SBIR 

SMALL BUSINESS INNOVATION

RESEARCH PROGRAM

PHASE I and PHASE II

AWARDS FOR FISCAL YEAR 2020

National Institute of Standards and 

Technology

Department of Commerce
INTRODUCTION

Abstracts of Awards for Fiscal Year 2020 SBIR Program

Note: Certain non-ASCII characters may not be represented accurately in this 

document. In cases where there may be doubt, please direct your questions 

to sbir@nist.gov. 
Fiscal Year 2020 List of Awardees

Award Number Company Name Phase 

70NANB20H128 Additive Manufacturing Innovations LLC Phase I 

70NANB20H129 AirFlow Sciences Corp. Phase I

70NANB20H121 Awayr, Inc. Phase I

70NANB20H130 BigHat BioSciences Phase I

70NANB20H120 Emergent Healthcare Solutions LLC Phase I

70NANB20H127 HelloMaxwell, Inc. Phase I

70NANB20H117 NanoElectronic Imaging, Inc. Phase I

70NANB20H119 Poseidon Systems Phase I

70NANB20H118 Reax Engineering Inc. Phase I

70NANB20H122 RedShred Phase I

70NANB20H124 STF Technologies LLC Phase I

70NANB20H123 WW Technology Group Phase I

70NANB20H114 Airflow Sciences Corp. Phase II

70NANB20H115 Intact Solutions, Inc. Phase II

70NANB20H111 nVariate, Inc. Phase II

70NANB20H109 OmniVis, LLC Phase II

70NANB20H112 Parman Tech, LLC Phase II

70NANB20H116 QATCH Technologies LLC Phase II

70NANB20H110 Robotic Materials Inc. Phase II

70NANB20H113 SandBox Semiconductor Inc. Phase II

 

 
FY 2020 PHASE I AWARD

Title: An Optical Imaging System to Characterize Mechanical Deformation at 

Microscopic Length Scale

Firm: Additive Manufacturing Innovations LLC

65 Main St.

Potsdam, NY 13676

Principal Investigator: Ajit Achuthan

Phone: (315) 262 4998

Email: achuthan@aminnov.com

Award Amount: $99,998.80

Abstract: Additive Manufacturing Innovations LLC will develop a new optical imaging 

system to characterize microscopic deformation of materials. The system, named as a 

Mechanical Testing at Microscale system (MT@micro), uses a micro-tensile testing 

device to mechanically load the specimen uniaxially and an optical imaging platform to 

capture high resolution, in-situ images of the evolution of heterogeneous deformation on 

the specimen surface for a large global strain. Such an enormous advancement in the 

characterization capabilities could be achieved by addressing the fundamental 

challenges of the state-of-the-art technology, primarily the narrow depth of field of the 

microscope, large rigid body motion of the field of view, and lack of a data processing 

algorithms to extract critical engineering data from a large number of image 

frames. MT@micro will have three innovative solutions to address these challenges: 1) 

a smart imaging subsystem, 2) a features detection and tracking subsystem, and 3) a 

material modeling subsystem. Solutions will be developed following an interdisciplinary 

approach. The proposed system is expected to advance the mechanical 

characterization at microscale with an optical imaging technique into a robust material 

design and development tool.

Commercial Applications: The customer discovery interviews conducted by our team 

as part of our participation in NSF I-Corps site program validated a large demand for the 

characterization of microscopic deformation of materials among scientists and

engineers who work in defense, aerospace, automotive, healthcare, and manufacturing 

sectors. This demand is growing exponentially due to an emerging interest in tailoring 

material microstructure to derive high-performance materials and structural components 

with spatially gradient properties.
Title: Advanced Measurement Probe System for Non-Nulling Velocity Testing

Firm: AirFlow Sciences Corp.

12190 Hubbard St.

Livonia, MI 48150

Principal Investigator: Matthew Gentry

Phone: (724) 525-0300

Email: mgentry@airflowciences.com

Award Amount: $100,000.00

Abstract: Industrial facilities and power plants that burn fossil fuels exhaust the 

combustion products to atmosphere through their smokestacks. Stack pollutant 

emissions are quantified using manual testing method developed in the 1960s, which 

are prone to error due to biases from human operators and any non-axial flow in a 

stack. 

NIST has been working on an improved technique of performing 3D flow testing for 

smokestacks, which significantly reduce the complexity, duration, and potential for error. 

Advanced data acquisition systems were developed to accommodate the NIST test 

methodology. These systems are highly accurate but still rely on human intervention to 

move and control the test probe. The systems are also relying on probe technology and 

manufacturing techniques developed 30 years ago.

This project will develop an advanced probe movement system suitable for the “nonmuling” technique. The system requires highly accurate probe positioning with the 

capability of moving up to four probes simultaneously. The probe movement will be 

computer-controlled and coordinated with the data collection operations of the existing 

non-mulling hardware. The project will also investigate the optimum prove geometry and 

manufacturing techniques. A prime objective will be to optimize the manufacturability 

and cost to the end-user of the non-mulling probe.

