A Forensic Workshop Report Concerning

(1) The Measurement and Analysis of

Energetic Materials and (2) Databasing

by Joseph M. Heimerl

ARL-SR-77 January 1999

Approved for public release; distribution is unlimited.

Precediny Pag&iank
The fmdings in this report are not to be construed as an official

Department of the Army position unless so designated by other

authorized documents.

Citation of manufacturer’s or trade names does not constitute an

offkial endorsement or approval of the use thereof.

Destroy this report when it is no longer needed. Do not return

it to the originator.

.
Army Research Laboratory

Aberdeen Proving Ground, MD 21005-5066

ARL-SR-77 January 1999

A Forensic Workshop Report Concerning

(1) The Measurement and Analysis of

Energetic Materials and (2) Databasing

Joseph M. Heimerl

Weapons and Materials Research Directorate, ARL

Approved for public release; distribution is unlimited.
Abstract

On Thursday, 20 June 1996, this workshop was convened at BRL Hall (Bldg. 330), located

at the U.S. Army Research Laboratory (ARL), Aberdeen Proving Ground (APG), MD. The

purposes of this workshop were: (1) to define the critical problems that cut across forensic

agencies; (2) to provide an open, multidisciplinary forum in which these problems could be

addressed and better defined; and (3) to lay the foundation for practical, timely, and

cost-effective solutions.

The technical focus of the workshop pivoted on two questions: (1) What is required for a

sample to be traceable to a unique compound (or unique class of compounds)? (2) How can the

present databases be made into user-friendly, complete, and current databases, accessible by all

forensic laboratories?

This report documents the background leading up to the calling for a workshop, the

manuscripts of the talks given, and the discussion held.

ii
Acknowledgments

The fact that this workshop took place was due to a large number of people who saw its potential

worth and were willing to put forth the effort to make it happen. At the risk of omitting the names

of some folks, who have my apologies in advance, I would like to thank each agency’s point of

contact (POC): Dr. Frank Fox at the Federal Aviation Administration (FAA), Mr. Raymond

Voorhees at the Postal Inspection Service, Dr. Steve Burmeister at the Federal Bureau of

Investigation (FBI), and Mr. Charlie Midkiff at the Bureau of Alcohol, Tobacco, and Firearms

(BATF).

Kudos to Dr. Rose A. Pesce-Rodriguez of the U.S. Army Research Laboratory (ARL) for her

roles as technical reviewer, sounding board for ideas, and moderator of the afternoon session.

Dr. Kevin McNesby, also of AI& is thanked for his roles as technical reviewer and presenter.

Mr. Ed Simmons of the Aberdeen Proving Ground (APG) Audio/Visual Section is thanked for his

audio recordings of the presentations and discussions; the discussion section owes its existence to

those audio tapes. (Next time, we will avoid placing a live microphone in the vicinity of the

viewgraph’s exhaust fan.)

Thanks are also due to Ms. Rhonda Cumpton of ARL for a variety of logistical services and to

the Chemical Propulsion Information Agency (CPIA) for permission to publish Dr. Caulder’s earlier

work. Finally, I want to express my thanks to Ms. Kathleen Higgins at the National Institute of

Justice (NIJ) for partial financial support of this workshop and for reviewing this report.

. . .

111
Table of Contents

. .

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

Acknowledgments . . . . . . ..*OD..-*O.... .*..0*0.....*00.. . . . . . ..a*.00

List of Figures 0...~.0.,.~.~~.~..~...~~.~~~~~~........~~...~...~..~~

List of Tables . ..D..~.~.....~.~~..~...~~~..~.....~.....~..~.~.~~.~.

Background . . . . . . . . . . ..~.~00..~.~....~~~~.......~...........~~.~.

Agenda . . . . . . ..0...........~~~~...~.~~......0~....0....~....~~~~~

“Analytical Methods Used in Identifying Trace Constituents in Energetic

Materials” (presented by Steve Burmeister, Federal Bureau of

Investigation~I]) . . . . . . . . . . . . . . . . . . . . . . . . . . . ..a...O.....+........

“Characterization and Identification of Explosive and Propellant

Formulations Using Raman Spectroscopy” (presented by Kevin L. McNesby,

U.S.ArmyResearchLaboratory[ARL])...... . . . . . . O.OO.e...e . . . . . e.eO

“Trace Detection Activities” (presented by Frank T. Fox, Federal Aviation

Administration FAA] Technology Center) . ..*.....*....*.....00**.**..

detection of Hidden Explosives” (presented by Frank T. Fox, Federal

Aviation Admimstration [FAA] Technology Center) . . . . . _ . a o o . . . . . . e . . e .

“The Explosive Standard Analytical Reference Material (SARM) Program”

(presented by S. M. Caulder, Naval Surface Warfare Center WSWC],

Indian Head [M9 Division) *....*.....a...*........*.......... . . . . . . .

“Problems in Characterizing Smokeless Gunpowders” (presented by

Allan N. Walters, Postal Inspection Service) . . . . . . . . . . . . . . . . . . . . . . . . . . . .

“Simplifying Access to Complex Databases” (presented by Ralph D. Semmel,

The Johns Hopkins University [JHU], Applied Physics Laboratory [APL]) . .

DiscussionSection e n........e.e.s.o..a *. ..~.~.*......0~......Q*.~..

Appendix A:

Appendix B:

Appendix C:

Details of the Chemical Characterization Research Facilities

at the Ignition Combustion Branch (ICB), U.S. Army Research

Laboratory (ARL) . . . . . . . . . . . . . . . . . . . . . . ..**....*......

Development of the Workshop Focus Questions . . . . . . . . . . . . .

Workshop Details . . . . . ..*0.............................

V

&

. . . 111

vii

ix

1

3

5

6

17

21

28

46

56

70

81

89

93
F&g

Appendix D: Workshop Evaluation .................................. 101

Distribution List ................................................... 105

ReportDocumentationPage ......................................... 109

.

vi
List of Figures

Figure m

4-l. Simple Diagram of the Experimental Apparatus Used in These Experiments ....

4-2. The FT’R Spectra of RDX and HMX ....................................

4-3. TheFTRSpectraofPETNandNQ .....................................

4-4. TheT;TRSpectraofADNandTNT .....................................

4-5. The FIR Spectra of b-HNIW and TNAZ ................................

4-6. TheFTRSpectraofHNS andPDNPA ..................................

4-7. TheFI'RSpectraofNCandDMNA ....................................

4-8. The FTR Spectra RDX, HMX, and XM39 ...............................

4-9. The FTR Spectra of RDX, XM39, and a Chinese Propellant Formulation .......

4-10. The FTR Spectra of XM39 During Heating ..............................

7-l. Cl3 NMR Spectrum 2-Amino-4,6-DNT ..................................

7-2. Cl3 NMR Spectrum 4-Amino-2,6-DNT ..................................

7-3. Cl3 NMR Spectrum Mixture 2% 2-Amino-4,6-DNT/98% 4-Amino-2,6-DNT ....

7-4. HPLC Pure 2-Amino-4,6 DNT and 4-Amino-2,6-DNT ....................i

7-5. HPLC 1: 1 Ratio and 9: 1 Ratio 2-Amino-4,6-DNT/4-Ao-2,6-DNT .........

7-6. C’32,4,6-Trinitrobenzaldehyde ........................................

7-7. Proton NMR 2,4,6_Trinitrobenzaldehyde ................................

7-8. HPLC-2,4,6_Trintrobenzaldehy@e ......................................

7-9. HPLC - 12.6% NC ..................................................

7-10. HPLC-NGWith2-NDPAinAcetone ..................................

vii

8

10

11

11

12

12

13

13

14

15

32

33

34

35

36

38

39

40

41

42
Figure

7-11. HPLC - PETN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44

7-12. HPLC - Normal Lead Styphnate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

8-1. Schematic of Alliant Double-Base Smokeless-Propellant Manufacturing

Process . . . . . . . . . . . . . . . . . . . . ..~....................................

49

9-1. A Banking Example ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

. . . Vlll
List of Tables

Table Page

4-1.

7-l.

7-2.

8-1.

8-2.

8-3.

8-4.

8-5.

Energetic Materials Used in These Experiments ........................... 9

Compounds in the Standard Analytical Reference Material Repository ......... 29

Compounds Being Synthesized ........................................ 30

Physical and Chemical Data of Hercules Canister Powders .................. 47

Relative Burning Rates of Various Powders .............................. 50

BATP Database for Hercules 2400 ...................................... 52

BATP Database for Hercules Blue Dot .................................. 52

BATI? Database for Hercules Bullseye .................................. 53

8-6. Hercules Large-Die Smokeless Powders . . . . . . . . . . . _ . . . . . . . . . . . . . . . . e . . . . 54

ix
1. Background

.

As the presentation of findings in court have made more and more demands on the forensic

scientist, the time to do the science that underpins forensic investigations has diminished. With the

idea that the Ignition and Combustion Branch (ICB) Chemistry Laboratory at the U.S. Army

Research Laboratory (ARL), Aberdeen Proving Ground (APG), MD (see Appendix A) could aid

forensic laboratories in keeping up with scientific advances, members of the Bureau of Alcohol,

Tobacco, and Firearms (BATF), the Federal Bureau of Investigation (FBI), and the U.S. Postal

Inspection Service were invited to tour the ICB physical chemistry facilities on 31 January 1996.

Eleven forensic scientists from these agencies and one researcher each from the National Institute

of Standards and Technology (NIST) and the U.S. Army Edgewood Research, Development, and

Engineering Center (ERDEC) participated in this tour. ARL’s detection and characterization

capabilities and three current problems from each of the forensic agencies were briefed. During the

wrap-up session, there was a consensus that a workshop ought to be held to bring problems

concerning energetic materials into sharper focus. Later, the Federal Aviation Administration

(FAA), at their request, also toured the ICB/ARL facilities and expressed a desire to participate in

the planned workshop.

On Thursday, 20 June 1996, a workshop entitled “Forensics: Energetic Materials Measurement,

Analysis, and Databasing” was convened at BRL Hall (Bldg. 330), located at ARL, APG, MD. The

purposes of this workshop were:

(1) to define the critical problems that cut across forensic agencies;

(2) to provide an open, multidisciplinary forum in which these problems could be addressed and

better defined; and

(3) to lay the foundation for practical, timely, and cost-effective solutions.

1
The technical focus of the workshop pivoted on two questions.

(1)

(2)

What is required for a sample to be traceable to a unique compound (or unique class of

compounds)?

How can the present databases be made into user-friendly, complete, and current databases,

accessible by all forensic laboratories?

These two focus questions had been formulated in conjunction with the forensic community (see

Appendix B). Subsequently, an agenda was established and is given in section 2. Next, a letter of

invitation was extended to approximately 30 forensic scientists and researchers. A copy of this

letter, the response fax form, and a list of the workshop attendees are given m Appendix C.

As can be seen from the agenda, the format for the workshop consisted of six topical speakers

followed by an extensive discussion session. In anticipation of this report, the entire set of talks was

audiotaped, transcribed, and returned to the respective authors, who then reviewed the transcription

to produce the written manuscripts contained herein. The discussion session was prepared by this

author. Because of the rather poor quality of the audiorecordings, each author was free to substitute

work already in progress or published elsewhere, provided that the manuscript covered in essence

what was presented at the workshop. The body of this report (six manuscripts of the presentations

and a discussion section) follows the agenda. Because of a demanding court schedule,

Dr. Bermeister could not provide his manuscript; therefore, only his abstract has been included. In

addition, for a variety of cumulative reasons, Dr. Caulder was not able to supply a manuscript of his

talk. In its stead, by mutual consent of all parties, we have chosen to substitute an earlier version of

the talk given, which covers the same kind of material and may be found on pp. 53-67 in the

Chemical Propulsion Information Agency (CPIA) Publication No. 588 of August 1992. At the end

of the workshop, an evaluation was made, and these results are presented in Appendix D.

2
2. Agenda

.

Forensics: Energetic Materials Measurement, Analysis, and Databasing Workshop

Held at the U.S. Army Research Laboratory

BRL Hall (Bldg. 330)

2OJune1996

08304855 Parking (See Directions)

Badging (Bldg. 328)

Walk to BRL Hall (Bldg. 330)

0900-0915 Welcome, by Al W. Horst, Chief, Propulsion and Flight Division (PFD)

0915-0920 Administrative Remarks, by Joseph M. Heimerl, Workshop Coordinator

Chair of Morning Sessions: Rose A. Pesce-Rodriguez

09204940 “Analytical Methods Used in Identifying Trace Constituents in Energetic Materials,”

by Steve Burmeister, Federal Bureau of Investigation (FBI)

094&1000 “Characterization and Identification of Explosive and Propellant Formulations Using

Raman Spectroscopy,” by Kevin E. McNesby, Nicholas F. Fell, Jeffrey M. Widder,

Jeffrey B. Morris, and Rose A. Pesce-Rodriguez, U.S. Army Research Laboratory

(ARL), Ignition and Combustion Branch (ICB)

1000-1020 Break

1020-1040 “Trace Detection Activities,” by Frank T. Fox, Federal Aviation Administration

(FAA) Technology Center

3
1040-l 100 “‘The Explosive Standard Analytical Reference Material (SARM) Program,” by

Stan M. Caulder, H. E. Turner, and W. F. Bryant, Naval Surface Weapons Center

(NSWC), Indian Head (JH) Division

1100-l 120 Break

1120-1140 “Problems in Characterizing Smokeless Gunpowders,” by Allan N. Walters, Postal

Inspection Service

1140-1200 “Simplifying Access to Complex Databases,” by Ralph D. Semmel and E. A. Immer,

The Johns Hopkins University (JHU), Applied Physics Laboratory (APL)

1200-1300 Lunch at Top 0’ the Bay

Chair of Afternoon Sessions: Josenh M. Heimerl

1300-1400 Open Discussion (Bldg. 330)

1400-1420 Break

1420-1530 Wrap Up

Tour Director: Rose A. Pesce-Rodriguez

1530-1630 Minitour of Laboratory Facilities (Optional)

4
3. “Analytical Methods Used in Identifying Trace

Constituents in Energetic Materials”

Steve Burmeister

Federal Bureau of Investigation

(Abstract Only Available)

The talk addresses techniques in use today for the analysis of certain explosives and how these

results are applied to the sourcing of materials. It iucludes a discussion of database information

currently available.

5
4. “Characterization and Identification of Explosive

and Propellant Formulations Using

Raman Spectroscopy”

Kevin L. McNesby, Nicholas F. Fell, Jeffrey M. Widder,

Jeffrey B. Morris, and Rose A. Pesce-Rodriguez

LJ.S. Army Research Laboratory, Ignition and Combustion Branch

Aberdeen Proving Ground, MD 21005-5066

Abstract

Fourier transform Raman (FTR) spectroscopy employing near-infrared (NIR) laser radiation at

9394.5 cm-’ is used to characterize neat energetic materials and several propellant formulations.

Raman spectra are reported over the region from 100 cm-’ to 3,000 cm-‘, relative to the Rayleigh

line. The technique is extended to the study of crystalline components of propellant formulations

during heating. The utility of the technique in deter-mining the principal crystalline ingredient in a

propellant formulation is demonstrated.

Introduction

Fourier transform Raman (FIR) spectroscopy using near-infrared (NIR) laser light as the

scattering source has been shown to be useful in the identification of crystalline components of

propellant formulations [ 13. The ease with which many principle components of propellant

formulations may be identified is due to the generally good scattering characteristics of crystalline

energetic materials combined with the poor scattering characteristics of most plasticizers and

binders. Although using NJR laser light as the scattering source limits the signal intensity compared

to that obtained using visible laser light scattering, the lack of fluorescence interference when

working in the NIR spectral region outweighs this disadvantage. If a disadvantage exists in trying

to measure Raman spectra of propellant formulations using NIR laser scattering, it is that most

6
colored formulations will absorb the laser light and combust. Fortunately, most crystalline neat

energetic materials are not colored; so, the technique can often yield excellent results.

Recently, we published a short paper consisting of Raman spectra of several neat energetic

materials and several propellant formulations 123. It was our goal at the time to have that paper be

the first in a series to aid in the assembly of Raman spectral database of energetic materials and

propellant formulations. Since that time, modifications to our instrument have enabled coverage of

a broader spectral range; so, we have remeasured the spectra of neat energetic materials not measured

in the first paper and have also included some new energetic formulations and techniques. We hope

most readers find this paper a useful continuation of our initial effort toward the characterization of

energetic materials by FIR spectroscopy.

Experimental

The experimental apparatus has been described previously 121. Briefly, the experimental

apparatus consists of a Bomem DA-8.02 Fourier transform spectrometer to which a Raman accessory

has been added. Incident radiation is provided by a Quantronix Series 100 Nd:YAG laser. A simple

sketch of the experimental apparatus is shown in Figure 4-l. Raman-shifted radiation is collected

using a back-scattering geometry and detected after filtering and interferometer modulation using

a liquid-nitrogen-cooled InGaAs detector. Recently, the interference filters used to exclude the

Rayleigh line at 1.06 p (9394.5 cm-‘) were replaced by holographic notch filters (Kaiser Optical).

This enabled measurement of Raman transitions that shifted from 100 cm-’ to 3,000 cm-’ with

respect to the Raleigh line (the previous spectral range of the instrument was from 400 cm-’ to

3,000 cm-‘). Also, we are able to observe anti-Stokes lines that shifted from the Rayleigh line from

150 cm-’ to 600 cm-‘.

