Using Visualization Tools 

to Understand Drug 

Evidence Handling 

Processes

Matthew Staymates and Edward Sisco

Material Measurement Laboratory

National Institute of Standards and Technology

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Quick background – flow visualization and scientific imaging

Many uses in the Surface and Trace Chemical Analysis Group, NIST

Schlieren imaging, high speed videography, laser-sheet imaging

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Two visualization methods 

Fluorescent powder handling and visualization

Created fluorescently tagged, mock drug evidence 

and had examiners handle it as they normally would. 

Recorded the entire process under a blacklight

Laser-sheet visualization

Lasers and optics help illuminate microparticles 

during net-weight operations. Provides 2D slice of 

the transport of particles during these activities.

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NO TEXT 

– Fluorescent powder visualization

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Take -aways from fluorescent power experiments

• Net weights were quickly identified as one of the most 

concerning practices

• Emptying the entire contents of the drug evidence to 

obtain the weight of the material (powder) without the 

packaging

• Required for prosecution based on weight

• Repackaging of evidence also of concern

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NO TEXT 

– Laser-sheet visualization ~2 g powder

Picture In 

Picture 
Measuring the Distribution

• Wet swabbing was completed in a 

grid-pattern to collect residue that 

settled onto the bench after several 

minutes

• As expected, the highest background 

was observed in area immediately 

surrounding the weigh paper

• Surface concentrations in excess of 

10 μg/in2 observed

• Airflow was not controlled in these 

experiments 

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The spread of 

material is linked 

to the amount of 

material present

Emptying of 

baggie 

containing 100 g 

mixture

NO TEXT – Laser-sheet visualization ~100 g powder

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New contamination visualization laboratory

Current efforts are focused on:

• Particulate transport in the third 

dimension?

• Expanding studies to other 

workplace processes

• Visualize process modifications that 

minimize exposure risks

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Picture In 

Picture New facility that improves visualization and imaging techniques
Summary

• Our goal is to increase the safety of drug chemists due to the increasing 

presence of extremely toxic substances

• We are developing imaging tools and techniques that help visualize the 

processes that increase exposure risk, and evaluate the efficacy of process 

modifications

• Collaborations with other agencies have aided in interpretation of analyst 

risk and development of best practices

• While the current focus is on seized drugs these processes and approaches 

could easily be translated to other areas

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Thanks for listening!

Questions or Comments?

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Picture 

Many thanks to Amber Burns (Maryland State Police) and Ed Sisco (NIST)!

matthew.staymates@nist.gov

A snapshot of drug background levels 

A multi-laboratory investigation of 

drug background levels 

Visualizing particle spread 

Net weights: Visualizing and 

quantifying

Cleaning agents removing drugs
Development of Novel Workflows for 

Seized Drug Analysis

Edward Sisco - NIST 

Amber Burns – MSP-FSD
Certain commercial products are identified in order to adequately specify the procedure; this 

does not imply endorsement or recommendation by NIST, nor does it imply that such products 

are necessarily the best available for the purpose.

A portion of this work was supported by Award No. 2018-DU-BX-0165, awarded by the 

National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The 

opinions, findings, and conclusions or recommendations expressed in this 

publication/program/exhibition are those of the author(s) and do not necessarily reflect 

those of the Department of Justice.

Certain commercial products are identified in order to adequately specify the procedure; this 

does not imply endorsement or recommendation by Maryland State Police, nor does it imply that 

such products are necessarily the best available for the purpose.
Novel Workflows

Sample Handling and Preparation Sample Analysis Data Analysis & Interpretation

Atlas Logistic Network

Screening Approaches – Expanding DART-MS Capabilities

Confirmatory Analyses – Targeted GC-MS Methods

3
Workflow Shift

Current Approach

New Approach

A large part of the development and implementation of this work has been done in collaboration 