Commercial Applications: The equipment developed during this research will apply to 

smokestacks at large industrial facilities, allowing them to more accurately quantify and 

minimize their pollution emissions. The system will be accurate but easy to use, 

allowing stack testing personnel (from the plant site or third-party stack testing 

companies) to obtain high-quality data with less effort than current methods. In addition, 

the instrumentation will be applicable to other industrial personnel that use EPA Method 

2F test procedures. This encompasses activities such as large horsepower fan 

performance testing, combustion optimization of boilers, an HVAC equipment testing at 

nuclear power plants.
Title: Awayr Security: Validating the NIST Phish Scale Toward Artificial Intelligence 

Approaches Toward Human Cybersecurity

Firm: Awayr, Inc.

500 Massachusetts Ave.

Cambridge, MA 02139

Principal Investigator: Adam Beal

Phone: (303) 350-9175

Email: BEAL@GETAWAYR.COM

Award Amount: $99,999.41

Abstract: Remote operated social engineering (ROSE) attacks account for a surprising 

share of successful cyberattack. For example, The Verizon Data Breach Incident 

Response 2019 report found that approximately 94% of all malicious code was 

introduced into systems via email. Threat actors, illegally, benefit from knowledge 

gained from repeated massive remote operated social engineering operations. 

Defensive actors can leverage simulation, but unlike the advanced state of computer 

penetration simulation, human behavior in the face of social engineering is presently not 

easy to simulate. This means that descriptive and predictive understanding of the 

human attack surface is vested in threat actors. Defensive forces today operate at a

disadvantage, despite the efforts of a rapidly growing market: phishing alone is 

projected to grow to USD 1,401.6 Million by 2022. This clear and present problem is 

one Awayr can address through next-generation predictive models of vulnerability in 

cybersecurity. Specifically, this Phase I will prove out the technical and business 

feasibility of using NIST’s Phish Scale as the underpinning of Awayr Security. Awayr will 

1) validate the Phish scale using a cue-coded phishing email corpus, 2) explore the 

feasibility of automated cue detection systems, and 3) leverage this understanding 

toward a 5 year plan.

Commercial Applications: Awayr Security will provide a digital twin for the information 

workers of an organization. This simulation capability will allow security professionals to 

test phishing campaigns, bootstrap detection of vulnerable individuals, and better target 

training. Awayr sees this commercial product as a great equalizer to the asymmetry 

between ROSE cyberattack and cyberdefense, able to assign a score to individual 

phishing email difficulty, and to individual user security orientation. Awayr will then look 

toward a future where lessons and datasets collected toward email security can be 

applied to other ROSE domains, including chat, vishing, and social media.

Title: Feasibility of Using a High-speed AI-driven Experimental Platform to Develop an 

Optimized DNA Template for Cell-free Protein Expression
Firm: BigHat BioSciences 

733 Industrial Road

San Carlos, CA 94070

Principal Investigator: Peyton Greenside

Phone: (650) 394-7679

Email: peyton@bighatbio.com

Award Amount: $100,000.00

Abstract: BigHat Biociences will demonstrate the feasibility of using a high-speed 

artificial intelligence (AI)-driven experimental platform to optimize the DNA template for 

cell-free protein synthesis (CFPS) to maximize protein expression. Their platform is 

designed to rapidly find an optimal DNA sequence among millions of possible 

sequences through repeated design build-test cycles: AI is used to design the DNA 

template for each consecutive iteration based on the results from previous cycles, 

CFPS is used to build the protein from the DNA template, and testing is used to 

determine the protein yield. Using published datasets, BigHat Biosciences will run 

simulations to show that their platform can find optimal sequences among >250,000 

possibilities based on experimental testing of only about 1% of those sequences. As a 

proof-of concept case, they plan to optimize a ~30-nucleotide expression cassette 

region encompassing the sequences from the start of the ribosomal binding site through 

codon 5 of the protein coding sequence. It has been historically intractable to optimize 

cassette regions over ~10-nt because the search space if simply too large for 

comprehensive experimental exploration. The Phase I project aims to demonstrate the 

feasibility of optimizing larger cassette and coding regions through tight coupling of 

computational and experimental evaluation.

Commercial Applications: BigHat Biosciences’ AI-driven experimental platform can 

accelerate development of therapeutic antibodies by using machine learning to guide 

antibody engineering for improved biophysical attributes such as stability, solubility, and 

aggregation, which are important for manufacturing, storing, and delivery. Therapeutic 

antibodies are one of the most successful and fastest growing classes of drugs, but 

their development remains a challenging and painstaking endeavor largely due to the 

difficulty of optimizing multiple critical biophysical properties. Changes to the antibody 

that improve one property may negatively impact another, creating a 

complex optimization problem that can best be overcome with AI-guided protein 

engineering.