All spectra reported here were measured at 4-cm-’ resolution using coaddition of 256 scans.

Incident laser power was 200 mW. Scan time to collect each spectrum was 11 min. Neat samples

of energetic materials were placed in l-mm inside diameter (ID) glass capillary tubes, and the
Nd:YAG

LaserLigh

Interferometer I Ctdktion Ellipsoid

Holographic Filter B

I

sample c5

Deiecior

Figure 4-1. Simple Diagram of the Experimental Apparatus Used in These Experiments.

Nd:YAG laser focused on the front surface of the tube. Propellant formulations, when in the form

of cylindrical grains, were placed in a temperature-variable holder whose position could be

maintained from run to run. Powdered propellant formulations were placed in a sample spinner

operated at 2,400 rpm to minimize heating effects. No correction was made to any of the spectra to .

account for responsivity of the detector, interferometer, or filters used in the experiments. Samples

of hexanitrostilbene (PINS) and of a Chinese propellant formulation were provided by Atlas Wireline

Services Incorporated.

in-house sources.

All other propellant samples used in these experiments were obtained from

Results and Discussion

Neat Energetic Materials. Table 4-1 shows a summary of the energetic materials whose FIR

spectra are reported herein.

8
Table 4-l. Energetic Materids Used in These Experiments

.

.

.

Energetic Material

I

Laser Power

I

Composition

(mW)

neat

PETN

NQ neat

neat

b-HNIW

neat

PDNPA neat

xM39 I 76% RDX, 4% NC,

20% binder and plasticizer

Figure 4-2 shows the FIR spectra of the most common nitramine energetic material,

cyclotrimethylenetrinitramine (RDX), and of the eight-membered ring homolog,

cyclotetramethylenetetranitramine (HMX). There are several interesting features in these spectra.

First, unlike our previously published spectra, we are now able to observe in each spectrum

vibrational transitions to within 100 cm-’ of the Rayleigh line. Vibrational transitions due to ring

deformations in RDX and HMX, occurring at Raman shifts of from 100 cm-’ to 400 cm-‘, were not

measurable in our laboratory prior to installation of the new holographic rejection filters. Crystal

lattice modes [3] in nitramine crystals, which occur below 100 cm-‘, fall within the “notch” of the

holographic filters and are therefore not observable. An interesting result from the FIR spectrum

of HMX is that an intense peak is observed centered at a Raman shift of 150 cm-‘. This peak is close

to the intensity of the most intense ring &ret& features occurring in HMX from 800 cm-’ to

1,000 cm-‘.

9
500 1000 1500 2000 2500 3000

Raman Shift (wavenumbers)

Figure 4-2. The ETR Spectra of RDX and HMX.

Figure 4-3 shows the spectrum of the nitrate ester pentaerythritol tetranitrate (PETN), a common

ingredient in some commercial explosives. Also shown is the FT’R spectrum of nitroguanidine (NQ).

The N-H stretch in NQ at a Raman shift of approximately 3,200 cm-’ is not observed, since it occurs

beyond the range of the InGaAs detector. Figure 4-4 shows the spectrum of ammonium dir&amide

(ADN) and of trinitrotoluene (TNT). In both of these spectra, features below 400 cm-’ are among

the most intense in each spectrum. For the spectrum of TNT, sample heating is manifested by a

slightly elevated baseline. Neat TNT is light yellow in color and absorbs some of the incident

Nd:YAG laser radiation.

Figure 4-5 shows the spectra of beta-hexanitroisowurtzitane (b-HNIW), an energetic-caged

nitramine, and 1,3,3-trinitroazetidine (TNAZ), an energetic nitramine containing a geminal dinitro

group. Figure 4-6 shows the spectrum of FINS, an industrial explosive used in high-temperature

applications, and polydinitropropyladipate (PDNPA), an energetic polymeric binder used in some

propellant formulations. Both spectra were measured using incident radiation at a power of

400 mW, although the signal-to-noise ratio in the spectrum of HNS is superior to that of the

spectrum of PDNPA. These spectra illustrate the general observation that crystalline energetic

materials are better Raman scatterers than polymeric energetic materials.
500 1000 1500 2000 2500 3000

Raman Shift (wavenumbers)

Figure 4-3. The FTR Spectra of BETN and NQ.

ADN

0 500 1000 1500 2ooo

Raman Shift (wavenumbers)

2500

Figure 44. The FI’R Spectra of ADN and TNT.

Figure 4-7 shows the FIR spectra of dimethyh&amine (DMNA), a simple energetic nitramine,

and nitrocellulose (NC). While both spectra were measured using the same incident laser power and

sampling technique (1 -mm-ID capillary tube), the signal-to-noise ratio in the spectrum of crystalline

DMNA is superior to that of polymeric NC. Both samples were in the form of white powders.
Beta-HNIW

TNAZ

Raman Shift (wavenumbers)

Figure 4-5. The FTR Spectra of b-HNIW and TNAZ.

. . .R.

Raman Shift (wavenumbers)

Figure 4-6. The FTR Spectra of HNS and PDNPA.

Applications to Propellant Analysis. Figure 4-8 shows the FIR spectra of RDX, HMX, and

XM39 (a propellant formulation whose principal ingredient is RDX). The spectrum of XM39 is a

“best case” illustration of the utility of FTR spectroscopy for energetic material characterization.

While the spectra of RDX and HMX were measured using neat-powdered samples in capillary tubes,

the spectrum of XM39 was measured by scattering off the interior surface of cleaved propellant

12
-50'0 d 50; 1000 15ob 2oob 25dO 3060

Raman Shift (wavenumbers)

Figure 4-7. The ETR Spectra of NC and DMNA.

I I I I I I I I

200 400 600 800 1000 1200 1400 1600

Raman Shift (wavenumbers)

Figure 4-8. The FTR Spectra of RDX, HMX, and XM39. Arrows Indicate HMX Present in

Domestically Produced RDX.

grains. Even with this simple sampling technique, assignment of RDX as the principal crystalline

energetic ingredient in XM39 is unambiguous. Shown in the spectrum of XM39 is evidence of

HMX “impurity” in the formulation. HMX is normal by-product of RDX synthesis in most

domestically produced RDX. Also shown in the spectrum of XM39 is the increased intensity

13
(relative to neat RDX) of the feature near 150 cm-‘. This increased intensity (see Figure 4-2) may

be due to the small HMX “impurity” in domestically manufactured RDX.

Figure 4-9 shows the FTR spectra of RDX, XM39, and a commercially available propellant

formulation believed to be from China (PRC). Since the Chinese propellant formulation had a thin

graphite coating that would cause the sample to absorb some of the incident laser radiation, the

spectrum was measured by scattering the incident Nd:YAG laser radiation off the front surface of

a spinning (2,400 rpm) 5-mm-ID glass tube containing the propellant formulation. From Figure 4-9,

it is apparent that the principal crystalline energetic material in the Chinese propellant formulation

is RDX. Also, the spectrum of the Chinese propellant formulation shows no trace evidence of HMX,

supporting the likelihood that the RDX present in the Chinese propellant formulation is

manufactured by a different process than domestically produced RDX.

ChincsePmpcuant

I

I I I I I I I I 200 400 600 800 1000 1200 1400 1600

Raman Shift (wavenumbers)

Figure 4-9. The FTR Spectra of RDX, XM39, and a Chinese Propellant Formulation. Note

the Absence of HMX Impurities in the RDX Contained in the Chinese Propellant.

Arrows Indicate HMX Present in Domestically Produced RDX.

Figure 4- 10 shows a portion of the FTR spectrum of XM39 as it is heated from room temperature

to the onset of decomposition at 458 K (185” C). This spectrum was measured by scattering the

Nd:YAG laser light off the front surface of a slice of XM39 taken from the interior of a propellant

gram. As shown previously, the principal crystalline energetic ingredient in XM39 is RDX. The

14
A

XlVl39bemtedtodecompocition

.

Figure 4-10. The FTR Spectra of XM39 During Heating.

propellant slice was contained in a Reatable holder designed for the purpose of investigating changes

resulting from heating in energetic materials in propellant formulations. For XM39, as temperature

is increased, the scattering intensity decreases (as shown by the increased signal-to-noise ratio in the

spectra) and there is a slight red shift of the feature near 150 cm-‘. This red shift was most notable

for features at low Raman frequencies and was negligible at Raman frequencies above 500 cm-‘.

As the sample temperature approaches the melting point of RDX (477 K), the Raman intensity

rapidly diminishes, reflecting a loss of crystallinity in the principle energetic ingredient in the

( ~formulation.

Conclusion

.

It has been shown that FTR spectroscopy using NIR laser radiation as the scattering source can

be a useful tool in clharacterizing many neat samples of energetic materials and of some propellant

formulations. Raman spectra of most common energetic materials, as well as some new energetic

materials, have been measured from 100 cm-’ to 3,000 cm-‘. To our knowledge, Raman spectra

measured to within 100 cm-’ of the Rayleigh line have not been reported previously for most

. energetic materials. PerRaps the most interesting result of our ability to measure Ran& spectra over

a wider spectral range is the observation that the strongest peak in the HMX spectrum occurs near

15
150 cm-‘. Finally, we believe that, for many samples of energetic materials containing crystalline

principle ingredients, FT’R spectroscopy using NIR laser scattering sRould be the first choice of

investigators for forensic analysis. We are presently extending the technique to the investigation of

energetic materials at elevated temperatures, in formulations, and in solutions in high-pressure fluids.

Acknowledgments

Support is acknowledged from the National Research Council (NRC) Postdoctoral Associateship

Program and the Strategic Environmental Research and Development Program (SERDP) of the

Department of Defense (DOD). Many thanks are expressed to Dr. Monte Chawla of Atlas Wireline

Services Incorporated for providing the samples of hexanitrostilbene (HNS) and of the Chinese

propellant formulation.

References

1.

2.

3.

Alchaven, 9. Spectrochimica Acta. Part A, vol. 47, p. 1247, 1991.

McNesby, K. L., J. E. Wolfe, J. B. Morris, and R. A. Pesce-Rodriguez. Journal of Raman

Spectroscopy. Vol. 25, p. 75, 1994.

Goetz, F., and T. B. Brill. Journd of Physical Chemism. Vol. 83, p. 340,1979.

16
5. “Trace Detection Activities9

Frank T. Fox

Federal Aviation Administration Technology Center

I am Frank Fox with the Federal Aviation Administration (FAA). There are several headquarter

locations of the FAA in downtown Washington, DC, and a training facility in the Aerospace

Medicine facility in Oklahoma City, OK. In addition, there is a technical center located at the

William J. Hughes Technical Center in Atlantic City, NJ. That is where the portion of the research

is performed in which I am engaged. The overall mission of aviation security research is to get

commercially available explosives-detection instruments into use at airports. These will, of course,

have applicability to other transportation modalities as well. I will discuss two of the many ways that

the FAA is approaching this problem. One is to detect explosives inside of luggage, typically using

penetrating radiation, and the other is to detect explosives on the surfaces of luggage. External

contamination occurs due to inadvertent contact or touching ‘by the bomb maker or the dupe

delivering the package. Plastic explosives are very sticky, as many of you know. By analogy,

imagine that you are working with roofing tar. It is a substance that you cannot avoid getting on you,

and it does not wash off very easily either.

Trace detection does not actually detect the .explosive or even the presence of a bomb, but, rather,

it points a finger of suspicion, and that is the point of it. Congress has mandated that the FAA

approve commercially available trace-detection instruments, using a suitable test protocol. There

are presently six manufacturers of trace-detection instruments-namely, Barringer, Orion, Graseby,

Ion Track, Scintrex, and Thermedics. In order to be approved, it was first necessary to develop the

instruments. The FAA funded development of many of these instruments through grants and

contracts. It took nearly 10 years to develop trace-detection instruments. There are a number of

them currently available now, and they are purchased by other countries and used in many airports

around the world. The approval process in this country was a different story entirely because

extensive testing procedures for approval for use were mandated. A problem was that the FAA did

not have an accurate and repeatable standard to fairly assess the limits of detection or consistency

17
of detection. Many attempts had been made to produce a “standard” fingerprint, but it has been

found that there is quite a bit of variation in the amount of explosive from one fingerprint to another.

With my research group, I have developed a technique to accurately deposit nanogram amounts of

plastic explosives. The deposited material has the same characteristic-coated crystals that exist in

the bulk material. This procedure is described in the attached excerpt of a book chapter “Detection

of Hidden Explosives.“*

The tragedy of PAN AM 103 led to the start of this program approximately 10 years ago.

Subsequently, the Aviation Security Laboratory was built in New Jersey and this whole program

became active. Early on, the FAA went with thermal neutron analysis (TNA). The TNA instrument

is a bulky and heavy machine, which costs millions of dollars. The TNA instrument gave too many

false positives. Six of them were built, but none of them are in use at this time. For some years, the

development and evaluation of trace-explosives-detection technologies were done by the

manufacturers. The FAA has now reached the point where it has improved its testing procedures

for x-ray and other bulk-detection instruments, as well as trace instruments. It is now possible to

give feedback to the manufacturers, but it has taken a while to get to this stage.

As far as trace detection is concerned, there are two ways to go at it. You can sample either

particulate material or vapors; particulates are the solid particles on the outer surface of luggage left

by dust, lint, or inadvertent contamination. Another approach is to detect trace amounts of vapors

from volatilized explosives. For detection, the vapors need to emanate from a bulk explosive either

hidden inside the suitcase or deposited near the inner or outer surface. In this way, a vacuum inlet

to a detector could suck up the vapors. when the trace-work first started out some years ago,

analysis of vapors was the approach everyone was taking. The program was even called “Vapor

Detection” because everyone assumed that there was a high enough vapor pressure from the

explosives that could be detected. As time went by, workers in the field delved more deeply into this

research, and it turned out that the military explosives, particularly the ones that are available around

* Beveridge, Alexander (editor). “Detection of Hidden Explosives.” Forensic Investigation of Explosions, chap. 3,

ISBN No. 0748405658, Taylor and.Francis Publisher, London, 1998. This excerpt appears as section 6 of this report.

18
the world, have extremely low vapor pressures. So, nothing detectable would come through the

suitcase wall. Emphasis then turned to detecting particles on the surfaces of luggage.

If a person has his/her outer clothing contaminated (not just the suitcase), there is a possibility

that, in the normal evolution of body heat, lint will be carried off as tiny dust particles from the

clothing. You have to envision a person embedded in a cloud of particles all the time. You cannot

see the particles or the plume, but it is possible to take a Schlieren photograph of the heat plume.

In that case, you see heat plumes emanating all over a person with a much greater volume than you

might expect. If particles are swept off your clothing, such as lint and skin flakes, and, if those are

piggybacking little tiny pieces of explosives, then these particles present a detection capability. It

may be possible to have a person walk through a portal, just like you walk through a gun-detection

portal now, and collect those particles by inducing an air flow over the body, or by having some kind

of suction tubes pass over your clothing. Then you collect this particulate on a filter and have

concentrators that absorb and concentrate the particles. Heat desorption can release the explosives

into an air stream and into an analyzer. It will soon be possible to use these techniques in a walkthrough portal to analyze explosives on a person’s clothing. You would be amazed at how millions

of dollars have been spent on the development of a suitable portal.

There are now several working models, and a commercial version is getting close. The basic

elements of a portal walk-through trace-detection instrument consist of four components: (1) the

sampler, (2) the concentrator, (3) the discriminator, and (4) the detector. The detector is usually just

an ordinary ion mobility spectrometer or a chemiluminescent detector. The Thermedics EGIS

system uses the latter and all of the rest use the former. Scintrex uses an electrochemical detector.

The discriminator can be a gas chromatograph (EGIS and Orion) or a membrane (Ion Track). A

concentrator is usually a filter. The sampler is one of the most difficult parts of the analysis system;

it must be designed for high efficiency and without inherent interferences. Typical materials used

to wipe the surface are cotton cloth or gloves, synthetic fiber cloth, wire-mesh screen, or paper. The

point is to remove a very sticky material that is present in trace amounts at unknown locations off

a variety of surfaces. If you wipe too little, the contaminated spot may be missed; if you wipe too

19
much, you can easily relocate the sample onto uncontaminated areas of the suitcase, since the sample

transfer is a two-way process.

The two approaches being taken are: (1) to simply vacuum the surface and hope that the particle

explosives come off, possibly piggybacked onto dust, and (2) to take a cloth or paper patch and wipe

the surface. Incredibly, that is the degree of sophistication that exists to this day for sampling trace

amounts of explosives. That is as far as it has advanced. (Thousands of years ago, people were

polishing statuary in ancient Egypt during the time of the Pharaohs. And how did they do it? They

probably wiped it down with a cotton cloth or papyrus paper. That is as far as we have gone!)

Surprisingly, it is not easy to come up with more sophisticated methods because the sampler has to

be low cost, efficient, water resistant, simple to use, and absolutely safe; in addition, it cannot bleed

contaminants from its own matrix. So, it needs to be something clever. You cannot use solvents or

compressed gases, and you cannot use soaps because they will interfere with the analysis. As

improved samplers are developed, it is certain that they will be adopted.

Sampling by collecting particles in the air stream or by wiping the surface also picks up other

materials. For example, if a person has some material, perfume, mothballs, dry-cleaning fluid, you

name it, the question comes, “Will those compounds be analyzed and be detected as false positives

or false negatives?” So, to approve the machine, you have to determine interferents experimentally.