with Maryland State Police, Forensic Sciences Division

Color Tests Screening with 

GC-FID

GC-MS 

Confirmation

Screening with 

DART-MS or 

TD-DART-MS

Targeted 

GC-MS 

Confirmation

4
Expanding DART-MS Capabilities
DART-MS in Forensics

With the growing presence of novel drugs and increased 

complexity in cases, some labs are searching for technologies to 

aid in rapid screening

• DART-MS has been demonstrated as a powerful tool for this 

purpose

• Provides presumptive information in seconds with no sample 

preparation

• More specific than other presumptive tests

• Significant research effort at NIST surrounding DART-MS 

and its applications in the field

Rise of novel drugs

Data from Cayman Chemical

6
• One of many ambient ionization mass spectrometry 

sources

• Conventional DART-MS uses a heated helium 

metastable gas stream for sample desorption and 

ionization

• Allows for analysis of samples with minimal 

preparation or pre -treatment

• Analysis time 1 s to 5 s

• Typical LODs ppm to ppb

• Can be coupled to a range of 

mass spectrometers

What is DART-MS?

www.ionsense.com

DART-MS – Direct Analysis in 

Real Time Mass Spectrometry

7
352.238 Subtract [MS: 1 2414 1 3213 MS: 0 8950 1 0948] / Secondary Standards 09102020(#1) / DART+ / / (72422)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

x104

Intensity

50 100 150 200 250 300 350 400 450 500 550 600 650

m/z

2 x10

0

0.1

0.2

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0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

+EI TIC Scan 20-02155-1_AKB_41_M1.D 

5.934

9.553

1

Counts (%) vs. Acquisition Time (min)

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

DART-MS Use Cases

• We have been working with labs 

to identify unique use cases for 

DART-MS

• Utilizing GC-MS & DART-MS 

data can help identify unknowns

• Allows for determination of 

fragmentation and molecular ion 

of the compound

• Used to identify multiple 

unknown fentanyls and other 

NPSs

GC-MS

4 x10

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

+EI Scan (rt: 9.536-9.577 min, 8 scans) 20-02155-1_AKB_41_M1.D 86.1 107.0 58.0 44.0 77.0 135.0

207.0

Counts vs. Mass-to-Charge (m/z)

40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

8

DART-MS

100.112

352.238

Subtract [MS: 1 2081 1 5412 MS: 0 8484 1 1548] / Secondary Standards 09102020(#3) / DART+ / (117217)

0.00

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x101.20 5

Intensity

50 100 150 200 250 300 350 400 450 500 550 600 650 m/z
DART-MS Use Cases

TD-DART-MS

257 & 261 m/z

GC-MS

Utilize DART-MS to identify compounds that were completely not resolvable in the GC chromatograph

9
• Ongoing efforts to develop a DART-MS Validation package

• Includes validation plan, data workup document, SOPs, maintenance manuals, search lists, and 

training questions

• Available to labs who are interested

Validation Package Development

10
• Many recent research projects have 

used a TD-DART-MS configuration

• Glass T-junction mounted coupled 

with Vapur interface • Used to pull analyte towards 

mass spectrometer

• Thermal desorber attached to Tjunction • Allows for wipe -based sample 

insertion 

• Entire set-up can be removed and 

switched to traditional DART-MS in 

under 1 minute

• Increase sensitivity, reproducibility, 

safety

• Use nitrogen as the source gas

Non-Traditional TD-DART-MS

11
Evidence Screening Study

• To date >200 items sampled

• Inner packaging found to be the most 

representative (92 % accuracy)

• 100 % so far in determining the presence of 

synthetic opioids

• Typically enough material to saturate the MS or 

IMS

• False identifications attributed to plant material in 

foil bags or cases with large amounts of cocaine

Inner Packaging Extract Percent Occurrence Result Type

Drug Detected Same Drug Detected 79 % (n = 151) True Positive

Drug Detected No Drug Detected 1.5 % (n =3) False Positive

Drug Detected Different Drug Detected 2.5 % (n = 5) False Positive

No Drug Detected Drug Detected 4 % (n = 7) False Negative

No Drug Detected No Drug Detected 13 % (n = 25) True Negative

Overall Accuracy: 92 % 12
• Investigated if DART-MS could detect 

rodenticides (anti-coagulants) in illicit drug 

mixtures

• Six common compounds were easily 

detected by TD-DART-MS

• Form both positive and negative ions

• LODs in the 10’s ng range

• In binary mixtures, competitive ionization with 

less volatile drugs was observed

• Analysis in negative ionization mode 

eliminates competitive ionization 

concerns

Recent Application: Rodenticides in Drugs

13
• Investigated the detection of seed-based toxins such as scopolamine, oleandrin, hyoscyamine, and 

digitoxin

• Several toxins (oleandrin, digoxin, digitoxin) performed better in negative ionization mode

• Compared different platforms (DART, TD-DART, IRTD-DART) to identify the most useful approach 

for this application

Recent Application: Seed-based Toxins

14
Targeted GC-MS Methods
Targeted GC-MS Methods

Working with MSP-FSD to develop targeted GC-MS 

methods for different compound classes.

