DART-MS: A New Analytical Technique for Forensic Paint Analysis

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DART-MS: A New Analytical Technique for Forensic Paint Analysis Mark Maric, James Marano, Robert Bernard Cody, and Candice Bridge Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01067 • Publication Date (Web): 03 May 2018 Downloaded from http://pubs.acs.org on May 5, 2018

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Analytical Chemistry

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DART-MS: A New Analytical Technique for Forensic Paint Analysis

2

Mark Marić1, James Marano2, Robert B. Cody3, Candice Bridge1,4

3 4

1

5 6

2

7

3

8 9

4

National Center for Forensic Science, University of Central Florida, PO Box 162367, Orlando, FL 32816-2367 Florida Department of Law Enforcement, Orlando Regional Operations Center, 500 W. Robinson, St, Orlando, FL 32801 JEOL USA, Inc., 11 Dearborn Rd. Peabody, MA 01960

Department of Chemistry, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816

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1 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract:

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Automotive paint evidence is one of the most significant forms of evidence obtained in

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automotive related incidents. Therefore, the analysis of automotive paint evidence is imperative

14

in forensic casework. Most analytical schemes for automotive paint characterization involve

15

optical microscopy, followed by infrared spectroscopy and pyrolysis-gas chromatography mass

16

spectrometry (py-GCMS) if required. The main drawback with py-GCMS aside from its

17

destructive nature, is that this technique is relatively time intensive in comparison to other

18

techniques. Direct analysis in real time-time of flight mass spectrometry (DART-TOFMS) may

19

provide an alternative to py-GCMS, as the rapidity of analysis and minimal sample preparation

20

affords a significant advantage. In this study, automotive clear coats from four vehicles were

21

characterized by DART-TOFMS and a standard py-GCMS protocol. Principal component

22

analysis was utilized to interpret the resultant data and suggested the two techniques provided

23

analogous sample discrimination. Moreover, in some instances DART-TOFMS was able to

24

identify components not observed by py-GCMS and vice versa, which indicates that the two

25

techniques

26

desorption/pyrolysis DART-TOFMS methodology was also evaluated to characterize the intact

27

paint chips from the vehicles, in order to ascertain if the linear temperature gradient provided

28

additional discriminatory information. All the paint samples were able to be discriminated based

29

on the distinctive thermal desorption plots afforded from this technique, which may also be

30

utilized for sample discrimination.

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additional tool to the forensic paint examiner.

may

provide

complimentary

information.

Additionally,

a

thermal

Based on the results, DART-TOFMS may provide an

32 33 34 35 36 37 38 39

Keywords: Automotive paint evidence, DART-TOFMS, py-GCMS, thermal

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desorption/pyrolysis 2 ACS Paragon Plus Environment

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Introduction:

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Automotive paint evidence is located at incident scenes including; hit-and-run accidents,

43

vehicular homicides, automobile crashes or any scene where a vehicle has been used in the

44

commission of a crime. In the absence of eyewitness accounts or closed circuit television

45

footage, automotive paint evidence is often one of the more significant forms of trace contact

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evidence located at these scenes. As a result, increased emphasis must be placed on the analysis

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of automotive paint evidence to procure investigative information. Automotive paint is a

48

complex multilayered system, designed to provide aesthetic appeal and protect the frame of the

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vehicle.1 An original equipment manufacturer (OEM) automotive finish system, typically

50

consists of four layers; electrocoat primer, primer surfacer, basecoat and a clear coat. However,

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some new OEM paint systems, i.e. quad coats, will contain up to four tinted mid-coat layers

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between the basecoat and the clear coat to produce a higher quality color finish.2 The electrocoat

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primer is the first layer applied to the vehicle and provides corrosion resistance. Then, a

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relatively thick primer surfacer is applied to conceal surface imperfections and provide a smooth

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foundation for the application of the basecoat. The basecoat is a comparatively thin pigmented

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coating that provides the desired color and finish to the vehicle. The final coating applied is the

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clear coat, which is an unpigmented layer containing ultraviolet (UV) absorbers and hindered