The FAA has a big project going on now to determine interferences, and it is almost complete. So

far, the results indicate that, under approximately realistic conditions, there is little or no

false-positive or false-negative interferences from common substances.

20
.

6. “Detection of Hidden Explosives”

Frank T. Fox

Federal Aviation Administration Technology Center

The preparation of the type of bomb that involves the secretion of explosives into the cavities

of a portable item (such as a suitcase, laptop computer, radio boom box, or telephone) very

frequently leads to inadvertent contamination of the outer surfaces. This contamination can result

from the activity of the original bomb maker or intermediaries in the process of delivery to the final

destination. Consideration given by aviation security has been focused, to a large degree, on

detection of hidden-explosive devices in portable items, as these are most easily placed onto the

aircraft or in other locations. In addition to surface contamination, explosives vapors can be

detected. Many explosives have sufficient volatility inherent to the primary explosive or a

component of the mixture comprising the explosive to allow detection. This process is enhanced

by the use of taggants [l].

Over the past approximately 10 years, a variety of commercial instruments has become available

for the detection of trace amounts of explosives. These devices have a spectrum of capabilities,

features, and prices. Sampling time of 5-15 s is a feature that is common to virtually all of the

instruments. Total analysis time is approximately 5-20 s for most of the instruments. This speed

is crucial to keep up with passenger flow in a busy airport concourse, for example. The detectors

in most of the instruments consist of enhanced versions of classical techniques. They are robust and

highly sensitive to extremely low levels of explosive. One of the reasons for the high sensitivity is

the low efficiency of wipe or suction sampling to remove plastic and other particulate explosives

from surfaces. Eventually, a dramatic improvement in this area may lead to less-sensitive detector

requirements and subsequent lower instrument costs. All of the instruments are designed to be

portable, by use of built-in wheels for the heavier instruments. Nearly all require a 110-V alternating

current (AC) outlet.

21
Detection of trace contamination on the surfaces involves four steps: (1) sampling,

(2) concentration, (3) analysis, and (4) discrimination The fast step in the detection process,

sampling, is the transfer to the preconcentrator of the trace analyte from the suspect object

presumably containing a bomb. The analyte may be particulate matter from a wipe or vapor

emanating from the object. Development of efficient sampling methods has been elusive. The

sampling technique typically consists of the operator simply wiping the test object with a cotton

glove, followed by vacuuming the glove to collect particles from it onto a collector or

preconcentrator. This is often a metal or fiber filter that is subsequently heated to desorb the

explosives into the analyzer. Sampling in this way typically has removal effkiencies from less than

1% up to as much as 10% of the plastic explosives contaminating the surface.

Research efforts to develop an improved sampling method have previously been limited to

deposits made from solutions of explosives dissolved in organic solvents or particles of siliceous

material that has been coated with explosive crystals by evaporation of a solution 12, 31.

Unfortunately, dissolution of plastic explosives followed by recrystallization yields a material

without the particle size or adhesion character of the original plastic. Use of actual fingerprints as

a test standard also presents diffkuhies because of the inconsistent nature of the deposit due to

differences in fmger size, pressure used, nature of the bulk explosive touched, and many other factors

[4,51.

Many attempts have been made to achieve a consistent deposit that has the characteristics of the

original material, especially for plastic explosives. Attempts to standardize muhiple imprints or

determine the surface contamination upon bomb manufacture under controlled conditions have been

tried [6]. The requirement, when using multiple fingerprints to prepare a standard amount of

explosive on a surface, is to extract and quantitate alternate prints. These data allow one to assume

that an “average” unassayed alternate print in the imprint series prepared in the same experiment has

a known amount deposited. Unfortunately, the error in the amount of explosive analyzed from print

to print is greater than the amount of explosive removed by the wipe method of sampling.

22
This disparity is due to the very small amount of explosive removed using standard/inefficient

wipe sampling techniques. An “average” amount of explosive per fingerprint calculated in this way

has too much variation to allow determination of sampling efficiency or to use this method to prepare

explosive standards. In addition to fingerprints not being reproducible’ another drawback is the

inability to set the amount deposited at will [7]+

This laboratory has recently developed a method for the consistent accurate production of

deposits of plastic explosives that have the same sampling characteristics-namely, “stickiness” as

the bulk explosive. The method works with the major commercial and military plastics. The

technique takes advantage of the fact that plastic explosives are manufactured as crystals of

explosive that are stabilized and protected by plastic coating, such as polyisbutylene or

styrene-butadiene. The result is encapsulated crystals that are made into the final product by pressing

the particles together into bricks or sheets’ along with a little oil or other agent to aid flexing.

Normal production of a fingerprint occurs by touching the bulk explosive and removing some of the

coated particles mechanically from the bulk because they stick to the skin. The particles are then

transferred to the next object or surface touched.

Our method takes advantage of the encapsulation resulting from the manufacturing process. The

adhesion forces between the coated particle in the compressed bulk are overcome mechanically. A

small piece of plastic explosive is agitated with water, causing particles of the coated explosive to

be simply shaken loose from the bulk and suspended in the water. By analogy, touching the bulk

causes mechanical removal of particles due to pressure and adhesion. The explosive is not dissolved

by the water-agitation process, but particles from the bulk are merely suspended by gentle

mechanical means. Variations on the agitation include stirring, sonication, and shaking. Once the

explosive in its normal form of coated crystals is put into suspension, the actual amount is

determined by usual methods including high-performance liquid chromatography (HPLC), gas

chromatography (CC), and mass spectroscopy (MS). Dilution is then made to an appropriate level.

In order to prepare a deposit consisting of the desired amount of explosive, an aliquot of the

diluted and quantified suspension is pipetted onto the surface of a test object and the water is

23
evaporated. The dried residue consists of plastic-coated explosive crystals in the original state, as

would occur with a natural fingerprint or smear The amount of dried residue deposited can easily

be varied by simply controlling the degree of dilution of the original suspension of explosive

particles. Reproducible deposits have been prepared, ranging from high micrograms to low

nanograms. The concentration of the deposit is verified by back extraction of a test surface and

quantitation of the extract. Particle size of each preparation is verified by scanning electron

microscopy (SEM). Consistent particle size range in the deposit was the goal of this laboratory in

the preparation of a standard. Comparability of the particle size range of the deposit to actual

fingerprints was also considered important An unusable variation in the amount deposited would

be large if large particles appeared. Consistent analysis with relative standard deviation @SD) of

less than 10% was obtained on back extraction and analysis of the deposits. In addition, a close

comparison of particle size between a fingerprint and a deposit of the suspension of the explosive

(C4) was observed [8,9].

The controlled deposition of several plastic explosives on some typical carry-on electronic items

was used on commercial instruments at the 1995 International Civil Aviation Organization (ICAO)

meeting of the Ad Hoc Group of Specialists on the Detection of Explosives, held at the Federal

Aviation Administration (FAA) Technology Center in Atlantic City, NJ. Six commercial

instruments were tested over several days using the standard sampling technique proposed by the

trace-detection instrument manufacturer. Participants in the exercise included manufacturer

representatives and an international group of persons experienced in trace-explosives detection [ lo].

.

The older technique of simply dissolving the plastic explosive bulk material in an organic solvent

for deposit by recrystallization on evaporation removes the plastic coating and inevitably changes

the sampling characteristics observed. Coating inert material with a sohnion of plastic explosive and

evaporating the solvent has the same drawback [l I]*

The deposition technique provides a means to produce an accurate standard-so far limited to

plastic explosives-which can be used for testing and evaluating trace-detection instrument systems,

including the efficiency of the sampler and the effect of interferents. Jn addition, deposits onto fabric

24
may provide a standard for testing walk-through portals designed to detect traces of explosives on

passenger clothing. Several portals are in the developmental stage. Explosives removal techniques

in these portals are dependent on air flow onto a collector or intimate sampling by means of suction

of the surface of passengers’ clothing as they pass along tubes with suction holes along their length.

In both types of collection, the sample ultimately ends up on a prefilter and is desorbed and analyzed

by analytical means similar or identical to those used in commercial stand-alone trace-detection

instruments. Some advances in portal development were presented at an FAA-sponsored portal

conference in January 1995 [ 121 Q

The testing of checked-in and carry-on luggage, as well as passenger garments and hands, is an

obvious immediate application of trace-based hidden-explosives detection; however, there are other

applications. Trace-detection-based technologies are under development at several centers for cargo

inspection. Ultimately, the application may be adapted to postal needs and to other transportation

modalities.

Trace detection is, of course, not limited to explosives hidden in bombs, it may be used to detect

a wide variety of other chemicals used in hidden devices. Such hidden devices have been used

recently in Japan for toxic gases. There is a long list of flammable and toxic substances that may be

hidden and yet detected by trace methods, and there is a plethora of available literature on this

subject. Interested parties could begin by contacting either Delta Press, El Dorado, AZ , or Paladin

Press, Boulder, CO.

References

1. Danylewych-May, L., and C. Cumming. “Explosive and Taggant Detection With Ionscan.”

Advances in Analysis and Detection of Explosives: Proceedings of the Fourth International

Symposium on Analysis and Detection OfExplosives, held in Jerusalem, Israel, 7-10 September

1992, pp. 385-401, J. Yinon (editor), Kluewer Academic Publishers, Dordecht, Holland, 1993.

25
2.

3.

4.

5.

6.

7.

8.

9.

Neudofl, P., M. McCooeye, and L. Elias. “Testing Protocol for Surface Sampling Techniques.”

Advances in Analysis and Detection of Explosives: Proceedings of the Fourth International

Symposium on Analysis and Detection of Explosives, held in Jerusalem, Israel, 7-10 September

1992, pp. 373-384, J. Yinon (editor), Kluewer Academic Publishers, Dordecht, Holland, 1993.

Rodacy, P. “The Minimum Detection Limits of RDX and TNT Deposited on Various Surfaces

as Determined by Ion Mobility Spectroscopy.” SAND-92-0229, NTIS Order No. DE 93018521,

1993.

Langford, M. “Development of a Print Deposition Series Model.” Proceedings of the Third

Workshop of the International CivilAviation Organization (ICAO) AdHoc Group of Specialists

on the Detection of Explosives, Federal Aviation Administration, William J. Hughes Technical

Center, Aviation Security Laboratory, Atlantic City International Airport, NJ, 16-19 October

1995.

Cheng, C., T. E. Kirkbride, D. N. Batchelder, D. J. Lacet, and T. G. Sheldon. “In Situ Detection

of Trace Explosives by Raman Spectroscopy.” Journal of Forensic Sciences, vol. 40, no. 1,

pp. 31-37, JFSCA, January 1995.

Greshman, G., and S. Halowell. “Surface Contamination of Electronic Threat Devices Prepared

With Plastic Explosives.” Proceedings of the Third Workshop of the International Civil

Aviation Organization (ICAO) Ad Hoc Group of Specialists on the Detection of Explosives,

Federal Aviation Administration, William J. Hughes Technical Center, Aviation Security

Laboratory, Atlantic City International Airport, NJ, 16-19 October 1995.

Fox, F., S. Sisk, R. DiBartolo, D. Green, and J. Miller. “Preparation and Characterization of

Plastic Explosives Suspensions as Analogs of Fingerprint Derived Contaminants for Use in

Certification of Trace Explosives Detection Systems.” Proceedings of the Third Workshop of

the International Civil Aviation Organization (ICAO) Ad Hoc Group of Specialists on the

Detection ofExplosives, Federal Aviation Administration, William J. Hughes Technical Center,

Aviation Security Laboratory, Atlantic City International Airport, NJ, 16-19 October 1995.

Fox, F. “Evaluation Protocol for Trace Explosives Detection Instruments.” Proceedings of the

Fifth International Symposium on Analysis and Detection of Explosives (Available from

C. Midkiff, Bureau of Alcohol, Tobacco, and Firearms, National Laboratory Center, 1401

Research Blvd., Rockville MD, 20850), December 1995.

Fox, F., D. Green, S. Sisk,“R. DiBartolo, and J. Miller. “Particle Characterization: Trace

Explosives Solid Samples.” Proceedings of the Fifth International Symposium on Analysis and

Detection ofExplosives (Available from C. Midkiff, Bureau of Alcohol, Tobacco, and Firearms,

National Laboratory Center, 1401 Research Blvd., Rockville MD, 20850), December 1995.

26
10. Fox, F., and P. Jankowski. Personal communication (data reduction). Proceedings of the Third

Workshop of the International CivilAviation Organization (ICAO) Ad Hoc Group of Specialists

on the Detection of Explosives, Federal Aviation Administration, William J. Hughes Technical

Center, Aviation Security Laboratory, Atlantic City International Airport, NJ, 16-19 October

1995.

11. Elias, L. “NRC Procedures for Laboratory Evaluation of Trace Explosive Detectors.”

Proceedings of the Third Workshop of the International Civil Aviation Organization (ICAO) Ad

Hoc Group of Specialists on the Detection of Explosives, Federal Aviation Administration,

William J. Hughes Technical Center, Aviation Security Laboratory, Atlantic City International

Airport, NJ, 16-19 October 1995.

12. Jankowski, P. (editor). “Trace Portal Detection Portal Workshop Technical Note.”

DOT/FAA/CT-TN95/05 Revision A, Federal Aviation Administration, William J. Hughes

Technical Center, Aviation Security Laboratory, Atlantic City International Airport, NJ,

January 1995.

27
7, “The Explosive Standard Analytical Reference Material

(SARM) Program”*

S. M. Caulder, H. E. Turner, and W. F. Bryant

Naval Surface Warfare Center

Indian Head Division

101 Strauss Avenue

Indian Head, MD 20640-5035

Abstract

The U.S. Army Toxic and Hazardous Materials Agency (USATHAMA) has entered into an

agreement with the Naval Surface Warfare Center (NSWC), Indian Head (IH) Division, MD, for

them to maintain the USATHAMA explosive Standard Analytical Reference Material (SARM)

repository, consisting of 19 explosives compounds and 6 degradation products. The explosives and

their degradation products are purified and then analyzed using various instrumental techniques such

as high-performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR),

inductively coupled plasma (ICP) spectroscopy, x-ray diffraction (XRD), atomic absorption (AA),

and gel-permeation chromatography (GPC). The standards are packaged in blasting-cap containers

and Federal Expressed, only upon USATHAMA approval, to designated laboratories.

The analysis of the following compounds are discussed: trinitrobenzaldehyde, nitrocellulose

(NC), nitroglycerine (NG) (acetone and two nitrodiphenylamine [ZNDPA]), pentaerythritol

tetranitrate (PETN), normal lead styphnate, and the structural isomers 2-amino-4,6-dinitrotoluene

(DNT) and 4-amino-2,6-DNT.

* Financial support of this program is provided by the U.S. Amy Toxic and Hazardous Materiel Agency

(USATHAMA), Contract no. CC789000.

28
Introduction

The purpose of the explosive SARM repository is to furnish the U.S. Army Toxic and Hazardous

Materials Agency (USATHAMA) designated laboratories with reference standards that have a

minimum 99.0% purity. The repository presently consists of 19 explosive compounds and 6

degradation products. Four more explosive degradation products are presently being synthesized.

The compounds in the repository are shown in Table 7-l e The compounds presently being

synthesized are shown in Table 7-2.

Table 7-1. Compounds in the Standard Analytical Reference Materid Repository

Compounds

Cyclotetramethy~enetetranitramme (HMX) Cyclotrimethylenetrianitramine (EDX)

Nitroglycerine (NG) Nitrocellulose (NC)

2,4,6-Trinitrotoluene (TNT) I I ,3,5-Trinitrobenzene (TNB)

2,4,6-Trinitrobenzaldehyde I 4Amino-2,6-Dinitrotoluene (DNT)

2-Amino-4,6-DNT

2,6-DNT

2,&DNT

2,4-Dinitroaniline

Pentaerythritol Tetranitrate (PETN)

Tetracene

Tetryl

Nitroguanidine (NQ)

Picric Acid I Normal Lead Styphnate

I 2-Amino4Nitrotoluene

I 3,4DNT

I 2,4-Diaminotoluene

Basic Lead Styphnate

&Amino-2-Nitrotoluene

2,4-Diamino-6-Nitrotoluene

2.6-Diaminotoluene

29
Table 7-2. Compounds Being Synthesized

Compounds

2,2’,6,6’-Tetranitro-4,4’-Azoxytoluene

4,4’,6,6’-Tetranitro-2,2’-Azoxytoluene

2’,4,6,6’-Tetranitro-2,4’-Azoxytoluene

Hexabydro-I-Nitroso-3,5-Dir&o-1,3,5-Triazine

Procedure for Obtaining Stand&d Reference Material

In order to receive the explosive reference material, the government organization requesting the

compounds must submit a letter along with the request that the reference explosives are required for

government-sponsored contractual laboratory work This letter must be signed by the government

contracting offrcer and include the following information:

(1) the name of the laboratory with its full address (no post office box) and a point of contact

to receive the standards;

(2) the telephone and facsimile numbers for the person receiving the samples; .

(3) the government contract number under which the samples are being ordered;

(4) the list of standards requested; and

(5) tbe name, address, and telephone number of tbe government contracting officer associated

with the requested material.

A statement verifying the need and use for the compounds must also be included prior to

forwarding the material request form to the Naval Surface Warfare Center (NSWC), Indian Head

30
(IH) Division, MD. All of the aforementioned information in its entirely, must be sent to:

Commander, U.S. Army Toxic and Hazardous Materials Agency, ATTN: CETHA-TS-C

(Ms. D. Bader), Aberdeen Proving Ground, MD 21010-5401.

