with that of the surveyed 

households.

2022 SURF Colloquium 19
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Stephanie Estrella

University of Maryland, College Park Computer Engineering

Junior

Obtain my undergraduate degree and then attend graduate school

Communications Technology Laboratory, Smart Connected Systems Division, Smart

Connected Manufacturing Systems Group

Allison Barnard Feeney

Development and versions of standards repositories

The International Organization for Standardization (ISO), Technical Committee 184/Sub

Committee 4 for Industrial Data, develops the Standard for the Exchange of Product model data

(STEP). STEP, formally ISO 10303, is commonly used to exchange engineering design data in

computer-aided design (CAD) systems with other CAD systems, with computer-aided

manufacturing systems or coordinate measurement systems in manufacturing enterprises. STEP

is a complex standard, composed of many inter-related parts. STEP developers rely on custom

software to "build" the standards for ballot from technical content managed in a Git repository

and presentation rules from the ISO Directives.

While participating in Task Force 1, STEP Module and Resource Library (SMRL) Architecture

Innovation, I addressed ballot comments on the STEP Part 2 Vocabulary standard that is

generated from the hundreds of source files in the STEP Module Resource Library. I generated

my own Part 2 to check my work by installing a software environment that was helpful in adding,

editing, and removing documents in Part 2. In Part 2 I went through checking for terms, in clause

3 (English definitions), that had duplicate term definitions which didn’t follow the recommended

ISO directives and rules, edited them to fit the ISO directives, but if they were very distinct to

directive, I developed issues/tickets for these terms, then committed my current local repository

with my changes. To commit, edit, and retrieve the repository from GitHub I learned commands

from git. When installing the software development environment, many issues arose related to

the configuration, so more research into tool installation was required. Through this experience I

have learned the benefit of using git repositories for distributed development teams: you can

effectively work on one part of the standard while others work on other parts and efficiently check

errors or the change history. In the case of ISO standards, the ISO Directives which govern

development and documentation of the standards are always changing. Being able to efficiently

use git repositories will be worthwhile for other future standards or software development

projects.

2022 SURF Colloquium 20
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Prasun Guragain

University of Wisconsin-Madison Computer Science

Junior

Graduate school

CTL, Smart Connected Systems Division, Smart Connected Manufacturing Systems Group

Allison Barnard Feeney

Web API for visualizing data transference and vulnerabilities in systems

The current growth in the digitalization of businesses has been very beneficial, from better physical goods 

to faster services. However, due to increased reliance on digital data, they are exposed to many cyber 

threats, like data tampering. Data tampering is the act of changing data that is relied on by businesses. 

Tampering can be either intentional (i.e., cyberattacks) or unintentional (e.g., human or system error), 

which can lead to severe problems. Supply chains today are very complex, with many information 

systems running in the business. These system’s tampered data can easily and quickly propagate across 

many other systems and into other businesses. Therefore, understanding and preventing the exposure of a 

business to such a threat is crucial. The Smart Connected Manufacturing Systems Group in the 

Communications Technology Laboratory at NIST is working on helping organizations quickly identify 

data integrity threats and propagation paths within their applications and network. The software solution, 

driven by security controls and activities defined in the NIST Cybersecurity Framework and NIST SP 

800-53, allows users to: 1) build an inventory of their systems, applications, and networks, 2) be alerted 

of new vulnerabilities (from the NIST National Vulnerability Database - NVD) impacting their 

applications, and 3) understand and track all systems exposed (directly or not) to these vulnerabilities.

2022 SURF Colloquium 21
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Megan Lizambri

University of Maryland, Baltimore County Computer Engineering

Junior

Complete my undergraduate degree, then enter a masters program in Computer Engineering. 

Ultimately obtain a job in the STEM field that challenges me both analytically and creatively

Communications Technology Lab, Wireless Networks Division

Evan Black

NetSimulyzer - A 3D Network Simulation Analyzer for ns-3

The increased complexity of network protocols and scenarios simulated using the discrete-event, 

packet-network simulator, ns-3, is making the development, verification, and analysis of simulations a 

challenging task. To aid in those tasks the NetSimulyzer, a flexible 3D visualizer for ns-3, was developed 

to alleviate the workload of debugging, understanding, and demonstrating a scenario. The tool was 

conceived to easily integrate into any ns-3 scenario and provides core functionalities that are technology 

agnostic. The NetSimulyzer provides mechanisms to track a variety of simulation elements, including the 

network topology and system performance that can later be visualized using a 3D scene augmented with 

data visualization elements, such as charts and logs. 

This work presents a new feature implemented in the NetSimulyzer which allows for more detailed 

descriptions of individual elements of a wireless network simulation, as well as new and improved 3D 

models for the NetSimulyzer that were developed using an open-source, 3D modeling software, Blender. 

By shaping and combining basic geometry and coloring surfaces the new and improved models much 

more closely resemble the real-world items they represent and allow for additional information to be 

conveyed, while creating additional avenues for information facilitating the understanding of results. In 

conclusion, these efforts make for an enhanced user experience and further facilitate the development, 

verification, and analysis of simulations in ns-3. 

2022 SURF Colloquium 22
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Benjamin Philipose

Seattle University Computer Engineering

Junior

Pursue Masters Degree in Computer Science

CTL, Smart Connected Systems Division, IOT Devices and Infrastructure

Thomas Roth

Virtual Testing of ADS Equipped Vehicles

 Vehicles equipped with Automated Driving System (ADS), for example, automated vehicles 

must be tested to ensure their safety and reliability to minimize the chance of a collision and to ensure 

their driving behavior matches human expectations. There is a need to develop new testing methods for 

safety metrics to support the Federal Motor Vehicle Safety Standards for ADS-equipped vehicles. The 

current trend in the automotive industry is towards confirming that ADS-equipped vehicles exhibit 

appropriate driving behaviors on the road to contribute to the overall safety of its passengers and the 

environment. NIST can contribute to this area of measurement science by developing methods of virtual 

testing that are fundamental to measuring the behavioral competency of ADS-equipped vehicles. For a 

testing environment, NIST has developed a software tool called the Universal Cyber-Physical Systems 

Environment for Federation (UCEF). UCEF is an environment that can be used to test ADS-equipped 

vehicles by manipulating their external factors to allow for a good virtual representation of a driving 

scenario. This work develops a testing framework for UCEF to define and run tests against the simulated 

automated vehicle which helps improve UCEF support for ADS-equipped vehicle testing. The driving 

simulation helps figure out what and how to measure behavioral competency by checking the automated 

vehicle’s response to various driving scenarios. From the ADS-equipped vehicle’s response, one can 

modify the testing framework over time. The testing software which interprets the response, SoapUI is 

connected to a car simulation that outputs data at each time step just as a normal car would. SoapUI takes 

this data from the car and checks, from current or previous responses, if the car is acting in a dangerous 

way. With this system in place, it allows for the efficient development of new measurement methods for 

assessing ADS-equipped vehicles’ behavioral competency. 

2022 SURF Colloquium 23
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Aleysha Varghese

Virginia Tech Computer Engineering

Sophomore

Leadership in Corporate Technology 

Communications Technology Laboratory , Smart Connected Systems Division , Smart Grid Group

Dr. Eugene Song , Kang Lee

Assessment of Interoperability of Phaser Measurement Unit-Based Smart Sensors 

Smart sensors provide real-time data and the status of electrical power grids for monitoring, protecting, 

and controlling smart grids to improve their reliability and resilience. Smart sensor interoperability is a 

significant challenge for smart grids. Interoperability is the ability of two or more devices to use and 

exchange information through a standard protocol to achieve specific functions. To assure the 

interoperability of smart sensors in smart grids, a method for assessing needs to be developed and tested. 

This project primarily focuses on the software tool development for interoperability assessment of smart 

sensors based on standard communication protocols, the model of interoperability, and the 

Hennessey-Milner logic. An open-source software tool is designed and developed in the Java 

programming language to automatically assess the interoperability of phaser measurement unit 

(PMU)-based smart sensors based on the Institute of Electrical and Electronics Engineers (IEEE) C37.118 

standard communication protocol. This software tool parses the IEEE C37.118 packet dataset in 

extensible mark-up language (XML) format and automatically assesses the interoperability between 

phaser data concentrator (PDC) and PMU-based smart sensors. The assessment results are stored in a data 

file. These results determine whether the PMU-based smart sensors are inter-operable with PDC based on 

the IEEE C37.118 standard and provide an open-source software tool for any interested parties to use for 

interoperability testing of this specific smart sensor. 

2022 SURF Colloquium 24
2022

2022 SURF Colloquium 25
Summer Undergraduate Research Fellowship (SURF) - 2022 Participants

Engineering Laboratory (EL)

John Paul Alecia 

James Timothy Allen

Robert Bennett 

Noelle Crump 

Alex Dai 

Eric Matthew Fagan 

Joseph Fernandez

John Gallo 

Owen Hammer 

Khoa Huynh 

Sahil Kochar 

Sin Lin 

Emily Liu 

Linsen Liu 

Lindon Luu 

Catherine McCarthy 

Oren Minsk 

Savannah Moldvay 

Tejas Nazare 

Dominique Paige 

Robert Pang 

Diego Pantelis 

Matthew Pham

Ainsley Rexford 

Mary Ruxsarash 

Jonathan Smilovich 

Emmy Smith 

Ethan Sundel

Arin Zeng 

2022 SURF Colloquium 26
2022 SURF Colloquium 27
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

James Timothy Allen

University of Maryland, College Park Mechanical Engineering

Senior

Pursuing a career as a design engineer in smart industry or robotics

Engineering Laboratory, Systems Integration Division, Life Cycle Engineering Group

Dr. Guodong Shao

A Procedure for Implementing a Digital Twin

Recently, new technologies, such as machine learning and IoT devices, allow machines to collect and 

respond to real-time data in a meaningful way. These developments enable the realization of digital 

twins, which are fit-for-purpose, dynamic, and virtual representations of physical entities that are 

synchronized with their virtual entities. Using digital twins, conditions of the physical entity can be 

monitored, and optimal decisions can be derived to reduce costs and save time. However, it is still 

challenging for small and medium-sized enterprises (SMEs) to implement a digital twin due to the 

confusion surrounding the concept and lack of methodologies for implementing a digital twin. 

In this project, a generic procedure for implementing a digital twin has been developed. This procedure 

provides a step-by-step guideline that aims to assist SMEs in development of their own digital twins. 

Machine health digital twins have been used to exemplify the proposed procedure. Predictive 

maintenance and anomaly detection cases of a computer numerical control (CNC) machine spindle are 

performed to identify relevant machine health issues and derive optimal decisions.

2022 SURF Colloquium 28
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Robert Bennett

Bloomsburg University of Pennsylvania Mathematics (Statistics Concentration)

Senior

Graduate School : Georgia Institute of Technology : MS, Analytics ; Data Analyst->Data Scientist

Engineering Laboratory (EL) Division 735, Intelligent Systems Division

William Harrison

Deep Learning Applied to Industrial Robotics Simulation Environments

As industrial robotic systems become more complex, so does the process of teaching robots to perform 

desired tasks. Developing these agents (robots) to be intelligent could be conducive to learning 

efficiently, but the time and cost of learning on actual robots is substantial. Using 3D simulated 

environments to accomplish learning allows us to train agents more efficiently. In order for a robot to 

interact with its environment, a robot must be able to identify the locations of all objects in its 

environment as accurately as possible. This can be accomplished using pose estimation, a computer 

vision technique that tracks the locations of objects. The existing PoseCNN project successfully 

accomplished pose estimation of objects using only image data as input to a Convolutional Neural 

Network (CNN). The network estimates an object’s pose by decoupling the estimation of 3D rotation and 

3D translation. First, the 3D translation is estimated by localizing the center of an object and predicting 

the distance to its center. The 3D rotation is then estimated by regressing to a quaternion representation. 

This study seeks to assess how well PoseCNN performs using our own image data of manufacturing parts 

that might be present in robotics simulations. Additionally, this study analyzes the performance of the 

network on varying sizes of image data to understand to what degree accuracy is affected by the amount 

of training completed.

2022 SURF Colloquium 29
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Noelle Crump

Worcester Polytechnic Institute Mechanical Engineering

Senior

Finish Bachelors Degree in Mechanical Engineering

Engineering Laboratory, Fire Research Division, Engineered Fire Safety Group

Randall McDermott

 Validation Tests for the NIST Fire Dynamics Simulator

The NIST Fire Dynamics Simulator (FDS) simulates fire spread behavior by producing the trends of 

values such as heat release rate, wind velocity, and temperature. A suite of test cases called validation 

cases use the simulator to replicate the conditions of a controlled burn done in a physical lab. The values 

produced by the physical and simulated burns are compared to test the accuracy of the simulator’s values. 

A broad suite of these cases ensures the simulator can accurately recreate the progression of various fires. 

Two of the Validation test suites are the Memorial Tunnel and the Deep Fuel Beds test suites. The 

Memorial Tunnel validation suite tests FDS’s capabilities to simulate tunnel fires. Few data sets of 

well-controlled full scale tunnel fires exist, and the standards for tunnel fire safety are not drawn from 

comprehensive testing. The Deep Fuel Beds suite tests FDS’s ability to simulate burning vegetation with 

the Lagrangian Particle model. This validation case models the rate of flame spread depending on the 

spatial arrangement of discontinuous fuel beds. 

Validating these cases includes writing scripts for automating tasks such as reformatting old and 

non-standardized experimental data, extracting the correct data from FDS, and processing the data for 

plotting the comparison. It also includes challenges such as deciphering old reports and finding the most 

accurate way to replicate the experiment. When a validation case runs cleanly, it is added to the tracked 

version of the FDS code on an online GitHub repository, assuring that this free public resource is working 

as intended. 

2022 SURF Colloquium 30
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Alex Dai

University of Maryland, College Park Computer Science

Junior Year

I plan on completing my undergraduate degree, then finding a stable job ideally close to where I live

Engineering Labratory, National Fire Research Laboratory, Un-supervised Learning in Fire 

Calorimetry Database

Prasad, Kuldeep

Integration of Neural Networks to Aid in Identifying Flame Temperatures 

 The NIST Fire Calorimetry Database has maintained thousands of experiments regarding the burning 

of common materials, each with at least one video in addition to a comma separated value sheet 

containing information regarding the fire. With this information, it is possible to train an artificial 

intelligence in order to identify the heat release rate of the flame without specific lab equipment. If 

successful, a video or photo of any situation may yield corresponding heat release rate information.

 Because the information for the experiments are not easily publicly accessible, it was necessary to 

contact several staff members to gain access to the larger catalog of videos and corresponding files. Using 

the Python programming language and various libraries, we are able to extract frame data from the 

publicly available videos prior to this as a preliminary test. After the proof of concept succeeded, yielding 

a functioning neural network, we were able to move onto the private video files. Specifically, the neural 

network was constructed using Tensorflow-DirectML, and its corresponding keras libraries.

 As a result, we were able to create multiple models with varying data sets and procedures, ultimately 

finding not only the best model to use, but also the best method to make such models, with each step 

carefully documented. With these models, an image may yield an estimate of the heat release rate of a fire 

captured in the image, or a video may yield a significantly more accurate representation of the heat 

release rate of any given frame.

 If polished, not only can this be easily integrated into an application within a portable phone, security 

cameras can also warn staff of potential fires or hazards prior to an accident. Additionally, details 

regarding the construction of a neural network would undoubtedly be helpful for staff without the perfect 

hardware.

2022 SURF Colloquium 31
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Eric Matthew Fagan

University of Maryland College Park Civil Engineering

Junior undergraduate

Attend grad school for a master's degree in civil engineering and work for an engineering firm

Engineering Laboratory, Fire Research Division, Wildland-Urban Interface Fire Group

Dr. Shonali Nazare

WUI Fire Structural Hardening - Illustration Development

Several components of a residential structure require fire hardening in order to prevent ignition from stray 

embers or flames, and this becomes increasingly important in areas where man-made and natural 

environments intersect - also known as Wildland-Urban Interface Areas. For example, a roof has many 

vulnerable components present. Debris in the gutters will dry out and ignite unless an noncombustible 

barrier prevents embers from getting through and vents can have dust and lint ignitions unless the flap is 

closed. Besides roof vulnerabilities, the eaves and siding can ignite from a nearby burning auxiliary 

structure. The Hazard Methodology and Mitigation document describes the various hardening actions that 

are required, the precedents (if any) in the existing code, and elaborates on the goals and methods of the 

actions taken. This way, homeowners and construction-centered businesses can make the modifications 

necessary for existing and new constructions in Wildland-Urban Interface communities and other 

high-hazard zones. However, the document is rather wordy, and as a result some critical information may 

be glossed over. Labeled illustrations effectively supplement text for readers to better understand the fire 

hardening modifications. To facilitate this, a compilation of illustrations highlighting modifications 

recommended in the Hazard Methodology and Mitigation document were prepared to form an appendix 

of the said document. 

2022 SURF Colloquium 32
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Joseph Fernandez

Salisbury University Computer Science and Mathematics

Senior

Attend gradutate school and pursue a career in computer science

Engineering Laboratory, Intelligent Systems Division, Cognition and Collaborative Systems Group

Craig Schlenoff, Zeid Kootbally

Graphic User Interface for ARIAC Robotics Competition

The National Institute of Standards and Technology (NIST) Agile Robotics for Industrial Automation 

Competition (ARIAC) is a competition to test the agility of robots in the industry. Competing teams are 

given scenarios and objectives where they have to use the robots in the simulation to do kitting and 

assembly tasks. This competition helps to increase the agility of industrial robots, meaning they will be 

able to adjust to their environment even when unexpected changes occur. 

To run a simulation for the competition, a Yet Another Markup Language (YAML) file needs to be input 

into the program. This text file gives Gazebo, the simulation software, the necessary parameters. In the 

past, the group trying to run a simulation would make their own file, which could lead to many errors. If 

any errors were present in the file, then the simulation would not run at all. The main goal of this project 

was to develop a graphical user interface (GUI) that creates the YAML file for the user and avoids 

potential errors that could occur when done by hand. This GUI reduced the amount of time taken to 

produce one of these files and also help eliminate errors. 

After the interface was developed, a YAML schema was produced to check the output of the GUI to 

make sure that the outputted files are correct. Then the YAML files were checked for correctness by 

loading them into the simulation. Since the simulation would not run with an incorrect file, it was the best 

way that the output from the GUI was a correct and working file.

2022 SURF Colloquium 33
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϭ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

John Gallo

University of Maryland Mechanical Engineering

Junior

Graduate in December 2022

Engineering Laboratory, Energy and Environment Division, Mechanical Systems and Controls Group

Amanda Pertzborn

Estimating Steady State Conditions of HVAC Systems

The Intelligent Building Agents Laboratory (IBAL) is generating data on the performance of heating, 

ventilating, and air conditioning (HVAC) equipment. Some data sets are used to learn how to operate 

equipment or to understand the performance of equipment. Steady state data can be used to tune the 

operation of equipment and understand how it is performing under different circumstances. Steady state 

conditions are the final value of data after an infinite amount of time. Tuning is the process of modifying 

the input data (e.g., feedback from the system) through adding and multiplying values to change the 

output data (e.g., control signal for the equipment). This is required to ensure the output of the equipment 

matches the desired value. 

In order to accurately predict the steady state value of each data set, the gathered data is fit to a function 

and evaluated as it approaches an infinite running time. After an infinite time, the data should converge to 

a finite value. This allows the function to guess a final value without requiring an infinite data set. The 

accuracy of this function depends on how closely it follows the trend of the original data. 

I have been tasked with creating a MATLAB script to predict the steady state values for any data set. The 

estimation I've created functions based off of an exponential function with a decreasing rate. This allows 

the estimation to extend past the given data to approximate what the final value of the data would be if 

allowed to continue infinitely. Most of my work has been writing and optimizing the code to accurately 

and efficiently run the script to predict steady state values of each data set. The final version of the code 

can be used by researchers to process data sets of any size.

2022 SURF Colloquium 34
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Owen Hammer

University of Maryland, College Park Computer Engineering

Junior

Attend Graduate School

Engineering Laboratory, Intelligent Systems, Production Systems

Felix Kim

Mesh Optimization for AM Defect Detection Study using XCT Simulation

 Additive manufacturing (AM) is a very effective technique that can have a major impact upon the 

manufacturing industry, but defects in metal AM are preventing wider adoption of the technique. Several 

X-ray Computed Tomography (XCT) defect detection algorithms are being developed to resolve this 

issue, but it is challenging to evaluate their performance due to the lack of ground truth information of 

physical samples. Using XCT simulations with a voxelated surface mesh representing pores in the metal 

is important because they can produce benchmark data and eliminate uncertainty that would be present in 

ground truth image data. A mesh is a representation of a three-dimensional object with a series of points 

and two-dimensional shapes that connect them. In this study, a program was developed to generate 

surface meshes that follow voxel boundaries as this capability is not readily available in existing libraries. 

Two major functions were written: one that creates a voxelated mesh surface from a three-dimensional 

binary image, and another that converts a triangulated mesh surface into a three-dimensional binary 

image. These algorithms can be used together to voxelate a mesh, which is then used in an XCT 

simulation with randomly positioned pores. Three major methods of surface meshing, along with their 

advantages and disadvantages are discussed. The algorithm that converts a mesh in a stereolithography 

(.stl) file to three-dimensional voxel image and its capabilities are displayed. The usage of these functions 

in a larger program that simulates randomly positioned pores is then shown. Using procedural voxelated 

mesh generation allows for a more efficient, cheap, and accurate method for XCT AM defect detection 

simulations. Opportunities for optimization of these algorithms and modification and application of the 

overall program to other problems will be discussed at the end of the colloquium. 