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amine light stabilizers designed to protect the underlying layers and vehicle from UV

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degradation and weathering.3

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Although no current universal methodology exists for forensic paint examination, a general

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framework has been developed by the Scientific Working Group on Materials Analysis

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(SWGMAT)4 and the American Society for Testing and Materials (ASTM).5 These guidelines

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endorse microscopical examinations be conducted first, in order to determine layer structure and

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morphology. After this, forensic examiners typically use a combination of instrumental

65

techniques to characterize the coating(s), which may include but are not limited to;

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microspectrophotometry,6-8

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spectroscopy,12-14 dual column pyrolysis-gas chromatography (py-GC)15-16 and/or py-GC mass

68

spectrometry (py-GCMS),17-19 and elemental analysis techniques.20-22 The analytical scheme

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utilized to characterize the sample is flexible and is entirely dependent on the amount, quality,

70

morphology, physical/chemical characteristics of the sample and the analytical techniques

71

available to the forensic examiner.23 Most forensic examiners begin every automotive paint

Fourier transform-infrared

(FT-IR)

spectroscopy,9-11

Raman



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examination with some form of microscopy, usually followed by FT-IR spectroscopy of the

73

individual layers and py-GC or py-GCMS if required. FT-IR spectroscopy is routinely the most

74

employed instrumental technique for automotive paint layer analysis, owing to its ability to

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rapidly, non-destructively interrogate the paint system and provide information regarding the

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binder/resin, extender and pigment components.23 An advantageous feature of this technique is

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that many paint reference databases, such as the Paint Data Query (PDQ) database, are compiled

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entirely of IR data. This may provide actionable information in instances when there is no known

79

sample to compare to the questioned paint sample.24-25 While this technique rapidly provides

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generic information regarding the binder, additives and main pigments; if two paint samples

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contain similar binder systems, further characterization is required by a more sensitive technique,

82

such as py-GC or py-GCMS.19, 26 These techniques are arguably the gold standard in automotive

83

paint analysis, as they are the most sensitive techniques available for differentiating between

84

samples with similar paint binder formulations.27-28

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In fact, previous research by Burns and Doolan has demonstrated that py-GCMS is capable of

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discriminating between automotive clear coat formulations indistinguishable by FTIR

87

spectroscopy.17 While this technique is typically utilized to identify the various binder

88

monomer(s) employed in paint systems, it can also be sensitive to additive(s), pigment(s) and

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residual solvents.19,

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destructive techniques (e.g. FTIR spectroscopy) typically precede py-GCMS. Another drawback

91

to py-GCMS for forensic paint analysis is that it is relatively time intensive, with an analytical

92

run (including a bake method) potentially taking up to an hour. This could be a significant

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limitation in forensic casework and result in lengthy backlogs. Therefore, the rationale behind

94

this study was to investigate if an ambient ionization source, direct analysis in real time-time of

95

flight mass spectrometry (DART-TOFMS) could be utilized to provide analogous information to

96

py-GCMS, in a shorter timeframe.

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DART-TOFMS is capable of rapidly analyzing samples in any physical state, with minimal

98

sample preparation. This technique could be a viable alternative to py-GCMS, as the high

99

resolution and accurate mass detection afforded by DART-TOFMS enables component

100

identification based upon accurate mass measurements and isotopic ratios, thus preventing the

101

need for lengthy chromatographic separation. DART-TOFMS has routinely been utilized in the

26

It should be noted that this technique is destructive; consequently non-


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discipline of forensic science to analyze a variety of trace evidence including; drugs,29-32 inks,33-

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35

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a Q-Orbitrap tandem MS was recently utilized to characterize the organic pigments in

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automotive paint basecoats.42 The authors embedded organic pigments into a resin and coated it

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onto a metal panel. Small paint chips were crushed, suspended in water and introduced into the

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DART stream using a glass tip. DART-MS was capable of rapidly and accurately identifying the

108

organic pigments in these simulated samples, as well as real world samples obtained from hit-

109

and-run accidents in New Taipei City.42 This study centered entirely on the identification of the

110

organic pigments in the basecoat of automotive paint systems and did not investigate the other

111

components, such as binder(s), additive(s) and solvent.