The goal is to develop methods that:

1) Enhance separation of isomers

2) Increase sensitivity

3) If possible, shorten runtimes

4) Standardize reporting / methods across labs

Methods also build in retention time locking and 

retention indices to improve rigor

Column Comparison

Parameter Optimization

LOD Calculation & 

Comparison

Compound 

Expansion

Retention Index 

Measurements 16
Test Mixtures

• Worked with Cayman Chemical 

to develop custom text mixtures 

for each class

• Span range of elution times 

within class

• Include isomers to be able to 

measure resolution

Opioids Cathinones Cannabinoids

m-FIBF Phentermine FUB-AMB

p-FIBF Methamphetamine MDMB-FUBINACA

Cyclopropyl Fentanyl Dimethylone EMB-FUBINACA

Crotonyl Fentanyl Butylone MMB2201

Carfentanil Ethylone ADB-FUBINACA

Methoxyacetyl Fentanyl Dibutylone AB-FUBINACA

Furanyl Fentanyl Pentylone 5F-ADBICA

Etizolam Dimethylpentylone 5F-ABICA

Noscapine Ethylpentylone

Benzodioxole Fentanyl

17
Column Comparison

• First portion of study looked to identify the 

effect of different columns on test mixture 

response

• Evaluated six different columns

• DB1UI, DB5, DB5UI, DB35, DB200, 

and VF1701ms

• Utilized a uniform method across all 

columns to keep other parameters fixed

Temperature Program

1) 100 °C for 0 min

2) Ramp at 30 °C/min to 300 °C

3) Hold for 24 min

Flow Rate 1.8 mL/min (Constant Flow)

Injection Volume 1 μL

Inlet Temperature 275 °C

Split Ratio 30:1

Transfer Line 300 °C

Quad Temperature 150 °C

Source Temperature 230 °C

Tune Mode stune

Solvent Delay 1.30 min

Mass Scan Range m/z 40 – m/z 550

Threshold 150

Scan Speed N = 2

Total Run Time 30.667 min

Uniform method

18
Column Comparison

0.01

0.1

1

10

100

DB-1 UI

DB-5

DB-5 UI

DB-35

DB-200

VF-1701ms

1-2 2-3 3-4 4-5 5-6

6-7 7-8 8-9 9-10

0

1

2

3

4

5

6

7

8

9

DB-1 UI DB-5 DB-5 UI DB-35 DB-200 VF-1701ms

Area (cts, x106)

m-FIBF p-FIBF Fentanyl

Cyclopropyl Fentanyl Methoxyacetyl Fentanyl Crotonyl Fentanyl

Carfentanil Furanyl Fentanyl Etizolam

Noscapine Benzidioxole Fentanyl

Δ Retention Time (%)

Peak Area

Once a column was chosen, studies were completed to optimize temperature and flow programs. 19
Other Settings – Design of Experiments

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

230 280

Counts (x105)

Source Temperature (°C)

0.0

4.0

8.0

12.0

16.0

20.0

10 30

Counts (x105)

Split Ratio

0.0

5.0

10.0

15.0

20.0

25.0

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Counts (x105)

Injection Volume (μL)

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

200 300

Counts (x105)

Inlet Temperature (°C)

0.0

1.0

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5.0

10 30

Average % RSD

Split Ratio

0.0

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6.0

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Average % RSD

Injection Volume (μL)

0.0

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7.0

200 300

Average % RSD

Inlet Temperature (°C)

0.0

1.0

2.0

3.0

4.0

5.0

230 280

Average % RSD

Source Temperature (°C)

Peak Area Peak Height Retention Time Δ Retention Time (%)