 

2022 SURF Colloquium 35
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Khoa Huynh

University of Maryland, College Park Computer Science

Junior

Graduate School

Engineering Laboratory, Intelligent Systems Division, Sensing and Perception Systems Group

Shelly Bagchi

Object Recognition Framework for AR Interfaces

Manufacturing processes have become increasingly sophisticated, which has led many manufacturers to 

use robotics to enhance the speed, quality of production and complete many complicated tasks. However, 

in small and medium-size enterprises (SMEs), tasks change quite frequently so collaborating robots has 

become an important part of a successful and sustainable SMEs. However, controlling and operating a 

robot currently requires sophisticated knowledge of each robot and takes time to learn. 

To enable non-technical users to operate and work with robots, the Performance of Human-Robot 

Interaction (P-HRI) project at NIST aims to create an Augmented Reality (AR) system to enable 

human-robot collaborative tasks in manufacturing environments. An integral part of this AR system is 

object recognition that can identify parts and indicate which is the next-needed part in the task. The goal 

of our project was to create an object recognition framework that can integrate into AR interfaces for 

interactions between humans and robots. Using the program Unity along with the C# programming 

language, we created an app with Vuforia’s object detection to be able to pick out an object through the 

camera view. This allows the end user to see the object highlighted with AR annotations while interacting 

with their environment. We also experimented with openCV and Python to create a color detection 

framework but the accuracy of Vurforia’s object detection provided higher accuracy and lower time to 

detection. The end goal of this project is to integrate the object recognition framework into AR headsets 

for a more seamless experience. 

2022 SURF Colloquium 36
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Sahil Kochar

University of Maryland College Park Aerospace Engineering + Mathematics

Recent Graduate

TBD

Engineering Laboratory, Materials and Structural Systems Division, Infrastructure Materials Group

Dr. Xiaohong Gu

Development of Web-based Service Life Prediction Tool for Photovoltaics and Building Materials

Many kinds of polymers are used for solar photovoltaic (PV) systems. Yet the polymers chemically and 

mechanically degrade under their service environments with ultraviolet (UV) light, temperature, and 

humidity. In order to properly design solar panels and ensure warranties, it is critical that the PV industry 

is capable of efficiently assessing the long-term performance and lifetime of their materials is critical. 

The primary focus of my research this summer is to develop a user-friendly web tool comprised of 

physics-based statistical models that can describe the effects of key environmental factors on degradation, 

accelerated laboratory tests to real-world performance for backsheet polymeric materials. The statistical 

model uses a cumulative damage model which has been effective at predicting the chemical degradation 

of a commercial PV backsheet, with changes that allow it to more directly predict changes in mechanical 

degradation. In particular, the total UV exposure on a model PV backsheet material is correlated with the 

material's ductility, as measured via the elongation-at-break of specimens. The model is developed into an 

R Shiny web tool that allows users to quickly fit the model’s parameters to a specific backsheet material 

and compare the model’s predictions with measured data, providing significant value to companies that 

wish to design and/or test solar installations.

2022 SURF Colloquium 37
2022 SURF Colloquium 38
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Emily Liu

University of Maryland, College Park Civil Engineering

Sophomore

GPA

Industry

Engineering Laboratory/ Building Energy and Environment Division (BEED)/Mechanical Systems 

and Controls Group

Natascha Milesi Ferretti and Tania Ullah

Premise Plumbing: Pressure Loss in Pipe Fittings and Water Quality Effect on Microbial Growth

Premise plumbing distributes cold and hot water to consumers in buildings such as homes. The system 

consists of pipes, fittings, valves, fixtures, and appliances (e.g., water heaters). Pipes and heaters should 

be properly sized and operated not only to prevent energy losses, but also long periods of stagnant flow, 

which can create conditions for the growth of pathogenic microorganisms. Data is lacking to characterize 

premise plumbing that is more reflective of present building water usage and modern pipes and fittings to 

improve efficiency, safety, and public health. The objective of this SURF project was to assist two 

ongoing projects at NIST. For the first project focused on plumbing hydraulics at the cold-water 

distribution, a database was created by extracting information on pressure loss coefficients (i.e., an 

important pipe sizing parameter) from literature for pipe fittings. Using this database, visualization of the 

data was accomplished by developing Python scripts to create graphs comparing the Reynolds number 

and other flow parameters to pressure loss coefficients. Through the findings in the literature review and 

data processing, relationships between the analyzed parameters were observed. The second project was 

focused on water quality measurements in a hot water system. The objective was to extract information 

from manuals on the installation, calibration, and operation of sensors in a real-time monitoring 

instrument used to measure chemical and physical water quality parameters in a water heater laboratory 

test setup. By understanding the parameters measured and conducting a literature review, the potential 

effect of water quality on microbial growth in water heaters was studied. 

2022 SURF Colloquium 39
 SURF Student Colloquium 

 NIST – Gaithersburg, MD 

 August 2-4, 2022 

 Name: Linsen Liu 

 Academic InsƟtuƟon: University of Maryland Major: Applied MathemaƟcs 

 Academic Standing 

 (Sept. 2022): Sophomore 

 Future Plans 

 (School/Career): Graduate School 

 NIST Laboratory, 

 Division, and Group: Laboratory:Engineering Lab Division: 734-System IntegraƟon Division 

 Group: 734.12 InformaƟon Modeling and TesƟng Group 

 NIST Research 

 Advisor: Dr. Yan Lu 

 Title of Talk: 

2022 SURF Colloquium 40
 Abstract: 

 One of the prominent technical challenges powder-bed-fusion Additive Manufacturing process 

 faces is process variability and the resulting part-quality issues. This project investigates process 

 monitoring methods based on melt pool imaging. Various feature extraction methods are applied to the 

 melt pool images captured from the NIST Additive Manufacturing Metrology Testbed (AMMT) for process 

 anomaly detection. In addition to assessing features from individual MPM images and performing 

 anomaly detection based on individual features, such as melt pool size and shape, this study focuses on 

 examining melt pool variations by registering adjective melt pool images and extracting multiple features 

 for process fault detection and diagnosis. The advantage of the proposed method is that different melt 

 pool features are sensitive to different process abnormalities during a build. For example, the melt pool 

 size can reflect the total energy absorbed from the laser; the melt pool orientation may indicate the 

 deviation of a scan path. Measuring several features could lead to more efficient detection and 

 minimization of process variability due to the increased specificity of information conveyed by each 

 feature. 

 This study gathers and analyzes the melt pool images collected from the NIST AMMT. The data 

 set is a set of coaxial melt pool images (relative static melt pool, follows optical path) with a small field of 

 view of 120 ݌݅ݔ݈݁× 120 ݌݅ݔ݈݁ . The pixel size is 8 micrometers. The images are sampled at 10 kHz. The 

 normal scan speed is 800mm/sec.The study uses an ellipse fitting method to collect the features of 

 MPM images such as melt pool center location, melt pool width, melt pool height, melt pool orientation, 

 melt pool area, perimeter, circularity, and aspect ratio (ratio between width and height of non-rotated 

 fitted rectangle). The next step is to compare consecutive images for melt pool center, angle of rotation, 

 circularity, and aspect ratio and calculate the percentage changes. To implement the algorithms, the 

 study used Python with OpenCV, NumPy, Matplotlib, and Pandas. The study deploys the fitEllipse 

 function to fit a melt pool boundary based on the least square method, along with the boundingRect 

 function to find the aspect ratio. Finally, layerwise statistical features as mean, standard deviation, 

 maximum, and minimum are calculated for both the features itself and the changes. 

 The results of this study are summarized in two sets of data: one containing the nine features for 

 individual MPM and one containing the changes of these features. The study has also generated 

 statistics of these two sets in order to better understand the data set. Preliminary results indicate several 

 features have strong sensitivity to irregular melt pool. The study expects more results for direct image 

 registration method to compare the accuracy and efficiency. 

 This study proposes an innovative process control method based on image processing and 

 registration. It indicates that multiple features can help effectively control the process by more specific 

 fault indications. One preliminary conclusion that can be drawn is that image processing with an 

 emphasis on the change between adjacent images gives more useful information than image processing 

 based solely on the characteristics of the images themselves. 

2022 SURF Colloquium 41
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Lindon Luu

University of Maryland, College Park Fire Protection Engineering

Undergraduate Junior

Graduate School

Engineering Laboratory, Fire Research Division, WUI Group

Shonali Nazare

Literature Review on Recreational Vehicle Fires

Structural ignitions and structure-to-structure fire spread within Wildland-Urban Interface (WUI) 

communities during wildfire can occur by direct flame impingement, ember exposure, thermal radiation, 

and combinations thereof. WUI fire case studies have identified multiple sources that can directly cause 

structure ignitions. The sources include combustible fences, wood piles, combustible landscaping 

elements, storage sheds, auxiliary structures, non-commercial vehicles, and recreational vehicles (RVs). 

Structure Separation Experiments (SSE) project addresses structure-to-structure fire spread in the WUI 

communities. Phase 1 of the SSE project quantified fire hazard from storage sheds while Phase II will 

address quantification of fire hazard from recreational vehicles (RVs) and non-commercial vehicles. In 

preparation for the forthcoming experiments on RV fires, this project focused on reviewing literature on 

vehicle fires overall, and to analyze published data including peak heat release rate(PHRR), temperatures, 

smoke release, and mass loss rate. The experimental set-up, the instrumentation information, and test 

procedures including sources and locations of ignition were reviewed. 

The understanding of the burning behaviors of several vehicles including the sedans, minivans, and heavy 

goods vehicles(HGV’s) facilitated estimation of the RV fire hazards. Analysis of the HRR data from 

literature revealed PHRR ranging from 1.9 MW to 202 MW for the vehicle fires and showed a positive 

linear correlation between the mass of a vehicle and the PHRR. The estimated PHRR of the RV used in 

the upcoming experiments is expected to be in the range of 28 MW ± 10 MW given that the RV mass will 

vary approximately between 2485 kg ± 100 kg. The temperatures recorded during vehicle fires typically 

ranged from 900°C to 1300°C. Analysis of RV fire statistics indicated that the most common area for the 

origin of a fire was the engine, the running gear, and the wheel area and that the ignition occurred due to 

worn-out wires or friction that generated intense heat. 

2022 SURF Colloquium 42
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Catherine McCarthy

Georgetown University Physics

Graduated

Physics PhD at the University of Massachusetts, Amherst

Engineering Laboratory, Materials and Structural Systems Division (MSSD), Infrastructure Materials 

Group

Abhay Goyal 

Ion specificity and the Hofmeister series in charged colloidal systems

Charged colloidal systems, a class of materials that ranges from biological membranes to hydrated 

cement, contain charged nanoparticles suspended in an aqueous ionic solution. Cohesive forces between 

these charged particles arise due to the formation of hydrated-ion structures that dramatically reduce the 

dielectric screening capacity of water. This effect becomes most pronounced at high confinement. Since 

the size and geometry of the hydrated-ion structures depend on the size and valency of the ionic species, 

the cohesive forces in charged colloidal systems are naturally ion specific as well. We use semi-atomistic 

Monte Carlo and Molecular Dynamics simulations of cationic counterions and explicit water molecules 

confined between charged surfaces in order to investigate the role of ion specificity in charged colloidal 

systems. We find that the interplay of ion size and valency, in combination with the complex manner in 

which those factors interact with the surface charge density and confinement, is responsible for 

differences in the nanoscale structure and cohesive forces of systems with different species of 

counterions. Furthermore, for highly charged, dense systems, we link the magnitude of the cohesive 

force between colloidal particles in systems with different species of counterions to the order of the 

Hofmeister series and discuss the various aspects of ion specificity that contribute to the order.

*This research was supported by the Hichwa Fellowship at the Georgetown University Department of

Physics, the Clare Boothe Luce Program, and the NIST SURF Program.

2022 SURF Colloquium 43
SURF Student Colloquium

NIST – Gaithersburg, MD

August 2-4, 2022

Name: Oren Minsk

Academic Institution: University of Pennsylvania Major: Mechanical Engineering

Academic Standing

(Sept. 2022): Sophomore

Future Plans

(School/Career): Undecided, likely will pursue masters degree in some engineering field

NIST Laboratory,

Division, and Group: Engineering Laboratory, Systems Integration Division, Life Cycle Engineering Group

NIST Research

Advisor: Dr. Nehika Mathur

Title of Talk: Measuring and improving the performance of industrial synergistic networks

Abstract:

The circular economy is a paradigm for a more sustainable economy in which raw materials from the Earth have

a cyclical life that involves them being shared, repaired, reused, and recycled. Industrial symbiosis is a process

where byproducts or waste of one manufacturing firm are used as feedstock by another firm, and therefore

industrial symbiotic networks play crucial roles in facilitating a circular economy by reducing our dependence on

natural resources and mitigating supply chain risks. Only one large and efficient industrial symbiotic network has

existed for a number of decades, and it’s located in Kalundborg, Denmark. This project focuses on an industrial

network of transactions, mainly in Ohio, and aims to provide recommendations for how it can be improved, with

the motivation of increasing the presence of industrial symbiosis in American manufacturing. The network is

manipulated to experiment with how different real-life modifications could potentially improve the n twork.

These modifications include observing the network transactions year-by-year, duplicating the network, and

adding hypothetical nodes and edges. Social network analysis is implemented in an attempt to quantify the

improvements made by the modifications compared to the baseline network. Metrics including density, a

number of centrality measures, and network efficiency are calculated, analyzed, and used to gain understanding

of the network and aforementioned modifications. Breaking down the Ohio network year-by-year reveals that

almost no transactions were made in multiple years, suggesting that involvement and retention are challenges.

Existing literature aligns with our findings and would suggest improving personal relationships between firm

managers and increasing use of similar materials by firms.

2022 SURF Colloquium 44
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϭ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Savannah Moldvay

University of Maryland, College Park Civil Engineering

Sophomore

Purse a career in Civil Engineering

Engineering Laboratory, Building Energy and Environment Division, Mechanical Systems and 

Controls Group

Michael Galler, Natascha Milesi-Ferretti

Investigating Building Performance Using The Commissioning Software HVAC-Cx

Owners of large Commercial buildings, such as college campuses, are investing millions of dollars for 

heating, ventilation, and air-conditioning (HVAC) systems. The expectation for these systems is to 

consistently operate and function efficiently with minimal energy waste. Yet, many are performing 

inadequately. This leaves employees operating in less than ideal work environments, building owners 

paying larger energy bills, and huge productivity losses because of inefficient HVAC systems. 

 As a response, this project seeks to improve the performance of HVAC systems by identifying and 

addressing equipment performance faults. Michael Galler and Natascha Milesi-Ferretti developed the 

HVAC Commissioning (Cx) tool at the National Institute of Standards and Technology (NIST). 

HVAC-Cx is a semi-automated commissioning software tool designed to assist in the analysis of HVAC 

systems in commercial buildings. This software conducts its analysis through detecting faults related to 

improper operation, which are then illustrated through detailed graphs. These graphs are then 

incorporated in the documentation of the final results. 

 For this project, we were able to review HVAC data from both NIST and the Robert E. Parilla 

Performing Arts Center (PAC) at Montgomery College. The data we received is analyzed by HVAC-Cx 

to test the performance of the HVAC systems. This evaluation is formed into a final report and helps 

inform the building owners of the system’s current operation. From the PAC, we currently have a short 

period of time in which data was evaluated. This brief amount of data causes difficulty in assessing the 

full operation of the HVAC system. Therefore, in the future we plan to obtain larger spans of data. Yet, at 

this time we have analyzed the available PAC data from January 11th to February 24th of 2022. From this 

period, data was collected from multiple components of the HVAC system. A few examples of the types 

2022 SURF Colloquium 45


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Tejas Nazare

University of Maryland, College Park Economics/Mathematics

Rising sophomore

Pursue graduate studies in economics, enter industry or research

Engineering Lab, Applied Economics Office

Dr. Jennifer Helgeson

Understanding the Evolution of “Sustainability” and “Resilience” in Literature

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The climate crisis has been characterized by climate change, erratic weather patterns, environmental 

degradation, and increasing food insecurity. According to the United Nations website, climate change is a 

major threat to international peace and security. Climate change has been brought to a forefront in politics, 

popular media, and scientific research in recent years. Consequently, research on strategies to combat the crisis 

has increased significantly. 

Two major concepts are at the heart of this research — sustainability and resilience. The terms have been used 

with increasing frequency and consist of a significant segment of recent literature. This project takes on a 

structured literature review and analysis of trends in the study of sustainability and resilience. 

The methods of this research included a systematic review of the use of these terms in the literature and in the 

popular press. The Web of Science database was used to conduct an analysis of the use of the terms across 

disciplines. Using the Web of Science platform and Matlab, code was developed to create insightful 

visualizations regarding the number of pertinent papers, distribution amongst scientific categories, and 

citation/reference numbers. Furthermore, I augment this analysis with analysis using Lexis-Nexis and the NSF 

grants manager search engine to better understand the extent to which these areas are being studied (through 

funded grants) and communicated to the public.

I found that since 2012, both sustainability and resilience have become highly cited in the literature, with 

resilience following sustainability. In addition, the study of sustainability has evolved from being somewhat 

evenly distributed amongst categories to being heavily centered on environmental sciences and green and 

sustainable science technology. The study of resilience has expanded from primarily environmental fields to 

other fields, such as psychiatry. 

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2022 SURF Colloquium 46
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϭ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Dominique Paige

Morgan State University Cloud Computing

Sophomore

Tech Job

Engineering Lab, Materials and Structural Systems Division, Structures Group

Marc L. Levitan & Nico de Toledo

Development & Analysis of a Database of Tornado Impacts on Fire & Rescue Stations

Despite the severe threat that tornadoes pose, there is still much to learn about how they affect critical 

facilities in the United States. Due to the high likelihood of collapsed structures and other emergencies 

after a tornado, it is crucial that fire, rescue, and emergency medical services stations are not damaged. 

While other studies have documented the impacts of specific tornadoes and tornado outbreaks on fire, 

rescue, and emergency medical services stations, there is still a gap in knowledge about the cumulative 

national impact of tornadoes on these critical facilities. To fill this gap we are creating a database of these 

impacts by mining National Weather Service tornado narratives from the National Centers for 

Environmental Information’s Storm Events Database. Our database covers United States tornadoes from 

January 1993 through February 2022. The mining process consists of filtering all the narratives for ones 

that describe a tornado striking the property of a fire, rescue, or emergency medical services stations and 

then extracting details about the impact. 

First, we generated a list of keywords, like “fire” and “squad,” in an effort to discover every case when 

tornado impacts on fire, rescue, and emergency medical services stations were mentioned. We tested and 

refined the list of keywords to limit the number of false positives. We used the keywords to filter the 

narratives in Microsoft Excel and then read through each narrative to see if one of these facilities was 

indeed hit by a tornado. If so, we recorded details including a description of the damage, the station name, 

and station coordinates. 

This database will aid in decisions about whether to require tornado-resistant engineering design 

standards and/or storm shelters in fire, rescue, and emergency medical services stations to ensure that 

these critical facilities are able to provide emergency services after a tornado. 

2022 SURF Colloquium 47
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Robert Pang

University of Maryland College Park Mechanical Engineering

Junior

Finish bachelor's degree at UMD

Engineering Laboratory

Kenny Kimble, Joe Falco

A Biofidelic Impact Dummy to Evaluate the Safety of Collaborative Robot Applications

As technology advances, work in the manufacturing industry is automated by robots more and more. 

Collaborative robot systems allow humans and robots to work more closely to create a more efficient 

workflow. Collaborative robots need to be tested in order to determine their safety before being allowed 

to be used in a collaborative environment. ISO/TS 15066 provides a list of biomechanical limits on force 

and pressure for various body parts in case of human-robot impact. Testing with real humans is dangerous 

and risks harm, so a safety test dummy should be used instead. The test dummy should mimic the mass 

properties, mechanics, and anthropometry of the human body. The test dummy design should be 

repeatable, easy to manufacture, easy to set up, easy to use, easy to calibrate, and minimize cost. 

A dummy design based on an accurate model of a 50th percentile male, (determined by the US National 

Highway Traffic Safety Administration (NHTSA) THOR automotive crash dummy) is leveraged. Each 

region of the body is designed and tested separately beginning with the right forearm, then completing the 

arm, and eventually the entire body. The forearm and upperarm have been designed and tested, validating 

the process and results of the design. The design and implementation of a hand is next. If pressures at 

each location are below a corresponding threshold, the robot may be approved.

2022 SURF Colloquium 48




	

	







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2022 SURF Colloquium 49
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Matthew Pham

University of Maryland Mechanical Engineering

Junior

Pursue a career as a mechanical engineer

Engineering Laboratory, Materials and Structural Systems Division, Inorganic Materials

Dr. Nicos Martys

Structural Stability Analysis: Application to 3D Printing of Cement-Based-Materials

Concrete is one of the oldest and the most widely used building materials. Despite it's 

history of usage, there is interest in making it more sustainable, reducing its impact on the environment 

and in developing new ways of placing it. Recently, new technologies based on 3D printing 

have been developed that have the potential to revolutionize the process of placing concrete. 3D printing 

is less labor intensive and allows for more possibilities in architectural design. For a new technology to be 

accepted, standards and guidelines have to be developed for it's usage that insure the quality 

of construction. 

In this research project we investigate the stability of certain structural designs that may typically be 

3D printed. We varied the geometry of structures as well as investigated the tolerance for stability 

based on small variation in its placement and different applied forces. We also examine what minimum 

material properties are required to maintain the stability of various structures. 

We utilized commercial software to design and compute the response of structures to external forces as a 

function of design and material properties such as Young's Modulus, Poisson's ratio, and density. 

The results of these studies provide insight to the geometrical constraints as well as material properties 

necessary to perform a successful 3D print. 