USATHAMA forwards an approved request to NSWC-M, at which time it is filled and shipped.

A maximum of 200 mg of each ordered sample is packed in polyethylene bottles. The bottles are

then packaged in a blasting-cap container approved by the Department of Transportation for shipping

explosive materials. Along with the containers, a package containing the material safety data sheets

(MSDS) and the experimental data sheets for each compound is enclosed. The experimental data

sheet consists of high-performance liquid chromatography (HPLC) curves and/or nuclear magnetic

resonance (NMR) spectra for each compound. The entire package is then Federal Expressed to the

designated laboratory. The entire process takes about 3 weeks once NSWC-Mreceives the approved

request. The laboratories are required to ship the blasting-cap containers back to NSWC-M within

2 weeks.

Experimental Evaluation of Several Reference Compounds

Each compound is analyzed by various analytical techniques and purified by recrystallization,

if found necessary. The analysis results of several of the reference compounds are presented.

2-Amino-4,6-DNT and 4-Amino-2,6-DkT. In the case of structural isomers such as

2-amino-4,6-DNT and 6amino-2,6-DNT, NMR was used to identify each isomer and to ascertain

whether or not it was a pure compound. HPLC was also run on these compounds, as well as on a

mixture of them. The NMR curves for these compounds are shown in Figures 7- 1 and 7-2. As can

be seen, the Cl3 NMR differentiates the two isomers and shows that each of them is a pure

compound. Figure 7-3 shows the NMR curve for a mixture of the two structural isomers. The

mixture contained 2% 2-amino-4,6-DNT. The HPLC traces of the two compounds are shown in

Figures 7-4 and 7-5. The HPLC curves of each of the compounds give ahnost identical retention

times. However, the peak heights are different. HPLC of a mixture of the two compounds

(Figure 7-5) produces one peak. A mixture of the two compounds was not resolved into two peaks.

31
11l1~lIIl~llll~llll llllyll

8 0 6b

Figure 7-1. C?’ NMR Spectrum 2-amine-4,6-dinitrotoluene.
E-0

T

33
I i

34
.

HPLC CHROMATOGRAhU OF SARM STANDARDS

HPLC: WATERS MODEL202

COLUMNS: ALTECH Cl8 RP (5 MICRON)

SOLVENT SYS: fSO/3SA;rN”,: ,“‘O

FLOWRATE: .

DETECTOR: W(2S\tfANO;7ERS)

~;S~NSITO~:

5’0 MKROLlTERS

DATA ACQUI:

I-eMPERATURe

;;9;3iy lN’IEGRATOR

2+MNO-4,6-DNT

Pi . . s:

E:

CI

4-AMINO-2,6-DNT

: l Q

ii+! 8

a%. z

Figure 7-4. HPLC Pure 2-amine-4,6-DNT and 4-amino-2,6-DNT.
@3+*30000 * I =uoKluj 1rlY

91LE8E =w3Yw lW101

J46&-I’bCII LBI’ 8d VILESE L92’P

Xf3YW HUlIll 3dAl W3YB 111

zw311

0

dol.5 3l8w13lI1

i=

2

. .

:m

ew3@eee'r=Yo13w~ -MY

!x&1t =w3YW lW101

68088'001 66%' Bd Q&!Ib sa*b

::W3Ytr HlOIR 3dAl W3YW LY

ZERIW

YYWS

dOlS 318'jmIl

36
However one can analyze the peak heights and gain some insight as to whether one of the structural

isomers is contaminated with the other structural isomers,

Trinitrobenzaldehyde. Trinitrobenzaldehyde was analyzed using both proton and Cl3 NMR

and HPLC. The NMR curves are shown in Figures 7-6 and 7-7. The HPLC curve is shown in

Figure 7-8. In Figure 7-6, the Cl3 NMR curve is labeled to show the seven different carbon atoms.

In Figure 7-7, the proton NMR shows the two different hydrogens in the trinitrobenzaldehyde

compound.

Nitrocellulose (NC). The repository has two NC standards, one containing 12.6% N and the

other 13.15% N. Unless specified, the 12.6% standard is sent. Along with the standard, an HPLC

preparation sheet is enclosed, which outlines the precautions that must be taken when preparing

standards in acetonitrile for HPLC analysis. They are as follows: (1) the concentration of NC in

acetonitrile should be between 10 and 15 mg/rnl of acetonitrile, (2) a greater concentration of NC

may not go into solution, and (3) it is recommended that the solution be allowed to stand for 48 hr

to allow all the NC to dissolve. A representative HPLC curve is shown in Figure 7-9.

NG. The NG sample that is shipped is prepared in the manner described next. One gram of neat

NG is weighed into a IO-ml volumetric flask equipped with a stopper. Ten milligrams of two

nitrodiphenylamine (2-NDPA) are added to the lo-ml volumetric flask to stabilize the.NG. Five

milliliters of HPLC grade acetone are added using a 5-ml pipet. Each milliliter of acetone contains

200 mg of NG and 2.0 mg of 2-NDPA. One milliliter of each solution is placed into an amber vial

and sealed for shipment. The HPLC curves for NG, 2-NDPA, and acetone are shown in Figure 7-10.

BETN. As received in our laboratory, PETN is coated with polyethylene. The coating is

removed using a soxhlet extractor. The solvent is reagent grade acetone. The material is refluxed

for 16 hr at 56” C. After 16 hr, the acetone extract is concentrated on a rotoevaporator to dryness.

The white solid (PETN) is removed from the flask with a plastic spatula onto a drying dish. The

solid is dried in an oven at 50” C until a constant weight is obtained.

37
(187.1)

7

100 140

Illl~lllllllll~llll IIII~III’ IIIl~llll llll~llll llll~‘lil II

100 80 6 0 4 0 2 0 PPM 0

Figure 7-6. Cl3 NMR - 2,4,6-Triuitrobenzaldehyde.
B

9.30 PPfl

s.\r*. 4

A

11(1,,,

,,,,,,,,,

,,,,,,,,‘, ,,,, ,,,, ,, 10 I”“’ ’ “1 1 ‘1 ‘1 r

0 6 4 2 PPM

’

0

Figure 7-7. Proton NMR 2,4,6-Trinitrobenzaidehyde.
SCMVCINVLS NXVS d0 SNVtl~OLVINOlIH3 3?dH

:alnaeladtual

:ynbq saea

:IOA a=luI

:karATarsuas

:loasaaaa

:aaq PLOIJ

:sds auaAlog

:usnIo3

:31dll
. 1 .

HPLC CHROMATOGRAMS OF SARM STANDARDS

HPLC: WATERS MODEL 202

COLUMNS:

SOLVENTSYS:

ALLTECH Cl 8 RF’ (5 MICRON)

Kizc!i+F

65135 ACNIH20

l.OMLPE.RMlN

SENSITIVITY:

UV(254NANOMEmRS)

0.1 AUFS

LNJECTVOL

DATAACQUI:

2.0, 3.0, 19 MICROLITERS

IEhlPEtRATlJRE

;;WU'WU'~RATOR

NITROCELLULOSE

Figure 7-9. HPLC - 12.6% Nitrocellulose.
Lf0’6

i

tI:%j:II 1661 ‘b AON e1 t HII8 *

ti16S’S P91’ 8d LKZS ‘?14*t

,,EflP’ I 692’ dt. 81;t 2Lb ‘2

‘.LKli3 uarn 3du 1~3131i iY

Zuf3I3H

.

r;~:tE::; 1661 ‘t-2 130 6il t NM ’

42
This PETN is then triple-recrystallized from acetone and throughly dried to generate an analytical

standard. The HPLC curve of PETN is shown in Figure 7-l 1.

Normal Lead Styphnate. The lead styphnate is analyzed using HPLC and atomic absorption

(AA). The AA gives the percent of lead. The lead styphnate is dissolved in a 10% ammonium

acetate solution and filtered if necessary. A 2-ml aliquot of a solution containing 5 mg of lead

styphnate in the ammonium acetate solution is placed in a lo-ml volumetric flask and brought up

to volume with acetonitrile. The sample is filtered if necessary and analyzed by HPLC. The HPLC

curve is shown in Figure 7-12.

Discussion and Conclusions

The previous analysis of some representative Standard Analytical Reference Material (SARM)

Program compounds illustrates how our laboratory prepares and qualifies explosive samples for the

SARM repository. The compounds in the repository are reanalyzed every 3 months, except for the

NG, which is freshly prepared each time a request is received. Over the period September 1991 to

September 1992, more than 375 compounds were shipped throughout the United States.

43
HPLC CHROMATOGRAMS OF SARM STANDARDS

HPLC:

COLUhlNS:

SOLVENT SYS:

WATERS MODEL 202

ALLTECH Ct 8 RP (5 MICRON)

65 I35 ACN 1 H?O

FLOW RATE:

DETECTOR:

SENSITIVITY:

WJECT VOL

DATA ACQUI:

TEMPERATUREZ

1 .O ML PER Ml;

UV (2.54 NANOMETERS)

0.1 AUFS

2.0, 3.0, $Q MICROLITERS

3393 HP INTEGRATOR

28 DEGREES C PETN

Figure 7-11. HPLC - PETN
00+30e0e’1=u013u3 lI1y

E986Z q lH313H Xl01

80088’66 i +92’

::lHNW 8d C9862 SE+-2 HlaIR 3dAl lH313H I&

6Z:i::CI 2661 'S Hi%- 8 8 HllY *

88+38888-I=uoimj m

6LIZC =lH313H lUlO1

81&89't *

kX91C'Sb Es

88 L0SI zE*'2

w!q3ti L18'1

HlOIR :A1 :zH 18

ZlH3I3H

dOlS 378tU3HIl

45
8. “Problems in Characterizing Smokeless Gunpowders”

Allan N. Walters

Postal Inspection Service

I am Allan Walters, a forensic chemist for the U.S. Postal Inspection Service Forensic

Laboratory. We are located near Dulles Airport. Most of the work that I do involves the analysis

of explosives from improvised explosive devices, pipe bombs, and other types of devices that are

placed in or around postal property, mailboxes, or sent through the mail. Probably 99% of the types

of explosives that we see are low explosives, such as black powder; smokeless powder; Pyrodex;

flash powder; and, once in a while, improvised mixtures or pyrotechnic powders that have been

emptied from commercial fireworks or Class C fireworks, which you can buy at roadside stands.

Of particular interest to us is the use of smokeless powders in these improvised devices. We get

quite a bit of that, and it is easy material to obtain. Most people wlro make these sorts of bombs are

somewhat familiar with it. So, basically, we try to identify these. Our inspectors ask, “What was

the explosive material? Can you give me any leads based on the identity of the powder?” If there

are intact particles, we try to identify those. We try to give the inspectors as much information as

we can because they may conduct a search and obtain samples of powder for later comparison.

I am going to borrow some materials that were supplied by Hercules Corporation and were used

by Steve Burmeister in his course at the Federal Bureau of Investigation (FBI) Academy on

explosive residue analysis (Table 8-l). What we see are single- and double-base powders. I have

never seen a triple-base powder, but, maybe someday, I will. Basically, we go about this sort of

identification based on size and shape and whether it is extruded or ball powder and so forth. This

is all pretty basic stuff, and wlrat we see are the shot sRel1, rifle, and pistol powders. Some come

from reloading components, and others from ammunition that has been emptied. Shot shells contain

a little bit more powder than most rifle cartridges; so, they seem to be popular with bombers.

Propellants that are supplied to domestic manufacturers for factory-loaded ammunition are more

difficult to identify than the canister powders used by reloaders. There are over 100 types, and,

occasionally, if you get lucky, you might see a blue or red dot Hercules or something with an

46
.

. .

Table 8-l. Physical and Chemical Data of Hercules Canister Powders

Powder Type

Impetus (kft-lb/lb)

Heat of explosion at

25” C (cal/g)b

Relative quickness (%)

TV (K)

Molecular weight of

Gas (mopg)

Specific heat at constant

volume (Cal/g-“C)

Ratio of specific heats’

Covolume (cm3/g)

Solid density (gkm3)

Bullseye Red Green Unique Herco Blue Hercules Reloder Reloder Reloder Reloder Reloder

Dot Dot Dot 2400 7 12 15 19 22

384.5 366.7 368.2 367.7 372.2 364.5 359.8 338.4 346.0 345.3 353.1 344.0

1,232 1,084 1,035 1,088 1,065 1,047 1,008 934 914 952 951 939

100.0 94.1 77.9 61.6 56.1 37.8 27.0 19.4 14.4 13.7 11.3 11.1

3,740 3,433 3,391 3,379 3,486 3,312 3,214 2,859 3,001 3,015 3,113 2,964

27.1 26.0 25.6 25.6 26.1 25.3 24.9 23.5 24.1 24.3 24.5 24.0

Nominal Physical Dimensions

Gas Composition

(mole %)

’ Data obtained from theoretical calculations of nominal compositions.

b Data obtained from experimental measurements of reference lot.

’ Data obtained from close bomb testing of reference lot at 0.170 g/cm3 loading density.
identifier. Using powder from ammunition is pretty tedious. Imagine 300 little 0.22-cal. bullets to

make a pipe bomb, especially if you are using 0.22 “shorts!”

To understand the way these powders are manufactured, consider this diagram (Figure 8-l) of

the manufacturing process provided by Hercules Alliant. They make smokeless powder and then

add nitroglycerin (NG) and a couple of additives, which I would like to talk a little bit more about

later. One of the interesting things is, down here, we get to the extrusion part that provides our shape

and size and so forth. Then it is coated and dried. The particles are not all the same size. The lots

can be as large as 10,000 lb, which are then sorted into sublots. There is a room in a warehouse full

of barrels that are then blended for the ballistic characteristics rather than chemical composition. It

is sort of like brand identification of gasoline, which is blended for octane rating. Powders are

blended to optimize pressure, probably burn time and maybe accuracy and velocity of certain loads,

and packaged up and sent out. I have noticed that there have been a lot of differences among the

same ABC brand of powder from one lot to another. From 1 year to another, the differences could

be shape, color, and so forth. Some of the basic analyses (i.e., looking for the methylethyl centralites

and the diphenylamine, nitrotoluenes, and NG) have been well documented. Some of the other

classes of the ingredients that I am wondering about are the stabilizers, plasticizers, flash

suppressants, deterrents, and dyes. Is there any way we could do an analysis of these to further

characterize the powder with a little bit more accuracy?

Previously, Stan Caulder (see section 7) alluded to the nitrogen content of the various

nitrocelluloses (NC), NG, and nitroguanidine (NQ). Since these materials react over time, there is

a problem of not knowing the storage history of a propellant that we find in our bomb. This bomb

maker may have had the powder around in his/her house or shed for 20 years before it is used in a

bomb. With respect to the plasticizers, deterrents, and so forth, I do not know to what extent some

of these are used. The deterrents control the burning rate of the powder. Again, they are present in

small amounts, but, if you have intact particles, it might be a way to further analyze the powder.

There are two types that Hercules uses: (1) the penetrating type, which penetrates the actual grain,

and (2) the inhibiting type, which is a coating. They have dyes added for identification, opacifiers,

and graphite glazing. We have a relatively modest collection of smokeless powders in our

48
Water Wet Dehydration Pressure & Block

Nitrocelluiose - Alcohol Injection * Press

/

t

Nitroglycerin Molecular Wt. &

Acetone/Alcohol

Additives

Storage

I

Sort into I Shaker e Continuous Coating

Sub-Lots Screen Drvinq - Barrel ri Ballistic Test

.

Master Lot Acceptance

Blend - Testing Packaging

Figure 8-I. Schematic of Alliant Double-Base Smokeless-Propellant Manufacturing Process.

laboratory. Cincly Wallace at the Bureau of Alcohol, Tobacco, and Firearms (BATF) laboratory

maintains a large collection of commercial powders used in reloading. This chart (Table 8-2) shows

the relative burning rates of various powders. Number 1 is a real fast burner, and no. 94 is very slow.

I think by now (i.e., 1993), there are a lot more powders available at a local sporting goods store.

Mow do you determine the difference between Bullseye and 2400? Well, the NG level is 40%

in the Bullseye, but you need to do a chemical analysis, The thickness of the Bullseye is a lot less

than the 2400, but, as I said, these grain sizes are nominal; so, you could get different-sized grains

of the same powder.

49
Table 8-2. Relative Burning Rates of Various Powders

1No. Powder No. Powder No. Powder

1 R-1,Norma 33 HS-7, Hodgdon 65 IMR-4895, IMR Co.

2 N310, VihtaVuori 34 Blue Dot, Hercules 66 N135, VihtaVuori

3 Bullseye, Hercules 35 No. 9, Accurate Arms 67 IMR-4064, IhIR Co.

4 Solo 1000, Scot 36 2400, Hercules 68 Brigadier 4065, Scot

5 No. 2, Accurate Arms 37 NllO, Vihta Vuoii 69 2520, Accurate Arms

6 Red Dot, Hercules 38 R-123, Norma 70 MR-4320, IMR Co.

7 N320, VihtaVuori 39 Hl 10, Hodgdon 71 N-202, Norma

8 Royal Scot, Scot 40 296, Olin 72 N140, VihtaVuori

9 HP-38, Hodgdon 41 SR-4759, IMR Co. 73 Reloder 15, Hercules

10 231, Olin 42 N120, VihtaVuori 74 H380, Hodgdon

11 Trap 100, Hodgdon 43 5744, Accurate Arms 75 760, Olin

12 453, Scot 44 IMR-4227, IMR Co. 76 N150, VihtaVuori

13 Hi-Skor 700-X, IMR Co. 45 H4227, Hodgdon 77 H414, Hodgdon

14 452AA, Olin 46 1680, Accurate Arms 78 IMR-4350, IMR Co.