Title: Dynamic Incident Intelligence System, Utilizing Adaptation to Real-Time 

Information
Firm: Emergent Healthcare Solutions LLC

910 Shelley St.

Springfield, OR 97477

Principal Investigator: Paul Schlumpberger

Phone: (503) 475-9058

Email: pausch@emergenthes.com

Award Amount: $104,525.00

Abstract: Emergent Healthcare Solutions will research the feasibility of a dynamic 

cloud-based incident Intelligence System called Emergent Cloud. Emergent Cloud is an 

application service providing firefighters and Emergency Medical Technicians access to 

an interactive incident roadmap and real-time workflow and record keeping. The general 

aim of the project is to revolutionize the user experience and data management 

workflow for first responders to greatly enhance the accuracy of the collected date in 

real-time and to reduce the mental load and workload of first responders. Emergent 

Healthcare Solutions aims to transform the first responder user experience from an 

antiquated after-the-fact collection platform to a real-time, largely automated, userfriendly data management platform.

Commercial Applications: There are currently no products in the Emergency Services 

record-keeping market that address automated data collection to facilitate real-time 

decision making in the field, However, market research shows an immediate need and 

interest in this innovation. Fire and Rescue departments are the first potential 

customers, with an estimated 29,819 fire departments in the US and 46% of them 

providing medical services. The Emergent Healthcare Solutions team has extensive 

experience in product commercialization and marketing and has potential clients 

specifically interested in this product.

Title: An Integrated Data System for Machine Learning based Prediction of Radio 

Frequency Integrated Circuits (RFIC)

Firm: HelloMaxwell, Inc.

32 Tower Road

Lexington, MA 02421

Principal Investigator: Qiang Cui

Phone: (336)-541-4257

Email: qiangcui@mit.edu
Award Amount: $106,500.00

Abstract: Radio frequency integrated circuits (RFICs) are key components for wireless 

communication system. RFIC’s hard-to model parasitic effects and poor simulation 

accuracy require multiple trial-and-error tape-outs (fabrications) to meet product specs. 

Tape-out is very slow (2 months) and very costly (as high as $2M). In the coming 5G 

era, this problem gets worse as mmWave frequency parasitic effects are even harder to 

model, thus more tape-out rounds are needed HelloMaxwell research combines

physics-based models and customized machine learning (ML) to predict RFIC 

performance. This prediction method can improve RFIC’s prediction accuracy by 97%, 

which means engineers can reduce the RFIC development time by at least one tape-out 

round. In order to achieve this improvement, the ML training needs sufficient compatible 

data across design, simulation and test. Today’s RF semiconductor companies have 

indeed enough RFIC data points, however the existing data system is fragmented and 

inconsistent, so impractical for ML training. To overcome this problem, HelloMaxwell will 

design an integrated RFIC data system during this Phase I project. The proposed work 

sets the foundation for ML algorithm research planned for NIST Phase II, paving the 

way for commercializing our novel technology for the modern communications system.

Commercial Applications: Based on over 100 thorough customer interviews 

conducted during the National Science Foundation’s i-Corps program, the technology 

has very large commercial application potential. Though there are numerous 

applications for the technology, HelloMaxwell will first target the $50B global radio 

frequency integrated circuits (RFICs) industry. Out of the $50B, about $15B is spent on 

tape-out or tape-out related activities. Saving just one tape-out each product presents 

$3B market opportunity. The beachhead customer segment will be RF companies who 

design complex RFIC products for mobile phone applications.

Title: High-Precision TEM Topography Mapping

Firm: NanoElectronic Imaging, Inc.

1518 S Centinela Ave.

Los Angeles, CA 90025

Principal Investigator: William Hubbard

Phone: (617) 347-6436

Email: bhubbard@nanoelectronicimaging.com

Award Amount: $99,991.73

Abstract: NanoElectronic Imaging, Inc. (NEI) is developing techniques for mapping 

signals in the transmission electron microscope (TEM) that are normally inaccessible. In 

this Phase I SBIR project, NEI will build on preliminary results to develop a quantitative, 
TEM-based topographic mapping technique with the potential for atomic resolution in all 

three spatial dimensions. This precision would be akin to an AFM-in-TEM. The 

technique is based on scanning TEM electron beam-induced current (STEM EBIC) 

imaging, which maps pixel-by-pixel the current generated in a TEM sample by the 

electron beam. This project will quantify the relationship between a new EBIC signal 

(previously discounted as ``noise’’) and topography. STEM EBIC has already been 

demonstrated at atomic resolution laterally, and preliminary results suggest that nearatomic resolution in height is possible. In this Phase I project, the new signal's signal-tonoise will be maximized, and its exact relationship to height will be quantified by direct 

comparison to atomic force microscope (AFM) topographic maps. Finally, a 

statistical analysis of height precision, along with calibration procedures, will be 

developed. The reach goal of this project is to demonstrate topographic resolution 

comparable to, or even surpassing, NIST’s traceable AFM.

Commercial Applications: Despite its fantastic spatial resolution, standard STEM is 

blind to many electronic, thermal, and physical signals that directly relate to 

microelectronic device function and failure. NEI is developing a STEM EBIC imaging 

system that reveals these signals. The topographic imaging capability targeted by this 

Phase I project is an example. TEMs are ubiquitous in industry and academia, but TEMbased methods for measuring thickness and topography are awkward and inaccurate, 

and thus seldom used. STEM EBIC topography could make this capability accessible. A 

TEM-based topographic mapping technique competitive with AFM would be a truly 

disruptive development for sample characterization.