2022 SURF Colloquium 50
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Ainsley Rexford

University of Pennsylvania Mechanical Eng. and Applied Mechanics

Sophomore

Most likely pursue a masters degree in product design or robotics.

Engineering Laboratory, Intelligent Systems Division, Production Systems Group

Gregory W. Vogl, PhD.

Smarter Manufacturing: How images can help monitor thermal drift of machine tools

Precision manufacturing is increasing in importance as manufacturers seek to make their processes faster 

and cheaper while still producing quality parts. A major hurdle in the quest for precision machining is 

thermal drift, which can account for up to 70% of machining accuracy errors. This study focuses on 

developing and testing a new method to monitor the thermal drift of machine tools using videos of dot 

calibration grids taken with a high-magnification wireless microscope. To do this, a dot calibration grid is 

fixed at each corner of the machine tool worktable and a wireless microscope camera is mounted in the 

tool holder of the spindle. Since the machine tool can move in three dimensions (X, Y, and Z) and the 

spindle can rotate, videos with various types of machine motion can be captured and analyzed to 

determine the thermal drift of the spindle relative to the worktable. Next, these videos are parsed into 

images to segment the dots and track the individual X and Y dot center positions. The Z dot position is 

estimated by finding the Z position that yields the maximum contrast of dot edges. The tracked positions 

are then inputted into a two-dimensional (X and Y) or three-dimensional (X, Y, and Z) linear model to 

solve for the 2D or 3D changes within the machine tool. For example, the 2D model contains six 

thermally induced variables that are solved in a least-squares manner: X translation, Y translation, Z 

rotation, XY squareness change, and XY planar thermal growth coefficients. Experiments were conducted 

to test the ability of this new technique to measure commanded translations as large as 50 micrometers. 

Data analysis revealed that this method was accurate at a micrometer-level and is an effective way of 

measuring thermal drift. Overall, a relatively cheap wireless microscope and small calibration grids can 

be used to track thermal drift in near real time. Such an approach has significant future commercial 

potential for diagnosing and correcting thermal drift in machine tools which will advance manufacturing 

precision and efficiency.

2022 SURF Colloquium 51
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Mary Ruxsarash

American University Chemistry

Junior

PhD, Graduate School

Engineering Lab

Stephanie Watson

Characterization of Components for a Pyrrhotite Reference Material

Damage to residential and commercial concrete structures in Connecticut have been attributed to the 

oxidation of the iron sulfide mineral pyrrhotite. Sulfates that are released from pyrrhotite oxidation react 

to cause internal sulfate attacks and further expansions, contributing to long-term concrete cracking and 

crumbling. There are currently no standardized test methods to assess pyrrhotite occurrence and 

abundance in aggregate or in concrete. Therefore, developing a standard test method and a set of 

calibration reference standards is crucial in curating an accurate and consistent analysis of pyrrhotite in 

concrete. 

This project aims to assess and quantify the content of pyrrhotite in concrete foundations by 

developing reference materials, characterizing major mineral species in these reference materials, and 

determining consistent and effective standard test methods for pyrrhotite analysis. The reference material 

consists of a representative aggregate, Ottawa sand, and hydrated cement that can be mixed into various 

concentrations to create calibration curves for pyrrhotite measurements. We’ve approached testing the 

material with two methods: X-ray fluorescence (XRF) and X-ray diffraction (XRD). The data was 

analyzed for mineral composition using various methods and programs, such as Highscore, Profex and 

Microsoft Excel. This project will offer a valuable glimpse into developing a standard test method, 

including a set of calibration reference standards, which will provide a means for accurate and consistent 

analysis of pyrrhotite in concrete. 

2022 SURF Colloquium 52
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Jonathan Smilovich

The City College of New York Environmental Engineering

Senior

MBA Graduate School

Engineering Lab

Dr. Stephanie Watson

Automation of Data Analysis for Rapid Characterization of Spectroscopic Imaging Maps

Molecular spectroscopic techniques, including Fourier Transform Infrared (FTIR) and Raman 

spectroscopies, have made advancements using microscopy to collect spectroscopic maps of 

specific locations for chemical structure identification for materials applications. These 

spectroscopic maps can contain thousands of spectra, which can take many hours to process 

and analyze. Furthermore, each spectrometer vendor requires a unique macros or automation 

script to process (i.e., baseline correct, normalize and peak ratio) each series of spectra. 

Consistent data processing is essential to effectively track chemical changes in materials with 

our laboratory aging experiments. We would like to create a general script or series of scripts to 

process spectra from FTIR and Raman spectral maps using Python or other programs that can 

be applied to multiple instruments. This project focused on Raman mapping of photovoltaic 

backsheet materials. OPUS was used to extract the spectral data from the Raman maps. 

Baseline correcting is then applied to remove any background noise and align the spectra 

measurements. Origin was then used to normalize the data and implement methods to quantify 

the chemical changes with degradation. Improving efficiency of this process is the main focus of 

this project. Automation of selecting spectra, baseline correcting, and normalizing was then 

performed. OPUS Macros have been utilized in combination with elements of Python to 

automate the process. The automation script would then allow any spectral file to be efficiently 

analyzed. The output then consists of mapping the peaks of the various spectra for further 

analysis.

2022 SURF Colloquium 53
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Emmy Smith

The University of Alabama in Huntsville Industrial/Systems Engineering & Math

Graduated

Graduate School

Engineering Laboratory, Systems Integration Division, Information Modeling and Testing Group

Dr. Shaw Feng

Additive Manufactuirng Data Registration Software Development

Laser-powder bed fusion processes for metal Additive Manufacturing allow for more flexibility in metal 

component manufacturing. The quality of these parts needs to be observed and studied to ensure 

components meet design requirements for the targeted applications. The quality is determined through 

the amount and size of defects, or air pockets, present in the metal. The goal of this study is to take X-ray 

Computed Tomography images of the metal and develop software to determine the defects present. The 

main focus is creating ground truth images for artificial intelligence. The ground truth images will be 

used as baseline images for artificial intelligence training through U-NET. This method completes image 

segmentation which groups images based on their values and assigns them to classes. The ground truth 

images will train the images to recognize the classes that are defects. 

These ground truth images are created through a process of image segmentation and thresholding. The 

defects appear as dark spots and the algorithm detects dark areas and creates a rectangular bounding box 

that contains the entire dark area. These boxes are then used with a thresholding algorithm to determine 

the defects. This thresholding is run with multiple thresholding methods like Otsu. The new images with 

the specific boundaries are then compared to the originals. The images that are found to be accurate maps 

are selected as ground truth for the machine learning baseline. 

2022 SURF Colloquium 54


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Ethan Sundel

 University of Pennsylvania Mechanical Engineering

Sophomore

Finish my bachelor’s degree and pursue a master’s degree

Engineering Laboratory, Intelligent Systems Division, Emergency Response Robots and Drones

Adam Jacoff

Using Standard Tests to Create a Public Drone Park

NIST is creating a set of standardized test methods to quantify the performance of emergency 

response drones as well as pilot proficiency. First responders can use these standard tests to 

train and measure their ability to fly unmanned aerial vehicles in emergency scenarios. NIST’s 

main aerial tests include apparatuses that are inexpensive and easy to fabricate created from 

wood and plastic buckets. They are constructed in specific manners to ensure that they test the 

pilot and drone on proficiency in many different areas.

The first part of this project was for me to learn about NIST’s current aerial tests and learn how to 

proctor them in order to gain perspective into how the tests function. I was given the opportunity 

to travel to a robotics competition as well as a STEM camp which allowed me to gain firsthand 

experience proctoring these tests. Along with proctoring, by the end of the summer I will have 

obtained a few different credentials including FAA Safety Team Certificate for the NIST Open 

Test Lane, FAA remote pilot license (Part 107), and Airborne Public Safety Association (APSA) 

Advanced Proctor Certificate for NIST Open and Obstructed Test Lanes and Scenarios.

The end goal of this project is for me to design a more durable version of NIST’s tests. This 

version of the tests will create a public drone park in Philadelphia, Pennsylvania. By creating a 

more durable version of the tests using different materials, they will be able to withstand all 

weather conditions for an extended period of time, with little to no maintenance required. As 

drone technology advances, more pilots are being credentialed by the FAA in order to increase 

the use of drones in emergency response scenarios. This drone park will serve as an effort 

towards helping local first responders and anyone else in the Philadelphia area measure their 

proficiency and improve their skills in the field.

2022 SURF Colloquium 55
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Arin Zeng

University of Maryland College Park Computer Science

Sophomore

Earn a B.S in Computer Science and Applied Math

Engineering Laboratory

Helen Qiao

Optimizing Functionality of the Smart Target Data Collection System

Recently, the use of robots in high precision tasks have greatly increased such as 3D printing, automated 

manufacturing, robot assembly, etc. This has presented a challenge in keeping the robots performing 

these tasks precise and at high quality. The smart target detection system uses computer vision to 

recognize the positions of objects using a 6D (points and vectors) measurement system. This technology 

is useful in detecting mechanical degradation in robots. Currently, the industry best practice to fix 

degradation is to conduct local calibrations or in process inspection to confirm that degradation has not 

occurred to the extent to damage robot quality. The smart target vision system aims to improve 

maintenance responsiveness of the robot to maintain a higher quality of robot health. The smart target 

object is viewed through two camera lenses to determine its position. Functions to load in preset camera 

parameters and to identify the positions of each colored (red, green, blue) lines were added. The goal and 

task is to optimize performance of this tool and to debug the software tools of the Smart Target Data 

Collections program. Adding the individual color detection lines and loading preset camera parameters in 

will make it easier to collect data and measurement. The purpose is to increase the efficiency of 

measurement and improve the technology transfer to industry. Having a computer program that is able to 

accurately and efficiently detect the positions of objects that have the smart target sensor will help 

humans maintain the quality of robots and effectively prolong the healthy performance of the robots. 

2022 SURF Colloquium 56
2022

2022 SURF Colloquium 57
Summer Undergraduate Research Fellowship (SURF) - 2022 Participants

Information Technology Laboratory (ITL)

Robert Bao

Mitchell Campbell 

Maggie Kristina Cavitt

Sheldon Douglas

Francis Durso 

Jason Eveleth Samuel 

Galita Evelyn Hu 

Jaxon Ko 

Mikhail Krepets 

Ivy Liang 

Jane Liu 

Miguel Lopez 

Johannes Losert 

Abishay Reddy 

Samuel Rennich 

Vaibhav Sanjay

Yuvan Sundrani

Kyle Truong 

Ayush Varshney 

Joshua Zarb

2022 SURF Colloquium 58
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career):

NIST Laboratory, 

Division, and Group:

NIST Research 

Advisor:

Title of Talk:

Abstract:

Fluorescence guided surgery is an important tool for surgeons to accurately identify and remove cancerous 

tumors. This work is motivated by the spatial misalignment of real-time streaming brightfield images and 

fluorescent images with tumor indications acquired by a fluorescence guided hand-held imaging system during 

a head and neck surgery. The spatial misalignment of brightfield and fluorescent images pose challenges for a 

surgeon who is deciding where to remove tumor tissue with significant consequences for a patient. 

The problem of spatially aligning (registering) two multimodal images involves designing an automated method 

for estimating registration transformation parameters. The challenges include (a) achieving high spatial 

accuracy for tumor tissue removal, (b) overcoming limitations of existing algorithms that are optimized for 

monomodal images and assume many spatial features in both modalities and (c) decoupling the intermodality 

transformation and registration tasks in multimodal algorithms .

We approached the multimodal registration problem by (a) creating ground truth data by manually registering 

paired images (b) evaluating the effectiveness of traditional image registration algorithms such as SIFT, and (c) 

training and testing an unsupervised generative adversarial network (GAN) called NeMAR. The NeMAR 

method consists of a spatial transformation (registration) network, intermodality translation network, and 

discriminator network. 

By testing different configurations of NeMAR with artificially generated training images, training iterations, and 

network types, we found the optimal NeMAR configuration with respect to our ground truth registered images. 

The method’s accuracy increases with the number of input image pairs, but remains about the same for over 

200 epochs of training. 

In summary, while machine learning methods show promise in multimodal registration tasks, a robust 

GAN-based method would require a large training dataset sampling from a variety of surgical environments. 

Future research will compare a supervised machine learning approach to our unsupervised GAN-based 

approach with more training data.

Robert Bao

University of Illinois at Urbana-Champaign Computer Engineering

Sophomore

Attend graduate school

PML/ITL

Peter Bajcsy, Maritoni Litorja

Multimodal image registration for fluorescence guided surgery

Fluorescence guided surgery is an important tool for surgeons to accurately identify and remove cancerous 

tumors. This work is motivated by the spatial misalignment of real-time streaming brightfield images and 

fluorescent images with tumor indications acquired by a fluorescence guided hand-held imaging system during

a head and neck surgery. The spatial misalignment of brightfield and fluorescent images pose challenges for a 

surgeon who is deciding where to remove tumor tissue with significant consequences for a patient.

The problem of spatially aligning (registering) two multimodal images involves designing an automated method 

for estimating registration transformation parameters. The challenges include (a) achieving high spatial 

accuracy for tumor tissue removal, (b) overcoming limitations of existing algorithms that are optimized for 

monomodal images and assume many spatial features in both modalities and (c) decoupling the intermodality

transformation and registration tasks in multimodal algorithms .

We approached the multimodal registration problem by (a) creating ground truth data by manually registering

paired images (b) evaluating the effectiveness of traditional image registration algorithms such as SIFT, and (c)

training and testing an unsupervised generative adversarial network (GAN) called NeMAR. The NeMAR

method consists of a spatial transformation (registration) network, intermodality translation network, and 

discriminator network. 

By testing different configurations of NeMAR with artificially generated training images, training iterations, and 

network types, we found the optimal NeMAR configuration with respect to our ground truth registered images.

The method’s accuracy increases with the number of input image pairs, but remains about the same for over 

200 epochs of training.

In summary, while machine learning methods show promise in multimodal registration tasks, a robust 

GAN-based method would require a large training dataset sampling from a variety of surgical environments. 

Future research will compare a supervised machine learning approach to our unsupervised GAN-based 

approach with more training data.

2022 SURF Colloquium 59
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Mitchell Campbell

Virginia PolyTechnic Institute and State University Computer Science

Senior

Plan on pursuing a career in software development after obtaining my Bachelor's degree.

Information Technology Laboratory

Sandy Ressler

Enhanced Viewing of 3D Objects Scanned using Photogrammetry

 Most of a museum's collection is held in storage due to a lack of space for public displays. One solution 

for displaying these stored artifacts is to create 3D models of them. This can be done using 

photogrammetry, a technique for creating 3D virtual models of objects by taking many pictures of an 

object from different angles and using software that inputs camera images in order to reconstruct a virtual 

model mesh. These models are then saved as glTF files. glTF (GL Transmission Format) is a file format 

used to store 3D models and scenes, and is becoming an ISO (International Organization for 

Standardization) standard. 

 This study focuses on features that can be implemented to improve the user experience of viewing 

imported glTF models. Implemented features are presented to the user as a series of tools that can be 

interacted with through an on-screen HUD (heads-up display). Some of these tools include a light that 

follows the mouse cursor to brighten a model and annotations for describing individual parts of a model. 

Annotations are presented to the user through a separate HUD window that appears when clicking on an 

object. All tools were developed in AFRAME, a web framework that uses HTML and Javascript to create 

3D scenes which are viewable through a web browser and virtual reality devices.

2022 SURF Colloquium 60
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Maggie Kristina M. Cavitt

ECPI University CIS (Cybersecurity)

Junior

My goal is to obtain a graduate degree and work on high-performance or quantum computer research.

Information Technology Lab (ITL)

Hung Trinh and Katherine Schroeder

Visualizing Cybersecurity Vulnerabilities and their Role in Recent Cyber Attacks

The influx of recent large-scale cyber-attacks has created the need to understand how known 

cybersecurity vulnerabilities impact the integrity, availability, and confidentiality of network 

infrastructures across all business and government sectors. To aid in cybersecurity awareness efforts the 

NIST and its team of researchers are working to furnish the cybersecurity community with well-informed 

datasets/metrics. The goal if this project is to capture the process of enhancing cybersecurity related data 

through a wide range visualization software and resources. Utilizing the NIST’s National Vulnerability 

Database (NVD), a breakdown of verified cyber vulnerabilities with each vulnerability’s criticality score 

and influencing factors, we can identify some of the most common types of network/system 

vulnerabilities. Together with additional open-source resources such as CISA, the U.S. Department of 

Health and Human Services Office for Civil Rights, and others we can establish multipoint connections 

and create approximate reference relationships between the NVD’s vulnerability data and a significant 

number of reported cybersecurity incidents. From these connections we are then able to create 

visualizations that depict the correlations and possible influencing factors between many of the published 

vulnerabilities and recent incidents/breaches. By utilizing multiple data visualization services like 

PowerBI, Splunk, and ElasticSearch we can then create unique visualizations and compare similar 

findings from across the different services to validate the results. Once we are able to complete this 

process, we can then apply a method for tagging the data that will assist information security personnel 

and developers in determining how to prioritize implementing patches for these vulnerable systems.

2022 SURF Colloquium 61
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Sheldon Douglas

University of Maryland, College Park Computer Science

Sophomore

To pursue a master's degree in computer science leading to a career in software engineering or cyber 

security.

Information Technology Laboratory

Alexander J. Nelson

Geometric augmentations to file identifiers in file system forensics

Digital forensics is a process that is key to examining and interpreting data in cyber related investigations. 

File system forensics makes up a significant portion of digital forensics as it is logically sorting through 

hard drive storage to determine creations, deletions, and other data essential to event reconstruction. 

Important to the functionality of file systems is the principle of namespace uniqueness, which uses file 

paths and names as identifiers that can distinguish file objects. In the world of digital forensics, there are 

several libraries used, one being, The Sleuth Kit (TSK). Within it is a command, 'fiwalk', whose purpose 

is to convert raw disk images' metadata into extensible markup language (XML) but does not populate 

with the guarantee of namespace uniqueness due to its reporting of unallocated ("deleted") files. This 

reporting of unallocated files means that file names cannot be relied on as identifiers. Considering the 

need to review unallocated files, logical code changes that focus on incorporating new identifiers for file 

objects are made necessary. This work evaluates a practice that identifies: the start of an index node (a 

file's attributes), the start of the directory entry, and the start of the file's content, producing a 

three-dimensional address for each file object. Subsequently, reported results from a 2012 paper that 

contain a measurement discrepancy will be corrected and additionally, this research will enable better 

cross-tool comparison.

2022 SURF Colloquium 62
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Francis Durso

Johns Hopkins University Computer Science

Full-Time Student, Junior

Software Developer / Programmer / Cyber-Security Consultant

Information Technology Laboratory, Computer Security Division

Dr. Raunak and Dr. Kuhn

Addressing the Causes and Consequences of AI Failures

Artificial Intelligence (AI) has become increasingly prevalent in nearly all areas of life. It now controls 

our thermostats, drives our cars, produces our electronics, recognizes our faces, evaluates our resumes, 

predicts our purchases, and so much more. But what happens when AI systems fail, and how can we learn 

from these failures to prevent such incidents in the future? Our project proposes a framework for 

characterizing these AI failure incidents, and provides a structured way of documenting them in an online 

repository. 

This repository is designed to address some key concerns. First, it will provide all known, verifiable 

information about the AI failure incidents in a convenient, searchable manner to allow users to discover 

and learn about these incidents with some level of technical depth. Secondly, it will allow users to report 

incidents as new ones are discovered, ensuring that the data remains up to date. Lastly, it will address the 

issue that Machine-Learning based AI systems tend to be black boxes. While such systems often succeed 

in achieving remarkable levels of accuracy, they rarely provide much understanding of their decision 

making process or the factors that influence its decisions. Our proposed characterization of the AI-failures 

will help us gather information that could shed light on this aspect. 

Regular software vulnerability documentation efforts, such as NIST's National Vulnerability Database 

(NVD), require some knowledge about the inner workings of the code to be analyzed effectively. Such 

details are a lot harder to garner when it comes to AI/ML failures. Our proposed framework - Failures of 

Artificial Intelligence Learning Systems (FAILS) - captures the causes for the incident, the sources of 

weaknesses involved, and a measure of impact that the failure caused without needing to know the details 

of the code. In short, all the information needed to ensure it doesn’t happen again.

2022 SURF Colloquium 63
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career):

NIST Laboratory, 

Division, and Group:

NIST Research 

Advisor:

Title of Talk:

Abstract:

Jason Eveleth

Brown University Math-Computer Science

Senior

Pursue a PhD in math or computer science

ITL, Applied and Computational Mathematics Division, Mathematical Analysis and Modeling

Dr. Anthony Kearsley

High-dimensional consensus mass spectra comparison

 Mass spectrometry (MS) is an analytical chemistry technique for analyzing compounds. It provides a 

signature--called a mass spectrum--that can be used for compound discrimination. That signature is a 

scatterplot of charged fragments of the substance. One popular application is forensics chemistry, where 

drug chemists are trying to determine whether seized evidence is an illicit drug. 

 The traditional method for discriminating mass spectra in forensic chemistry is to bin the scatterplot 

into a vector (essentially a histogram) and take the cosine similarity between the vectors. While generally 

effective, this method can occasionally lead to misidentifications. We recently developed two novel 

methods for incorporating measurement variability when comparing mass spectra to limit the likelihood 

of misclassifications. 

 The first method works by binning the mass spectra--identical to the traditional approach--but then 

uses the mean and standard deviations of the bins across replicate measurements to form a 

summary-statistic vector. The second method works by taking the n highest y-valued points in the mass 

spectra and finding the mean and standard deviation across replicate measurements of that value and 

using the statistics to represent the compound. We use these summary-statistics as a more informative 

way to compare compounds. 