20
• Results of relevant parameters from the DOE were 

furthered refined 

• Final optimization looked at tune type

• After optimization, ran expanded panel of drugs to 

ensure method parameters worked 

Final Optimization

0.0

1.0

2.0

3.0

4.0

5.0

Area (cts, x106)

atune stune

21
Approximate LODs

Opioids LOD (μg/mL) Cathinones LOD (μg/mL) Cannabinoids LOD (μg/mL)

m-FIBF 1 Phentermine 0.5 FUB-AMB 1

p-FIBF 1 Methamphetamine 0.5 MDMB-FUBINACA 1

Fentanyl 1 Dimethylone 0.5 EMB-FUBINACA 5

Cyclopropyl Fent. 1 Butylone 0.5 MMB2201 1

Carfentanil 10 Ethylone 0.5 ADB-FUBINACA 10

Crotonyl Fentanyl 10 Dibutylone 0.5 AB-FUBINACA 10

Methoxyacetyl Fent. 10 Pentylone 0.5 5F-ADBICA 10

Furanyl Fentanyl 1 Dimethylpentylone 0.5 5F-ABICA 10

Etizolam 25 Ethylpentylone 0.5

Noscapine 25

Benzodioxole Fent. 10

22
% Change (Average) Area Height Delta RT % RSD (RT)

Opioids 327 % 37 % 135 % 93 %

Cathinones 66 % -19 % 262 % 0 %

Cannabinoids 6518 % 4045 % 220 % 537 %

Comparison to Current Method

0

10

20

30

40

50

ΔRT (%)

Compound #

Current Target

0

5

10

15

3-4 4-5 5-6 6-7 7-8 8-9

ΔRT (%)

Compound #

Current Target

0

10

20

30

40

1-2 2-3 3-4 4-5 5-6 6-7 7-8

ΔRT (%)

Compound #

Current Target

Opioids Cathinones Cannabinoids

23
Comparison to Current Methods

2 x10

0

0.2

0.4

0.6

0.8

1

+EI TIC Scan CM2C.D 

4.351

1.903

4.217

3.791

2 x10

0

0.2

0.4

0.6

0.8

1

+EI TIC Scan 100C.D 

1.284

3.645

4.303

3.196

3.417 4.070

2.112 2.671

1

Counts (%) vs. Acquisition Time (min)

1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 8 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 9.8 10 10.2 10.4 10.6 10.8 11 11.2 11.4 11.6 11.8

2 x10

0

0.2

0.4

0.6

0.8

1

+EI TIC Scan CM1A.D 

7.498

8.326

11.322

9.684 22.350

1

2 x10

0

0.2

0.4

0.6

0.8

1

+EI TIC Scan 1-100B.D 

8.449

9.434 10.448

7.854

11.544 14.749

26.214

23.423

20.642

2.719 21.435 5.942

1

Counts (%) vs. Acquisition Time (min)

1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 25.5 26 26.5 27 27.5 28 28.5 29 29.5 30

General

General

Targeted

Targeted

Opioids

Cathinones

24
Compound Expansion

2 x10

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

4.25

4.5

4.75

5

+EI TIC Scan P0532.D 

1.342 2.106 2.589 3.889 4.787 6.331 6.978

1

Counts (%) vs. Acquisition Time (min)

1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4

2 x10

0

0.1

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0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

+EI TIC Scan P0540A.D 

7.417

1.285 2.888

1

Counts (%) vs. Acquisition Time (min)

1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6 6.2 6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 8 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 9.8 10 10.2 10.4 10.6 10.8 11 11.2 11.4 11.6 11.8

• Once developed, 

additional compounds 

were analyzed

• Made adjustments to 

methods as needed

• Replicate analyses to 

evaluate locked RT and 

RI

• Build library with RT 

and RI information

• All compounds had 

>1% RT difference or 

differentiable MS

Cannabinoids

54 Compounds to Date

Cathinones

61 Compounds to Date

25
Compound Expansion

1 x10

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

4.25

4.5

+EI TIC Scan 2F9-B.D 

1

Counts (%) vs. Acquisition Time (min)

1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5 24 24.5 25 25.5 26 26.5 27 27.5 28 28.5 29 29.5

• Utilized Fentanyl Analog Screening kit for expansion of opioid method

• Method has 8 pairs of compounds that have similar MS with <1 % RT difference

• Six sets were ortho / meta isomer pairs

• Currently building out automated data analysis and reporting features

Opioids

212 Compounds to Date

26
The next step of this work is looking to quantify a comparison between the current 

workflow and a novel workflow.