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The main emphasis in this study will be on the analysis of automotive clear coats. The rationale

113

behind this is that automotive paint evidence typically comes in the form of paint chips or

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smears; paint chips have the entire layer system intact, while paint smears are comprised of the

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clear coat and in some instances the basecoat. As the only constant in paint chips and smears is

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the clear coat, this study will focus predominantly on the analysis of the clear coat. The objective

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of this study was to develop a DART-TOFMS protocol capable of rapidly pyrolyzing automotive

118

clear coats. The information obtained from this technique was compared to a standard py-GCMS

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methodology

120

desorption/pyrolysis DART-TOFMS was utilized to investigate if a temperature dependent

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DART protocol could characterize intact paint systems and provide additional information to the

122

examiner.

explosives,36-38 sexual assault evidence39-40 and chemical warfare agents.41 DART interfaced to

that

is

utilized

in

forensic

paint

casework.

Additionally,

thermal

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Experimental:

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Automotive Paint Samples

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A subset of paint chips from four black (i.e. 2012 or newer) vehicles were randomly selected

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from a large collection of samples assembled by the Florida Department of Law Enforcement

138

(FDLE). Paint samples were selected from black vehicles, in an effort to limit the contribution of

139

the basecoat pigments to sample discrimination. The make, model, year, generic paint clear coat

140

formulation type, presence of metallic and pearlescent pigments in the basecoat and the Vehicle

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Identification Number (VIN) for each vehicle is provided below in Table 1. It is important to

142

note, the information provided in Table 1 was not known prior to sample selection.

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Table 1: Table containing information regarding the manufacturer, model, year, paint formulation, the presence of metallic or pearlescent pigments in the basecoat and the VIN for the paint samples utilized in this study (ACR – acrylic, MEL – melamine, STY – styrene, PUR – polyurethane).

Sample Manufacturer No.

Model

Year

Formulation

F1104

Hyundai

Elantra

2013

ACR-MEL-STY

F1111

Toyota

Camry

2012

ACR-STY

F1126

Chevrolet

Camaro

2014

ACR-PUR

F1177

Toyota

Camry

2012

ACR-STY

Metallic or Pearlescent Pigments Sparse Pearlescent Moderate Pearlescent Metallic and Pearlescent Moderate Pearlescent

VIN KMHDH4AE1DU850483 4T1BF1FK6CU189901 2G1FG1E30E9147410 4T1BD1FK0CU010497

146 147

DART-TOFMS Methodology

148

Mass spectra were acquired in the positive-ion mode using a IonSense® DART® ion source

149

equipped with an AccuTOF™ 4G LC-plus mass spectrometer (JEOL USA, Peabody, MA, USA).

150

The DART ion source was operated using a helium gas flow rate of approximately 3.6 L/min and

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a gas heater temperature of 550 °C. The gas heater temperature was maintained at 550 °C, as this 6 ACS Paragon Plus Environment

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temperature was required to pyrolyze and thermolytically breakdown the polymeric

153

macromolecules of the clear coat samples. The needle electrode potential was held at 3 kV and

154

an exit grid voltage of 250 V was utilized. As automotive paint systems are chemically complex

155

consisting of a number of components, the orifice 1, orifice 2 and ring lens voltages were set at

156

20 V, 5 V and 5 V respectively, so as to minimize fragmentation of the molecular ions and aid in

157

interpretation. The peaks or ion-guide voltage was fixed at 600 V. All mass spectra were

158

obtained over the m/z range of 60-1000, at a sampling interval of 0.25 ns and a recording interval

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of 1 s. The microchannel plate (MCP) detector voltage was maintained at 2150 V for the

160

duration of analysis.