15 Green Dot, Hercules 47 680, Olin 79 H4350, Hodgdon

16 N330, VihtaVuori 48 N-200, Norma 80 N-204, Norma

17 PB,IMRCo. 49 N133, VihtaVuori 8 1 Brigadier 435 1, Scot

18 No. 5, Accurate Arms 50 Brigadier 4197, Scot 82 Reloder 19, Hercules

19 Unique, Hercules 51 H4198, Hodgdon 83 N 160 VihtaVuroi

20 SR-7625, IMR Co. 52 IMR-4198, IMR Co. 84 IMR-483 1 , lh!lR Co.

21 473AA, Olin 53 Reloder 7, Hercules 85 3100, Accurate Arms

22 HS-6, Hodgdon 54 JMR-3031, IMR co. 86 H450, Hodgdon

23 N340, VihtaVuori 55 N201, Norma 87 H483 1, Hodgdon

24 540, Olin 56 H322, Hodgdon 88 MRP,Norma

25 Herco, Hercules 57 2230, Accurate Arms 89 N165, VihtaVuori

26 SR-4756, IMR Co. 58 Brigadier 3032, Scot 90 Reloder 22, Hercules

27 Solo 1250, Scot 59 748, Olin 91 m-7828, IMR Co.

28 3N37, VihtaVuori 60 BL-C(2) Hodgdon 92 8700, Accurate Arms

29 Hi-Skor 800-X, MR Co. 61 2460, Accurate Arms 93 N170, VihtaVuroi

30 No. 7, Accurate Arms 62 H335, Hodgdon 94 H870, Hodgdon

31 Solo 1500, Scot 63 H4895, Hodgdon

32 N350, Vihta Vuori 64 Reloder 12, Hercules

ate: These listings are approximate; faster burning powders have lower numbers.

Here is another characteristic: the larger “dot” propellants (red dot, green dot, and blue dot) all

hysically look the same; the NG level is the same. If you do not have the little dots in your sample

tnd there are not a lot of dots in these powders), how do you distinguish them? Do you need to

50
distinguish them? Here are some characteristics: the diameters may be similar and you could have

some particles that fit one category, but maybe it is another type. Some were made in this country

and some were made in Sweden. Tables g-3,8-4, and 8-5 are based on some work done by Cindy

Wallace at BATB. I know she found some differences among the various lots of Hercules 2400, blue

dot, and Bullseye, based on diameter and thickness. Basically, we keep a collection of smokeless

powders that are commercially available; but, when trying to identify powder, we do measurements

of the applicable characteristics that I described in those tables. It was difficult to distinguish

between the two. Sometimes it is impossible, but we keep trying to do it because we cannot always

know or understand the future significance of the forensic comparison we do. Here is a chart on

physical data (Table 8-6). Is there anything in the physical data that could help us distinguish among

various types of powder? Down on the left side of the chart, we have a lot of other properties of the

powders.

To sum up, we have large-sized lots on some of these powders, and there is a lot of variation

among lots of the same ABC powder.

We have a case now that has two pipe bombs that are about an inch and a half or so in diameter,

6 in long, full of powder, and rendered safe by an Army Explosive Ordnance Disposal (EOD) squad.

They sorted through this debris and recovered 1 gr of tubular smokeless powder. It was a smokeless

powder used in the bomb, as they were able to eliminate the powder that was used by the EOD

people in the de-arming procedure. There were some further complications: (1) when they hit the

pipes with the de-arming cannon, both pipes detonated, (2) the dearming was done in the middle of

a road, and (3) there was a rain storm. As you can see, the environment we deal with does not

resemble a controlled laboratory environment.

I looked in my cupboard the other day and noted where the powders were manufactured. I found

powders made in the United States, Czechoslovakia, Scotland, Israel, and Australia. Since my

collection is not complete, I know there are still other countries involved. Hodgdon imports a lot

of the powders and repackages them. But, today’s Hodgdon ABC may be different in size and shape

from tomorrow’s Hodgdon ABC. Again, the powders are blended for the ballistic properties.
Table 8-3. BATF Database for Hercules 2400

Notes: d = Diameter.

SD = Standard deviation.

T = Thickness.

EC = Ethyl centralite.

DPA = Diphenylamine. X = Ingredient found in propellant

DNT = Dinitrotoluene. A = Always found.

DBP = Dibutyl phthalate. S = Sometimes found.

Table 8-4. BATF Database for Hercules Blue Dot

Manufactured

R-BLUE.lb 1 Lot BD038 1 50 1 1.336 1 0.073 1 0.246 1 0.039 1 X 1 X 1 - IXIX

Notes: d = Diameter.

SD = Standard deviation.

T = Thickness.

EC = Ethyl centralite.

’ Newer blue dot with filler in IDS.

b Older blue dot.

DPA = Diphenylamine.

DNT = Dinitrotoluene.

DBP = Dibutyl phthalate.

X = Ingredient found in propellant.

A = Always found.

S = Sometimes found.

.
L . .

Table 8-5. BATF Database for Hercules Bullseye

Inventory No. Manufactured Count Diameter Thickness 1 NG 1 DPA 1 2,4DNT DBP 1 EC

Notes: d = Diameter.

SD = Standard deviation.

T = Thickness.

EC = Ethyl centmlite.

DPA = Diphenylamine. X = Ingredient found in propellant.

DNT = Dinitrotoluene. A = Always found.

DBP = Dibutyl phthalate. S = Sometimes found.
Table 8-6. Hercules Large-Die Smokeless Powders

[

Characteristics Red Dot Green Dot

Diameter” 65 65

(thousandths of inches)

Thicknessa 3 4

(thousandths of inches)

NG Level” 20 20

(%)

Appearance of Flakes black black

lnside Diameter (ID) Grains Red Green

l-lb cans Yes Yes

1 Packaging 4-lb cans Yes Yes

5-lb cans No No

8-lb kegs Yes Yes

a This information is nominal; actual values may vary.

Unique Herco

65 65

5 7

20 20

black black

None None

Yes Yes

Yes Yes

No No

Yes Yes

Blue Dot J

65

10

20

brown/black

Blue

Yes

No

Yes

No

Sometimes, more than one kind of smokeless powder is used in a pipe bomb. I had one pipe

bomb where I found four types of powder. Does it do any good to say the dominant one is double

based? Well, if double base were present, NG could migrate to the other particles. So, how then

does one distinguish between double base and single base? Were these powders taken from

reloading-type powders, or did someone empty ammunition? I had a case recently where a man sent

a bomb, I think it was to his wife or ex-wife. She is lucky; it did not go off, and it was rendered safe.

During the render-safe procedure, the pipe burned for quite a while and left some black residue, but

there was tape on the outside of the pipe. (Bomb makers love tape, and there was black electrical

tape wrapped around the pipe.) I looked at it under the microscope and noticed several types of

smokeless powder particles, indicating three or four different types of smokeless powder. The

conclusion is that this bomber used a mixture of propellants. Some of the smokeless powder

particles that survived were in one of the end caps that had a cotton wad in it. But the powder

seemed to be from factory ammunition. When the bomb maker eventually confessed to making the

device, he said he emptied out the propellant and then threw the rest of the ammunition components

54
away. So, the ammunition that I had identified in tRe search warrant had powder that was vastly

different in appearance from what the suspect actually used in the device.

55
9. “Simplifying Access to Complex Databases”

Ralph D. Semmel Elisabeth A. Immer

(240) 228-6179 (240) 228-6277

Ralph.Semmel@jhuapl.edu Lis.Immer@jhuapl.edu

The Johns Hopkins University, Applied Physics Laboratory

Milton S. Eisenhower Research and Technology Development Center

Johns Hopkins Road

Laurel, MD 20723-6099

Abstract

Database query formulation is a difficult task. While a domain expert may have a good

understanding of the information stored in a specific database, successful data retrieval requires

detailed implementation knowledge. Thus, query formulation typically involves significant effort

on the part of a database expert, and decision makers are restricted from immediate access to the

information they need. QUICK (Universal Interface With Conceptual Knowledge) is an automated

query formulation system that uses database design knowledge to generate SQL (Structured Query

Language) queries from high-level user requests. Providing more intuitive access to the database,

QUICK facilitates information retrieval by end users and supports the development of query

processing systems and knowledge discovery systems over complex databases.

Introduction

Query formulation is a difficult task that requires knowledge of both the information to be

retrieved and details about how that information is stored in the database. In SQL (Structured Query

Language), the most common database query language, a data request must identify (1) the attributes

of interest, (2) the specific constraints on those attributes, (3) the relations that contain them, and (4)

the join path through the database relations that connects the requested data. In real-world databases,

it is not unusual for a request over a few data items to result in a query that is multiple pages in

56
length. Thus, significant effort is required to develop correct queries to return the required data.

Query formulation is further complicated when the database implementation does not correspond

directly to its conceptual design due to ongoing maintenance modifications, performance-driven

optimizations, or poor design decisions. These factors make it diffkult, even for a domain expert,

to understand the database well enough to formulate a correct query in a timely manner. In

particular, it is unlikely that anyone who is not intimately familiar with the details of the database

implementation will be successful in retrieving the desired data. As a result, direct information

requests are typically limited to preformulated queries, and ad hoc queries must be supported by the

time-consuming handcrafting of queries by database experts.

Information access is further restricted when data are distributed throughout an organization

Typically, enterprise information that evolves over time is often collected at multiple locations and

may be separated into different databases developed using various design models and database

management system (DBMS) implementations. Not only is it difficult to locate specific data within

such a collection of distributed and possibly heterogeneous databases, but those databases may

contain conceptually related information that would be quite valuable to decision makers if they

could access it in a consolidated manner. Unfortunately, tools to support wider data access are costly

to develop and difficult to maintain because of their tight coupling to the implementation details of

the underlying databases.

This paper describes a query formulation approach that facilitates information retrieval from

complex database systems. Utilizing conceptual-level database design knowledge, the approach

supports the user’s intuitive understanding of the problem domain, promotes direct ad hoc querying,

permits the conceptual integration of related databases, and supports database integration into larger

knowledge discovery systems. The Database Design and Implementation section reviews the

fundamentals of ,database design and implementation. Then, the Conceptual-Level Query

Formulation section shows how conceptual-level bowledge can be used to automate query

formulation. The QUICK (Universal Interface With Conceptual Knowledge) section describes a

system that implements the approach and reviews some of its applications. Finally, the Conclusion

and Future Research section discusses areas for future research.

57
Database Design and Implementation

Database development typically proceeds along the information system life cycle from problem

specification to design to implementation to maintenance. Once the problem domain has been

analyzed and the system requirements have been discovered, a conceptual-level design is produced.

This design provides an abstraction of the real-world problem area and restricts the focus to those

aspects of the domain that are critical to the application requirements. An accurate conceptual-level

design is vital for successful database implementation, and, upon completion, it provides a detailed

and intuitive understanding of the database.

The entity-relationship (ER) model is one of the principal mechanisms for conceptual-level

database design [ 1,2]. Objects in the real-world problem domain are modeled as entity types and

described by a collection of attributes. Relationship types are used to identify associations between

entities and provide additional semantic information in the form of cardinal@ ratio and participation

constraints that indicate the number of times an entity may participate in a relationship and whether

or not it is required to do so. A number of extensions to the ER model have been developed to

support increased descriptive detail and data complexity in database designs 133. In particular,

modeling concepts, such as complex attribute specifications, Nary relationship types, and subset and

generalization hierarchies, provide significant additional support for modeling complex real-world

systems.

An ER diagram provides a graphical representation of the database design information, where

the entity types are indicated by rectangles, the relationships between entity types are represented by

diamonds, and the descriptive attributes of entity (or relationship) types are indicated by ellipses.

Figure 9-l shows the ER diagram for a simple banking database example, where bank branches

offer accounts, and loans, which are in turn held by customers. The entity types (branch,

customer, account, and loan) contain information about each of these objects in the problem

domain as indicated by their associated attributes, Closely related attributes may be conceptually

grouped into composite attributes; customer_address and street-address are examples

58
Figure 9-f. A Banking Example ER Diagram.

in Figure 9- 1. The set of attributes that uniquely identifies a particular entity is called the identifier,

those attributes are underlined in the ER diagram. For example, each account is identified by its

account-id.

The relationship types (account-branch, customer_account, loan-branch, and

cus tamer_loan) identify the conceptual associations between the objects in the banking problem

domain. The participation constraint for each entity type associated with a particular relationship

type is specified by the connection to that relationship type, where a single line denotes partial

participation and a double line indicates total (or mandatory) participation. Consider, for example,

the customer_account relationshiptypeinthe banking database. An account mustberelated

to a customer, but each customer need not be related to an account. This design models the

real world, where a customer may have a loan but not an account with the bank. The

cardinality ratio constraints that indicate the number of times each entity may participate in a

59
relationship is denoted by the labels on the lines connecting the relationship type to each of its

participating entity types. The ER diagram in Figure 9-l contains N-to-M and N-to-l relationship

types. Again, looking at the cus tomer_account relationship type, the N-to-M notation indicates

that there is a many-to-many relationship between customers and accounts, Thus, an

account must be related to at least one customer but may be related to many, while a

customer need not be related to any account, but may be related to many. This corresponds to

the real-world notions of joint bank accounts and customers that have more than one

account with a particular branch. Simillarly, the N-to-l (i.e., many-to-l) relationship type

account-branch denotes that, while a branch may be associated with any number of

accounts, each account must be associated with a single branch.

There is a straightforward algorithm for converting a database ER diagram to a logical-level

implementation in the form of a set of relations (see Ehnasri and Navathe [2] for a detailed

explanation). These relations define the tables in a relational database, where the cohunns

correspond to the included attributes, and the rows contain specific data entries in the database.

Applied to the database design in Figure 9- 1, this process results in the following six relations (where

the underlined attributes indicate the primary key, which is the set of cohunns that uniquely identifies

each row in the table).

(1)

(2)

(3)

(4)

(5)

(6)

branch (branckname, branch-address, branch_phone_number)

account (account-id, balance, branch-name)

loan (loan-id, amount, branch-name)

customer (m, customer_name, street_num, street-name, city,

state, zip)

customer_account (ssn, account-id)

customer_loan (ssn, loan id)

Qnce the database is implemented and the data loaded, queries may be submitted. Consider, for

example, a request for the names and addresses of all customers with a loan amount greater than

$10,000. The SQL query is as follows.

60
SELECT

customer.customer_name,

customer.street_num,

customer.street_name,

customer.city,

customer.state,

customer.zip

FROM

customer,

customeyloan,

loan

WHERE

loan.amount > 10000 AND

customer.ssn = customer_loan.ssn AND

customer_loan.loan_id = loan.loan_id

The SELECT clause identifies the attributes to be returned, the FROM clause identifies the relations

required to process the query, and the WHERE clause specifies the constraints on the requested

attributes and the join path through the database tables to connect related data. Notice that the query

for this simple request is not trivial to formulate, Not only must each attribute be located within its

particular table, but this query requires knowledge of the customerJoan relation that connects

specific customers to their loans,

Conceptual-Level Query Formulation

Unfortunately, converting a database design to a relational implementation removes a significant

amount of conceptual knowledge. For example, in the banking relational database previously

defined, the tightly coupled sets of data items that formed composite attributes are no longer

distinguished. Similarly, relationships are not necessarily explicit, cardinality ratio constraints can

only be inferred from relational schemas, and the participation constraints have been removed.

Much of the semantic meaning that is so clearly defmed in the design of Figure 9-l and that so

closely corresponds to the user’s view of the problem domain has been discarded in the database

implementation

Query formulation is further complicated by the duplication of attributes in the relational schema,

In the banking database implementation, for example, the column branch-name appears in three

61
relations: (1) branch, (2) account, and (3)loan. It may not be clear whiclr subset of these

relations is required to formulate a query for branch-name information. Notice that there is no

ambiguity at the conceptual level; however, the branch-name attribute is associated only with the

branch entity type.

Recall that an SQL query must specify the relations that contain the desired attributes and the

join path through the database that connects the requested data. Since this information is specific

to the database implementation and is typically not known to an individual with a data request, a

mismatch exists between the user9s understanding of the database at the conceptual level and the

standard method for accessing information contained in that database at the logical level. One

approach to bridge this gap is to permit the user to request data in a more intuitive manner. The

universal relation (UR) concept [4] refers to a single, virtual relation that contains all of the attributes

from all of the tables in the database. Queries over a universal relation would simply specify the

attributes of interest and any constraints that must be applied to those attributes; there is no need to

identify the implementation-specific details required in an SQL query. As a result, this type of

representation provides a more natural mechanism to pose requests.

While a UR-like interface simplifies a user’s data request, an additional burden is placed on tRe

supporting query formulation system to identify semantically reasonable join paths for the requested

attributes. This can be accomplished by using the semantic lorowledge contained in the ER database

design to define maximal subgraphs consisting of strongly related conceptual objects [5]. Known

as contexts, these overlapping subsets of the database schema provide the knowledge necessary to

define associations between requested attributes and to develop the relational join path required for I

query formulation. In particular, if a context covers the set of attributes in a Ihigh-level user request,

then it is inferred that the user request corresponds to the join path defined by that context.

Conversely, if no context is found, then it is assumed that the requested attributes are not related.