• Take a subset of cases and have drug chemists analyze using one of the workflows

• Evaluate the level of detail gained at each step

• Quantify the time taken for each step

• Identify strengths and weaknesses in the novel workflow

Workflow Comparison

27
Thank you.

edward.sisco@nist.gov

DARTdata@nist.gov
NIST Mass Spectral Libraries and Search Tools for Seized Drug Analysis 

Arun S. Moorthy

National Institute of Standards and Technology

Gaithersburg, MD, USA 20899

November 6th, 2020.
Unidentified 

Analyte

Possible 

Compounds in 

Analyte

Search 

Algorithm

Pure 

Compound 

Database

NIST R&D

Pure 

Compound 

Database

DART-MS 

Forensics Database

• A new database available now

• focus on NPS’s, synthetic opioids, cutting agents

• spectra measured at multiple orifice energies

• Developed new manual and automated

evaluation workflow 

• Implemented workflow to facilitate rapid 

updating of database

• open-source software

• Database and workflow available from 

DARTdata@nist.gov

4

NEW DART-MS 

Forensic 

database:

663 compounds,

1989 spectra

Visual summary of library (e.g. 

Histogram?)
Unidentified 

Analyte

Possible 

Compounds in 

Analyte

Search 

Algorithm

Pure 

Compound 

Database

NIST R&D

Search 

Algorithm

1. EI-MS

AMDIS

MS Search + Interpreter

Fentanyl Classifier

2. DART-MS 

Inverted Search Algorithm
Q

LA

LB

LC

LD

LE

LF

LG

LH

LD

LA

LH

LC

LE

LB

LF

LG

(1) Measure Similarity (2) Rank by Similarity

Mass spectral library searching

sqa

sqb

sqc

sqd

sqe

sqf

sqg

sqh

EI-MS: Fentanyl Classifier
Q

LA

LB

LC

LD

LE

LF

LG

LH

LD

LA

LH

LC

LE

LB

LF

LG

(1) Measure Similarity (2) Rank by Similarity

Mass spectral library searching

sqa

sqb

sqc

sqd

sqe

sqf

sqg

sqh

Mass spectral similarity mapping

Q

LA

LB

LC

LD

LE

LF

LG

Q LA LB LC LD LE LF LG

sqa

sqb

1

sqc

sqd

sqe

sqf

sqg

1

sab

sac

sad

sae

saf

sag

sba

1

sbc

sbd

sbe

sbf

sbg

sca

scb

1

scd

sce

scf

scg

sda

sdb

sdc

1

sde

sdf

sdg

sea

seb

sec

sed

1

sef

seg

sfa

sfb

sfc

sfd

sfe

1

sfg

sga

sgb

sgc

sgd

sge

sgf

1

saq sbq

scq sdq seq

sgq sfq

EI-MS: Fentanyl Classifier
Q

LA

LB

LC

LD

LE

LF

LG

LH

LD

LA

LH

LC

LE

LB

LF

LG

(1) Measure Similarity (2) Rank by Similarity

Mass spectral library searching

sqa

sqb

sqc

sqd

sqe

sqf

sqg

sqh

Q

LA

LB

LC

LD

LE

LF LG

Mass spectral similarity mapping

EI-MS: Fentanyl Classifier
N

N O

a

b

c

d

e

a

b

Software Availability:

1. NIST Fentanyl Classifier (2020): 

http://github.com/asm3-nist/FentanylClassifier

Relevant Publications:

1. Moorthy et. al. "Combining fragment-ion and 

neutral-loss matching during mass spectral library 

searching: A new general purpose algorithm 

applicable to illicit drug identification." Analytical 

chemistry 89, no. 24 (2017): 13261-13268.

2. Moorthy et. al. "Mass spectral similarity mapping 

applied to fentanyl analogs." Forensic Chemistry 19 

(2020): 100237.

3. Moorthy & Kearsley. ”Pattern similarity measures 

applied to mass spectra”. To appear in “Progress in 

Industrial Mathematics” (2021)

4. Kearsley & Moorthy. “Mathematics and Mass 

Spectra: Model problems to study the Fentanyl 

epidemic”. Submitted July 2021. 