Title: Standardizing Grease Sampling and Characterization By Automated Online 

Device

Firm: Poseidon Systems

200 Canal View Blvd.

Rochester, NY 14623

Principal Investigator: Ryan Brewer

Phone: (585) 633-8550

Email: ryan.brewer@poseidonsys.com

Award Amount: $98,750.00

Abstract: Scheduled grease sampling is one of the more reliable methods for detecting 

mechanical issues within machinery. Offline laboratory‐based analysis of the grease 

can indicate component failures, such as spalling via high concentrations of ferrous 

particulates. However, consistency, costs, and frequency of sampling are less than 

ideal. Manually collecting grease samples can put personnel in danger due to the 

location of the sampling location and potential for other machinery still in operation. In 
addition, grease sampling is a bit of an art form requiring experience with consistency in 

how samples are taken directly affecting the results and benefits. To help 

address, Poseidon Systems will develop a repeatable inline grease sampling 

mechanism to automatically monitor the ferrous particle content of grease from an in‐

operation machine. The device will automatically collect samples from the target 

bearings in a repeatable controlled manner. These samples will then be analyzed by the 

device for ferrous particle content in order to provide in situ indication of wear debris 

contamination. The ferrous content could then be trended over time and monitored 

remotely for condition monitoring of these crucial grease packed bearings.

Commercial Applications: During the Phase II effort, Poseidon Systems will mature 

the prototype design created during the Phase I effort further enhance system capability 

and robustness. Phase II efforts will focus on improving the design developed during 

Phase I needed to make the automated grease sampler a commercially viable system. 

The goal is to create a robust system that can be deployed on a variety of systems. A 

specific targeted market is the wind energy generation sector. Scheduled grease 

sampling is one of the more reliable methods for detecting issues with the large critical 

bearings in today’s wind turbines. Offline laboratory‐based analysis of the grease from 

generator, pitch (blade), yaw, and main bearings can indicate component failures, such 

as spalling via high concentrations of ferrous particulates. However, consistency, costs 

and frequency of sampling are less than ideal. A critical need will be in the offshore wind 

industry. Manually collecting grease samples from the planned offshore arrays will be 

even more costly and difficult to schedule. New active pitch and yaw control systems 

maximizing power production will increase the wear on corresponding components, thus 

leading to stronger need to monitor the health state of those components robust system 

that can be deployed on a variety of systems. A specific targeted market is the wind 

energy generation sector. Scheduled grease sampling is one of the more reliable 

methods for detecting issues with the large critical bearings in today’s wind turbines. 

Offline laboratory‐based analysis of the grease from generator, pitch (blade), yaw, and 

main bearings can indicate component failures, such as spalling via high concentrations 

of ferrous particulates. However, consistency, costs and frequency of sampling are less 

than ideal. A critical need will be in the offshore wind industry. Manually collecting 

grease samples from the planned offshore arrays will be even more costly and difficult 

to schedule. New active pitch and yaw control systems maximizing power production 

will increase the wear on corresponding components, thus leading to stronger need to 

monitor the health state of those components.

Title: Pyrocast: Wildfire Forecasting & Data Services

Firm: Reax Engineering Inc.

1921 University Ave.

Berkeley, CA 94704

Principal Investigator: Christopher Lautenberger
Phone: (510) 387-2155

Email: lautenberger@reaxengineering.com

Award Amount: $100,000.00

Abstract: This work will develop a real-time wildfire forecast system that automatically 

detects and forecasts the spread of every fire in the Continental US and updates each 

forecast in real-time as additional information becomes available. Fire forecast data, 

which are generated “in the cloud” using high performance computing resources, are 

transferred to a web server hosting a mobile-friendly web map that facilitates 

visualization by stakeholders such as first responders and government entities. 

Subscribers will also be able to initiate a fire spread forecast by clicking a location on 

the web map and specifying an ignition time; the resultant forecast will then also be 

visible to others, enabling coordination among first responders and other agencies. This 

system also quantifies near-term fire risk by modeling ignition and spread of millions of 

hypothetical fires at various times in the future. This provides a means to quantify 

spatially and temporally granular near term fire risk in terms of fire size and number of 

structures impacted to inform decisions regarding resource allocation and fire 

prevention.

Commercial Applications: There is clear commercial potential for this fire forecasting 

system. Government agencies (federal, state, and local), first responders, and public 

utilities desperately need reliable fire forecast data to make informed decisions relative 

to issuing effective warnings to the public, strategic allocation of suppression resources, 

fire prevention via powerline de-energization and other measures, and evacuations 

during ongoing fires. Through a subscription-based service, the fire forecasting system 

developed under this work will provide data that meets those needs.