Contexts are developed from a database ER design using an extension of the concept of a lossless

join. If a join between two relations is lossless, then a strong association exists between the tables.

In particular, a lossless join ensures that all the rows that should be connected are, and that no

62
extraneous rows are added to the result (see Ehnasri and Navathe [2] for more details). Applying

this notion to the conceptual-level database design, two ER objects are strongly associated if their

corresponding tables can be joined in a lossless manner. Thus, contexts are generated by extending

outward from each relationship type in the ER schema to include all strongly associated conceptual

objects. Any context that is a subset of another is removed, leaving the set of maximal ER

subgraphs.

Applying this procedure to the banking database example of Figure 9-1, two contexts are

generated.

Context 1:

(customer, customeyaccount, account, account-branch, branch)

Context 2:

(customer, customer_loan, loan, loan-branch, branch)

These contexts correspond to the strong semantic associations between customers and bank

branches through their accounts (Context 1) and through their loans (Context 2).

QUICK

QUICK is an automated query formulation system developed at The Johns Hopkins University

(JHU) Applied Physics Laboratory (APL) [6]. QUICK exploits conceptual-level database design

knowledge to generate semantically reasonable SQL queries from high-level requests. Once a

text-based representation of the graphical ER database design is entered into the system, the

conceptual schema is mapped to the corresponding logical implementation and contexts are

generated. Then, requests can be processed.

63
QUICK allows the user to submit queries in a language known as USQL (a simplified version

of SQL that operates over a UR-like interface). Consider again the banking database request for the

names and addresses of all customers with a loan amount greater than $10,000, originahy

presented in the Database Design and hnplementation section. The USQL query, specifying only

the attributes and constraints of interest, is simplly as follows.

SELECT

customer_name,

customer_address

WHERE

amount > 10000

Given this query as input, QUICK exploits its conceptual knowledge of the banking database

design to convert the composite attribute customer_address to its constituent components, to

provide the necessary relation names and join path information, and to generate the SQL query

previously presented.

Looking at this process in more detail, consider a request for the customer-name and

branch-name corresponding to each account with a balance greater than $5,000. The USQL

version of this query is as follows.

SELECT

customer-name,

branch-name

WHERE

balance > 5000

The process followed by QUICK to convert this request to SQL for execution on the underlying

database is straightforward. First, find the contexts that cover the requested attributes. In this case,

Context 1 (the account portion of the schema) is the only candidate. Next, prune the contexts of

extraneous ER objects until all leaves in the remaining ER subgraph cover the requested attributes.

Since the entity types branch and customer both account for requested attributes, no pruning

is necessary. In general, pruning multiple candidate contexts may yield identical results; in that case,

remove the duplicates. Next, map the remaining ER objects in the pruned context to their

64
logical-level relations and identify the join path. Finally, generate the SQL query. For this example,

the result is as follows.

.

SELECT

customer.customer_name,

branch-branch-name

FROM

customer,

customeyaccount,

account,

branch

WHERE

account.balance >

5000 AND

customer.ssn =

customer_account.ssn AND

customer_account.account_id =

account.account_id AND

account.branch_name =

branch.branch_name

QUICK facilitates meaningful information retrieval and supports an ad hoc querying capability

without requiring detailed understanding of me underlying database implementation. In particular,

the system provides the relation names for all attributes in the query, the complete SQL FROM clause,

and the join path required in the SQL WHERE clause from the conceptual-level knowledge contained

in the ER design. Notice that even in this simple example, the resultant SQL query is significantly

more complex than the original USQL request. Jn real-world databases, it is quite common to

generate a page or more of SQL from a relatively simple USQL request. Thus, the time required to

generate a semantically meaningful and syntactically correct SQL query is greatly reduced through

the use of QUICK, regardless of the user’s database expertise. In addition, query formulation can

be further simplified through the use of high-level user interfaces that assist in the selection of query

” attributes. By encapsulating the query formulation function, QUICK supports the development of

user interfaces that focus on problem domain specifics,

Having demonstrated how QUICK provides decision makers with simplified access to

information within databases, we now consider the larger problem of integrating related information

across multiple data sources. This has become a critical issue as organizations seek to exploit recent

65
knowledge discovery advances by applying data-mining approaches to extract previously hidden

patterns and relationships from their large and diverse databases [7]. Prior to implementing such

efforts, however, a unified view of the disparate data sources and database implementations, as well

as uniform methods to access these data, must be made available. Extending the conceptual-level

approach across multiple databases, QUICK may be used to support the formation of such an

organization-wide data access infrastructure.

In particular, QUICK has been used to demonstrate the retrieval of information across

semantically related heterogeneous databases [8] for the U.S. Army Research Laboratory (ARL).

Two distinct databases, the ARL Electronic Intelligence (ARL-ELINT) relational database and the

Military Intelligence Integrated Data System and Integrated Database (MlIDS/IDB) inverted file

database, contain an entity type that corresponds to the same conceptual object in the intelligence

domain. By extending the ER model to include a gateway relationship type that describes the

association and join specification between the common entity types, a conceptual model of the fused

databases was developed. After mapping to the logical database implementations and generating

contexts, USQL requests were processed as before. The user’s perspective was a single, high-level

interface over the fused databases, permitting straightforward access to all the data.

A typical query over these combined intelligence databases might, for example, request the

last-known activities, mobility status, and equipment types and quantities of any known enemy unit

associated with a site where a pulse repetition frequency of 50 Hz has been detected. In USQL, the

query is as follows.

SELECT

site-name,

unit-name,

unit-role,

echelon,

activity-name,

mobility-status,

equip-code,

equip-quantity

WHERE

allegiance = 'ENEMY' AND

psf = 50

66
Applying the query formulation process described earlier, QUICK generates the following SQL

queryovert.heMIIDS/lDB andAF&EUNTdatabases.

SELECT

miids.site.site_name,

miids.unit.unit_name,

miids.unit.unit_role,

miids.unit.echelon,

miids.unit.activity_name,

miids.unit.mobility_status,

miids.equipment_type.equip_code,

miids.equipment.equip_quantity

FROM

miids.equipment,

miids.unit,

miids.equipment_type,

miids.site,

elint.eob_site,

elint.eob_component,

elint.elint_report,

elint.elint_prf

WHERE

miids.unit.allegiance =

'ENEMY' AND

elint.elint_prf.prf =

50 AND

miids.equipment.unit_name =

miids.unit.unit_name AND

miids.equipment.unit_role =

miids.unit.unit_role AND

miids.equipment.echelon =

miids.unit.echelon AND

miids.equipment.equip_code =

miids.equipment_type.equip_code AND

miids.equipment.site_name =

miids.site.site_name AND

miids.site.site_name =

elint.eob_site.eob_site_name AND

elint.eob_component.eob_site_db_id =

elint.eob_site.eob_site_db_id AND

elint.elint_report.elnot_id =

elint.eob_component.elnot_id AND

elint.elint_prf.elint_report_db_id =

elint.elint_report.elint_report_db_id

I .

This que~isparticul~l~interestinginthaetherequesteddataattributes(asidentifiedinthe SELECT

clause) are all contained in the MIIDS/IDB database but the specific data items to be returned are

constrained by information located in the ARL-ELINT database (specifically, the restriction that

67
pr f = 5 0). Thus, by providing a combined access capability, QUICK permits the straightforward

exploitation of additional data found in related databases to enhance the quality of information

returned to decision makers. Similarly, through this unified view of enterprise data, QUICK can

facilitate the development of larger data analysis and decision support systems.

Conclusion and Future Research

Query formulation can be a complicated and time-consuming task. By providing a

conceptual-level query interface and utilizing the semantic knowledge contained in the

conceptual-level design, query formulation can be greatly simplified. The QUICK system supports

a user’s intuitive understanding of the data items in the database, facilitates the Ad Hoc group

querying to provide information on demand, and provides a mechanism to integrate conceptually

related databases into enterprise-wide information systems. Finally, this consistent view of

organizational data, unconstrained by DBMS implementation details, provides the underlying

support necessary to implement data-mining and data-analysis systems.

Future research is focused on several areas. First, gateway relationship types are being extended

to provide a general mechanism to integrate conceptually related database schemas. Next, contexts

are being enhanced to help disambiguate queries wlrere multiple contexts cover the requested

attributes. Finally, integrating alternative conceptual and logical models into QUICK is being

explored.

68
References

1.

F

2.

3.

4.

5.

6.

7.

a.

.

Chen, P. P. “The Entity-Relationship Mode% - Toward a Unified View of Data.” ACM

Transactions on Database Systems, VOP, I, no. 1, pp* g-36,1976.

Elmasri, R., and S. B. Navathe. Fundamentals of Database Systems. 2nd edition, The

Ben$unin/Cummings Publishing Company, Jnc., Redwood City, CA, 1994.

Teorey, T. J., D. Yang, and J. P. Fry. “A Logical Design Methodology for Relational Databases

Using the Extended Entity-Relationship Models.” ACM Computing Surveys, vol. 18, no. 2,

ppo 197-222,1986.

Ulhnan, J. D. Principals of Database and Knowledge-Base Systems. Vol. 2, Computer Science

Press, Rockville, MD, 1989.

Semmel, R. D. “Discovering Context in an Entity-Relationship Conceptual Schema.” Journal

of Computer and Soj?ware Engineering, vol. 2, no. 1, pp. 47-63, 1994.

Semmel, R. D., E. A. hnmer, D. P. Silberberg, and R. P. Winkler. “Knowledge-Based Query

Formulation for Integrated Information Systems.” Johns Hopkins APL Technical Digest,

vol. 18, no. 2, pp_ 261-270,1997.

Fayyad, U., G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (editors). Advances in

Knowledge Discovery and Data Mining. AAN Press, Menlo Park, CA, 1996.

Semrnel, R. D., and R. P. Winkler. “Reverse Engineering Complex Databases to Support Data

Fusion.” Proceedings of the 4th Systems Reengineering Technology Workshop, pp. 192-199,

1994.

69
10. Discussion Section

Workshop Questions and Responses

What Is Required for a Sample to Be Traceable to a Unique Compound (or Unique Class

of Compounds)? A database can be forensically useful, even if it does not uniquely identify the

sample in question. For example, suppose the residue from a bomb is examined and adhesive tape

is found. Subsequent analysis of the backing thickness shows that the adhesive composition is made

by the 3M Company and it is one of their specialty tapes. If the possibilities can be narrowed to just

a few and one of these possibilities is found in a suspect’s house, there are grounds for a legal case.

For energetic compounds, the sampling question has two facets. First, there is the domestic

concern with low-order explosions, mainly based on smokeless powders. Second, there is the

international concern, generally with high-order explosives. Because of the different types of

energetic materials, these can be thought of as two separate problems.

Smokeless powders are involved in the majority of domestic bombing incidents. There are not

many cases,@volving high explosives. Detonators are interesting, but they are not seen in very many

cases either.

How Can the Present Databases Be Made Into User-Friendly, Complete, and Current

Databases, Accessible by All Forensic Laboratories? There are two questions here: one is

database structure, the other is database entry.

It was concluded that a user-friendly and user-accessible set of databases can be and, in fact, is

being constructed. More research is required, and several problems have been identified. How

complicated must a database be to serve its intended purpose? How much information is needed?

How is this information validated? Apparently, some of these questions, as well as others, are being

addressed by the already-established Technical Working Groups for MATerials analysis

(TWGMAT).

70
General Aspects of Databases

The following general aspects of a database were considered: completeness, maintenance,

validity, modeling, accession, and security. The first four topics are considered internal aspects of

a database; the last two are considered external aspects.

Internal Aspects.

Completeness. It was the consensus of the participants that one never has everything in a

database; it is never complete. A constant updating of the database was envisioned.

Proprietary Inform&on. 1t was recognized that, sometimes, the most important information is

proprietary, which can create problems. If proprietary data were put into a database, then anybody

accessing the database would have access to the proprietary data.

An irony was noted for the proprietary status of some information. Personal experiences have

shown that much that is labeled “proprieta@ is really common knowledge throughout the industry

or readily accessible in the open literature.

in

Processing Changes. It had been observed that manufacturers will routinely make slight changes

their manufacturing, either in the processing steps or in the ingredients used. Either the

manufacturers themselves do not recognize the changes, or they choose to remain silent to the fact.

Such changes usually do not become a part of the audit record.

These changes may be beneficial. They may alter the characteristics of the material so that a

previously common material becomes unique. On the other hand, these changes may be confounding

when, say, an altered ingredient shows up in analysis when it is not supposed to be there.

71
Maintenance. One or two people could maintain a sufficiently small or simple database (i.e.,

sometimes data would be input, and, at other times, data would be deleted). For example, in the

Federal Bureau of Investigation (FBI) automotive paint file, paints that are over 25 years old are not

typically needed; so, they are deleted, As a counterexample, one probably would not delete entries

from the Bureau of Alcohol, Tobacco, and Firearms (BATF) smokeless powder database because

very old powders are still encountered in the field.

For sufficiently large or complex databases, a central location or “clearing house” might be

required. A central location leads to the question of wlro bears the cost of maintaining and upgrading

the database? If a clearing house were employed, it might be easier to control who has access to the

data; so, a security benefit might be gained.

Validity.

Consistency. Two levels of error checking were distinguished. At data input, how certain is the

entry? For multilevel bases, how can entry common to several databases be simuhaneously

changed? The first situation more or less is dependent upon the experimenter or analyst to determine

the sensibility of the entry and is commonly encountered by those who maintain their own (private)

databases. The second situation is not yet routinely encountered within the forensic community,

according to the participants.

There are solutions built into database systems that automatically perform consistency

maintenance to some extent. There are commercially available tools that do what is called “data

scrubbing.” Turn one of these operations loose on a database, and it will look for and report likely

inconsistencies within the data records and data sets.

However, there is a bigger problem. For example, suppose A maintains database a, and B

database b, with X as the common column between a and b. If B changes an entry in X, does A

know about it? Some sort of data administrator or central clearing house charged with control of the

data was recommended. Someone has to know the structure of the entire database if cl-ranges are to

72
be made consistently. Ifb is changed and the appropriate change in a is not made, consistency will

have been lost. ,

Structure. Some ideas were put forth on advanced databases. The desired database system that

combines text, analytical information, and image data was defmed.

It was recommended to start with a small, constrained set of databases. The idea was to find out

what works at the smaller domain before building up to the larger. This was recommended simply

to conserve resources (i.e., if the system fails, let it fail in the smaller domain).

Databases that contain about 50 tables and several thousand attributes have been dealt with. The

size, structure, and interrelationships of the databases (i.e., tables) are more important than simple

volume of data entries. Terabytes (1012 B) of data can be handled with no problem. The numbers

of tables in the current smokeless powder and in the paints and coatings databases are 6 and 20,

respectively. It was thought that the paints and coatings database might provide an adequate exercise

of the database tools discussed, whereas the smokeless powder database would probably be too small

to make effective use of these tools.

Modeling. Whether one has a specialized database for one’s own data or is meshed with a

complex series of interlinking tables of data, one must decide at the outset what the data should look

like. In the more complex cases, one normally goes through some conceptual and logical modeling

efforts associated with the data. If one has a thousand attributes and a hundred entities that are all

related, with many multiple relationships existing among those entities, one will probably have a

complicated model, requiring several modeling approaclres before it is adequately formulated. This

kind of modeling requires a major effort.

Consider this counterexample. The Army has many legacy systems that are not well structured

from a data viewpoint. Essentially, they consist of gigantic files that are simply record, after record,

after record. Such a database might consist of 10,000 records, but it is just one table. The database

tools discussed here are not designed for this kind of database and, so, do not exercise the power of

73
this tool. If the data have some inherent structure, then it probably makes sense to model them.

Most databases that have any relational richness at all should be structured so that one ends up with

perhaps 40 or 50 tables. The main point is that such a modeling effort is not necessarily easy or

trivial.

External Aspects.

Accession. Consideration was given to question of retrieval design. Specifically, could the

receiving format be universal, or would it have to be specialized? At this time, one would build

some sort of program that parses the data (e.g., this number of bytes goes here; that number goes

there). In other words, this program would be a translator, which is not difficult to construct but has

a low priority just now.

A dialog-type access is the ultimate goal. But right now, the reality is more of a Windows-based

interface in which much more material is presented on screen to decrease the time spent narrowing

down selections.

Security and Release Authority. Concern, based on individual experience, was voiced about

the ability to obtain release authority from controlling agencies for specific propellants or explosives.

Such authority appears to be a function of the agency and the releasing officer, and, without it, the

particular energetic material could not be legally entered into an open database. However, the

individual energetic ingredients were thought to be relatively immune to this obstruction to database

entry.

The Chemical Propulsion Information Agency (CPIA) based at The Johns Hopkins University

(JHU) has been trying to assemble a spectral database of energetic materials. They are attempting

to massage their data so that they can meet the release requirements for information to be distributed

by wire (e.g., the Internet). (Note added in proof: CPIA now has a web site that requires a password

to gain access.)

74
Particular Database Topics

Energetic Materials.

Smokeless Powders. Some smokeless powders are easy to identify because they are fairly

unique: for example, in their size, shape, or coating. Others are difficult to identify by brand.

Production lots of smokeless powders are blended together so that the blend meets some ballistic

limit (a velocity or a pressure). Thus, there may not be a unique identification because of the random

way the smokeless powders are blended. In these cases, one gathers as much information as one can

to limit the number of possibilities.

What would be useful is to further characterize smokeless powders beyond that which is done

now.