161

Thin shavings of the clear coat (~ 15 µg) were obtained from each paint sample using a scalpel

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and a stereomicroscope and these shavings were mounted onto the mesh grid of 12-sample

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QuickStrip™ cards (IonSense, Saugus, MA, USA). The sample cards were placed on a linear rail

164

system (IonSense, Saugus, MA, USA), which allowed lateral movement of the sample between

165

the DART source and mass spectrometer inlet. The linear rail was used to optimally position the

166

samples reliably in the sample gap, and the samples were held at this position until analysis was

167

complete. The DART ion source was positioned 1 mm away from the QuickStrip™ sample cards

168

and 1.5 cm away from the mass spectrometer inlet. Samples were held in the metastable stream

169

for a period of approximately 2 minutes, to allow for sufficient time to pyrolyze and characterize

170

the specimens. Each clear coat sample was characterized in triplicate. Polyethylene glycol, with

171

an average molecular weight of 400 was used as the internal mass calibration standard, in order

172

to provide accurate mass measurements. All data acquisition was performed using JEOL Mass

173

Center. TSS Unity (version 1.0.6.1; Schrader Software Solutions, Inc., Detroit, MI, USA) was

174

used to produce high resolution, spectrally averaged (i.e. over the two minute analysis period),

175

background subtracted, peak centroided mass spectra. Mass spectral interpretation, elemental

176

composition determination and isotope ratio matching was conducted using Mass Mountaineer™

177

(version 3.3.2.0; RBC Software, Peabody, MA, USA). The individual data files were collated

178

into a data matrix, by using diagnostic ions with a relative intensity greater than 10 % to define

179

the variable list, and a 5 millimass unit tolerance to ensure all masses were binned correctly.

180

Py-GCMS Methodology



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Thin shavings of the clear coat (~ 7.5 µg) were pyrolyzed using a CDS Analytical 5150

182

pyroprobe. The pyrolysis temperature was fixed at 750 °C for 20 seconds and the temperature of

183

the interface was maintained at 300 °C. The pyroprobe was coupled to a Hewlett Packard HP

184

6890 GC equipped with a 30 m phenyl methyl siloxane (5 %) capillary column and interfaced to

185

a Hewlett Packard 5973 quadropole mass selective detector with an electron ionization source.

186

Helium was used as the carrier gas at a constant flow rate of 1 mL/min. The GC oven

187

temperature was held at 45 °C for 4 minutes and ramped at 15 °C/min to a final temperature of

188

260 °C, which was maintained for an additional 10 minutes. Each clear coat was characterized in

189

triplicate. Between analytical runs any residual organic matter was removed from the pyrolysis

190

coil by heating the quartz tube to 1100 °C for 10 seconds, a total of four different times.

191

Additionally, any trace pyrolysates in the column were removed by utilizing a bake method with

192

a starting temperature of 120 °C which was ramped to 270 °C at a rate of 30 °C/min. This final

193

temperature was held for an additional 10 minutes.

194

Thermal desorption/pyrolysis DART-TOFMS Methodology

195

The paint chips were characterized using the temperature gradient system ionRocket

196

(BioChromato, San Diego, CA, USA) interfaced to the DART-TOFMS. Fine particles of the

197

intact paint chips (roughly 1 mm in diameter) were mounted onto a copper sampling “pot”,

198

which was placed onto the heating block and positioned in the sample gap. The temperature

199

program was 50 °C with a 0.5 minute hold time, followed by a ramp rate of 100 °C/min to a final

200

temperature of 600 °C and this final temperature was held for 1 minute. A glass T-junction was

201

positioned over the sample mounted on the copper sampling stage and as the heating element

202

desorbs components from the paint chips they were directed into the sample gap. Mass spectra

203

were acquired in the positive-ion mode utilizing a gas heater temperature of 550 °C and a 1

204

second spectral storage rate. The ion-guide voltage was 600 V and the orifice 1, 2 and ring lens

205

voltages were set to 20, 5 and 5 V, respectively. Thermal desorption plots were generated by

206

using MZmine 2.28.43

207

Statistical Analysis

208

An unsupervised pattern recognition technique in the form of principal component analysis

209

(PCA) were performed on the individual datasets using Rx64 (version 3.1.2). This statistical

210

technique was used to objectively interrogate the structure of the data, reduce the dimensionality 8 ACS Paragon Plus Environment

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211

of the dataset and identify relationships between the samples and variables (i.e. retention time

212

and m/z). PCA was conducted using the non-linear iterative partial least squares algorithm and

213

the first two principal components (PCs) were utilized to reconstruct and model the datasets.