Title: ENT: Extended Nestor Tagging

Firm: RedShred

5520 Research Park Drive

Baltimore, MD 21228

Principal Investigator: Maryam Esmaeilkhanian

Phone: (443) 681-9938

Email: mesmaeilkhanian@redshred.com

Award Amount: $100,000.00

Abstract: In order for manufacturers to adopt data-driven decision making, they must 

tap into unstructured data that sits unused. The process of annotation and knowledge 
extraction from unstructured text data is a time consuming and challenging bottleneck to 

adopting machine learning at scale. This Phase I SBIR will implement an enhanced 

cloud-based platform enabling maintenance and other organizational users to tag and 

extract knowledge from large volumes of unstructured text data more efficiently. In 

Nestor, NIST provides an NLP toolkit that assigns tags and rules to unstructured data 

by a ranked tagging procedure. Combining this approach with RedShred’s state of the 

art enrichment platform will accelerate an organization’s ability to rapidly develop and 

deploy these models to support evolving organizational needs. The extended system, 

ENT, will include customizable ranking allowing organizations to tailor which data are 

prioritized based on business value as well as interactive UI dashboards providing realtime feedback. This combined system provides a valuable low-friction solution to 

accelerate adoption of NLP technologies in manufacturing to unlock value from 

previously idle log data repositories.

Commercial Applications: ENT fills gaps in existing market solutions by leveraging 

NLP to analyze work orders and manuals in manufacturing and maintenance. 

Technicians gain efficiencies by reducing time recording work order data without 

significant changes to existing workflows. Data scientists gain an order of magnitude 

increase in time-savings to clean, organize and tag data for downstream analytics. 

Nestor has demonstrated significant impact by reducing tagging time from 12 hours to 

45 minutes for 1,200 work orders. By deploying ENT at scale organizations reduce the 

$50+billion spent on maintenance costs annually by putting existing organizational data 

and processes to work.

Title: High Throughput, High-Pressure Small-Angle Neutron Scattering Sample 

Environment

Firm: STF Technologies LLC

18 Shea Way

Newark, DE 19713

Principal Investigator: Ryan Dombrowski

Phone: (716) 799-5935

Email: rdombrowski@stf-technologies.com

Award Amount: $100,000.00

Abstract: STF Technologies addresses a need of the neutron user community by 

creating a minimum viable prototype of the only high-throughput hydrostatic pressure 

small angle neutron scattering sample environment (HTHP-SANS-SE). Our HTHPSANS-SE will greatly improve the ease of use and reliability of measurements under 

extreme environments, thereby increasing throughput on the beamline and expanding 

feasibility. Current HP-SANS cells are difficult to load/unload and require significant 
sample amounts, often prohibitive for soft matter users. The HTHP-SANS-SE will be 

controlled via existing data acquisition software (NICE) and enable queueing multiple 

cells compatible with standard sample block environments, to optimize the use of 

beamtime while greatly simplifying support required from beamtime scientists.

Commercial Applications: STF Technologies addresses a significant unmet need to 

improved HP-SANS measurement capabilities for R&D in healthcare/biology, energy, 

and consumer products. A reliable, efficient HP-SANS sample environment will 

maximize valuable beamtime, increasing operational efficiencies and improve output at 

neutron scattering facilities, such as NCNR, and programs such as nSOFT. Their 

commercialization plan envisions stages Phase I/II/III development that directly meets 

the needs of our primary target customers (beamline facilities( by improving support of 

HP-SANS users and efficiency. This well-defined initial market is p art of the rapidly 

growing $1.12b total SANS market, and complements other SANS SE produces 

engineered by STF Technologies.

Title: Model-Based Application of NIST Cybersecurity Standards

Firm: WW Technology Group

4519 Mustering Drum

Ellicott City, MD 21042

Principal Investigator: Richard Le Boeuf

Phone: (254) 485-7809

Email: rleboeuf@wwtechnology.com

Award Amount: $100,000.00

Abstract: The innovation will use a model-based approach to streamline understanding 

and application of standards. NIST standards addressing cybersecurity, presented in 

the form of documents, spreadsheets, and database tools, provide thousands of 

complimentary and overlapping items for users to track. Significant effort is expended 

understanding the standards before attention can be focused on the system of interest. 

Model-based representations of both the standards and cyber-physical systems in a 

single tool will provide advantages over current costly and labor-intensive approaches. 

The tool will give stakeholders at all organizational levels access to the information 

specific to their domain, enable a better understanding of both the standards and the 

system, and be the basis for analyses and generation of certification artifacts. WW 

Technology Group will model NIST SP 800-53, NIST SP 800-53A, the NIST 

Cybersecurity Framework, NIST IR 8183, and the new Cybersecurity Maturity Model 

Certification (CMMC) standards and trace them to a cyber-physical system model. 

Analyses will be developed to automatically assess compliance gaps in the system 

relative to the standards. Stakeholder-specific reports with analysis results and 
recommendations will be generated automatically. The extensible tool will improve the 

efficiency of understanding and applying existing, evolving, and new NIST standards.