 We have implemented these methods in C and performed preliminary evaluation using experimental 

data collected with two different types of mass spectrometers. We have found good performance in the 

discrimination of current drugs of interest (methamphetamine vs phentermine, nicotinamide vs 

isonicotinamide) and are currently evaluating the performance of these new methods across a larger test 

set of mass spectra that are difficult to discriminate by the traditional method, including applications 

outside of seized drugs.

2022 SURF Colloquium 64
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Samuel Galita

University of Maryland Finance & Mathematics

Sophomore

Considering a career in data science or quantitative finance

Information Technology Laboratory

Min Ding

Exploring Graph Analytics on Nisaba GPU cluster with cuGraph 

Graph algorithms and Graph analytics are designed for manipulating and analyzing graph types of data to 

determine the relationships between graph objects and the structural characteristics of a graph as a whole. 

They have been adopted and used heavily in fields such as social networking, route optimization, fraud 

detection, and so on. A major issue with modern graph analytics is that it is usually challenging to 

perform the algorithms quickly or with high computational efficiency at a large scale. Graphics 

Processing Units (GPUs) can be utilized to accelerate graph data analysis and machine learning. Recently, 

NVIDIA produced the open-source graph analytics library cuGraph, which operates directly on GPU 

DataFrames and provides a collection of GPU accelerated graph algorithms with NetworkX-like API that 

can be treated as an efficient graph analytics solution for Python users. 

The purpose of this project is to evaluate benchmark graph analysis algorithms on NIST’s Nisaba GPU 

cluster and compare the quantitative performance of cuGraph with other CPU-based graph analysis tools, 

such as NetworkX and NetworKit. Both synthetic and real world datasets are employed to benchmark the 

common network analysis algorithms among six categories, namely Katz for centrality analysis, Louvain 

for community detection, Breadth-First-Search (BFS) and Single-Source-Shortest-Path (SSSP) for graph 

traversal, Weakly Connected Components for component detection, and PageRank for link analysis. 

Furthermore, cuGraph supports multi-GPU and multi-node operations (MNMG) in conjunction with Dask 

(Dask cuGraph). Dask cuGraph was also benchmarked alongside cuGraph and NetworkX. Through our 

reproducible experimentation, we were able to identify large performance increases when using the 

cuGraph library compared to both NetworkX and NetworKit, as well as unprecedented scalability of 

graph analytics using multiple GPUs. A GitLab repository was created to allow future users to test the 

cuGraph benchmarks with their own specifications and provide implementation examples for using the 

library. 

2022 SURF Colloquium 65
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Evelyn Hu

 UC Berkley Computer Science

Sophomore

Graduate with a bachelor’s degree and pursue a career in technology

Information Technology Laboratory, Computer Security Division

Dr. Michaela Iorga and Nikita Wootten

Dynamic Access Review and Control Implementation and Enforcement (DARCIE)

As cloud services are becoming more widely adopted, the amount of data available to members of an 

organization is vastly increasing along with the risk of data breaches.This project develops an access 

control mechanism that dynamically reviews, implements and enforces access control policies in real-time. 

The mechanism ensures granularity of control through privilege access management, allowing the system 

to control user access to resources. The access control mechanism enforces zero-trust policies so that users 

are continuously authenticated and are granted or denied access to the sensitive information based on their 

geolocation, organization’s network availability and historic pattern of accessed resources.

Our proof-of-concept system uses two devices to simulate a user accessing local or cloud data. A virtual 

machine acts as the end user's device and another device acts as a router that simulates different 

geolocations from where data in the cloud is accessed. The system demonstrates a dynamically changing 

policy generated by the state of a sensor and enforced by the kernel using Security-Enhanced Linux 

(SELinux). In this case, the demo system limits a user’s access to some system resources based on the 

device’s connection to an access point, simulating a dynamically generated access policy based on 

geographic location. Future work will focus on access control policy review and control implementation 

and enforcement rules also derived from the user's historic pattern of accessing the resources of interest.

2022 SURF Colloquium 66
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Jaxon Ko

University of Maryland, College Park Computer Science

Senior 

Complete my bachelor's degree in Computer Science and work full-time as a Software Developer

Information Technology Laboratory, Software and Systems Division, Information Systems Group

Derek Juba

Benchmarking Queries from Zeno against FCPW

NIST has developed a software called Zeno, which estimates material properties from a geometric model 

of a particle of said material. One of the main computational tasks Zeno performs is to compute the 

closest point on the model to a query point. In addition, a newly proposed algorithm in Zeno would need 

to determine whether a part of a geometric model is contained within another. Currently, Zeno uses an 

internally-developed library to compute its closest point queries. However, using another open-source 

library may prove to be more optimal. In this project, we benchmark the closest point and contains 

queries performed by the current Zeno library against those performed by the “Fastest Closest Points in 

the West” (FCPW) library. The results of these benchmarks will help us decide whether or not the 

internally-developed Zeno library should be replaced with the FCPW library when implementing the new 

Zeno algorithm. 

To obtain the benchmarks, we created C++ programs using either library. Users can specify a .obj file for 

the program to construct its geometric model, a query type (either the closest point or contains query), 

and a number of random query trials to run. The programs will time how long it takes to construct the 

geometric model (preprocessing time) and how long it takes to compute all the query trials. We then used 

Python scripts to calculate benchmarking statistics for different .obj files, query types, and trial runs. 

These statistics were plotted using various double bar graphs to help visualize patterns and directly 

compare each library's preprocessing and query times. Early tests suggest that the FCPW library is more 

efficient for a larger number of trial runs and is less error-prone than its existing counterpart. However, 

through more in-depth testing and analysis, we will be able to determine whether the FCPW library will 

be optimal for Zeno’s next implementation.

2022 SURF Colloquium 67
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Mikhail Krepets

Montgomery College Computer Science

Junior

I plan to pursue my BS and likely also my MS in Computer Science with a focus on AI/ML at UMD, 

then do my best to start working full time for NIST or NASA I am considering also going for a PhD

Information Technology Laboratory, Division 771, Mathematical Analysis and Modeling Group

Professor Michael Mascagni

Creating an Algorithm for Searching RNGs to Link with Test Results

Random number generators (RNGs) are often used in many aspects of everyday life from simulation and 

decision making to video games and other recreational activities. For a category of objects used so often, 

there must be a reliable method to test the quality of individual objects in that category. One of the most 

popular methods to test RNGs today is through a software library known as TestU01. Unfortunately, 

despite being effective at testing the quality of RNGs, TestU01 is expensive to run with the biggest test 

battery, BigCrush, consistently taking multiple CPU hours to test one RNG, which will inevitably take 

more wallclock hours. The original task was to research and figure out how to store RNGs and their 

TestU01 test results in a database such that they would be searchable, but figuring out a working 

algorithm to make said RNGs easily searchable ended up being so big that it turned into a project of its 

own. 

Initially, a lot of time was spent on reading about RNGs and experimenting with the TestU01 software 

library in order to gain an understanding of TestU01 and the relevant RNGs. While becoming acquainted 

with them, we were also thinking of ideas as to how we could classify RNGs such that they would be 

searchable. Many potential algorithms were thought of, but the algorithm eventually proposed contains 

features from multiple of the potential algorithms we came up with along the way. It ended up being 

complicated to explain, but it should be relatively easy to use. This algorithm will likely be used in the 

database that the original project idea was supposed to create. However, it will also be usable in other 

contexts as long as they involve TestU01.

2022 SURF Colloquium 68
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Ivy Liang

Harvard University Computer Science

Sophomore 

Graduate School and Research

Information Technology Laboratory, Software and Systems Division, Information Systems Group

Peter Bajcsy and Mylene Simon

Interactive Online Histogram-Based Visualization of AI Model Fingerprints

Previously, NIST has generated hundreds of thousands of artificial intelligence (AI) models for the 

TrojAI Challenge focused on detecting poisoned (trojaned) AI models. The main motivation for this 

project is to support discoveries/analyses of relationships between various clean and poisoned AI models 

by measuring their model utilization and relating it to Trojan characteristics.

In order to draw connections between AI models, the problem lies in creating interactive and traceable 

histograms that allow researchers to group AI models according to their characteristics, select pairs of AI 

models to perform qualitative/quantitative comparisons, share and discuss AI model comparisons 

remotely. Challenges include: interactivity over thousands of data points, traceability of histogram 

contributing points (AI utilization fingerprints) to their training images, and reusability of existing 

libraries and of the visualization prototype.

Our approach is based on the D3 JavaScript Library and Papa Parse CSV parser followed by the design 

of interactive, traceable, and reusable histograms. Histograms are dynamically created based on AI model 

attributes, including architecture name, predicted classes, Trojan triggers, and measurement probes. By 

selecting two contributing data points to a histogram bin, a side-by-side comparison of two AI model 

utilization fingerprints is enabled to quantify AI model similarities. 

The resulting visualization presents a histogram of AI model utilization fingerprints with drop-down 

menus to allow users to select attributes for binning. Interactive images in histogram bins can be selected, 

new comparisons of utilization values are rendered, and buttons can trigger computations of distribution 

statistics.

2022 SURF Colloquium 69
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Jane Liu

University of Illinois Urbana-Champaign Computer Science

Sophomore

Finish undergraduate

Information Technology Laboratory, Software and Systems Division, Information Systems Group

Timothy J. Blattner

Implementing Real Time Constraints in Hedgehog API

An operating system (OS) is system software. Among its various capabilities, the OS can manage 

multiple threads and rapidly switch between their executions. A real time operating system (RTOS) 

provides more fine grained control over multithread behavior, allowing for a deterministic response and 

guaranteed execution time. For example, threads with higher priority values in RTOS would be 

guaranteed to run over threads with lower priorities. 

The ability for RTOS to provide guaranteed real-time responses is significant especially for jobs needing 

consistent responses within a time constraint, such as monitoring a metal additive manufacturing process 

in real time by keeping up with data collected from a high-speed camera. 

Over the past several years, NIST has been developing a C++ library called Hedgehog, which creates task 

graphs for algorithms to obtain performance across CPUs and multiple co-processors. The library relies 

on the OS to schedule its threads and provides no real time guarantees. 

The focus of this research is to extend Hedgehog to provide access to real time priorities and scheduling 

algorithms, so that applications utilizing Hedgehog can be more deterministic when launched on an 

RTOS. In this presentation, we will present the implementation efforts to add the real time capabilities 

into Hedgehog, and the associated performance costs. To evaluate the performance, we have implemented 

two algorithms; (1) the Hadamard product and (2) Matrix multiplication. We will explore the 

performance behaviors with and without real time constraints of these algorithms by varying priorities 

and thread configurations within the algorithms.

2022 SURF Colloquium 70
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Miguel Lopez

University of Maryland, College Park Computer Science

Senior

Pursue a career in the continuously evolving field of cybersecurity

Information Technology Lab, Applied and Computational Mathematics Division, 771.02

Howard Cohl, Ph.D.

Translating Mathematica Source Code to a Presentable LaTeX Format

Mathematica is a powerful programming language that is often used to handle and process mathematical 

data and equations. Mathematica is powered by the Wolfram Language, enabling it to define, display, and 

calculate essentially any level of mathematics, namely hypergeometric series in this use case. While 

Mathematica is well suited to manipulating, defining, and calculating these series, it is often very difficult 

to read and present longer equations. Through utilizing the programming language Perl, string analysis, 

regular expressions, and the Wolfram Engine, provided Mathematica source code is translated into the 

markup language LaTeX. The result is a much more user-friendly and discernible view of the 

hypergeometric series and other expressions contained within, and the ability to export these results 

easily. Translating Mathematica source into LaTeX allows for the intense computational power of 

Mathematica to be combined with the compatibility and readability provided by LaTeX to display the 

results.

2022 SURF Colloquium 71
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Johannes Losert

Columbia University Computer Science 

Sophomore

I plan to attend graduate school

Information Technology Lab, Software and Systems Division, Information Systems Group

Derek Juba 

Scientific Reproducibility of AI Trojan Detector Results 

AI Trojans are malicious and intentional attacks that change the behavior of an AI by inserting hidden 

classes. To motivate research into Trojan detectors, NIST administered the TrojAI competition, where 

teams submit algorithms that detect Trojan AI models. The detector algorithms are known to output 

slightly different results across systems. These differences are problematic for scientific study of the 

algorithms because it means that results aren’t reproducible. This problem was the motivation for my 

NIST SURF project in which my mentor, Derek Juba, and I researched how algorithms submitted to the 

TrojAI competition behave when run in different environments. Submitted algorithms are containerized 

using Singularity which allows them to be easily run on broad range of machines. We tried to test the 

algorithms on as many combinations of software and hardware as possible (CPU core count, GPU 

drivers, etc.) in order to deduce potential causes of differing results. 

We theorized that one of the main reasons for differences in the results across systems was changes in the 

orders in which floating point arithmetic operations were being performed. With this in mind, we 

attempted to quantify the uncertainty resulting from the choice of system without running the container 

on different systems. We simulated different orders of operations by tweaking the weights and biases of 

an AI model by a small amount. We used multiple random samples of such tweaks to find the variance 

we can expect in results if someone were to run an algorithm on a given model across different machines. 

Early analysis of the data suggests that results produced on other machines agreed with the variance we 

predicted with our tweaks and that the statistical distributions of tweaked models are largely reproducible 

across machines. Additionally, we propose that that the variance of the tweaked distributions can be used 

to score the confidence of detector algorithms. 

2022 SURF Colloquium 72
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Abishay Reddy

University of Maryland, College Park Computer Science 

3.2

Pursue a Masters Degree then work in Artificial Intelligence.

ITL, Software and Systems Division, Cyber Infrastructure Group

Dr.Ram D. Sriram

Multimodal Fusion with Modality-Specific Factors for IEMOCAP dataset

In the scope of human-computer interaction, technology that can quickly analyze and identify emotion 

from varying data sources, is a coveted development. Potential applications of emotion recognition span 

from healthcare to gaming, only increasing demand for methods with efficient analysis and identification. 

Humans convey emotion through various mediums, most common of which are speech, facial expression, 

body language, etc. Emotion recognition technology frameworks are built upon foundational fusion 

methods, which synthesize various data modalities into features, utilized by prediction algorithms. This 

work mainly focuses on processing speech, text, and video data, and extracting the features from multiple 

modalities to develop a fusion model for emotion recognition tasks. We consider the IEMOCAP 

benchmark dataset by the processing of spliced data from modalities which includes features from audio 

data, video data, and embeddings from text data. These three modalities were processed for multimodal 

representations to recognize human emotions. 

2022 SURF Colloquium 73
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Samuel Rennich

Howard Community College Computer Science

Junior Year of Undergraduate Career

Attending the University of Maryland, College Park to finish my B.S. Computer Science and earn a 

minor in Agronomy. Plan to work in the agricultural modeling industry.

Information Technology Laboratory, Information Access Division, Retrieval Group

Dr. George Awad

Making TRECVID Results More Accessible and Coherent

 NIST has run the TREC Video Retrieval Evaluation (TRECVID) program since 2001, allowing

institutions to evaluate how successful their systems are at retrieving video content from textual queries.

As the results of these evaluations were simply sent back to the submitting institution(s), discussed at the

annual TRECVID workshop, and only reported in published papers, there was no another means for

teams or the public to examine the results. The website also enables the displaying of data in more

organized and visually appealing ways, such as playing the video results, corresponding to the tested

queries, based on different result conditions across participating teams. 

 The data from these evaluations was also stored locally, with minimal organization, making it

difficult to perform many statistical analyses. Building a comprehensive web interface with a suitable

relational database to house the TRECVID result information was the clear solution to the problem. By

developing an easy-to-use website, the information not only becomes more easily accessible to

participating institutions, but it allows them to compare their tools across like systems, and over time. The

website was developed with a focus on simplicity and maintainability, while also striving to remain

lightweight. All data is displayed in simple tables, with a user interface that allows for easy navigation

and finding of important data points with visualized results.

2022 SURF Colloquium 74
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Vaibhav Sanjay

University of Maryland, College Park Computer Science and Mathematics

Sophomore

Graduate School/PhD

Information Technology Laboratory, Software and Systems Division, Cyber Infrastructure Group

Jacob Collard

Term and Relation Extraction in Mathematical Texts

There exist a variety of Natural Language Processing tools for term and relation extraction. Examples 

include Parmenides, a framework that applies structured and normalized terms to represent natural 

language, as well as DyGIE++, a deep learning system for entity and relation extraction. However, while 

these tools may be effective in extracting terms from scientific texts, their performance is less substantial 

with mathematical texts.

The two tools have previously been tested on their ability to extract terms from a collection of abstracts in 

the Theory and Application of Categories (TAC) journal. Parmenides extracted many valid mathematical 

terms, however it also extracted several times as many non-term phrases. We now hypothesize that term 

candidates that are part of relations, that is, subject-verb-object patterns, are more likely to be terms. 

Thus, a filter that removes words that cannot be found in relations reduces false-positives generated by 

the Parmenides term extractor.

In the case of DyGIE++, the model was retrained on TAC abstracts using author provided keywords as 

training data. Since the model was trained on more domain specific text, it performed stronger than the 

default model.

These measures increased the precision and recall of both tools by a noticable margin. In future research, 

we will utilize this term extraction for the creation of comprehensive knowledge graphs for mathematical 

domains. Further, the relations extracted by Parmenides and DyGIE++ can be employed for the 

evaluation of these knowledge graphs.

2022 SURF Colloquium 75
 SURF Student Colloquium 

 NIST – Gaithersburg, MD 

 August 2-4, 2022 

 Name: Yuvan Sundrani 

 Academic Institution: University of Maryland, College Park Major: Information and Computer Science 

 Academic Standing (Sept. 2022): Junior 

 Future Plans (School/Career): 

 Pursuing a Masters degree in AI/ML or Blockchain technology while continuing to grow and expand my personal 

 app company, Soshi. 

 NIST Laboratory, Division, and Group: ITL, Software and Systems Division 775.02, Information System Group 

 NIST Research Advisor: Antonio Cardone , Marcin Kociolek 

 Title of Talk: Artificial Intelligence-based texture analysis 

 Abstract: 

 Texture analysis is ubiquitous, and it finds application in both biomedical and nanomaterial research. 

 The ability to address it in an automated fashion is greatly beneficial. However, in most cases, visual 

 analysis and custom-tailored approaches are employed. Convolutional neural networks (CNNs) 

 represent a viable approach to characterize image texture accurately, and in particular properties that 

 humans can detect: directionality and granularity. 

 NIST researchers have been addressing AI-controlled texture analysis for years, however, they have only 

 used synthetic data to train Artificial intelligence, not real-life data. To further advance the CNNs and 

 our AI as a whole, we need to change the testing data to real-life images. The only barrier is that there 

 is no efficient software allowing users to annotate real-life images to be then used for testing. 

 Another contribution of the GUI I created is associated with a step forward my NIST mentors are 

 envisioning on this project. Basically, the software will enable the creation of a public database of 

 annotated texture images that will be globally available to other scientists. Images annotated using our 

 software will be uploaded to a public database where others can view, source, and use it. To the best of 

 our knowledge, there is no global database that contains this information. 

 This would not only help researchers around the world train AIs but help advance machine learning 

 texture analysis as a whole. 

2022 SURF Colloquium 76
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Kyle Truong

University of Maryland, College Park Computer Engineering

Senior

Career in Software Engineering

Information Technology Laboratory, Applied Cybersecurity Division, National Initiative for 

Cybersecurity Education

Danielle Santos

Evaluating the Implementation of NIST SP 800-181 in Cybersecurity-Related Job Descriptions

With technology and data science becoming so prominent in society, it’s becoming increasingly 

imperative that companies and organizations protect themselves from malicious cybersecurity threats. 

However, in the United States alone, there are over 700,000 unfilled cybersecurity positions. The 

National Initiative for Cybersecurity Education (NICE) created the NICE Framework (NIST SP 800-181) 

to provide a set of building blocks for describing the tasks, knowledge, and skills that are needed to 

perform cybersecurity work. Through these building blocks, the NICE Framework enables organizations 

to develop their workforces, and helps learners to engage in appropriate learning activities to develop 

their knowledge and skills. 

The purpose of this research is to evaluate if employers are using this framework by examining job 

descriptions found on online hiring platforms and measuring the extent of their alignment to the 

Framework. The results of this research will provide insight into whether or not actions need to be taken 

to increase industry awareness of the Framework or to modify the Framework to better apply to employer 

needs. 

 Two methodologies will be explored to complete this project. In the first methodology, job descriptions 

from multiple hiring platforms such as LinkedIn and USAJobs will be graded using a rubric to determine 

how well they align with the framework. A job description which matches a larger amount of key words 

found in the knowledge, skills and tasks of a work role will score higher on the rubric. In the second 

methodology matching keywords and qualifications will first be found between job descriptions. After 

compiling a list of the most common keywords and qualifications, this list will then be compared to the 

Framework work role to determine how well the Framework covers what employers desire. 

2022 SURF Colloquium 77
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Ayush Varshney

California Institute of Technology Computer Science

Sophomore

Pursue graduate studies in a field of quantum science

Information Technology, Applied and Computational Math, Computing and Communications Theory

Dr. Thomas Gerrits

Optimizing Data Communication for Low Latency Quantum Network Metrology

Quantum networks currently require various in-situ measurements from their components to ensure good 

network fidelity. Communications between quantum network nodes are carried out with single photons 

through the use of single-photon sources and single-photon detectors. One undesirable characteristic of 

these photon transmissions is substantial timing jitter associated with the single-photon detection process. 