Dynamite. The situation for dynamites in the United States is almost the complete opposite of

the smokeless powders. There is one dynamite production facility in the United States, maybe two.

But they make all the dynamite sold in the United States and Canada. Thus, identifying a unique

brand name like Atlas or Hercules means little or nothing.

Detonators. Some time ago there was an identification guide for detonators, but, now; there are

many new types of detonators and no standard classification of these newer types. Such a database

is needed because, in a recent case, a fact-finding mission had to be undertaken throughout various

parts of the world to find a standard for the newer detonators.

On the one hand, the Federal Aviation Administration (FAA) might have an interest in an

updated detonator database; on the other, the BATF has not seen a case with a nonelectric blasting

cap in many years. (Note added in proof: The National Institute of Justice [NIJ] has subscribed to

the Raman database generated by Dr. K. McNesby, U.S. Army Research Laboratory CARL].)

75
Paints. NIJ, through the National Institute of Standards and Technology (NISI’), is working on

a project that includes the entire forensic community: specifically, the FBI, TWGMAT, the forensic

associations, the federal and state forensic laboratories, and the local forensic laboratories. Some

countries, such as Canada, are also participating. The basic idea is to create a database of paint for

all automobiles manufactured in the last 25 years and, also, all paints or coatings for everything from

a building to a utensil or an appliance.

It is a large database that contains pigments, resins, and additives and all the different chemicals

and formulations with their spectral analysis, (e.g.g Fourier transform infrared [FTIR], ultraviolet

[WI, or infrared [IR] spectra). The system operates from a central location where it can be

administrated and maintained. All of the laboratories that participate either supply paint samples or

collect paint samples. Only the laboratories that participate have access to this database, and in a

read-only mode.

Automotive Parts. As another example, another forensic laboratory is cataloging parts and

collecting part numbers of all automobiles manufactured, both foreign and domestic. (The idea was

originally championed and developed by the Royal Canadian Mounted Police.) In an explosion, the

identifying number might be broken into two or more parts. The goal is to be able to access that

number, or number fragment, and identify the part and the manufacturer.

In this case, even though one can envision a huge number of records, the number of tables would

probably be small. A large number of records is not an issue. Say that one has 5,000 company

entries and a couple of million part entries; there are still only two tables, and the structural

complexity of this database is relatively minuscule, even though one has millions of records.

Database complexity is not obtained when one simply has millions of records, but rather when one

has these records spread over many different tables.

Crystal Patterns. There exists a diffraction pattern database whose structure is simple. A

crystal structure for a selected material is obtained from an x-ray experiment, and the lattice

parameters are submitted to the database manager for incorporation. Here, though, there might be

76
three or four different submissions for nominahy the same material. A record of crystallographic

information may be deleted after 20-25 years, or whenever a better recognized structure has been

determined. This database in constantly upgraded and published every 2 years; one must purchase

a license to access it.

Legal Aspects

Uniqueness of Forensic Samples. By definition, physical evidence is circumstantial evidence.

This means that, in court, one tries to provide as much circumstantial evidence as possible to

convince a judge and jury. From a forensic point of view, one attempts to identify samples by as

many techniques as are practical. This strategy increases the probability that the substance is what

one thinks it is. If it is not possible to uniquely identify samples in this manner, then one tries to

supply a limited list of possibilities for the forensic sample,

Comments on Courtroom Procedures. In the past, the only questions have been those of

sample collection, sample custody, and procedures. It is important how the sample was collected,

where the sample has been, whether it was contaminated, and whether it was properly stored.

Sample custody procedures and processes are attacked in court; lawyers are not known to attack the

validity of forensic analysis.

On the other hand, BATE has recently run into a problem with their firearms database. Error

checking and the validity of the database entries have become a significant legal issue. Questions

have been asked in court: How well is your error checking done? How is the integrity of your

database routinely checked?

The two opposing views may both be correct. The one looks to past events, while the other to

possible future courtroom strategies.

77
TWGMAT - The Next Step

Ultimately, databases, not only from the forensic people in the United States, but also from all

over the world, will need to be joined together or otherwise accessed. The concerns will not be

limited to the energetic materials discussed here but will encompass a whole host of other topics.

There are legal problems and many interesting computer problems (e.g., just getting the data

gathered together). A working group ought to be formed to hammer out a proposal that would be

submitted to NLJ for funding a technical working group to address these rather massive database

problems.

TWGMAT is a collaborative effort among forensic agencies: the FBI, state, local, and several

international forensic laboratories. The T?VGMkT subcommittees are developing standards (e.g.,

standards for databases and methods). They have the capability of expanding into other areas such

as terrorist applications and improvised explosives. The TWGMAT approach is a systematic way

of addressing important forensic issues.

Right now, TWGMAT has three major technical subcommittees operating: (1) the group for

fiber analysis; (2) the group for paints and coatings analysis; and (3) the group for glass analysis, the

newest group. Each of these groups has a database subcommittee, and the issues that have been

discussed here are under consideration by each of the TWGMAT groups. For example: How does

one database fiber information? Paint information.3 Glass information? How does one handle

proprietary information?

Some of the participants of this workshop ought to consider becoming part of TWGMATbecause

the issues discussed today have been and are confronting the other TWGMAT groups. They do not

have the level of expertise demonstrated here and could benefit from such expertise. This new group

would compliment the other TWGMAT groups,

78
Summary of Discussions

Energetic materials in the forensic community can be broken down into two major classes:

(1) domestic, mainly smokeless powders and (2) international, mainly high-order explosives.

Up to the present time, the smokeless powder database has been sufficient for legal cases. Two

situations were distinguished. First, the forensic sample does not have to be uniquely identified.

And second, the veracity of the database entries have only recently been questioned in court.

Database complexity is a function of the number of tables and interrelationships, and not sheer

volume of records. Specific examples in the areas of energetic materials, paints, automotive parts,

and crystal patterns were discussed.

The construction, verification, and modeling of large, complex databases are subjects of ongoing

research. To date, databases that contain 50 tables and several thousand attributes have been dealt

with.

There was a strong endorsement that a database working group be formed within the TWGMAT

organization. The objective of this group is to address database construction, maintenance,

verification, and management problems that are currently impeding progress of the three TWGMAT

database working groups in fiber analysis, paints and coatings analysis, and glass analysis.

79
Appendix A:

Details of the Chemical Characterization Research

Facilities at the Ignition Combustion Branch (ICB),

U.S. Army Research Laboratory (ARL)
Techniques and Facilities

Physical Chemistry, High-Temperature Chemical Kinetics, and Combustion Diagnostics.

Shock-Tube Chemical Kinetics. A gas-driven shock tube (for gas-phase kinetic studies to

4,000 K), including gas handling system; light sources, monochromators, filters, detectors and digital

oscilloscopes for microsecond absorption and emission measurements in the ultraviolet (W),

including Lyman alpha vacuum W; visible (VIE) and infrared (IR), including IR diode laser system;

and other laser systems available from other groups.

Low-Pressure Burner Flame Facility. Huntington/Newport low-pressure chamber; McKenna

burner with meters-kilograms-seconds (MKS) digital pressure and flow measurement systems; and

Laser Photonics IR Diode Laser System, including liquid helium cryogenic refrigerator, mode

selection monochromator, etalon, mercury-cadmium-telluride (MCT) detector, lock-in amplifiers

(two), and personal computer (PC)-based data-acquisition board and software. Argon ion laser for

fluorescence/Raman measurements. Other available flame diagnostics include thermocouples,

W/VIS absorption and emission spectroscopy, using Fourier transform infrared (FTIR) and mass

spectroscopy (MS) instrumentation listed in the following sections.

Imaging and Visualization of High-Speed Events. Cordin Image Converter Camera Systems

(two each) with streak and framing plug-ins for nanosecond to millisecond visualization, absorption,

and emission and Raman spectroscopy in the visible spectral region.

Analytical (OrganWInorganicPPolymer) Chemistry, Moderate-Temperature Chemical

Kinetics, and Ignition (Pyrolysis) Diagnostics.

ThermalAnalysis (Differential Scanning Calorimeter [DSC], Thermal Gravimeti Analyzer

[TGA]). Mettler 3300 DSC (with cryogenic accessory), Mettler TGA, and Calm microbalance.

83
FTIR-Raman Spectroscopy and UVIVIS Spectroscopy.

(2)

(3)

(4)

(5)

(6)

Bomem DA8 FTIR, 0.02-cm-’ resolution9 with PTIR-Raman accessory and Nd:YAG laser.

Mattson RS 1 PllR Includes Polarization Modulated (PM) FTIR spectrometer driven by

a Dell Pentium processor. Includes a Harrick refractor reactor for temperature, an ambient

atmosphere control, and in situ characterization of surfaces. The Mattson PM-FI’IR

employs a differential reflectance spectra obtained by the polarization modulation of the

F?CTR interferograms from measurements of the signal during each modulation cycle. This

instrument is valuable in probing the characteristics of thin films and monolayers at

reflective surfaces.

Mattson Sirius 100 FTIR, 0.12-cm-’ resolution, Bullseye MCT, InSb, Deutrated Triglycine

Sulfate (DTGS) detectors, and external optics for burner flame diagnostics.

Mattson Polaris l?TlR (0.5cm-’ Spectra Tech RX-Plan IR microscope system with MCT

detector), reflectance, and transmission modes.

FTIR accessories for photoacoustic spectroscopy (with room-temperature and

high-temperature capability), diffuse reflectance, pyrolysis diffuse reflectance, variable

angle automatic target recognition (ATR), cylindrical internal reflection (CIR), specular

reflectance, long-path (20 m) gas studies (including photolysis), variable temperature (- 190

to +200” C) measurements, and high-pressure work (diamond anvil cell). Complete

collection of digital Sadtler condensed vapor phase and specialty IR spectral libraries.

Hewlett Packard (HP) model 8453 diode array UV/VIS (190-820 nm) spectrometer with

UV-VIS ChemStation and full-feature Windows-based software control.
High-Pressure LiqquidChroma$ography (HPLC) andGelPerme&n Chromatography (GPC).

(1) HPIX: The ISCO Company, model 2350 pumping system with 2360 gradient controller,

Therm0 Separation Products SpectraFocus (1-, 2-, 3-wavelength or scanning) W/VIS

detector with PC-1000 operating system (OS)/Zbased acquisition software (90~mHz

Pentium Workstation), ISCO ChemResearch data acquisition software, ISCO V-4 variable

wavelength detector, and Gilson 201 programmable fraction collector.

(2) GPC: ISCO 2350 pumping system, and W/VI!3 and refractive index detectors.

High-Pevormance Liquid Chromatography-Mass Spectioscopy (HPLC-MS). HP 5988Amass

quadrupole mass spectrometer with electron import (El) and chemical ionization (CI) and negative

ion, HPLC-MS interface, HP 1050 isocratic HPLC pump, HP 5980 GC (for gas chromatograplry

[GC]-MS), l.Cmbyte HP 9000/236 (59970B) computer, 81- and 15-mbyte disk drives (Local-Area

Network [LANJ, National Institute of Standards and Technology [NISIJ) mass spectral library.

Supercritical Fluid Extraction (SFE), Reaction, and Chromatography.

w

(2)

(3)

(4)

Computer Chemical Systems (CCS) model 3100, lO,OOO-psi SFE system.

CCS model 3 100-100, lO,OOO-psi SFE system with dynamic and static modes and CCS

model OM-300 solvent modifier addition module.

CCS model 5000 supercritical fluid chromatography (SIC) system, (configured for

operation up to 10,OO psi and 250” C and can be configured as reactor up to 6,000 psi and

400 ’ C) includes CCS model 301 B extractor/accumulator.

CCS model OM-220 solvent modifier addition and flame ionization detector; specialized

(custom built) CCS supercritical fluid equipment includes 3,000-psi 50-ml view cell, gas

separator, and variable restrictor/collection modules

85
(5) ISCO SFC500 (with PC interface and software) 50 ml (to 10,000 psi, 0.08 to 600 yl/min)

and ISCO 26OD 260 ml (to 7,500 psi, up to 100 mknin) syringe pumps.

(6) ISCO heated variable linear flow restrictors.

(7) Harrick high-pressure (to 5,00 psi) variable path length optical cell for FTIR and FT Raman

studies.

(8) Aabspec high-pressure (to 10,000 psi) baclc-scattered Raman cell.

(91 High-pressure equipment manual pressure generators (5,000 and 10,000 psi).

(101 Heise digital-pressure measurements systems (2,000 and 10,000 psi).

(111 Water heater/chiller/recirculators (two).

(12) High-pressure flow cells for ISCO V-4 and TSP SpectraFocus HPLC detectors (can be used

with SFC).

(13) High-pressure extraction vessels (all up to 10,000 psi) including l-, 6-, 7.5-, lo-, 50-, and

103~ml vessels from CCS, Keystone Scientific, and Autoclave Engineers. This collection

of supercritical fluid (SF) handling equipment allows for custom assembly of specialized

systems such as a closed-loop CO, extractor (for bulk extraction applications) or a

spectroscopic solubility measurement system using combinations of high-pressure pumps,

flow cells, and extractors.

MS, GC-MS9 and Pyrolysis Mass Spectroscopy (PMS). Finnigan Incas 50 quadrupole mass

spectrometer with HP 5890 GC, solids probe, cRemical ionization, and NM/EPA mass spectral

library. Interface for ionization volume laser photolysis.

86
Pyrolysis-Gas-Chromatography (PGC)-FTIR-MS (Simultaneous FTIR and MS Data From

Single GC Run). Communication Detection System (CDS) 122 Pyroprobe, CDS 330 cryogenic trap

concentrator with column cryofocuser, HP 5890 GC with thermal conduction detector (TCD) and

flame ionization detector (PID), HP 5965B dedicated IR (FTlR) detector (IRD) with array processor,

HP 5980 mass selective (quadrupole) detector (MSD), HP ChemStation Software, EPA vapor phase

FTlR and NIST/Wiley MS libraries.

Suflace Microscopy and ElementalAnalysis. JEOL 820 scanning electron microscope (SEM),

and Kevex Delta Class x-ray fluorescence attachment with light element (down to boron) detector.

Visible LaserlRaman System.. Eexe1 Model 95 Argon ion laser, Jarrel-Ash 0.25-m

monochromator, EI&G Model 1455 intensified linear diode array, and an EI&G Model 1460/1463

controller.

Novel Optical Chemical Sensors.

Laser-Induced Breakziown Spectroscopy (LIBS) Sensors. LIBS is based on laser induced plasma

emission spectroscopy. We are developing LIBS for trace metal and trace explosives detection.

LIBS utilizes a compact Nd:YAG laser (up to 200 m J/pulse, capable of field application), fiber-optic

delivery, and photodiodekapacitor-coupled detector (CCD) array detection.

Near Infrared Tunable Diode Laser Sensors (NIR-TDL). Compact NIR-TDLs operating at room

temperature can be used for the detection of numerous gases of environmental interest by sensitive

frequency modulated vibrational/electronic absorption spectroscopy.

Acoustic Optic Tunable Filter (AOTF)-Based Sensors. AOTPs are inherently compact and

rugged with very low power requirements. Their use in fieldable sensors for chemical contamination

is being explored utilizing spectroscopic techniques such as LIBS, Raman, and near-infrared

(NIR)-absorbance/reflectance. AOTFs cover the wavelength range from 0.2 to 2 pm.

87
Fast Flow ReactorlTriple Quadrupole Mass Spectrometer (TQMS). A flow reactor is capable

of examining radical-radical reactions and their kinetic rate constants. The flow reactor has two

microwave discharges to produce the radicals. The temperature of the flow reactor can be varied so

that temperature-dependent measurements of kinetic rates can be determined. The output of the flow

reactor is sampled by a molecular beam sampler to introduce small portions to a triple quadruple

mass spectrometer to allow for product determination from the reactions studied.

Miscellaneous Equipment Available in the Propulsion and Flight Division (PFD).

Mettler-Parr model DMAs ultrasonic calculating density meter, Brinkman 672 autotitrator with

computer interface, accelerating rate calorimeter, Parr combustion calorimeter, IS1 SEM, dynamic

mechanical analyzer, high-rate mechanical properties testers, low- and high-pressure (50 Kpsi) strand

burners, closed bombs to 200 Kpsi, and all kinds of lasers and related spectroscopic techniques, etc.

88
Appendix B:

Development of the Workshop Focus Questions

89
In order to address the problems actually facing the forensic agencies, it was decided to formulate

a set of potential workshop questions and send them to the four forensic agencies for comment.

* These questions were based on the dialog, comments, and notes from the forensic tour of 3 1 January

1996.

.

(1) It is assumed that the major forensic interest in energetic materials lies with black powder

and smokeless powder and that these energetic materials have the highest priority. Are there

any other high-priority energetic materials? If so, which ones?

(2)

(3)

(4)

(5) Is ISO- important? If so, how?

(6) What is the top item(s) on your wish list (i.e., complete this sentence: “We really

need...XX...;” where XX = a method, or a type of information, or a device, etc.)?

What specific entries in a database are desired (e.g., spectra, physical parameters, etc.)?

What items (types of entries) already exist in database format? How do you access them?

(i.e., do you think entry and retrieval could be improved? If so, what would you like to see?)

What do you think is necessary to bring existing or contemplated databases to “courtroom”

quality?

All four agencies responded, and, from their written comments, we determined that the two focus

questions for this workshop should be:

(1)

(2)

What is required

compounds)?

for a sample to be traceable to a unique compound (or unique class of

How can the present databases be made into user-friendly, complete, and current databases,

accessible by all forensic laboratories?