Commercial Applications: A tool providing model-based representations of both 

standards and cyber-physical systems and their interrelationships has potential 

commercial applications in civilian government, DoD, commercial, and academic 

markets. Cybersecurity risks have been increasing in all sectors and legal and 

regulatory requirements pertaining to cybersecurity continue to mature. So, a tool that 

continually captures those requirements and the system characteristics throughout the 

system life cycle will be useful in many areas. It could be applied in organizations 

generating, using, assessing, or authorizing systems. It would also be useful for 

academic institutions for improving cybersecurity standards coverage in degree and 

certificate programs.

FY 2020 PHASE II AWARD

Title: Advanced Instrumentation for Non-Nulling Stack Velocity Testing

Firm: Airflow Sciences Corp.

12190 Hubbard St.

Livonia, MI 48150

Principal Investigator: Matthew Gentry

Phone: 734-525-0300

Email: mgentry@airflowsciences.com

Award Amount: $400,000

Abstract: Industrial facilities, manufacturing plants, and electric power plants that burn 

fossil fuels exhaust the combustion products to atmosphere through their smokestacks. 

Stack pollutant emissions are quantified using manual testing methods developed in the 

1960s, which are prone to error if complex flow patterns exist, which is common. 

Recently, NIST has been working on an improved technique of performing 3D flow 

testing for smokestacks, which will greatly reduce the complexity, duration, and potential 

for human error. Advanced data acquisition systems and software are required to 

accommodate the NIST test methodology. These systems must be highly accurate but 

also able to operate in potential harsh environmental conditions since the testing is 

conducted year round on outdoor platforms of smokestacks. The Phase II SBIR will 

continue the research and development of the Phase I effort for NIST. During Phase I, a 

prototype data acquisition device was developed, and proved to successfully meet the 

project goals, proving feasibility of the system. During the Phase II effort, the 
development will be expanded to create a complete system, capable of full-scale testing 

at industrial plants. A test site in Michigan will be selected, likely a DTE Energy site, to 

prove out the system and verify operation.

Commercial Applications: The instrumentation developed during this research will be 

applicable to smokestacks at large industrial facilities, allowing them to more accurately 

quantify and minimize their pollution emissions. The system will be accurate but easy to 

use, allowing stack testing personnel (from the plant site or from third-party stack testing 

companies) to obtain high-quality data with less effort than current methods. In addition, 

the instrumentation will be applicable to other industrial personnel that use EPA Method 

2F test procedures. This encompasses activities such as large horsepower fan 

performance testing, combustion optimization of boilers, and HVAC equipment testing 

at nuclear power plants.

Title: Direct Performance Evaluation of Additive Manufacturing Process Plans 

Firm: Intact Solutions, Inc.

211 S Paterson St.

Madison, WI 53703

Principal Investigator: Goldy Kumar

Phone: 608-334-9646

Email: gkumar@intact-solutions.com

Award Amount: $400,000

Abstract: Additive manufacturing is steadily advancing towards fulfilling its promise of 

customized and on-demand production of functional parts. However, performance of asmanufactured parts can differ significantly from the as-designed parts because the asmanufactured geometry differs from the as-designed geometry and the asmanufactured material properties are unknown. Attempts to predict performance of asmanufactured parts are hampered by the complexity of the as-planned geometry and 

the unknown material properties. In Phase I, Intact Solutions successfully demonstrated 

the feasibility of performing accurate structural simulation directly from AM plans while 

querying material information directly from NIST’s AMMD. This was achieved by 

extending our moment-based interoperable simulation technology that incorporates 

material properties through a combination of analytical, experimental, and data-driven 

methods.

In Phase II, Intact Solutions will develop a novel Part-Scale Process–Structure–

Property–Performance (PSPP) architecture and implement a robust commercial grade 

system for performance prediction of AM parts. The developed system will support 

adaptive exploration of process and design spaces through direct integration with AM 
material databases, as well as simulated data. In addition to rigorous validation and 

testing, we will demonstrate how our system can be used to evaluate, compare, and 

optimize AM process plans, leading to significant savings and improved performance.

Commercial Applications: The ability to computationally assess performance and 

optimize process planning of AM components directly from process plans will lead to 

dramatic savings in time and cost in additive manufacturing throughout US industry.

The technology will serve three major customers: (1) manufacturing service industry 

that depends on rapid generation and evaluation of process plans, (2) aerospace, 

defense, and medical industry where performance of mission-critical printed 

components is determined by the process plan and its parameters; and (3) small 

businesses and maker communities that increasingly rely on additive manufacturing but 

are unable to predict whether 3D printed components will be functional.

Title: Watertight CAD for Integrating Isogeometric Analysis into the Model-Based 

Enterprise

Firm: nVariate, Inc.