To monitor this issue, each photon’s emission time and absorption time is recorded with picosecond 

accuracy and sent to the quantum network’s management system for analysis. This time-data transfer can 

become a considerable bottleneck in the network due to bandwidth limitations in classical data 

communication. Thus, we seek to reduce network overhead and optimally compress this data. In our 

investigation, we tested several lossless compression methods such as delta encoding, different types of 

variable length quantity encoding, and a hybrid approach on a sample of such data. We found that the 

hybrid approach produced the best results by compressing the data by 83.11% (a 5.92 compression ratio). 

Implementing this compression technique into quantum network metrology toolsets could significantly 

speed up quantum network analysis and allow for more data to be analyzed as well.

2022 SURF Colloquium 78
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August 2-4, 2022 

Name: Joshua Zarb

Academic Institution: University of Maryland, College Park Major: Computer Science 

Academic Standing Senior 

(Sept. 2022): 

Future Plans Become a Software Engineer after receiving my bachelor’s degree 

(School/Career): 

NIST Laboratory, Information Technology Laboratory 

Division, and Group: 

NIST Research Dr. Ian Soboroff 

Advisor: 

Title of Talk: Understanding Neural Search Algorithms 

Abstract: 

Search engines have models for predicting if a document is relevant to a query. Furthermore, in search engines, deep 

learning methods for predicting relevance are an emerging area of research. To determine if a document is relevant 

or not based on a query, search engines may use three different models. The first model is manual (non-automatic) 

where there is human intervention to determine whether a document is relevant or not. The next two models are 

considered automatic in that the query is created from the textual description of the user information needed. The 

first automatic model is traditional where it looks at how often terms appear in documents and uses formulas to 

calculate its relevance. The second automatic model is neural where neural networks are used to determine the 

document’s relevance. The question then becomes how do all these three models compare with one another? 

 To answer this question, we use a query-by-query analysis approach by examining traditional, neural, and manual 

 outputs on lots of search queries, then trying to identify patterns of success and failure for each model. I then 

 conducted a qualitative analysis of traditional, neural, and manual ranking methods to understand the differences. 

2022 SURF Colloquium 79
2022

2022 SURF Colloquium 80
Summer Undergraduate Research Fellowship (SURF) - 2022 Participants

Material Measurement Laboratory (MML)

Marquesa Calderon

Brooke Calvo

Charlotte Dohne

Caressia Edwards

Nitin Elavarasu

Grace Finch

Melissa A. Flores-Rivera

Emily Frashure

Ross Gunther

Maggie Hampson

Aidan Knab

Bintou Koroma

Jennifer Li

Richard Ma

Jacqueline Maloney

Leilani Meyers

Terence Murphy

Yemi (Christine) Ogunsula

Ryan Puthumana

Hrishikesh Ram

Alexandra Terres

Dennis Zhao

2022 SURF Colloquium 81
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Marquesa Calderon

Hawai’i Pacific University Environmental Science

Junior/3rd Year

To pursue a Master’s degree in Environmental Science, conduct research in Antarctica

Material Measurement Laboratory, Chemical Sciences Division, Biochemical and Exposure Science 

Group

Dr. Katherine Shaw

Analysis of existing NIST SRM for microplastics

Microplastic research continues to grow in both its impact on and increasing ubiquity in the environment. 

However, there is no current standard method for the extraction of microplastics from the matrices in 

which they exist. This creates the need for a reference material with certified microplastic values to 

standardize methods. The existing National Institute of Standards and Technology Standard Reference 

Material 2782 Industrial Sludge (SRM 2782) was split into three subsamples and evaluated for 

microplastics. SRM 2782 was collected from an industrial pharmaceutical research site in northern New 

Jersey and has been certified to contain inorganic constituents such as silver and aluminum. This study 

will determine if the SRM 2782 certificate can be updated to include microplastic concentrations. It was 

predicted that there would be consistent concentrations of microplastics in all three replicates since they 

were from the same well mixed bottle homogenized in a controlled NIST laboratory. A novel density 

separation device (DSD) was used to extract microplastics from the sludge. Separated materials were 

then analyzed on a Thermo Scientific iN10 MX microFTIR. The produced IR spectra were compared 

against libraries of known materials to identify each particle. Ten plastic particles were identified in 

replicate 1 (n = 6357 particles). The most prominent polymer found in replicate 1 was nylon. However, 

nylon is easily mistaken for natural polyamide because they have a very similar spectra, and the majority 

of nylon in this sample is likely natural polyamide. Data from other subsamples will be presented. This 

study is a critical step toward the establishment of standard materials to test microplastic extraction 

methods.

2022 SURF Colloquium 82
SURF Student Colloquium 

NIST – ŽƵůĚĞƌ͕K

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Brooke Calvo

University of Memphis Mechanical Engineering

Senior

I plan to complete my BSME degree and potentially attend graduate school.

Material Measurement Laboratory, Applied Chemicals and Materials Division

Enrico Lucon and May Martin

Assessment of Shear Fracture Appearance in Charpy Specimens of Modern Steels

The Charpy test is a method used to characterize the impact toughness of materials. The instrumented 

version of the test uses strain gauges applied to the striker to measure applied forces. The Charpy test has 

been standardized by the American Society for Testing and Materials (ASTM) and the International 

Organization for Standardization (ISO). The standards (ASTM E2298 and ISO 14556) include four 

empirical correlations to estimate the shear fracture appearance (SFA, ductile portion of the fracture 

surface) based on the instrumented force data. 

Many steels exhibit a ductile-to-brittle transition as the test temperature decreases. Within this range, both 

ductile and brittle features are present on the fracture surface. In this project, we analyzed data from four 

steels with significantly different properties to determine the SFA. This analysis is usually done optically, 

however for modern steels this method is often difficult to apply. We performed instrumented Charpy 

tests on 4340, A302B, F82H, and X52 steels at temperatures ranging from fully ductile to fully brittle 

behavior. The force-displacement curves were analyzed using NIST Instrumented Charpy Analysis 

Software (NICAS). We took photos and estimated SFA optically. Scanning Electron Microscopy (SEM) 

was utilized to get higher magnification images to determine the SFA more precisely. All the analyses 

were performed independently by an experienced user and a novice user. Regardless of experience, the 

results were similar across most specimens. We will compare SFA estimates from optical methods and 

those obtained from instrumented Charpy data to the SEM results to determine which of the published 

empirical correlations is more appropriate for the investigated steels.

2022 SURF Colloquium 83
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Charlotte Dohne

University of Maryland Criminal Justice

Freshman

BS in Biology at UMD, medical school, and career as forensic pathologist.

Material Measurements Laboratory, Division 645, Applied Genetics Group

Becky Steffen

Creating Reference Libraries and STRBase Topic Pages on Emerging Areas in Forensic DNA

The Applied Genetics Group in the Material Measurement Laboratory has a great need for a centralized 

reference library and updated group website. The work completed throughout this program includes 

searching for, adding, and organizing references for the group's publication library using EndNote, which 

is a commercial reference software package used to manage references when writing articles. Many of 

these references are being compiled and sorted on the subject of Massively Parallel Sequencing (MPS) 

for a workshop at the International Society for Forensic Genetics this fall. PubMed and the group's 

website STRBase (https://strbase-b.nist.gov/) are the main search engines being used to find scientific 

articles for the library, which are then stored in EndNote. A detailed search was performed for the 

workshop based on various forensic search terms to find a broad range of articles to cover the September 

2019 to present time frame. For example, the first general search term “forensic massively parallel 

sequencing” resulted in 478 articles to sort through, and when five similar search terms were added over 

500 results were found. In parallel, spot checks and updates of the links and pages on STRBase have also 

been performed before a new version 2.0 is released in the fall. Additionally, a topic page has been 

created for the website about an emerging technology in the forensic field, specifically, forensic 

microbiome research which is the study of the individualization of a person’s microbiome by specific 

body sites that could be useful evidence in forensic investigations. By performing advanced literature 

searches in emerging areas of the DNA forensic community for the group library and adding relevant 

content to the STRBase website, there is more current background information and knowledge of recent 

advances in the field of forensic genetics.

2022 SURF Colloquium 84
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

 Caressia Edwards

 Fayetteville State University Chemistry-Materials Science

 Recent Graduate (Spring 2022)

 Graduate School

Material Measurement Laboratory, Materials and Engineering Division, and Functional Polymers 

Group

 Dr. Frederick R. Phelan Jr. and Dr. Lilian C. Johnson

 Atomistic Molecular Dynamics Analysis of a Model Polycarbonate/Silica Composite System 

Polymer composites are a polymer matrix reinforced by fiber or filler phase, and are valued because of 

their high strength to weight ratio as well as their stiffness. Fiber reinforced composites transfer loads 

from the weaker polymer phase to the more durable reinforcement phase creating a stronger combined 

material. Load transfer ensues at the molecular level between the polymer phase and filler phase, known 

as the interface and interphase, this makes them a focus of scientific study. The interface is the surface 

area where matrix material contact the reinforcement. Interphase is the region in the vicinity of the filler 

where the matrix properties are modified from bulk. The goal of this project is to use molecular dynamics 

(MD) calculations of a polymer composite system consisting of polycarbonate restrained between fused 

silica surfaces to understand the interphase properties in these materials. We used MD to characterize the 

properties of bulk carbonate for comparison with the composite properties. A bulk polycarbonate system 

of 50 chains with eight monomers per chain was created. We then equilibrated the bulk polycarbonate 

system at a temperature of 800 K for 10 ns using the LAMMPS MD package. We cooled the surface and 

bulk system at three different rates 25, 50, and 100 K/ns to study the effect of cooling rate on the glass 

transition temperature (Tg). These runs were used to characterize the Tg as a function of cooling rate. 

Results for Tg as a function of cooling rate are consistent with the values in literature for this material. 

We then calculated structural and dynamic properties at several temperatures for the polymer matrix for 

comparison with properties observed in the PC/silica system. The mean square displacement and the 

non-Gaussian parameter of the polymer matrix are compared at varying temperatures to study how 

polymer motion slows and becomes more non-Gaussian as temperature decreases. Results for the bulk 

system will be compared with the composite system as results become available. 

2022 SURF Colloquium 85
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Nitin Elavarasu

University of Virginia Chemical Engineering

3rd year of a 4-year Program

Master's in Chemical Engineering and/or a Law Degree; hoping to enter Patent Law or some STEM + 

Law Field; Technical Consulting

Material Measurement Laboratory, Materials Measurement Science Division, Microscopy and 

Microanalysis Research Group

Dr. Donald Windover

Separation of composition from structure in Micro-X-ray fluorescence measurements

X-Ray Fluorescence (XRF) is a type of fluorescence spectroscopy that is advantageous for its relatively 

simple preparation process and non-destructive nature. Fluorescence spectroscopy is a type of elemental 

analysis that measurers light waves emitted by a sample after subjecting it to high-energy waves. 

Micro-XRF is a powerful tool especially for characterizing high-Z elemental composition, however a 

challenge with it is separating the fluorescence data that determines the elemental composition from the 

diffraction peaks that cover structural information of the compound. 

Through utilization of different software analysis techniques, it was possible to distill the fluorescence 

data from the initial micro-XRF data file that was fed through. The initial steps of isolating the requisite 

energy reading information from the overall file via a reader method was the same for each process. This 

method categorized the data file into folders and subfolders using a data structure and then searched 

through the structure to determine the energy reading values. These values were then processed using 

different analytical methods, including averaging the lowest and highest readouts from a set of data of the 

same sample to eliminate noise through sampling size and breaking down single samples into energy 

channel ranges to specify parts as either fluorescence or diffraction information. 

The averaging method best removed diffraction data but took the longest time and memory to process, 

whereas the single-sample approach focused on reducing noise in a specific sample but had inconsistent 

success with identification. Overall, it would be of interest to strike a balance with these approaches to 

determine minimum and maximum channel ranges that still effectively identify data with an 

average-sampling method. These results serve primarily as a new roadmap for further micro-XRF 

software analysis that can better eliminate noise from more structurally complex data samples for higher 

accuracy of identification going forward. 

2022 SURF Colloquium 86
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Grace Finch

Virginia Tech Biochemistry

Sophomore

I plan on becoming a forensic chemist. 

Material Measurement Laboratory, Chemical Sciences Division, Chemical Process and Nuclear 

Measurements group

Nicholas Sharp

Exploring Neutron Activation Analysis at the University of Maryland

The nuclear reactor that the National Institute of Standards and Technology (NIST) was using to run 

Neutron Activation Analysis (NAA) has recently gone down for repair. However, there are still routine 

measurements involving NAA that need to be done. The goal of this experiment was to explore the 

capabilities of the reactor at the University of Maryland (UMD) to perform NAA by investigating the 

homogeneity of the elements lanthanum, thorium, neodymium, cerium, rubidium, and uranium in three 

forensic glass samples. Experimentation was also done to determine whether a graphite backing matrix, 

or lack thereof, affects the precision of NAA measurements. 

In NAA, samples are placed in polyethylene vials, which are then wrapped in aluminum foil. The 

wrapped foils are placed in a polyethylene container called a “rabbit,” which is irradiated in the reactor. 

After the vials are irradiated, they are placed on high purity germanium detectors, which acquire a 

gamma-ray spectrum. The gamma-ray spectrum can be used to calculate the mass fraction of the elements 

in the sample. 

 Results from the forensic glass measurement will inform the suitability of the UMD reactor for routine 

NAA analysis. Results from the matrix test show that the standards with no backing had greater precision 

than the standards with backing, which is the opposite of what was expected. This may indicate a problem 

with permeation of the ~5% nitric acid solution through the graphite backing. Repeating this experiment 

with varying volumes for both the graphite backed standard and no backing standard, and varying 

quantities of material for the standards with no backing may reveal why this occurred. 

2022 SURF Colloquium 87
SURF Student Colloquium 

NIST – ŽƵůĚĞƌ͕K

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Melissa A. Flores-Rivera 

Interamerican University of Puerto Rico, San Biology

Senior 

Pursue a Doctor of Veterinary Medicine (DVM)

Material Measurement Laboratory, Applied Chemicals and Materials Division, Thermodynamics 

Research Center

Dr. Demian Riccardi 

Structural Characterization of Extracellular Signal-Regulated Kinase 2 

Extracellular Signal-Regulated Kinase 2 (ERK2) is a mitogen-activated protein kinase (MAPK) that 

participates in the Ras-Raf-MEK-ERK signal transduction cascade. Physiological regulation of ERK2 is 

important to cell growth, inflammation, proliferation, transcription, and many other cellular processes.1 

The ERK signaling cascade has been implicated in many pathological conditions like cancer, arthritis, 

chronic inflammation, and osteoporosis.1 The cascade upstream of ERK2 may be expressed using 

acronyms (each K is a kinase): MAPKKKs phosphorylate MAPKKs, such as MEK, which phosphorylate 

MAPKs, such as ERK2. Once activated, ERK2 phosphorylates numerous cytoplasmic and nuclear 

substrates.2 MEK phosphorylates ERK2 at two specific threonine and tyrosine residues: T183 and 

Y185.3,4 The addition of the two phosphate groups activates ERK2 enabling the transfer of a phosphate 

group from ATP to ERK2 substrates, such as RSK1 and ELK1.3 For this summer project, the Visual 

Molecular Dynamics (VMD) program and MDAnalysis python scripts were used to analyze the structural 

features of ERK2 with and without phosphate groups, (pdbids 2erk and 5umo, respectively).3,4 We will 

present activation loop conformations observed in crystal structures and long conventional MD 

simulations carried out previously. 

References: 

1) Lu, N., & Malemud, C. J. (2019). Extracellular Signal-Regulated Kinase: A Regulator of Cell Growth, 

Inflammation, Chondrocyte and Bone Cell Receptor-Mediated Gene Expression. International Journal of 

Molecular Sciences, 20(15), 3792. https://doi.org/10.3390/ijms20153792 

2) Fey, D., Croucher, D. R., Kolch, W., & Kholodenko, B. N. (2012). Crosstalk and Signaling Switches 

in Mitogen Activated Protein Kinase Cascades. Frontiers in Physiology, 3. 

https://doi.org/10.3389/fphys.2012.00355 

2022 SURF Colloquium 88
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Emily Frashure

University of Maryland, College Park Materials Science and Engineering

Senior

Peruse a Masters Degree in Materials Science and Engineering

MML, Materials Science and Engineering Division, Polymer and Complex Fluids Group

Debra Audus

Polymer Database Pipeline for The Circular Economy

Currently, there is a need to improve how polymers are sorted at recycling facilities. Polyolefins, such as 

polypropylene and polyethylene, are some of the most common polymers. Due to insufficient sorting, 

when these polymers are reprocessed, there is a significant decrease in their performance and properties. 

However, this can be mitigated by correlating properties of recyclable polyolefins back to easy-to-take, 

near-infrared measurements. Doing so will thus enable improved sorting of polyolefins and allow 

polymers to stay within The Circular Economy. To accomplish this goal, the Community Resource for 

Innovation in Polymer Technology (CRIPT), an in-development platform to with the goal of increasing 

FAIR (Findable, Accessible, Interoperable, and Reusable) polymer data is used to make the polyolefin 

dataset publicly available. The platform functions by organizing, storing, and connecting data from a 

range of sources in a single, cloud-based database to address outstanding challenges in polymer data. 

More specifically, the goal of this project was to create, using python and the CRIPT Application 

Programming Interface, a data pipeline starting with research data files and ending with the online 

database platform. The research files consist of measurement data and materials characterization 

information. The code splits and sorts the information, then uploads it to the corresponding nodes in the 

platform. These nodes connect the polymers to various property data gathered by experimentalists. By 

having easy-to-take measurements connected to properties measured from time-consuming, detailed 

experiments available to the public, recycling facilities will ultimately be able to sort the polymers with 

greater specificity by taking IR measurements. This would result in the reprocessed polymers being better 

qualified for new applications due to improved properties. These polymers would then be able to remain 

in use for longer, bringing the US one step closer to the goal of a circular economy. 

2022 SURF Colloquium 89
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Ross Gunther

Georgia Institute of Technology Materials Science and Engineering

Junior

Materials engineering R&D work

Material Measurement Laboratory, Materials Science and Engineering, Polymers Processing

Leanne Friedrich

Investigating Filament Fusion in Embedded 3D Printing

Embedded 3D printing is a novel approach to creating objects from soft materials. It has the potential to 

transform tissue engineering which uses soft materials to fabricate cartilage, vessels, tissues, and organs. 

3D printing these structures with patient-specific cells would accelerate drug development, eliminate 

organ shortages, and reduce transplant rejection. Embedded 3D printing involves extruding a filament 

into a support fluid which stops soft filaments from slumping and allows creation of complex 

non-self-supporting shapes. While using a support expands the range of feasible printing materials, it also 

complicates filament fusion and print quality due to its interaction with the filament. Defects can arise at 

many parts of the printing process. Movement of the nozzle can deform already printed lines, filament 

extrusion can deform already printed lines, support material can become trapped between lines, and lines 

can fuse too strongly and deform into a droplet. This work investigates factors which influence these 

defects—namely nozzle movement, line spacing, interfacial tension, filament type, and support type. We 

analyze these factors by simulating numerous variations of a line printed beside or atop a second 

preexisting line. We use the fluid dynamics simulator OpenFOAM along with Python and ParaView to 

create and analyze these simulations. The findings indicate the following trends which serve as guides for 

others creating a printing setup. Closer line spacing increases fusion but also increases disturbance. A 

nonzero interfacial tension leads to smoother interfaces between lines and less trapped support. A shear 

thinning ink encourages less disturbance to already printed lines. Vertically-oriented lines benefit from a 

further spacing compared to horizontally-oriented lines. Understanding the interplay of these factors 

serves to improve the quality of prints and makes embedded 3D printing a more feasible method for 

fabrication of soft structures. This is an important step towards realizing large-scale soft material 

manufacturing.

2022 SURF Colloquium 90
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Maggie Hampson

University of Maryland, College Park Bioengineering

Sophomore

Pursue a career in research and development of pharmaceuticals

Materials Measurement Laboratory

Andrew Slifka and Matthew Connolly

Finite Element Modeling For Hydrogen Transportation & Storage

In a world pushing to use fewer fossil fuels, finding safe and effective renewable energy sources is 

crucial. Hydrogen has been looked at increasingly as a clean energy carrier. Hydrogen is gaseous and 

therefore it is critical that it is transported and stored safely. Few laboratories can safely test the effects of 

hydrogen on metals, one of these is NIST. This project focuses on developing safe transportation and 

storage for hydrogen so it can be used as an alternative fuel and energy carrier without the associated 

risks. To do this, the strength, stability, and functional lifetime of the metal used in the pipelines and 

pressure vessels for hydrogen must be determined. My work consists of using Autodesk inventor to 

replicate specific conditions similar to a laboratory setting. Through testing and finite element analysis 

researchers can begin to understand how hydrogen affects metals. Studies performed with Autodesk 

inventor don’t require the waste of precious materials and researchers are kept safe from potentially 

hazardous conditions. Through the SURF experience, I tested various conditions, including applying an 

uncentered force. The first step was to develop a model for testing. The chosen model I constructed was 

designed to replicate specimens and test conditions used in cyclic fatigue testing. In this SURF 

experience, I developed and applied this model to determine the strain distribution in a specimen when a 

load is applied correctly and when a load is applied off-center. This can be useful for both tensile and 

compressive loads because off-axis loading produces different amounts of bending stresses in each case. 

Results are still being examined and plotting strain relative to how the load is applied will be essential to 

understanding the findings. The finite element analysis calculations performed during my SURF 

experience will help NIST researchers understand how the application of load affects the damage 

evolution of the specimen, and will improve the ability to run successful mechanical tests. 