Examples of fentanyl and fentanyl analogs, with colored shapes demonstrating the sites 

at which the analogs differ from the fentanyl

Example of 2D mass spectral similarity map created by the NIST Fentanyl Classifier. Each circle 

represents a mass spectrum. Based on where a query spectrum lands in this space, an analyst can 

determine whether it is a fentanyl analog (with up to two modifications) or not.

EI-MS: Fentanyl Classifier


DART-MS: Inverted Search Procedure

12

DART-MS 

Spectra of 

Analyte GIVEN: (q) L1 L ... 2 Ln

DART-MS Spectral Database (pure compounds) 

Assumption 1: The component molecules contained in a mixture will each present an [M + H]+ peak in the low

energy spectrum and the relative intensity of these peaks will be greater than a threshold intensity.
13

mm2 mm1

(q)

Assumption 2a: 

Reference mass spectra of 

the component molecules 

contained in the analyte 

are available in a 

searchable database. 

Assumption 2b: 

The difference between 

protonated molecule m/z

values of database entries 

and those observed in the 

query is accurate to a 

known resolution ± εε0. 

DART-MS: Inverted Search Procedure
L7 L7 L7

Assumptions 3a-b: If a molecule from the reference database is a component of the mixture:

(a) peaks from its reference mass spectrum are likely to be represented within the mass spectrum of the

mixture, with likelihood proportional to the rel. intensities of the peaks in the reference spectrum, and

(b) the (m/z) difference between its reference spectra peaks and the corresponding peaks in the mixture

mass spectra will be consistent.

φφmm1,LL4= gg ff1 qq, LL4, qq, LL4, qq, LL4, PP , ff2 qq, LL4, qq, LL4, qq, LL4, PP , ff3(qq, LL4)

L4 L4 L4

14

Target: mm1

q q q

weighted fraction of abundance explained weighted mass bias mass difference

(q)

DART-MS: Inverted Search Procedure

Summary of Tools

AMDIS: Automates extraction of GC-MS data files to generate 

consistent/reproducible mass spectra.

- Built-in “standard” library search procedure

MS SEARCH/Interpreter: A comprehensive tool for interacting 

with mass spectral libraries, including a variety of useful search 

algorithms and data interpretation tools.

Fentanyl Classifier: A tool specifically for interacting with mass 

spectra of potential fentanyl analogs, attempting to localize the 

site of modification.

Available: https://github.com/asm3-nist/FentanylClassifier

Inverted Search Algorithm: A new method currently in 

preparation for identifying components in DART-MS. 

For status updates: DARTdata@nist.gov

16

Unidentified 

Analyte

Possible 

Compounds in 

Analyte

Search 

Algorithm

Pure 

Compound 

Database

NIST R&D
Questions?

arun.moorthy@nist.gov
Benchtop NMR for Forensic 

Drug Analysis

Aaron Urbas

Chemical Sciences Division, Material Measurement Laboratory

National Institute of Standards & Technology

Gaithersburg, Maryland USA
Outline

2

• NMR at a Glance 

• Benchtop NMR

• Fentanyl Analog Differentiation with 1H low-field/benchtop NMR 

Spectra

• Fluorine (19F) low-field/benchtop NMR

• Quantum Mechanic Spectral Analysis (QMSA) of 1H NMR Spectra 

and Translation of 1H Spectra Across Magnet Field Strengths

• Recent Sample Investigations

• Conclusion & Acknowledgements
NMR at a Glance

• NMR Active Nuclei (Spin 1⁄2)

• 1H, 13C, 15N 19F, 31P mainly

• 2D experiments offer a wealth of connectivity information 

• COSY : 1H-1H single bond correlations

• TOCSY : 1H-1H multi-bond correlations

• HSQC : 1H-X single-bond single bond connectivity

• HMBC, HMQC : 1H-X multi-bond single bond connectivity

There are MANY more methods including variants of these and others.