1101 W 34th St.

Austin, TX 78705

Principal Investigator: Benjamin Urick

Phone: 512-934-4765

Email: benurick@nvariate.com

Award Amount: $399,968

Abstract: Today’s Computer-Aided Design (CAD) applications utilize restrictive 

mathematical assumptions to approximate the compound geometric intersections 

necessary to represent real-world products. As a result, critical information is not 

represented within the Model-Based Definition (MBD) for downstream users in the 

digital thread, forcing engineers to manually repair CAD models and convert them into 

degraded formats, such as polygonal meshes for Finite Element Analysis (FEA). For 

Isogeometric Analysis (IGA), the problem is worse as the input required is a watertight 

spline representation, Although there are current solvers in both academia and industry 

to run IGA models, there is no automated means to produce the watertight spline 

representation input form CAD models, a gap in the Model-based Enterprise (MBE). 

nVariate’s Watertight CAD technology solves this problem by creating geometrically 

gap-free representations from trimmed surfaces, natively within the CAD application., 

This requires minimal change to the user’s high-level software interface while the 

technology fixes the flaws within the CAD model. The resulting model is IGA-ready for 

existing industrial and academic implementations, as well as for use in forthcoming IGA 
ISO standards. This is critical in bridging gap in the MBE, integrating IGA into the digital 

thread.

Commercial Applications: The product will be a CAD plugin that produces IGA-ready 

watertight spline representations without the need of any importing or exporting files 

between applications, file conversions, or unique representations that are non0native to 

CAD systems. To the frustration of users, an IGA preprocessing tool from CAD data is 

currently unavailable in the technology space of the MBE. This product uniquely fills that 

void with the least disruption in the digital thread. T pheroduct will provide substantial 

benefit to design engineers who create complex and/or precision designs in aerospace, 

automobile, and medical device industries.

Title: The Full Integration of a Portable Bacterial Concentrator with a Pathogen Detector 

Device

Firm: OmniVis, LLC

2042 Malibu Dr.

West Lafayette, IN 47906

Principal Investigator: Katherine Clayton

Phone: 415-309-9524

Email: kclayton@omnivistech.com

Award Amount: $400,000

Abstract: This SBIR Phase II project will integrate an easy to use, inexpensive, and 

portable bacterial concentrator with a handheld pathogen detection system to enable 

ultra-low cholera pathogen (Vibrio cholerae) detection. Cholera affects communities 

across 41 countries, including in Yemen, where in the first 5 months of 2020 has had 

110,000 suspected cases. Current methods used to detect the cholera pathogen in 

water involves a 3 to 5-day procedure due to the low concentrations of the bacterium 

found in the water. The proposed device intakes 1L of water from an environmental 

water source and concentrates the solution down to 1mL, enriching the bacteria 

contained within the sample. The enriched sample is then immediately deposited into a 

handheld detection system to identify low, but dangerous, bacterial levels. OmniVis will 

expand upon their SBIR Phase I work by designing a user-friendly and fully integrated 

device for sample-to-answer environmental V. cholerae detection. They will identify a 

membrane that best captures and releases V. cholerae cells from environmental water, 

for robust operation in the concentrator. Following, they will design, bench test, and 

perform user-centered studies on a concentrator that integrates a disposable test kit, to 

seamlessly integrate our concentrator to a pathogen detector system with minimal user 

steps. Lastly, they will make mixed cultures of environmentally relevant bacteria, pass 

the sample through the concentrator, collect, and test the sample. The final step will 
provide critical knowledge in the effect complex environments have in detecting Vibrio 

cholerae and will demonstrate a full sample-to-answer system for water-based 

pathogen detection.

Commercial Applications: The commercial potential of this project is an inexpensive, 

portable, and easy to use device that concentrates the Vibrio cholerae in environmental 

water sources for pathogen testing. Contaminated water sources place populations at 

risk for contracting cholera where patients exhibit symptoms of severe diarrhea, 

vomiting, and dehydration and if left untreated, death. OmniVis proposes a concentrator 

integrated with a detection device for a sensitive test. The platform would enable aid 

organizations to discover low, but dangerous, levels of Vibrio cholerae. Early detection 

will save the time and costs currently associated with cholera outbreaks.

Title: Commercialization of NIST Technology for Separating Particles with Light

Firm: Parman Tech, LLC

9231 Shafers Mill Dr.

Frederick, MD 21704

Principal Investigator: John Curry

Phone: 240-405-9670

Email: jjcurry@parmantech.com

Award Amount: $400,000

Abstract: Parman Tech will build a first prototype embodying the NIST-owned 

technology ‘Optical Particle Sorter.’ The prototype will demonstrate feasibility of a 

commercial implementation of this technology. The prototype will also serve as the 

model for subsequent production of several beta-units to be used for obtaining customer 

feedback and eventually validation of the product.

Commercial Applications: The technology developed by Parman Tech under this 

grant will enable the directors of research in companies developing gene therapy and 

nano-therapeutics to produce safer and more effective treatments for some of the most 

persistent and devastating human diseases.