2022 SURF Colloquium 91
SURF Student Colloquium 

NIST Gaithersburg, MD 

August - , 20

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk:

Abstract:

Aidan X Knab

University of Maryland, College Park Materials Science and Engineering

3.3

Graduate Dec 2022, Find industry job in LA

Material Measurement Laboratory, Applied Chemicals and Materials Division, Fatigue and Fracture 

Group

Andrew Slifka

Machine Learning Automation of Charpy Impact Verification

Charpy testing is a destructive test method useful for predicting the failure of structures. Engineers using 

Charpy testing in accordance with ASTM E23 can have their machines indirectly verified through NIST. 

The MML at Boulder is designing a neural network to automate the verification of submitted images of 

tested samples. The neural network should determine if presented samples were tested on machines in 

good condition or poor condition. First, the submitted images must be prepared for training. The 

submitted images are cropped and stretched to all be 1000x605 pixels using a LabView program. The 

neural network then applies a series of convolution filters to the images before passing them through 

layers of nodes with activation functions. The results are compared to the correct classifications, and the 

error is used to reweight the paths to the nodes incrementally. This process is repeated as the error 

decreases. Models will be written and trained using Tensorflow with Python. The images will be 

duplicated and rotated slightly to enhance the dataset and avoid overfitting of the model. A dropout 

function that skips various nodes during training will also help reduce overfitting. Trial runs of a network 

designed in Perceptilabs proved successful, with ~86% accuracy. This automation will allow customers 

to perform an initial verification without NIST. Future models should be able to determine the nature of 

damage to the Charpy machines.

2022 SURF Colloquium 92
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Bintou Koroma 

University of Maryland, College Park Bioengineering

Junior

Graduate School/Biotech

Material Measurement Laboratory, Materials Science and Engineering Division, Polymers and 

Complex Fluids Group

Dr. Robert Ivancic

The effect of randomness in branch spacing on LLDPE dilute solution properties

Today, the United States collects only 16% of plastics for recycling. Plastic recycling remains 

challenging because most polymers are incompatible, leading to materials with poor mechanical 

properties that easily break when mixed. Thus, it is essential to characterize plastic waste better to 

improve our ability to sort it. In this project, we attempt to improve this characterization by examining 

linear low-density polyethylene (LLDPE), a large and growing source of plastic waste, via a previously 

designed validated coarse-grained model of polyolefins in an implicit good solvent. We developed code 

that computes LLDPE structures with branches placed randomly along the backbone at a certain 

percentage analogous to industrial LLDPE. For each architecture, we used the ZENO code to calculate its 

radius of gyration, intrinsic viscosity, and hydrodynamic radius as functions of molecular weight and 

branch length . We then compared these results to previous results in which branch spacing is fixed. This 

comparison may facilitate future research into developing structure-property relationships for industrial 

LLDPE, allowing more efficient sorting of these materials in recycling streams. 

2022 SURF Colloquium 93
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Jennifer Li

University of Maryland College Park Neuroscience and Information Science

Junior

I plan to pursue an MD or MD/PhD degree.

Material Measurement Laboratory, Biomolecular Measurement Division, Bioprocess Measurements 

Group

Dr. Kurt Benkstein

Characterization of Micrometer-Scale Particle Motion through Optimization of Analysis Parameters

Brownian motion refers to the random motion of particles suspended in a medium. Particle movement is 

relevant in a variety of biological and environmental applications, including the diffusion of 

pharmacological molecules throughout the human body. Understanding Brownian movement can 

improve and optimize pharmaceutical delivery to the intended area of interest. One way to elucidate 

particle movement is through particle tracking analysis (PTA), which provides information on the size of 

the particles. Particle size is related to how quickly a particle moves by Brownian motion in a medium 

according to the Stokes-Einstein equation. In PTA, video frames of micrometer- and 

sub-micrometer-scale particles of various sizes in an aqueous medium were captured using different 

frame rates. Initially, the expected particle movement between frames for the varied sizes and frame rates 

was calculated using a Rayleigh probability distribution. Next, the videos were analyzed using an image 

analysis particle tracker to filter particles from background noise and track their position over time. A 

second analysis of each particle’s motion was completed using custom and open-source software 

developed through Python to account for frame rate and track length differences. Preliminary results on 

all particle sizes indicate that higher frame rates generally produce average particle size values with lower 

standard deviations and relative errors. Furthermore, the analyses highlight the importance of optimizing 

relevant video analysis parameters, including jump distance, quality threshold, track count, and track 

length in producing more diameters with lower relative errors. These results demonstrate the significant 

effects of parameter adjustments and highlight the need for parameter optimization in particle tracking 

methodology for estimating particle size and movement.

2022 SURF Colloquium 94
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Richard Ma

UC Berkeley Materials Science and Engineering

Senior

Pursue PhD

Material Measurement Laboratory, Materials Measurement Science Division, Materials for Energy 

and Sustainable Development Group

Brian DeCost, Howie Joress

Improving robustness of active learning to accelerate XRD measurements of metal alloys using NMF

X-ray diffraction (XRD) is a powerful analytical technique that allows complex insight into a material’s 

structural properties. Such experiments can be very time-consuming. When mapping complex samples, 

such as a composition spread thin-film requiring tens to hundreds of measurements, experimentation can 

take several days. Our objective is to improve mapping efficiency by applying active learning techniques 

to reduce the number of points to be measured while still maintaining near-complete knowledge of the 

sample. This is achieved by using previous knowledge to intelligently select which points to sample. 

 First we perform non-negative matrix factorization (NMF) on the existing XRD dataset, decomposing it 

into a set of constitutive basis patterns and weights which when multiplied approximate the original data. 

Nominally, each basis pattern should correspond to a distinct phase in the material. Peak detection 

algorithms are then used to detect and log the peaks in each basis pattern. 

The peaks from the basis pattern are used to initialize and constrain least squares fits to the data, serving 

as reference points to observe changes between patterns and allowing individual patterns to be fitted 

using knowledge informed from the entire dataset. Fitting the data with a set of pseudo-voigt peak 

profiles and a background polynomial, a parameterized peak list (parameters including peak intensity, 

amplitude, location, etc) may be obtained for each pattern. In this manner, peaks can be tracked as they 

shift and broaden, and can be identified with context even when they are locally/individually difficult to 

distinguish from the background. 

Once modeled, a set of mathematical relationships, such as a Gaussian process, may be established 

between the sample coordinates and the parameters of the measured peaks. By applying these methods, 

these relationships can be leveraged during data collection to intelligently select which points to sample, 

reducing knowledge-poor measurements. 

2022 SURF Colloquium 95
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Jacqueline Maloney

SUNY Geneseo Biochemistry

College Senior

Graduate or medical school to obtain my doctorate

Material Measurement Laboratory, Office of Data and Informatics, Group 0

Dr. June Lau

Evaluation of the Microscopy NexusLIMS Search Functionalities

NexusLIMS is a laboratory information management system that curates and manages electron 

microscopy session data at NIST. NexusLIMS strives to maintain data that is consistent with the FAIR 

data principle1. It is also tool for researchers to search, explore, and download data their data through a 

web interface powered by NIST's configurable data curation system (CDCS). 

The emphasis of this work centers around human and machine discoverability of data already residing in 

the NexusLIMS database. Some context: In any scientific discipline (e.g. electron microscopy), the 

research community often uses different words and phrases with similar meanings interchangeably. 

Although the researcher themselves know and understand the relationships among similar terms, this 

knowledge is not machine actionable (i.e., when a search is performed) until those relationships are 

explicitly defined for the machine. 

Previously, this project had developed an electron microscopy thesaurus of commonly use terms and their 

relationships represented as a similarity score, using natural language processing. My work consists of the 

implementation of this thesaurus into the three different search modalities supported by CDCS: elastic 

search, search operator, and auto complete. Auto complete is a whole-word-based function that generates 

search options as you type in the search bar. The search operator functionality allows the user to filter 

their search results based on the specific operator in use. Elastic search enables one to search large 

volumes of data quickly by searching an index instead of text directly. We hope to demonstrate how these 

search modalities differ and can improve the search quality from both the human and the machine 

perspective. 

1. Jansen, Mascha. “Fair Principles.” GO FAIR, 21 Jan. 2022, https://www.go-fair.org/fair-principles/. 

2022 SURF Colloquium 96
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Leilani Meyers

Boston University Biochemistry and Molecular Biology

Junior

Attend dental school

Material Measurement Laboratory, Biomolecular Measurement Divison

Dr. Tytus Mak

Assessing Ambiguities in Metabolite Identification when Analyzed by LC-MS/MS

Liquid chromatography-mass spectrometry (LC-MS/MS) is a technique that pairs the chromatographic 

separation of a biological sample with an analytical tool that elucidates information on the molecular 

weight and abundance of its contents by ionizing the molecular components, resulting in a retention time, 

relative abundance, and mass-to-charge (m/z) ratio value for each analyte in the sample. This method is 

used for metabolomics, the comprehensive study of the small molecules involved in metabolism, because 

it allows the composition of large quantities of diverse and complex biological samples to be determined 

efficiently without extensive preparation. Metabolomics allows biological pathways to be evaluated to 

study biochemical mechanisms and to further understand diseases. 

A major challenge encountered when using LC-MS/MS is the ambiguous identifications of compounds. 

In this work, a program was created to facilitate the analysis of metabolites with uncertain identities. To 

highlight ambiguities, a CSV parser was written in Python to isolate data from CHO cell metabolite 

extract samples with duplicate identifications (facilitated by matching InChIKeys). The adduct type and 

retention times of these compounds were displayed for comparison. A dot-product-based score (ranging 

from 0 to 999) is assigned for each library match, with higher scores indicating a better match to the 

library’s reference spectrum. Analytes with a second hit having a score within 50 points were flagged as 

ambiguous to be further studied using MetaboPique and MetaboVision, where users can visually examine 

extracted ion chromatograms associated with each analyte and inspect MS2 spectra. This work helps to 

improve compound identification confidence and highlights the importance of discretion when 

interpreting hits by revealing data ambiguity. The program located many ambiguities throughout the data, 

showing up to nine duplicates of a certain analyte. The cause of these ambiguities can be further studied 

to understand how they can be reduced to maximize identification certainty. 

2022 SURF Colloquium 97
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Terence Murphy

Purdue University Computer Engineering

Junior

R&D in the field of computer engineering.

Material Measurement Laboratory, Biosystems and Biomaterials Division, Cellular Engineering Group

Samuel Schaffter

A Versatile Simulator for Cotranscriptionally Encoded RNA Strand Displacement Circuits

Cotranscriptionally encoded RNA strand displacement (ctRSD) circuits are an emerging technology for 

programmable and scalable molecular computations. ctRSD circuits can execute multilayer cascades, 

logic, and signal amplification, and these elementary functions can be integrated to orchestrate 

sophisticated information processing tasks like digital calculations and pattern recognition. To design, 

predict, and understand the behavior of ctRSD circuits, a theoretical model that describes the kinetics of a 

broad range of possible reactions is needed. A user-friendly, scalable simulator that encompasses all 

conceivable ctRSD reactions with arbitrary circuit connectivity would fill this need and could be used to 

prototype new designs and compare experiments to theory. Here we develop a comprehensive kinetic 

simulator written in Python. We first derived the mass action differential rate equations that describe how 

the ctRSD species change with time for any combination of 40+ possible reactions. These equations are 

then numerically integrated in Python to simulate the kinetics of a given system. Instead of creating 

specific rate equations for each simulation, we implemented the equations with matrix algebra. This 

allows ctRSD components to be connected in any arbitrary way, and linear algebra populates the various 

matrices based on the components specified by the user so that one set of governing equations 

encompasses all possible systems. Lastly, we developed an intuitive user interface that enables 

straightforward definition of custom parameters. We validated the simulator with 50+ detailed examples 

spanning all possible reactions. A current limitation is the time required to simulate systems involving 

reactions at very different time scales. Future work could address this issue by integrating our simulator’s 

front end to alternative solvers, such as BioGenNet and Bioscrape, that handle disparate timescales. To 

aid experimentalists in prototyping ctRSD circuits and facilitate the broader adoption of this technology, 

we have launched a website with in-depth simulator instructions and examples.

2022 SURF Colloquium 98
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϭ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Christine "Yemi" Ogunsola

Montgomery College Chemical and Biomolecular Engineering

Sophomore

I plan to continue my education into graduate school to gain a job combining AI and natural Sciences.

Material Measurement Laboratory, Materials Measurement Science Division, Surface & Trace 

Chemical Analysis Group

Dr. Edward Sisco and Dr. Arun Moorthy

Building and Expanding the Drug DART-MS Forensics Database with Automated Tools

With the ability to create a new drug with simple modifications during its synthesis, identifying illegal 

drugs has become increasingly challenging. At NIST, we research new tools such as Direct Analysis in 

Real-Time Mass Spectrometry (DART-MS) that forensic scientists can use to get rapid and more accurate 

data than traditional instrumentation. Hence, the NIST DART-MS Forensics Database, which contains 

mass spectra of various drugs and can help assist forensic laboratories in drug identification, was created. 

Building a mass spectral database is a complex and financially expensive process. Due to time and 

financial constraints, it is imperative that we understand what drugs are valuable and prioritize them in 

our database. To help identify which drugs should be included, mathematical models were created to 

propose optimal combinations of drugs to expand the database. These models consider and evaluate 

various value paradigms for optimal solutions using several algorithmic approaches. 

Additionally, a database sometimes has the potential to include human and computer-generated errors. 

For the NIST DART-MS Forensics Database, one tool that is regularly used for identifying 

inconsistencies is Validation User Friendly (VUF). VUF checks the metadata of every entry in the 

intermediary step of the database building process. Each chemical identifier (molecular mass, canonical 

SMILES, InChI code, InChI key, and class) is validated against the provided chemical formula (assumed 

as fact). The data is verified by a custom script and compiled into a user-friendly validation application. 

Depending on the user’s needs, the dataset can be filtered by multiple values and downloaded. 

This presentation will cover these tools' automated methods for building and expanding our database, and 

thereby ensuring accurate and relevant information for the forensic community. 

2022 SURF Colloquium 99
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August 2-4, 2022 

Name: Ryan Puthumana

Academic Institution: University of Maryland, College Park Major: Bioengineering

Academic Standing 

(Sept. 2022): Senior 

Future Plans 

(School/Career): Biomedical research/industry and possible graduate school 

NIST Laboratory, 

Division, and Group: Material Measurement Laboratory 

NIST Research 

Advisor: Dr. Tyler Martin 

Title of Talk: Optimization of Clustering Algorithms for Small Angle Scattering

 of Polymer Formulations 

Abstract:

There remains a persistent need in the field of materials development to characterize the structural properties of 

polymer samples dissolved in oil and water solvents. Oftentimes, the amphiphilic nature of these polymer-solvent 

mixtures means that a variety of microstructures are formed, including micelles, lamellae and cylinders. Neutron 

scattering is a useful technique to characterize these structures, but the process of data analysis is time consuming 

and requires deep expertise. In order to develop automated analysis strategies that characterize microstructure, we 

examined three common clustering methods — KMeans, affinity propagation, and DBSCAN. While affinity 

propagation and DBSCAN automatically determine the number of clusters in a dataset, KMeans requires a 

secondary method for this purpose and we additionally explored three potential options: silhouette, gap-statistic, 

and elbow methods. We found that while the DBSCAN method had the best performance with default parameters, 

the affinity propagation method offered the optimal combination of simplicity and flexibility with some 

modification. When compared to our gold standard of manual clustering by hand, affinity propagation offered a 

high Fowlkes-Mallows score indicating optimal clustering. Additionally, affinity propagation contains a “preference” 

input term that can be modified based on data-specific knowledge for added tunability. As KMeans required a 

separate step to determine the optimal number of clusters it was more inefficient compared to the other 

algorithms. In conclusion, we have benchmarked several data clustering methods and other ancillary analyses 

related to the clustering of small-angle scattering data of polymer materials. This project serves to establish a 

relatively simple and efficient pipeline for researchers and inexperienced lay people alike to analyze small angle 

scattering data of soft materials. 

2022 SURF Colloquium 100
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Hrishikesh Ram

North Carolina State University Chemical Engineering + Chemistry

Rising Sophomore

Graduate School in Chemical Engineering/Chemistry/Polymer Science

Material Measurement Laboratory, Materials Science and Engineering Division, Polymers and 

Complex Fluids Group

Jennifer A. Clark (NRC)

Interaction Energies of Zwitterionic Polybetaines with NaCl 

Polybetaines are a family of zwitterionic monomers with both positively and negatively charged groups 

that exhibit stimuli-responsive properties with applications in biomedicine and membrane design. The 

tendency of these materials to expand in response to increased salt concentration is the opposite of 

entirely anionic or cationic polyelectrolytes. Thus, this response to salt is termed the anti-polyelectrolyte 

effect, and is likely caused by competitive ionic interactions between the polyzwitterions and salts. 

Because such complex interactions are difficult to deconvolute with experimental methods, computational 

simulation (e.g., density functional theory (DFT)) will aid in understanding polybetaine interactions with 

salt. In an ongoing project, systems consisting of the 2-(acrylamidoethyl-dimethyl-ammonium) 

propyl-1-sulfonate sulfobetaine monomer interacting with NaCl in aqueous solution were probed using 

molecular dynamics (MD). Geometries of target ionic groups (monatomic and polymeric) were extracted 

from these MD trajectories to serve as input structures for DFT calculations. Water molecules within 

approximately 2.8Å of hydrophilic groups were included to explicitly represent the first hydration shell. 

Frames were then grouped into categories by anticipated interactions based on the proximity between 

ionic groups. Random samples were extracted for each category and input into DFT calculations. With 

the IEFPCM/B3LYP-D3/aug-cc-pVDZ model chemistry, the absolute solvation free energies of Na+ and 

Cl- are determined using the primitive quasichemical theory (pQCT) for sodium-water and chlorine-water 

clusters of varying coordination. The accuracy of the pQCT setup compared to experimental values for 

monatomic ions allows the application of the same method to sampled sulfobetaine monomer 

configurations. Subsequently calculating gas-phase binding energies in conjunction with solvation free 

energies will help quantify the free energy contributions involved in various competing ionic interactions 

within ion-polymer systems. Thus, this work will help characterize the competitive ionic interactions that 

result in the stimuli-responsive phenomena observed with polybetaines. 

2022 SURF Colloquium 101
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Alexandra Terres

University of Maryland Materials Science and Engineering

Junior 

Plan to pursue a Masters Degree or Ph. D. in Materials Science 

Material Measurement Laboratory, Materials Measurement Science Division, Materials Structure and 

Data Group

Benjamin Dolata, Lawrence Friedman

COMSOL Simulations of Contact Line Propagation in Additive Manufacturing with Ceramics

Ceramic slurries in additive manufacturing (AM) have the potential to revolutionize biomedical, 

aerospace, and electrical engineering. The direct ink write (DIW) process provides a reliable way of 

producing custom and individualized products with slurries, capable of expanding the AM industry. One 

aspect of the on-going research into this process is building a better model for slurry extrusion with 

regards to bead shape that will allow for better control and development of DIW printing. We used 

COMSOL Multiphysics software to simulate the propagation of laminar single-phase flow of a 

non-Newtonian, Herschel Bulkley-Papanastasiou slurry through a pipe using a moving-mesh method to 

refine the simulation at the point where the slurry/air contact line means the pipe wall. Previous 

constitutive models utilizing a phase-field or level set method have been insufficient at defining this 

point, as the contact angle exceeds 180Û, thereby “flowing” outside the bounds of the pipe. Two 

conditions are integral in determining the dynamic behavior of the contact angle: the capillary number 

and the Navier slip boundary condition. The former describes the ratio of surface forces to viscous forces, 

analogous to surface tension and fluid friction; the latter is a condition applied at the wall that allows the 

line to move freely through the simulation. By kinematically coupling the contact line dynamics with 

fluid velocity and refining the mesh significantly at this point, we demonstrate that the necessary mesh 

refinement for a successful simulation is a function of capillary number. 

2022 SURF Colloquium 102
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Dennis Zhao

University of Maryland College Park Materials Science and Engineering

Sophomore

Graduate School/Researcher

Material Measurement Laboratory, Materials Measurement Science Division, Materials for Energy 

and Sustainable Development Group

Dr. Aaron Gilad Kusne

Analyzing Ternary Phase Diagrams Using Machine Learning

Autonomous physical science is the future of materials science. In autonomous systems, machine learning 

controls experiment design, execution, and analysis in a closed loop, speeding up the process of finding 

new useful materials. Autonomous systems use active learning, the machine learning field of experiment 

design, to select subsequent experiments that maximize knowledge gained. As active learning strategies 

evolve, there is a growing need for reference datasets to benchmark strategies. We present three reference 

datasets. The datasets describe three ternary systems and contain both phase composition and simulated 

X-ray diffraction for different material compositions. The datasets are then demonstrated for 

benchmarking active learning strategies. The active learning strategies of random selection, entropy 

minimization, and risk minimization are benchmarked for their ability to maximize knowledge of the 

composition-structure relationship, i.e., the 'phase diagram'.

2022 SURF Colloquium 103
2022

2022 SURF Colloquium 104
Summer Undergraduate Research Fellowship (SURF) - 2022 Participants

NIST Center for Neutron Research (NCNR)

Benen Crombie

Shriya Haravu

Amy Musser

John Nunez

Jack Rooks

Christopher Stallard

Kim Taylor

Washat Ware

2022 SURF Colloquium 105
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϭ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Benen Crombie 

University of Virginia Mechanical Engineering

Junior

I intend to pursue a career in engineering and potentially attend a graduate progam for engineering or 

computer science.