• Quantification

• Absolute purity determinations against a reference material

• Quantification of multiple compounds from a single internal (or external) standard

• Powerful screening method for unknowns

• In most cases, if it’s soluble and has a proton you can see it 

Powerful Structure Elucidation Tool

Analytical Tool 

13

C 1H

1H

1H

3
Benchtop NMR • 40 – 90 MHz Permanent Magnet 

Systems

• Range from ~ $40K - $100K

• No cryogens, little maintenance • Easy to Use • Portable to varying extents • Some 2D spectral capabilities • Drawbacks • Sensitivity & Resolution

4
Fentanyl Analog Benchtop NMR Evaluation

General fentanyl structure labeling 

functional groups and opportunity for 

modification

the list goes on....

65 fentanyl analogs and related compounds were examined

All samples were prepared in CDCl3 (~5 mg in 0.6-0.7 mL)

Duffy J, Urbas A, Niemitz M, Lippa

K, Marginean I, “Differentiation of fentanyl 

analogues by low-field NMR spectroscopy.” 

Anal Chim Acta, 2019, 1049:161-169

5

R1) N-propionyl group

R2) phenethyl group

R3) aniline ring

R4) piperidine ring

In the case of fentanyl:
Furanyl Fentanyl Analogs (1H NMR, 62 MHz) 

A) para-methyl furanyl

fentanyl

B) ortho-methyl furanyl

fentanyl

C) furanyl fentanyl

D) furanyl fentanyl 3-

furancarboxamide isomer

furanyl fentanyl

6
Butyrl Fentanyl Analogs (1H NMR, 62 MHz)

7

α-methyl 

Butyryl 

fentanyl
Fluorofentanyl Analogs (1H NMR, 62 MHz) 

A) fentanyl

B) o-fluorofentanyl

C) m-fluorofentanyl

D) p-fluorofentanyl

E) 3-fluorofentanyl

Aromatic-H

Region 

8
Water

Solvent

(MeOD)

Ar-H

Region 

Fluoromethcathinone Isomers (1H, 62 MHz, MeOD)

9
4-FMC

3-FMC

2-FMC

MAM-2201

AM-2201

MAM-2201 AM-2201

19F NMR Spectra (~58 MHz)

10
19F NMR of Fluorinated Fentanyl Analogs (1H Decoupled) Meta Para Ortho Meta Para Ortho

ortho-fluorofentanyl

ortho-fluoroacryl fentanyl

ortho-fluorobutyrl fentanyl

meta-fluorofentanyl

meta-fluoroisobutyrl fentanyl

meta-fluorobutyrl fentanyl

para-fluorofentanyl

para-fluoroacryl fentanyl

para-fluorobutyrl fentanyl

despropionyl ortho-fluorofentanyl

despropionyl meta-fluorofentanyl

despropionyl para-fluorofentanyl

3-fluorofentanyl

11
Outline

12

• NMR at a Glance 

• Benchtop NMR

• Fentanyl Analog Differentiation with 1H low-field/benchtop NMR 

Spectra

• Fluorine (19F) low-field/benchtop NMR

• Quantum Mechanic Spectral Analysis (QMSA) of 1H NMR Spectra 

and translation of 1H Spectra Across Magnet Field Strengths

• Recent Sample Investigations

• Conclusion & Acknowledgements
Can We Better Utilize 1H Spectra?

 Wealth of structural information available

 Proton counts

 Chemical shift structure correlations

 Connectivity via couplings and coupling 

constants

 Indirect heteronuclear information through 

coupling, e.g. 19F

Predicted Chemical Shifts &

Coupling Constants for 

para-fluorofentanyl

para-fluorofentanyl ...

13
Predicting 1H NMR Spectra

Measured

Predicted

δ(ppm)

Measured vs Predicted Para-Fluorofentanyl 1H NMR Spectra (600 MHz)

While predicted 1H spectra can be useful for spectral interpretation they often differ quite 

considerably from observed spectra in both chemical shifts and coupling constants. 

14
...

...

Predicted Chemical Shifts &

Coupling Constants

Fit Chemical Shifts &

Coupling Constants

There are a total of 117 chemical shifts 

and couplings in the spin system utilized 

for this molecule, the tables only 

represent a subset.

Para-fluorofentanyl

Long story short....

Quantum Mechanic Spectral Analysis (QMSA) 

15
Quantum Mechanic Spectral Analysis (QMSA) 

Para-fluorofentanyl

δ(ppm) δ(ppm) δ(ppm) δ(ppm)

Measured (600 MHz)

QMSA Model

16
Field Translation of 1H NMR Spectra using Spin-System Models

 QMSA models are field 

independent and thus portable 

to different magnetic fields for 

reproducing spectral 

information.