Title: Microcapillary Quartz Sensors for Screening Injectability of High Concentration 

Protein Formulations
Firm: QATCH Technologies LLC

551 Dairy Glen Rd.

Chapel Hill, NC 217516

Principal Investigator: Zehara Parlak

Phone: 678-908-3112

Email: zehra.parlak@qatchtech.com

Award Amount: $400,000

Abstract: The main focus of this project is to develop a prototype instrument and test 

cartridges for injectability pre-screening of high concentration protein formulations 

(HCFs). HCFs are non-Newtonian fluids with shear-thinning behavior and they are 

formulated to be administered by subcutaneous or muscular injections. The injectability 

and manufacturability of HCFs depend on the viscosity behavior over a very wide range 

of shear-rates. In Phase I, QATCH demonstrated the feasibility of wide shearrate viscosity measurements by its proprietary microcapillary quartz technology. In 

Phase II, QATCH will determine the 1) Sensor design for wafer-scale productions, 2) 

Processing steps for deployability of the sensors, 3) Prototype instrument firmware and 

software, and 4) Linearity and accuracy of the viscosity measurements on a wide range 

of solutions. The finalized prototype will be tested by external partners and be ready to 

be deployed to industry experts. 

Commercial Applications: Injectable protein-based therapeutics are preferred by 

patients and health-care providers over IV infusion, yet only 40% of the recently 

approved protein-based therapeutics are injectable. Because, biopharma researchers 

do not have proper analytical tools for optimizing protein molecules for injectability and 

selecting injectable candidates in early drug development. QATCH aims to fix this by its 

proprietary injectability screening technology, which is an extremely low sample volume 

and broad dynamic range viscometer for protein formulations. This technology will not 

only enable early stage injectability screening of protein formulations, but also deliver 

significant material and time savings for biopharmaceutical industry.

Title: Easy-to-use, Autonomous Bin-picking and Assembly Operations for the 

Manufacturing Industry

Firm: Robotic Materials Inc.

1860 38th St.

Boulder, CO 80309

Principal Investigator: Nikolaus Correll

Phone: 303-717-1436
Email: nikolaus@roboticmaterials.com

Award Amount: $400,000

Abstract: Robotic Materials will develop a series of object manipulation primitives to 

pick up and assemble standard mechanical parts such as screws, gears and pulleys 

that can be configured without any programming skills. Building up on a smart robotic 

gripper, 3D perception and machine learning algorithms, they will design a graphical 

user interface that allows an user to label arbitrary 3D objects, identify assembly points, 

and issue pick-up and assembly commands, which are modeled by standard industrial 

assembly tasks that have been identified by NIST’s manufacturing group. Specifically, 

they will provide routines for peg-in-hole and hole-on-peg assemblies that are defined 

by circular or rectangular holes or extrusions, as well as pick-up routines for arbitrary 

objects from clutter or in bins. The underlying algorithms have already been validated, 

making the focus of this proposal to develop interfaces that make their configuration as 

simple as possible, only requiring a user to present an individual object, labeling it, and 

selecting a desired action. This Phase II project will extend this approach to a larger 

class of objects and tasks and lead to deployment with real users.

Commercial Applications: Making operations such as bin picking and assembly 

available to non-programmers at a fraction of the cost of existing systems will 

dramatically broaden the possible applications for collaborative robots and enable use 

cases that were previously too expensive or too difficult to automate. Combining 3D 

perception and computation inside the gripper also enables automation in spaces where 

space constraints prevented the use of external camera systems. Not requiring any 

additional infrastructure also enables quickly adapting a robot for different applications, 

even at the same day.

Title: Advanced Manufacturing and Material Measurements Software Tool WEAVE for 

the Accelerating and Automation of SEM image analysis in the Semiconductor Industry

Firm: SandBox Semiconductor Inc.

54 Rainey St.

Austin, TX 78701

Principal Investigator: Meghali Chopra

Phone: 214-288‐7926

Email: meghali.chopra@sandboxsemiconductor.com

Award Amount: $399,991

Abstract: The goal of this project is to create the software WeaveTM to automate the 

extraction of critical dimensions (CDs) from scanning electron microscope (SEM) 
images for the microelectronics industry. Current best practices for extraction of CDs 

are that personnel analyze the images one by one, which is tedious, prone to human 

bias, time-consuming and expensive. Successful implementation of WeaveTM will save 

almost 10% of process engineers’ time, freeing them for more productive and creative 

work. Two methods will be developed, tested and compared for edge and material 

detection and automated extraction of CDs; level set (LS) contour or edge detection and 

machine learning (ML) edge detection. Both methods will be developed because both 

show promise for this application, and they need to be evaluated for speed and 

accuracy on a variety of typical device nanostructures. The software will allow the user 

to import a prototypical image and specify the measurements to be extracted. The user 

then submits all the images with similar features (e.g., trenches or finfets) to be 

analyzed, and the software will produce tables of CD measurements and annotated 

images. WeaveTM will plug directly into our current SandBoxTM Studio software tool 

providing semiconductor process engineers with an end-to-end recipe development 

solution.

Commercial Applications: Semiconductor process engineers currently spend at least 

10% of their time extracting measurements from microscope images, a procedure that 

is prone to significant human error. The development of WeaveTM in this research will 

enable these process engineers to improve the accuracy of their process metrology 

workflows, reduce their total metrology costs, and accelerate their process development 

cycles. WeaveTM will be deployed as a plug-in for SandBoxTM Studio and as a 

standalone software.