Center for Neutron Research, Reactor Engineering

James Whipple

Automating the Fuel Element Visual Inspection Process at the NCNR Reactor

On February 3, 2021, the NCNR reactor experienced a fuel element failure resulting from an improper 

latching of one of the fuel elements. This caused the element to exceed its allowable temperature. This 

was a failure of the inspection checks that must occur before the reactor is operational, and new checks 

were implemented at the NCNR in response. One of these checks is a visual check of each fuel element. 

Reactor operators check that the latch bar of the fuel element is in line with the fuel element itself. This 

inspection process takes about two hours. The goal of this research project is to automate the process in a 

custom computer application using video and image processing techniques. 

Our program is built in Python and utilizes a Computer Vision library called OpenCV. The program takes 

the video from the visual inspection tool camera as the input and returns a report consisting of pictures of 

each fuel element and their respective offset angles. Reactor operators can then view the output report to 

perform a final human inspection to ensure that all fuel elements are latched properly. To accomplish this 

we implemented contour detection, which outlines large shapes that have the same light intensity. 

Afterward, the next step in the project was calculating the offset angle of the latch bar. Once we found the 

bounding boxes of the fuel element and the fuel element latch bar, the angle was calculated. 

Our program automated the inspection checks and shortened a 2 hour process to a few minutes. This 

program can be modified to ensure safe and efficient inspections of reactors at other facilities and save 

reactor operators upwards of 10 hours of manual inspections every year. 

2022 SURF Colloquium 106
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August 2-4, 2022 

Name: Shriya Haravu 

Academic Institution: UNC Chapel Hill Major: Astrophysics + Mathematics 

Academic Standing Senior

(Sept. 2022): 

Future Plans 

(School/Career): 

Hope to work in industry before going to graduate school 

NIST Laboratory, 

Division, and Group: 

NIST Center for Neutron Research, Neutron-Condensed Matter Science Group 

NIST Research 

Advisor: 

William Ratcliff 

Title of Talk: Automatic Identification of Regions of Neutron Diffraction Patterns Changing During Phase Transition 

Abstract: 

Neutron scattering is an exquisite probe of the crystal and magnetic structure of materials. Increasing the 

efficiency with which neutron scattering data can be taken is important because there are a limited 

number of neutron facilities in the world. We know from previous experiments that there exist specific 

regions of diffraction patterns which change with temperature upon a phase transition. These changes in 

the diffraction pattern can inform us about changes in the crystalline and magnetic order in the material. 

Our goal is to automatically identify the regions mentioned above using diffraction patterns. In this 

project, I work with simulated diffraction patterns and use the DREAM (DiffeRential Evolution Adaptive 

Metropolis) algorithm to automatically fit peaks to the data. Following this identification of peaks, we 

determine how individual peaks shift with temperature while also monitoring the 

appearance/disappearance of peaks. By measuring a diffraction pattern above and below the transition, 

this will allow us to determine which regions to follow during the phase transition. 

2022 SURF Colloquium 107
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Amy Musser

University of Maryland College Park Bioengineering

Recent Graduate 

Industry

NIST Center for Neutron Research, NCNR Neutron Condensed Matter Sciences

Yimin Mao

Multi-Scale Structures of Starch as Revealed by Scattering Techniques: From Unit Cell to 

Nanostructure

Starch is a form of energy storage produced by plants, abundant in seeds, roots, and tubers. As a common 

source of dietary calories, starch structure and crystallinity play a pivotal role in its digestion, which is an 

important aspect of human dietary health, gut health, and overall well-being. We examined the multiscale 

structures of four different starches, specifically, waxy maize, normal maize, high amylose maize, and 

pea starch, using wide- and small-angle x-ray scattering techniques (WAXS/SAXS). WAXS was used to 

probe starch crystal structures as well as the content of crystalline portion in starch granules. In addition, 

grain size of starch crystals can be calculated by diffraction line profile analysis. SAXS was used to 

characterize nanoscale structures. It was identified that starch crystals in waxy maize and normal maize 

are of the A type, whereas those in high amylose maize are of the B type. Pea starch possesses a unique 

crystal type, which is arguably considered as a mixture of type A and B. It was found that water can 

significantly enhance starch crystallization: after hydration, a dramatic increase of crystallinity and 

perfection of crystals were revealed by WAXS, and a formation of nanoscale lamellar structure consisting 

of amorphous and crystalline layers occurring in an alternating manner by SAXS. Our quantitative 

analysis can provide insights into structural roots of physical properties of starch, such as pasting and 

enzymatic hydrolysis efficiency. Neutron and X-ray scattering will be used to further decipher water 

partition in starch multi-scale structures. 

2022 SURF Colloquium 108
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

John Peter J. Nunez

Virginia Commonwealth University Mechanical Engineering

Junior

Aeronautical Engineer (Government (Department of Defense)/Defense Company)

NIST Center for Neutron Research

Dr. Jonathan Gaudet

Revealing the magnetism of Weyl semimetal CeAlGe

Weyl fermions are massless and highly mobile particles. They also possess a form of chirality in which 

their spin is either in the same direction as motion (right-handed) or in the opposite direction 

(left-handed). Their basic nature means that Weyl fermions move very quickly on the surface of the 

crystal with no backscattering, which hinders efficiency and generates heat in normal electronic materials. 

Given that Weyl fermions have not yet been directly observed in nature, it has been discovered that they 

accurately characterize the emerging electrical properties of select crystalline semi-metallic materials that 

either break inversion or time-reversal symmetry. This research focuses on the semi-metallic CeAlGe 

material, whose crystal structure deviates from inversion symmetry to allow the emergence of Weyl 

fermions. Additionally, the time-reversal symmetry is broken by the Ce3+ low temperature collective 

magnetism, which affects the Weyl characteristics. Thus, CeAlGe offers a unique template for 

researching the relationship between Weyl fermions and magnetic properties. 

In this presentation, we will go into the specifics of the CeAlGe compound's magnetic structure and 

spin-order analysis, which we conducted using neutron diffraction. We will present the single crystal 

neutron diffraction data we carried out to determine the CeAlGe nuclear and magnetic unit cell. We 

discovered that the Ce spins in the magnetic unit cell are pointing within the basal plane and have 

compositions that are both ferromagnetic and antiferromagnetic. Given this, since zero field neutron 

diffraction cannot definitively reveal the specifics of the collinear structure, we will also discuss our 

in-field neutron diffraction data that can be refined to resolve this uncertainty. The relationship between 

the magnetism and the Weyl fermions in CeAlGe will then be presented. 

2022 SURF Colloquium 109
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Jack Rooks

SUNY at Buffalo Chemical engineering

Graduated

Chemical engineering PhD program at University of Delaware

NIST Center for Neutron Research (NCNR), Neutron Condensed Matter Science Group

Peter Gilbert

Particle orientation in soft materials from small-angle neutron scattering

Many soft materials, like polymers, derive their mechanical strength and flexibility from molecular and 

nanostructure orientation. Any particle or molecule that is longer than it is wide has orientation, and this 

orientation can be manipulated by an external force, such as flow, to modify orientation-dependent 

material properties. Small angle neutron scattering (SANS) can measure nanoscale orientation within a 

material, and traditional analysis yields an estimate of the average orientation angle. Particles are often 

imperfectly oriented in soft materials, resulting in a distribution of particle orientations about an average 

angle. We introduce a novel analysis method to extract the width of the orientation distribution from 

experimental SANS data for flow-aligned cylindrical micelles. This analysis requires data collected from 

different perspectives of micelle orientation. These perspectives are then assembled into a single estimate 

of the orientation distribution. Our analysis provides more information on the orientation distribution than 

previous approaches. This new orientation information will enable more precise tuning of material 

properties that are linked to orientation.

2022 SURF Colloquium 110
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Christopher Stallard

University of Minnesota Chemical Engineering

Sophomore

Go to graduate school, get a PhD, and work in either industry or government labs

NIST Center for Neutron Research, Neutron-Condensed Matter Science Group

Ryan Murphy

Developing a User-Friendly Framework for Stopped Flow SANS at the NCNR

Small-Angle Neutron Scattering (SANS) is a technique that can measure structural properties of materials 

at nanometer to micrometer length scales. Recently, there have been developments in the use of 

time-resolved SANS to study kinetic processes and the resulting changes in small-scale structure. This 

technique is particularly important in the study of biological and self-assembled systems. In order to 

probe shorter timescales (from 100ms-10s), it is necessary to begin measurement as quickly as possible 

after mixing. Stopped-flow techniques mix components using a series of pumps, valves, and one or more 

flow-through mixer. This lowers the "dead time" between mixing and observation to <1s. 

The Center for High Resolution Neutron Scattering (CHRNS) is developing a stopped-flow SANS system 

for routine use, in order to expand its measurement capability for facility users. As a complementary 

measurement technique, UV-Vis spectra are collected simultaneously with SANS measurements. In this 

project, I created a single program to control all the experiment hardware, including pumps, valves, 

pressure sensors, and the UV-Vis spectrometer. This program provides a simple way to control, monitor, 

and automate experiments. Using this program, I characterized the response of the UV-Vis spectrometer 

to various concentrations and mixing ratios of fluorescent dye/quencher pairs. Additionally, I propose a 

standard method for data analysis of both time-resolved neutron flux and 1D SANS data, and test that 

method using simulated data based on past experimental scenarios. This work sets the stage for consistent 

experimentation and analysis of stopped-flow SANS data at the NCNR.

2022 SURF Colloquium 111
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Kim Taylor

University of Maryland College Park Material Science Engineering

Junior

Completing my bachelors in Material Science Engineering at UMD and continuing on to Grad school.

NIST Center for Neutron Research: NCNR Neutron Condensed Matter Sciences

Yimin Mao, Susana M. Teixeira, and Susan Krueger

Surfactant-Free Oil-in-Water Emulsion Stabilized by Chitin Nanocrystals: A Green Recipe

An emulsion is a colloidal mixture of two or more incompatible liquids, typically in a form of droplets of 

one phase dispersed in a matrix, such as oil droplets dispersed in their water surrounding, termed 

oil-in-water emulsion. The food industry is one of the most significant users of emulsion technology 

since many food products, such as milk, butter, and creams, need to be manufactured using multiple, 

incompatible liquid components. Emulsions need to be stabilized by amphiphilic species such as 

surfactants to prevent them from agglomerating, which often leads to environmental and health concerns. 

Alternatively, a new type of emulsion, Pickering emulsion, can be produced when using colloidal 

particles as an emulsifier. In this study, we used chitin nanocrystals (ChNCs) to produce oil-in-water 

Pickering emulsions, with the goal to provide a green recipe applicable to various food products. Chitin is 

mainly sourced from fishery wastes; it is the second most abundant biomass on the earth. We applied 

phosphoric-acid (PA) hydrolysis and 2,2,6,6-tetramethylpiperidine-1-oxyl(TEMPO) mediated oxidation 

methods to produce ChNCs (PA-ChNC and TEMPO-ChNC) with varied size and surface chemistry. 

Nanostructure of the rod-like ChNCs were characterized using dynamic light scattering (DLS) and 

small-angle X-ray scattering (SAXS). It was found that both types of ChNCs were able to form stable 

Pickering emulsions at an oil (medium chain triglycerides)-to-water ratio of 1:9 (volume ratio). 

PA-ChNC produced a smaller droplet size of ~2 microns with a relatively narrow size distribution, while 

TEMPO-ChNC produced droplets with a broad size distribution ranging from a few microns up to ~20 

microns. On-going investigation focuses on using scattering and microscopic techniques to characterize 

the oil-water interface stabilized by ChNC, to correlate the ChNC characteristics to emulsion structures 

and properties.

2022 SURF Colloquium 112
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Washat Roxanne Ware

Fayetteville State University Chemistry w/ conc. Materials Science

Recently Graduated 

PhD in Materials Science and Engineering and eventually work in prosthetics research

NIST Center for Neutron Research, NCNR Neutron Condensed Matter Sciences

Rachel Ford

Determining the validity of Guinier analysis in slit-smeared small angle scattering data

Small angle scattering (SAS) techniques are powerful methods for probing structure ranging from 

nanoscopic to mesoscopic scale. A common method for analyzing SAS data is Guinier analysis, which 

enables the determination of the radius of gyration, Rg, without prior knowledge of the particle 

morphology. Guinier analysis was developed specifically for unsmeared data. The Guinier model may 

work well for certain instrument smearing, namely smearing from instruments with slit geometry. 

Therefore, Guinier analysis is typically not used on slit-smeared data, such as data from ultra-small angle 

neutron scattering (USANS) instruments. We seek to determine whether the Guinier approximation holds 

for slit-smeared data. We apply Guinier analysis to simulated data for a variety of particle morphologies 

both with and without slit smearing. We report the models for which the Guinier approximation holds in 

slit-smeared data, as well as boundary conditions for models where slit-smearing produces a 

non-negligible deviation of the extracted Rg.

2022 SURF Colloquium 113
2022

2022 SURF Colloquium 114
Summer Undergraduate Research Fellowship (SURF) - 2022 Participants

Physical Measurement Laboratory (PML)

Nicolas Aguilar

Max Buskirk

Briana Chen

Hayden Craun

Aagam Dalal

William Dienstfrey

Michael Gabe 

Michael Gutowski 

Samir Kulkarni 

Connor Lewis 

Gabirel Lewis

Faith Makumbi 

Satvik Manjigani 

Evan McClintock 

Shawn Meyer

Daniel Quinter

Abrar Sheikh

Hunter Staiger

Grace Tang

Travis White 

Lucy Yagodich 

Joshua Young 

Helen Zhang

2022 SURF Colloquium 115
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Nicolas Aguilar

University of Maryland, College Park Mechanical Engineering

Senior

Currently planning on obtaining a master's degree and pursuing a career in engineering

PML, Quantum Measurement Division, Mass and Force Group

Jon Geist

Testing Calibration Uncertainty With Low Cost Inertial Measurement Units With A Pendulum Method 

NIST serves as the primary measurement and calibration laboratory for the nation and as part of that, the 

Mass and Force Group within the Physical Measurements Laboratory focuses on advancing measurement 

technologies for inertial measurements. A big part of this includes the use of Inertial Measurement Units 

(IMUs), which consist of a mix of tri-axis accelerometers, gyroscopes and magnetometers. IMUs are used 

in a vast amount of technologies in today's era, including smartphones, vehicles, and drones as they are 

essential in determining the motion and position of the device. 

This project focuses on testing the calibration uncertainty of the Adafruit nRF52840 Bluefruit Sense (a 

micro-electromechanical IMU that sends acceleration and rotational data to a device via Bluetooth) by 

using a calibration method that was previously developed by NIST. The first phase of this method uses 

gravity and the intrinsic properties of the IMU to calibrate the sensor in static conditions with a predicted 

uncertainty under one tenth of a percent (based on previous results done by NIST). This process is done 

by running the IMU under different orientations in a cube to see the offset within each accelerometer. The 

second phase of the method consists of using a pendulum to collect both gyroscope and accelerometer 

data to calibrate the gyroscope with the accelerometer data and with the effective length of the pendulum 

including uncertainties. We will compare the manufacturer's calibration with the calibration done on the 

accelerometer to see its differences and effectiveness.

2022 SURF Colloquium 116
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career):

NIST Laboratory, 

Division, and Group:

NIST Research 

Advisor:

Title of Talk:

Abstract:

Max Buskirk

California Polytechnic State University Chemistry, Physics

Senior

Graduate School in Physics or Chemistry; preferred topics near AMO Physics or Chemical Physics.

Career involving the physical sciences somehow.

Physical Measurement Laboratory; Nanoscale Device Characterization Division; Advanced 

Electronics Group.

Yaw S. Obeng

Analysis of Solder Joint Failures using Resistance Measurements

Solder joints found in electronic systems are susceptible to failure in two main groups: hard failures and 

no failures found (NFF). NFF failures are responsible for half of all failures in solder joints. The focus of 

this research is to detect NFF failures before complete failure. By determining how solder joints fail in 

NFF failures, the Department of Defense could save upwards of $2 billion annually.

Sample boards were analyzed in a thermally controlled chamber cycling between 0 °C and 100 °C, while 

measuring some electrical properties of the joints. Comparing cycle number, the corresponding 

temperature, and the microwave signal return loss yielded some previously unidentified details, including 

nearly abrupt jumps. These gave some insight into these NFF failures, with the jumps possibly 

corresponding to stages of failure of the solder joint. But, some past work suggested that these 

measurements detected complete failures after when they would be detected by DC resistance 

measurements. In this work, DC resistance measurements of the joint failure are analyzed in order to, 

similarly, find any previously unidentified detail. This will help further characterize solder joint failures 

under thermal cycling, and improve measurement techniques. Preliminary results show a lack of 

temperature dependence of the resistance, but some dependence on the cycle number has been found.

2022 SURF Colloquium 117
SURF Student Colloquium 

NIST – ŽƵůĚĞƌ͕K

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Briana Chen

Purdue University Aerospace Engineering

Sophomore

Graduate school!

Physical Measurement Laboratory, Applied Physics Division, Quantum Nanophotonics Group

Dr. Krister Shalm

Developing resources for understanding an enhanced randomness beacon

Public sources of randomness have powerful applications, widely affecting societal processes and 

outcomes such as lotteries, jury selections, and spot checking. Currently, NIST operates a public 

randomness beacon that periodically publishes pulses containing random values, creating a blockchain of 

randomness. 

The research team I have been involved with aims to replace this current beacon with a next-generation 

beacon that is efficiently verifiable and incorporates better quantum randomness. However, both beacons 

utilize blockchains and specific protocols that are difficult to understand and implement, which 

discourages people from using the beacons in various applications. Because of this, the general public 

requires an educational resource that explains why randomness beacons are useful and how they can be 

used.

In this talk I will discuss an interactive web-based tutorial I created that introduces the fundamental 

concepts behind the new beacon's protocols to create trustworthy randomness. The tutorial also highlights 

how the beacon safeguards against illegitimate claims and manipulation. By learning about the inner 

workings of a randomness beacon, the tutorial's users will better understand how to apply public sources 

of randomness to their own ventures. This will ultimately make the randomness beacon more widely 

accessible and help it reach its full potential. 

2022 SURF Colloquium 118
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Hayden W. Craun

Virginia Tech Computer Engineering

Senior

Finish my undergraduate degree and possibly go to graduate school for electrical engineering

PML, Microsystems and Nanotechnology, Photonics and Plasmonics

Amit Agrawal

Design of Multiresonant Plasmonic Nanocavities for Label-Free Nonlinear Optical Voltage Sensing

Multiphoton voltage imaging with voltage-sensitive fluorophore molecules has been instrumental in 

monitoring deep-tissue electrophysiological activities but suffers from challenging issues, including 

photobleaching, phototoxicity, and disruption of native cellular behaviors. In this project, we investigate 

elliptical nanolaminate plasmonic nanoantennas (ENLPNAs) as label-free solid-state voltage nanosensors 

to produce polarization-dependent plasmon-enhanced second harmonic generation (PESHG) signals with 

high conversion efficiency and large voltage sensitivity. Utilizing finite-difference time-domain 

simulations, we take advantage of the resonant plasmonic properties of gold nanostructures to engineer 

multiresonant, elliptical nanocavities to produce PESHG signals. We achieve multiresonant response with 

optimized sub-wavelength metal-insulator-metal (MIM) building blocks. By manipulating key 

geometrical parameters of our nanostructure, we widely tune the nanostructure's resonances with multiple 

degrees of freedom. By combining these properties, we find conditions for cross-polarized spatial mode 

overlap to enhance PESHG signals. Based on these simulation results, we plan to fabricate ENLPNAs via 

electron-beam lithography and lift-off to measure their nonlinear optical response and voltage sensitivity. 

With the development of these label-free voltage nanosensors, we aim to broaden the interface between 

the biological system and integrated nanosystem to monitor network-level brain activities using standard 

multiphoton processes. 

2022 SURF Colloquium 119
SURF Student Colloquium 

NIST 

August - , 20

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk:

Abstract:

Aagam Dalal

University of Pennsylvania Computer Engeineering

Sophomore

(Career) Computing Hardware/Firmware 

PML, Applied Physics Division, Quantum Nanophotonics Group

Dr. Krister Shalm

Testing and demonstrating a new toolkit for public randomness beacons

The NIST Randomness Beacon provides world-class public randomness based on the Interoperable 

Randomness Beacon project. The goal is to produce a source of randomness that sensitive applications 

such as voting protocols or lotteries can use. However, the current application programming interface 

(API) of the beacon needs improvement. In partnership with the University of Colorado at Boulder, a new 

randomness beacon is being launched to overcome this issue and is based on an easy-to-use Javascript 

interface called TwineJS. 

In this talk, I will discuss my work testing and demonstrating the TwineJS library. The TwineJS library 

had not yet been thoroughly tested at the start of the summer. My first task was to write unit tests for 

TwineJS to test the API and remove bugs. During the second part of the project, I built a web game using 

the Vue javascript framework and TwineJS that demonstrates how to implement contracts based on a 

randomness beacon. In the real world, such contracts can help facilitate transparency and verifiability in 

allocating anything from admission into elite public schools to anti-corruption audits. In the web game, 

players try to predict the outcome of a coin flip, which is extracted from public randomness. As the game 

unfolds, users see the guarantees that Twine and the randomness beacon provide, and how they stop any 

player from having an unfair advantage. The results show how the NIST randomness beacon can be 

leveraged to build transparent applications. 