 QMSA models are free of 

solvent and impurity signals as 

well as instrumental artifacts

 QMSA models are adaptive and 

enable handling of small 

perturbations in chemical shifts 

and coupling constants 

between samples.

600

400

300

200

100

62

MHz

(1H)_

Spin-System Evaluated at Various Field Strengths

δ(ppm)
QMSA Fentalog Translation Examples (600 to 62 MHz)

Measured

Modeled
19

QMSA Sub-Systems as Spectral Building Blocks

para-fluorofentanyl

QMSA model

butyrl fentanyl

QMSA model

Measured

para-fluorobutyryl fentanyl

62 MHz 1H

δ(ppm)

Facilitates building new QMSA models and predicting spectra of unknowns
20

Bringing it all together.....

Simulated para-fluorobutyryl fentanyl

62 MHz 1H Spectrum

from

butyrl fentanyl & para-fluorofentanyl

QMSA Models

Measured para-Fluorobutyryl fentanyl

62 MHz 1H Spectrum

δ(ppm)
Outline

21

• NMR at a Glance 

• Benchtop NMR

• Fentanyl Analog Differentiation with 1H low-field/benchtop NMR 

Spectra

• Fluorine (19F) low-field/benchtop NMR

• Quantum Mechanic Spectral Analysis (QMSA) of 1H NMR Spectra 

and translation of 1H Spectra Across Magnet Field Strengths

• Recent Sample Investigations

• Conclusion & Acknowledgements
Synthetic Tryptamine Analog Example 1

4-methoxy MiPT (HCl)

(62 MHz Simulation)

5-methoxy MiPT

(62 MHz Simulation)

5-methoxy DET

(62 MHz Simulation)

Sample

Spectrum

Impurity

HDO

MeOD

22

Sample Spectrum Compared to 62 MHz QMSA Simulations
Synthetic Tryptamine Analog Example 1

4-methoxy MiPT (HCl)

(62 MHz Simulation, Optimized)

Sample

MeOD

(62 MHz Simulation)

23

62 MHz QMSA Model of Sample Spectrum

HDO

Impurity
Synthetic Tryptamine Analog Example 2

24

DPT

DiPT

N,N-Dipropyltryptamine

(DPT)

Sample

MeOD (included in QMSA)

HDO

Sample Spectrum Compared to 62 MHz QMSA Simulations 62 MHz QMSA Model of Sample Spectrum HDOMeOD
25

Conclusions & Future Efforts

 Demonstrated that analogs and isomers of fentanyl and some other classes of 

compounds were readily differentiated using low-field NMR spectroscopy

 Showed how 19F NMR might be useful in the analysis of fluorinated compounds 

 Demonstrated the potential utility of quantum mechanic spectral analysis (QMSA) to 

enable exchange of 1H spectra between NMR instruments of different field 

strengths.

 Broaden effort to develop QMSA libraries by enlisting collaborators.

 Resolution and sensitivity are significantly reduced at lower magnetic fields. 

Mixtures are anticipated to be challenging.

 Explore whether the use of Quantum Mechanic Spectral Analysis (QMSA) will permit 

effective mixture analysis with low-field NMR. Low-level components (< 5%-10%) would 

likely be difficult in many situations, though.

 Continue work with forensic lab partners to evaluate “real-world” samples.

Going Forward....

Going Forward....
Acknowledgements

26

• Support from the NIST Special Programs Office

• George Washington University (collaboration on the fentanyl analog project)

• Ioan Marginean and Jonathan Duffy

• Samples, reference materials or data in the past and present from

• Maryland State Police Forensic Science Division (Amber Burns)

• DEA Special Testing and Research Laboratory (Charlotte Corbett)

• US Postal Inspection Service (Mike Hitchcock)

• QMSA Assistance

• Matthias Niemitz (NMR Solutions)

• Pekka and Reino Laatikainen (Spin Discoveries) for assistance with QMSA

Commercial equipment identified in this presentation is not intended to imply recommendation or endorsement by the 

National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are 

necessarily the best available for the purpose.

Disclaimer
Thank You!

27