These two questions were then recirculated to the forensic agencies for additional comment. All

* agreed that the questions as stated were reasonable ones for the workshop. A letter of invitation was

then extended to approximately 30 forensic scientists and researchers.

91
Appendix C:

Workshop Details

93
Table of Contents

Page

LetterofInvitation................................................. 97

ResponseFaxForm ................................................ 98

ForensicWorkshopParticipants ..................................... 99

95
Dear Colleague:

.

You are invited to attend a l-day, unclassified workshop, entitled: “Forensics: Energetic

Materials Measurement, Analysis, and Databasing,” which will be held on Thursday, 20 June 1996

at BRL Hall (Bldg. 330), located at the U.S. Army Research Laboratory (ARL), Aberdeen Proving

Ground (APG), MD. The workshop focuses on two questions.

(1) What is required for a sample to be traceable to a unique compound (or unique class of

compounds)?

(2) How can the present databases be made into user-friendly, complete, and current databases,

accessible by all forensic laboratories?

The workshop purposes are:

(1)

(2)

(3)

to define the critical problems that cut across forensic agencies;

to provide an open, multidisciplinary forum in which these problems can be addressed and

better defined; and

to lay the foundation for practical, timely, and cost-effective solutions.

The workshop agenda is enclosed. The more formal presentations are made in the morning

sessions, while the afternoon sessions are reserved for open, intensive, and extensive discussions.

In order to keep the number of participants small (for effective interactions), this 1 -day workshop

is limited to government agencies and attendance is by invitation. Alternate or additional

government employees who would benefit from attending this workshop are welcome. A response

form is enclosed; please fill out and fax to the number stated before 28 May 1996. This will greatly

ease the logistics planning for 20 June. /.~

For efficiency and accuracy in assembling a proceedings, the audio portion will be taped and

transcribed. In order to encourage frank and open discussions, anyone stating his or her opinions has

right of “first refusal” with respect to correcting, deleting, or qualifying any of their verbal

statements. (No videotape of the workshop sessions has been planned.)

While the workshop is unclassified, you will need an ARL security badge to enter the compound.

To improve the efficiency of the badging process, kindly fax the enclose security form, unless you

are not planning to attend.

Sincerely,

Joseph M. Heimerl

Ignition and Combustion Branch

U.S. Army Research Laboratory

Encls: Response form, Security form, Directions to ARUAPG, Agenda for workshop, and list of

initial invitees.

97
Forensic Workshop Response Fax

WEAPONS TECHNOLOGY

DIRECTORATE

PROPULSION AND FLIGHT DIVISION

Aberdeen Proving Ground, MD

Fax Transmission Cover Sheet

To:

Organization:

Office Phone No.:

Fax No.:

Date (by 28 May 1996):

J. M. Heimeri

AMSRL-WM-PC

410-278-6111

410-278-6150

From:

Organization:

Office Phone No.:

Fax No.:

Subject: Response form for attendance at the Workshop: ‘(Forensics: Energetic Materials

Measurement, Analysis, and Databasing,” Thursday, 20 June 1996, at BRL Haii, Bldg. 330,

U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 210055066.

ADDITIONAL NAME(S):

WILL ATTEND

PLAN TO ATTEND

WILL NOT ATTEND

(IF ATTENDING OR PLANNING TO ATTEND, REMEMBER TO FAX SEPARATE

SECURITY FORM(S) TO CAROL FIFE, ARL SECURITY, FAX: 410-278-7675.)

98
I ’

Forensic Workshop Participants

20 June 1996

Name

Todd H. Anderson

Kenneth Basom

Steve Burmeister

Stan M. Caulder

Robert Daniel

Linda Dee1

Pete Dreifuss

Alim Fatah

Frank T. Fox

Joseph M. Heimerl

Mark Kind1

Kevin L. McNesby

Al Mercado

Charlie Midkiff

Andrzej Miziolek

Rose A. Pesce-Rodriquez

Ralph D. Semmel

Mary Tungol

Cynthia Wallace

Allan N. Walters

Lou Wasserzug

Jeffrey M. Widder

Organization

Office of Special Technology (OST), Technical Support Working

Group (TSWG)

Naval Surface Warfare Center (NSWC), Indian Head (III)

Division

Federal Bureau of Investigation (FBI)

NSWC-IH

Ignition and Combustion Branch (ICB), U.S. Army Research

Laboratory (ARL)

Bureau of Alcohol, Tobacco, and Firearms (BATF)

BATF

National Institute of Standards and Technology (NIST)

Federal Aviation Administration (FAA)

ICB-ARL

Information Science and Technology Directorate (ISTD), ARL

ICB-ARL

Federal Aviation Administration (FAA) Technology Center

BATE;

ICB-ARL

ICB-ARL

The Johns Hopkins University (JHU), Applied Physics

Laboratory (APL)

FBI

BATF

U.S. Postal Service

Naval Explosive Ordnance Disposal Technology Division

(NAVEOD’IECH)

ICB-ARL

99
Appendix D:

Workshop Evaluation

101
In order to obtain some feedback on the technical and administrative aspects of the workshop,

an evaluation form was constructed and distributed to each attendee at the very end of the sessions.

(A copy of this form is given at the end of this appendix.) There were seven written responses with

the distributions given as follows.

(1) How well were the purposes met relating to SAMPLE UNIQUENESS?

GOOD QJ$ POOR

(a) defined the critical problems 6 1 0

(b) provided an open, multidisciplinary forum 7 0 0

(c) laid the foundation 5 2 0

Comments

1) “Did not talk much about finding solutions.”

2) “Determination of sample uniqueness will only follow compilation and substantial use of

databases.”

(2) How well were the purposes met relating to CURRBNT DATABASES?

(a) defined the critical problems

(b) provided an open, multidisciplinary forum

(c) laid the foundation

OKGOOD POOR

4 3 0

5 2

3 4 :

Comments

1) “Have attendees who have databases provide an overview of the use and contents of them.”

2) “Databases flushed out well in discussion.”

3) “Need to include person more experienced in database warehousing to prevent rediscovering

the wheel.”

(3) What would you do differently?

Comments

1) “More focus on the problem to be solved.”

2) “Good workshop; do it again!”

3) “Less open seating to force people together.”

4) “Improve focus with each successive meeting.”

5) “Within the defined goals, would run workshop similarly.”

Interpretation

My interpretation of the responses: the workshop did a good job of addressing the SAMPLE

UNIQUENESS question, and the CURRENT DATABASES question was less well addressed.

103
6‘Forensic9’ Workshop Evaluation Form

This workshop was to focus on two questions:

(1) What is required for a sample to be traceable to a unique compound (or unique class of

compounds)?

(2) How can the present databases be made into user-friendly, complete, and current databases,

accessible by all forensic laboratories?

The workshop purposes were to:

(1) define the critical problems that cut across forensic agencies;

. (2) provide an open, multidisciplinary forum in which these problems can be addressed and

better defined; and

(3) lay the foundation for practical, timely, and cost-effective solutions.

(1) How well were the purposes met relating to SAMPLE UNIQUENESS?

(a) defined the critical problems GOOD OK POOR

(b) provided an open, multidisciplinary forum GOOD OK POOR

(3) laid the foundation GOOD OK POOR

Comments:

(2) How well were the purposes met relating to CURRENT DATABASES?

(a) defined the critical problems GOOD OK POOR

(b) provide an open, multidisciplinary forum GOOD OK POOR

(c) lay the foundation GOOD OK POOR

Comments:

(3) If you were to host a similar workshop, what would you do differently?

(4) Additional Remarks:

(5) Optional: (AGENCY): and/or (NAME):

THANK YOU!

104
NO. OF

COPIES

2

ORGANIZATION

NO. OF

COPIES ORGANIZATION

DEFENSE TECHNICAL 1 DIRECTOR

INFORMATION CENTER US ARMY RESEARCH LAB

DTIC DDA AMSRLD

8725 JOHN J KINGMAN RD RWWHALIN

STE 0944 2800 POWDER MILL RD

FT BELVOIR VA 22060-62 18 ADELPHI MD 20783-1145

HQDA

DAM0 FDQ

D SCHMIDT

400 ARMY PENTAGON

WASHINGTON DC 203 10-0460

1 DIRECTOR

US ARMY RESEARCH LAB

AMSRL DD

J J ROCCHJO

2800 POWDER MILL RD

ADELPHI MD 20783-l 145

OSD

OUSD(A&T)/ODDDR&E(R)

RJTREW

THE PENTAGON

WASHINGTON DC 20301-7100

1 DIRECTOR

US ARMY RESEARCH LAB

AMSRL CS AS (RECORDS MGMT)

2800 POWDER MILL RD

ADELPHJ MD 20783-l 145

DPTY CG FOR RDE HQ

us ARMY MATERIEL CMD

AMCRD

MG CALDWELL

5001 EISENHOWER AVE

ALEXANDRIA VA 22333-0001

3 DIRECTOR

US ARMY RESEARCH LAB

AMSRL CI LL

2800 POWDER MJLL RD

ADELPHI MD 20783-l 145

INST FOR ADVNCD TCHNLGY

THE UNJV OF TEXAS AT AUSTIN

PO BOX 202797

AUSTIN TX 78720-2797

ABERDEEN PROVING GROUND

4 DIR USARL

AMSRL CI LP (305)

DARPA

B KASPAR

3701 N FAIRFAX DR

ARLINGTON VA 22203-1714

NAVAL SURFACE WARFARE CTR

CODE B07 J PENNELLA

17320 DAHLGREN RD

BLDG 1470 RM 1101

DAHLGREN VA 22448-5 100

US MILITARY ACADEMY

MATH SC1 CTR OF EXCELLENCE

DEPT OF MATHEMATICAL SC1

MAJMDPHJLLIPS

THAYERHALL

WEST POINT NY 10996-1786

105
NO. OF

ORGANIZATION COPIES

FBI

LABDIV

MTRLSANLYsUNJT

S BURMEISTER

F -ST

lOTH&PENNAAVENW

WASHINGTON DC 20535

usss

FSD T CANTU

TSD B DES0

800 G STREET NW

WASHINGTON DC 20223

NSWC JH

S CAULDER

CODE 2900 BLDG 600

INDJAN HEAD MD 20640

BATF

EXPLOSIVES SECTION

G CZARNOPYS

FSL

R STOBLE

R TONTARSKI

1401 RESEARCH BLVD

ROCKVILLE MD 20850

PM OST

J DAVID

J LAWRENCE

M ASSELIN

R COCHR4N

BLDG 3

10530 RIVERVIEW RD

FT WASHJNGTON MD

207445841

BATF

L DEEL

R MOSLEY

650 MASSACHUSETTS AVE NW

WASHINGTON DC 20226

FBI

FSR&TR

D FETTEROFF

QUANTICO VA 22135

106

NO. OF

COPIES ORGANTZATION

4 FAA TECH CTR

F FOX (2 CPS)

AMERCADO

L WASSERZUG

ATLANTIC CITY LNTRNTL ARPRT

ATLANTJC CITY NJ 08405

1 DIR LLNL

FORENSICS SCIENCE CT

PGR4NT

PO BOX 808

L 371

LIVERMORE CA 9455 1

50 DIR OIES NIST

K HIGGINS

BLDG 225 RM A323

GATHERSBURG MD 20899

1 DJRIA

OCCT

DEPARTMENT OF STATE

M JAKUB

2201 C STREET NW

WASHINGTON DC 20520

2 POSTAL INSPECTION SVC

F&TSDIV

MAKELLY

R VOORHEES

22433 RANDOPH DR

DULLES VA 20104-1000

1 FAA

L MALOTKY

SCIENTIFIC ADVISOR

800 INDEPENDENCE AVE SW

WASHINGTON DC 20591

2 BATF

EXPLOSIVES STUDY GROUP

R CONRAD

L MCCUNE

650 MASSACHUSETTS AVE NW

WASHINGTON DC 20226
NO. OF

ORGANIZATION COPIES

1 FBI ACADEMY

HZRDS MTRLS RSPNS UNT

J PODLESNY

HMRU LAB ERF

QUANTICO VA 22135

2 BATF

NAT LAB CTR

CMIDKIFF

1401 RESEARCH BLVD

ROCKVILLE MD 20850

1 BATF

OFC OF LAW ENFORCEMENT

EXPLOSIVES DIV

D SHATZER

650 MASSACHUSETTS AVE NW

WASHINGTON DC 20226

1 FBI

LABDIV

EXPLOSIVES UNIT

TTHURMAN

lOTH&PENNAAVENW

WASHINGTON DC 20535

1 FBI

SClENTJFIC ANALYSIS SECT

M TUNGOL

gTH&PENNAAVENW

WASHINGTON DC 20535

4 POSTAL INSPCTN SVC

F&TSDIV

R VOORHEES

A WALTERS (2 CPS)

C WHlTCOMB

22433 RANDOPH DR

DULLES VA 20104-1000

1 BATF

NATLLABCTR

JLUND

C WALLACE

1401 RESEARCH BLVD

ROCKVILLE MD 20850

NO. OF

COPIES ORGANIZATION

1 NAVY EOD

TECH DIV

L WASSERZUG

2008 STUMP NECK RD

INDJAN HEAD MD 20640

1 NIMIC

USNATO/IS

PSC 81 BOX 116

APO AE 09724

12 JOHNS HOPKINS UNIV

APPLIED PHYSICS LAB

MJLTON S EISENHOWER RSRCH &

TECHLGY DEV CTR

E A IMMER (10 CPS)

R D SEMMEL (2 CPS)

JOHNS HOPKINS RD

LAUREL MD 20723-6099

1 DIR INTELL APPLICATIONS

US DEFT OF STATE

M JAKUB

2201 C ST NW

WAHSINGTON DC 20520

ABERDEEN PROVING GROUND

16 DJRUSARL

AMSRLWMB

B FORCH

R PESCE-RODRIGUEZ (3 CT’S)

K MCNESBY

J HEIMERL (3 CPS)

A JUHASZ

D DEVYNCK

AMIZIOLEK

J MORRIS

AMSRLWMTB

RFREY

J WATSON

AMSRLHR SC

DHARRAH

AMSRLHR

RKEESEE

107
t

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 07040188

4 Forensic Workshop Report Concerning (1) The Measurement and Analysis of

3nergetic Materials and (2) Databasing

lL161102AH43

MIF’R - 43NANB613680

loseph M. Heimerl

J.S. Army Research Laboratory

~TTNI AMSRL-WM-BD

4berdeen Proving Ground, MD 21005-5066

AGENCY REP

il. SUPPLEMENTARY NOTES

12a. DlSTRlBUTlON/AVAlLABlLlTY STATEMENT

Approved for public release; distribution is unlimited.

12b. DISTRIBUTION CODE

13. ABSTRACT(hfaximum 200 words)

On Thursday, 20 June 1996, this workshop was convened at BRL Hall (Bldg. 330), located at the U.S. Arm)

Research Laboratory (ARL), Aberdeen Proving Ground (APG), MD. The purposes of this workshop were: (1) tc

define the critical problems that cut across forensic agencies; (2) to provide an open, multidisciplinary forum in which

these problems could be addressed and better defined; and (3) to lay the foundation for practical, timely, am

cost-effective solutions.

The technical focus of the workshop pivoted on two questions: (1) What is required for a sample to be traceable to i

unique compound (or unique class of compounds)? (2) How can the present databases be made into user-friendly

complete, and current databases, accessible by all forensic laboratories?

This report documents the background leading up to the calling for a workshop, the manuscripts of the talks given,

md the discussion held.

14. SUBJECT TERMS 15. NUMBER OF PAGES

:omplex databases, explosive reference standards, Raman characterization, Raman 109

identification, smokless gunpowders, trace energetic materials 16. PRICE CODE

17. SECURITY CLASSIFICATION

OF REPORT

UNCLASSEIED

URU 7-i_3Rn-c;5nn

18. SECURITY CLABSlFlCATlON

OF THIS PAGE

UNCLASSIFIED

19. SECURKY CLASSlFlCATlON 20. LIMITATION OF ABSTRACT

OF ABSTRACT

UNCLASSIFIED UL

109 Standard Form 298 (Rev. 2-89)

Prescribed by ANSI Std. 23418 298-102
USER EVALUATION SHEET/CHANGE OF ADDRESS

This Laboratory undertakes a continuing effort to improve the quality of the reports it publishes. Your comments/answers

to the items/questions below will aid us in our efforts.

1. ARL Report Number/Author ARL-SR-77 (Heimerl) Date of Report Januarv 1999

2. Date Report Received

1

3. Does this report satisfy a need? (Comment on purpose, related project, or other area of interest for which the report will

. be used.)

4. Specifically, how is the report being used? (Information source, design data, procedure, source of ideas, etc.)

5. Has the information in this report led to any quantitative savings as far as man-hours or dollars saved, operating costs

avoided, or effkiencies achieved, etc? If so, please elaborate.

6. General Comments. What do you think should be changed to improve future reports? (Indicate changes to organization,

technical content, format, etc.)

Organization

CURRENT

ADDRESS

Name

Street or P.O. Box No.

E-mail Name

City, State, Zip Code

7. If indicating a Change of Address or Address Correction, please provide the Current or Correct address above and the Old

or Incorrect address below.

Organization

t

OLD

ADDRESS

Name

Street or P.O. Box No.

City, State, Zip Code

(Remove this sheet, fold as indicated, tape closed, and mail.)

(DO NOT STAPLE)