2022 SURF Colloquium 120
SURF Student Colloquium 

NIST – ŽƵůĚĞƌ͕K

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Will Dienstfrey

Amherst College Physics

Junior

Graduate school and pursue a career in atmospheric/geophysics

Physical Measurement Lab, Applied Physics, Molecular and Bio-Photonics

David Plusquellic

Monitoring Atmospheric Stability for Plume Emission Flux Measurements

Non-invasive, real time measurements of greenhouse gases (GHGs) at high-emission sources are critical 

for monitoring and regulation. It has been shown that one can observe emission flux of 

buoyancy-dominated smoke plumes using a Differential Absorption LIDAR system (DIAL). However, 

quantification of emission flux is challenging as variations in atmospheric conditions affect the shape, 

location, and density of the gas, which can lead to plume GHG concentration information becoming lost 

in the noise of ambient gas signal. Therefore it is important to point and adjust the DIAL beam to sample 

the optimal part of the plume. This work seeks to determine plume characteristics based on auxiliary 

atmospheric data to stabilize DIAL measurements of greenhouse gases. 

We use a Gaussian dispersion plume model to model smoke plume characteristics as a function of 

atmospheric conditions. In turn, atmospheric variations are tracked in the model using Turner and 

Pasquill-Gifford stability classes which account for changes in factors such as wind speed and the net 

radiation index. Real world atmospheric data from a NOAA Doppler LIDAR system and a VAISALA 

point source instrument are used to test the model and understand the model uncertainties. 

Atmospheric models face many challenges as there are numerous hidden interactions between parameters. 

We implement a Monte Carlo study to estimate uncertainty in smoke plume concentration predictions due 

to atmospheric variation. By introducing controlled stochastic variation in the input parameters, one can 

observe the statistical effect this variation has on the output. In our study, wind speed was varied, and the 

resulting concentration variations were determined via the model. This work found that the best time to 

observe a smoke plume is at night in stability classes 6 and 7. An interface using this model was 

constructed to help guide experimental measurements of the plume in real time.

2022 SURF Colloquium 121
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Michael Gabe

Case Western Reserve University Physics

Physics BS (Graduated)

I plan on attending graduate school in either physics or astronomy Fall 2023, with the intention of 

pursuing some cosmological research.

Physical Measurement Laboratory, Quantum Measurement Division, Quantum Optics Group

Dr. Zachary Levine

Nonperturbative nonlinear optics in a rubidium vapor cell

Because of their ability to decrease shot noise, squeezed states of light have been a hot topic of research 

interest. They have famously been used to increase the sensitivity of LIGO's gravitational wave detector. 

Paul Lett has been examining relative-intensity squeezing by four-wave mixing in Rb-85 vapor. Lett's 

experiment in 2007 featured Ti:sapphire lasers sent through Rb-85 vapor, while his current experiment 

feeds laser components back through the Rb-85 vapor. The current experiment outputs an anomalous 

oscillatory effect in the amplitude of the laser. To further our understanding of this optical system, this 

project computationally models the experiments' induced dipole moment from Rb-85 D1 transitions due 

to excitations from the laser. As perturbation theory likely breaks down due to the laser's near-resonant 

tuning, our code calculates the density matrix in the time domain, which we will further analyze in 

frequency space using Fourier transformations. In this project, we verify the code by matching analytic 

solutions such as the Rabi frequency for an ideal two-level system, then calculate chi_zzzz and chi_xxzz 

for the Rb-85 vapor.

2022 SURF Colloquium 122
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Michael Gutowski

Villanova University Electrical Engineering

Sophomore

Pursuing Masters in Engineering

Physical Measurement Laboratory, Sensor Science Division, Dimensional Metrology Group

Braden Czapla

Computer Vision Identification and Analysis of Interferometric Fringes

The Dimensional Metrology Group in the Physical Measurement Laboratory at NIST uses a specialized 

interferometer to precisely measure sphere diameters with nanometer uncertainty. This instrument, called 

the Strang Viewer, is crucial in the measurement of reference spheres used to calibrate equipment 

internally at NIST and for external customers, such as ball-bearing manufacturers. 

The Strang Viewer’s operating procedure includes capturing images of the fringe pattern created by the 

interference of laser beams reflecting between two optical flats, in between which the sphere is located. 

Encoded in the fringe pattern is the distance between the two flats. To access the information, the current 

analysis software requires the operator to identify key features in the images to assist the algorithm in its 

calculations. This method can be time-consuming and can possibly introduce human error into its 

calculations. 

This project utilized computer vision techniques to automatically detect the positions of the sphere and 

fringes in the images, decreasing the potential for human error. The new algorithm also proved to be 

faster than the existing software. In the future, the new algorithm could be improved by implementing 

additional image filtering to enhance the identification of the fringes. 

2022 SURF Colloquium 123
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Samir Kulkarni

UC Riverside Physics

Sophomore

Graduate School

Physical Measurement Laboratory, Quantum Measurement Division, Atomic Spectroscopy

Dr. Joseph Tan

Simulating electron beam behavior in a miniature electron beam ion trap

Highly charged ions (HCIs), traditionally found in hot plasmas, may have colder applications, such as

precise atomic clocks, probing fundamental physics, and quantum information processing. NIST has an

electron beam ion trap (EBIT) with a magnetic field of approximately 3 T to facilitate the production of

HCIs. An ongoing project at NIST is to create a more compact EBIT, or mini-EBIT, using NdFeB

permanent magnets rather than large superconducting coils. This construction is more effective at creating

HCIs with ionization energies lower than 1 keV. The highest charge states an EBIT can produce depends

on the energy and current density of the electron beam. Using a program called LORENTZ, we will

model the mini-EBIT, simulate the electron beam's space-charge-limited motion, and estimate the current

density when passing high currents through the drift tubes of the mini-EBIT. The goal is to understand

better the electron beam optics in the mini-EBIT and provide predictions to be compared to actual

measurements for evaluating performance.

2022 SURF Colloquium 124
2022 SURF Colloquium 125
		





	



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2022 SURF Colloquium 126
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August 2-4, 2022

Name: Faith 

Makumbi 

Academic 

Institution:

 University of Kentucky Major: Physics 

Academic Standing 

(Sept. 2022):

Senior 

Future Plans 

(School/Career): 

Go to graduate school 

NIST Laboratory, 

Division, and Group: 

Physical Measurement Laboratory, Sensor Science, Remote Sensing 

NIST Research 

Advisor: 

Susana Deustua 

Title of Talk: Examining the Thermal Properties of an Artificial Star for Flux Calibration 

Abstract: An important aspect of modern observational cosmology is that data must be precise and 

accurate. For example, to measure the properties of dark energy using Type Ia Supernovae 

with an uncertainty of 1% or less means that the calibration of supernova color must be no 

greater than 0.5%. Astronomers use color to represent the ratio of light measured through 

any two band passes (filters). Currently, the uncertainty in color is limited by how well the 

stars used as standard candles are calibrated. One way to improve flux calibration 

techniques of modern telescopes entails utilizing an artificial star whose light output is well 

characterized and traceable to the SI standards. The artificial star can then be used to 

calibrate real stars. Stars are suitable calibration standards because of their numerosity and 

easy availability. The focus of my research is to study the design of a prototype artificial star 

known as CANDLE (Calibration using an Artificial star with NIST-traceable Distribution of 

Luminous Energy). CANDLE is a proposed instrument that will be designed to go on a small 

satellite in space, and project light calibrated to a telescope. I am using COMSOL to simulate 

the thermal properties of CANDLE under different configurations. This study will provide a 

better understanding of CANDLE' s thermal properties under different conditions and be 

used to inform the final design of the payload.

2022 SURF Colloquium 127
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Evan McClintock

University of Maryland College Park Physics, Computer Science

Junior

Graduate School, Physics and CS research

Physical Measurement Laboratory

Dr. Richard Steiner

Automating instrumentation for the calibration of DC power meters

In recent years, DC power usage has become increasingly common, driven by renewable energy sources 

and electric vehicle charging. However, the need for precise DC power meter calibration is not yet met. In 

this project, we begin fabricating a method to meet this demand. To ensure resulting uncertainty on the 

order of tens of ppm, all elements of the final setup must individually maintain high degrees of precision. 

Reliable methods of automatically recording and analyzing large amounts of data are vital in reducing the 

uncertainty in these calculations and are therefore a large focus. We have concentrated primarily on the 

calibration of a DC voltage source and current transducer. The source must have correction coefficients 

developed and usable up to 1V, building off previous SURF work with the Fluke 8588A multimeter. The 

corrections for the transducer must be known up to 100A. It requires a voltage source to set the output of 

a current transconductance amplifier and measurement of the corresponding voltage from the current 

transducer and across a low-ohm shunt resistor, the latter of which is dependent on temperature from 

self-heating. The results of this project will serve as a foundation for following work, allowing 

completion of the calibration of DC power meters with high degrees of precision. 

Disclaimer: Any mention of commercial products within this paper is for information only; it does not 

imply recommendation or endorsement by NIST. 

2022 SURF Colloquium 128
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Shawn Meyer

University of Texas at Austin Physics

Senior

Graduate School or Industry Research

Physical Measurement Laboratory, Nanoscale Device Characterization Division, Atomic Scale Device

 Group

Richard Silver

Analysis of Dopant Quantum Dots in Silicon

Strong electronic interactions in quantum materials are responsible for phenomena such as high-Tc 

superconductivity or Mott insulators The Fermi-Hubbard model provides a low-energy description of 

electrons and is believed to have the ingredients to explain such exotic behaviors. The model consists of a 

lattice of sites where electrons are situated, allowed hopping between nearest-neighbor sites, and an 

interaction term representing the Coulomb repulsion between, which can be long-range. However, 

solutions of the model in its generic form are not accessible to current theory or numerics. It has been 

shown that arrays of dopant quantum dots in Silicon effectively simulate the Hubbard model. With the 

ability to place atoms with atomic precision using scanning tunneling microscopy, it is possible to make 

arbitrary lattices and tune hopping and interaction parameters for electrons. 

We analyze a two-by-two array of quantum dots with four gates to vary the chemical potential landscape, 

and source and drain leads to allow electrons to tunnel onto or off the array. The primary focus is to 

establish a connection between device parameters and those of the Hubbard model by comparing a 

simulation of the quantum dot device to experimental data. However, we also explore the effects of 

disorder in the system; this is important because fabrication can be imprecise. We use the open-source 

Python package QmeQ to construct the Hamiltonian of the system and by finding its ground states, map 

out the electronic configurations as the gate voltages are varied. QmeQ gives us a test bed to tune 

parameters such that the experiment and simulation agree. 

2022 SURF Colloquium 129
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Daniel Quinter

Brown University Physics

Junior

Currently intend to attend graduate school for a PhD in a physics program.

PML, Quantum Measurement Division, Laser Cooling and Trapping Group

Dr. Charles Clark

Quantum Random Number Generation in Trapped-Ion Computing with Error Mitigation

In the field of cryptography, random number generators provide a consistent method for generating 

secure keys which serve as the backbone of encryption. Quantum random number generators (QRNGs) 

offer an appealing path toward true randomness and consequently greater cryptographic security, 

motivated by relatively simple quantum mechanical principles. Unfortunately, computations including 

the generation of secure keys or bitstrings via projective measurement are hampered by noise present in 

real-world quantum circuits. The predictability and consistency of this noise across experiments reduces 

the overall entropy of any key generated in this fashion. We thus investigate the statistical randomness of 

bitstrings encoded by quantum cryptography protocols utilizing the cloud-based trapped-ion computers of 

IonQ in College Park, Maryland. 

In this talk, I will discuss noise mitigation techniques and performance results for quantum random 

number generation using a variety of circuits on IonQ's backend. Mitigation is achieved through several 

techniques, chiefly calibration and general error mitigation, with each technique demonstrating some 

level of error mitigation which is quantified and compared. In order to assess the efficacy of each form of 

mitigation, they are applied to random quantum circuits of varying depths, with special emphasis placed 

on GHZ and W-state circuits due to their attractive entanglement properties. Preliminary findings 

demonstrate positive noise minimization, but on a low order of magnitude, partly due to the relatively 

high fidelity of quantum states generated by the IonQ backend. These approaches are then applied to the 

randomly generated bitstrings in order to determine whether or not error mitigation can cause an increase 

in statistical significance for the results of any given randomness test.

2022 SURF Colloquium 130
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Satvik Manjigani

University of Maryland, College Park Physics and Astronomy

Junior

I plan to pursue a degree in graduate school, followed by research in either academia or the private 

sector

Physical Measurement Laboratory, Microsystems and Nanotechnology Divison, Biophysical and 

Biomedical Measurement Group

Michael Zwolak

Characterizing DNA Structures with Machine Learning

Modern advances in machine learning allow for the rapid automation of classification (unsupervised) and 

prediction (supervised) procedures, which has the potential to expedite biophysical and biomolecular 

research considerably. The classification of biomolecules based on distinct physical parameters has a 

particular application in studying proteins for drug development. Here, we use a Convolutional Neural 

Network to predict physical binary states (i.e. folded vs. unfolded, high vs. low temperature) of simulated 

DNA sequences using time series data. Specifically, we calculate the radius of gyration of the DNA for 1 

μs of real time, and adjust the neural network to improve the machine learners' performance. This work 

serves as a baseline that can be expanded to characterize biomolecules into more classes using different 

physical properties.

2022 SURF Colloquium 131
2022 SURF Colloquium 132
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Hunter Staiger

Clemson University Electrical Engineering

Senior

Attending graduate school in order to become a professor.

Physical Measurement Laboratory, Quantum Measurement Division, Atomic Spectroscopy Group

Yuri Ralchenko

An automatic calibration algorithm and the efficiency determination of the NIST spectrometer.

Extreme ultraviolet (EUV) radiation is produced by transitions between energy levels of highly charged 

ions. By precisely measuring the wavelength of the emitted radiation, valuable information about the 

atomic structure can be obtained and compared to the most advanced quantum theories. Theories today 

are so precise that uncertainty in the size of the atomic nucleus is the primary limiting factor in the 

calculations. Therefore, precisely comparing EUV experiments and theory can determine nuclear 

parameters. 

At NIST, EUV wavelengths are measured using a spectrometer, but factors such as temperature variation 

and the spectrometer's position can significantly impact the daily calibration. Additionally, the detection 

efficiency of the spectrometer varies with the wavelength of incoming radiation. The changing efficiency 

can affect the measured line intensities and even systematically shift the measured wavelength. In the 

past, time-intensive calibrations were completed manually, and the efficiency curve was approximated 

using ray-tracing software. To improve the precision of EUV wavelength measurements, a novel 

automatic calibration algorithm was developed and implemented over a database of NIST data. The 

efficiency curve of the spectrometer was reconstructed by comparing theoretical and experimental 

relative intensities. 

Overall, the automatic calibration method produces precision comparable to or better than manual 

calibrations in a fraction of the time. Utilizing the efficiency curve in the calibration process seems to 

produce minimal systematic shifts. The generality of the automatic calibration algorithm suggests that 

similar methods can be implemented with other instruments in the spectroscopy field, reducing the time 

needed to analyze complex data. Shortly, I aim to distribute the automatic calibration software in an 

easy-to-use format to assist the experimental work done by NIST's atomic spectroscopy group. 

2022 SURF Colloquium 133




	

	







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2022 SURF Colloquium 134
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Travis White Jr.

Mercer University Mechanical Engineering

Sophomore

Attending graduate school and conducting engineering in aerospace

Physical Measurement Laboratory, Office of Weights and Measures, Metric Program

Elizabeth Benham

Metric Program Resource Development

Utilizing the International System of Units (SI) in the United States provides an opportunity to 

significantly increase efficiency and decrease unit errors in trade and commerce. The Office of Weights 

and Measures Metric Program aims to develop and publish informational and educational resources for 

U.S. students, teachers, businesses, government, and the public. Throughout the SURF Program, a 

centralized curation of metrication websites has been developed to inform the Metric Program’s audience 

of potential benefits and risks related to metric system conversion. A webpage template was developed 

and implemented for NIST’s educational STEM registry, NEST-R, to permit detailed article entries on 

the public website. With this template, multiple entries have been created for existing SI education 

publications. Additionally, metrication inquiries from U.S. businesses, Congress, and international 

government representatives have been analyzed to ensure that the new websites included appropriate 

information for these technical requests. These resources developed for the Metric Program provide 

solutions that will be instrumental in education outreach and stakeholder engagement for learning and 

implementing the International System of Units.

2022 SURF Colloquium 135
SURF Student Colloquium 

NIST Gaithersburg, MD 

August - , 20

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk:

Abstract:

Lucy Yagodich

Penn State University-Harrisburg Electrical Engineering

Senior Undergraduate Student

I plan to have a career in medical imaging and/or nuclear medicine.

Physical Measurement Laboratory- Quantum Measurement Division 

Dr. Angela Hight Walker

 Interconnecting and Controlling In-Operando Spectroscopic Measurements

Quantum materials characterized with Raman spectroscopy involves the excitation of samples with a 

specific laser wavelength (or color) and collection of inelastically scattered light (Raman process), 

uniquely at NIST, as a function of polarization, temperature, magnetism, and applied electric fields. Yet 

any contiguous operation of instrumentation that controls experimental conditions such as temperature 

and magnetism often involve use of several distinct proprietary programs and devices. Here, I present my 

work in the design and development of a highly customized LabVIEW program (named MagnetoRaman 

Spectroscopic and Electrical Measurement System "MrESMS") that consolidates the control and 

operation of three independent instruments: Raman spectrometer (HORIBA JYT64000), a cryostat with a 

superconducting magnet (attocube attoDRY 2100), and a source measurement unit (Keithley 2400). 

Using the MrESMS program, we demonstrated successful remote control and automated measurements 

with the cryostat and source measurement unit (SMU). A device made from an exotic 2D quantum 

material 2M-WS2 was measured for the demonstration. We showed that the program is capable of 

measuring the superconducting transition as well as the dependence of the transition temperature as a 

function of applied magnetic field. The MrESMS is designed for expandability such that the addition of 

the Horiba spectrometer, or any other additional future devices, will be relatively seamless.

2022 SURF Colloquium 136
SURF Student Colloquium 

NIST – Gaithersburg, MD 

August 2-4, 2022

Name: Joshua Young 

Academic Institution: University of Kentucky Major: Physics 

Academic Standing 

(Sept. 2022):

4.0 

Future Plans 

(School/Career): 

Graduate School, Teaching/Private Sector 

NIST Laboratory, 

Division, and Group: 

PML, Radiation Physics Division 682, Dosimetry Group 

NIST Research 

Advisor: 

Dr. Ronald Tosh 

Title of Talk: Development of an Open-Source Workflow for End-to-End Simulation of an Ultrasonic 

Calorimeter for Imaging of Radiation Dose.

2022 SURF Colloquium 137
Abstract: 

Radiotherapy for cancer treatment seeks to deliver radiation dose to tumors while sparing surrounding healthy 

tissue. Such spatially variable dose fields differ significantly from the highly uniform fields used to calibrate 

detectors, which introduces large systematic uncertainties in measurements of dose delivered to patients and 

concomitant risks for safety and effectiveness of therapeutic radiation. To address this, NIST is working on a 

primary standard for dose calibration that works in nonuniform fields. One embodiment involves the use of 

ultrasonic imaging of temperature distributions in water. An experimental prototype developed here has 

demonstrated proof of principle, but speedups in data acquisition and image processing are necessary to make it

practicable for eventual use as a new standard. The present project is developing computational tools to aid the 

design process, consisting of finite-element analysis to simulate dose delivery to a water phantom, discrete 

sampling of that dose distribution to simulate data acquisition, and image reconstruction based on the simulated 

acquired data. It is expected that this work will provide needed tools for optimizing sensor arrays and image 

processing algorithms to affect the needed speedups. 

2022 SURF Colloquium 138
SURF Student Colloquium 

NIST – ŽƵůĚĞƌ͕K

August Ϯ-κ, 20ϮϮ

Name: 

Academic Institution: Major: 

Academic Standing 

(Sept. ϮϬϮϮ):

Future Plans 

(School/Career): 

NIST Laboratory, 

Division, and Group: 

NIST Research 

Advisor: 

Title of Talk: 

Abstract: 

Helen Zhang

University of California, Berkeley Biology, Data Science

Junior

Graduate school

Physical Measurement Laboratory, Applied Physics Division, Molecular and BioPhotonics Group

Jeeseong Hwang

Hyperspectral Imaging for Tumor Margin Analysis in Breast Tissue Resection

Breast lumpectomy is a surgical procedure that removes cancerous tissue with a margin of healthy tissue 

around it to ensure that all tumorous tissue is removed. 32-63% of breast cancer patients are recalled to a 

second surgery due to incomplete removal of the tumor, which is costly and reduces the quality of life of 

patients. To analyze tumor margins, pathology imaging and dark-field hyperspectral microscopy were 

applied. Pathology imaging was used to categorize tissue and confirm a tumor margin. Dark-field 

hyperspectral microscopy was implemented to image the tumor margin in resected human breast 

lumpectomies. Normal and cancerous tissue cases were collected using the hyperspectral imaging 

technique, which produces a hyperspectral data cube made up of a reflectance spectrum and a series of 

narrow and contiguous wavelength bands. The spectral angle mapper classification technique was 

employed to quantify the similarity between a reference image and an unknown image. The alpha value 

formulated in the spectral angle mapper is the spectral correlation angle formed between the reference 

spectrum and the image spectrum. For each tissue type, the optimal alpha value was determined through 

an alpha validation process. Based on histological annotation, regions of interest were taken to extract 

endmembers, the set of spectrally unique surface materials existing within an image. The alpha value was 

adjusted with samples of non-cancerous tissue and then validated by generating a spectral angle mapper 

classification image on the original cancerous tissue image to ensure a match to the pathology 

interpretation. Adjustment of the alpha value was found to control tumor margin detection. Extended 

alpha value validation against a broader range of tissue types enhanced tumor margin detection by 

minimizing data errors and improving the accuracy of tissue classification. Thus, the alpha value can 

serve as a biomarker and measurable indicator of biological conditions.

2022 SURF Colloquium 139