214

Scores plots were generated to visualize the position of samples by projecting them into a 2-

215

dimensional PC space, in order to group similar samples together whilst simultaneously

216

discriminating samples that have markedly different scores. The loadings plots provided further

217

information by detailing the variables in the original datasets, which provide a significant

218

contribution to the PCs and aided in identifying unique chemical markers that discriminate

219

between groups of samples.

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Results & Discussion:

221

A small subset of automotive paint samples were utilized in this study. By decoding the VIN,

222

which is a 17-digit alphanumeric code, we were able to obtain unique latent information about

223

the vehicles (Table 1).44 Sample F1104, which was obtained from a Hyundai Elantra, was

224

determined to be manufactured in South Korea based upon the first three characters of the VIN,

225

which is designated as the World Manufacturer Identifier (WMI). The paint specimens obtained

226

from the two Toyota Camry vehicles (i.e. F1111 and F1177) were determined to be both

227

manufactured at the Georgetown, Kentucky manufacturing plant in the US. While these two

228

vehicles are markedly similar based upon information extracted from the VIN, sample F1111

229

was finished using midnight black metal (218) and sample F1177 contained a cosmic grey mica

230

(1H2) finish. Sample F1126, obtained from the Chevrolet Camaro, was manufactured in Canada

231

according to the WMI of the VIN. These descriptors identified were used to rationalize the

232

similarity or dissimilarity between the paint samples.

233

DART-TOFMS

234

The high resolution mass spectra obtained from the automotive clear coats revealed that DART-

235

TOFMS was capable of thermolytically decomposing the polymeric macromolecules into the

236

individual binder monomers, additives and cross-linking agents. By examining the spectra

237

displayed in Figure 1, all samples contained acrylic as the main binder type, however variations

238

were identified in the acrylic co-polymers utilized to create the backbone of the clear coat

239

enamels. Table S-1 provided in the supplementary information contains the peak identification

240

and mass information for the main peaks in the spectra obtained from the four clear coat 9 ACS Paragon Plus Environment


Page 10 of 27

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enamels. As can be seen from Table S-1, characteristic peaks were identified that were indicative

242

of a variety of different acrylic binder monomers including; methyl acrylate, methyl

243

methacrylate, glycidyl methacrylate, n-butyl acrylate and allyl methacrylate. Another major

244

component, styrene, was also identified in 3 of the automotive clear coats (i.e. F1104, F1111 and

245

F1177), due to the fact that this component imparts favorable qualities such as gloss and

246

hardness to the finished film.3 Only one sample, F1104, contained unique peaks indicative of an

247

amino resin melamine, which was utilized to cross-link the acrylic backbone. Based upon the

248

components identified in the clear coats (Table S-1), sample F1104 was correctly classified as an

249

acrylic-melamine-styrene enamel, samples F1104 and F1177 were classified as an acrylic-

250

styrene enamel and sample F1126 was categorized as an acrylic urethane coating. As can be seen

251

from Figure 1, spectra acquired from samples F1111 and F1177 appear visually similar, which is

252

not surprising considering these samples were obtained from Toyota Camry vehicles assembled

253

in the same manufacturing plant in the same year. However, PCA was also used to objectively

254

determine the inter-sample variability.

255 256 257


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258 259 260

Figure 1: Representative peak centroided high resolution DART mass spectra obtained from the clear coats for the four black vehicles.

261

PCA performed on the entire mass spectral dataset revealed that 87.02 % of the variability in the

262

dataset could be accounted for in the first two PCs. A 2-dimensional scores plot was generated

263

by projecting the scores of the samples from the first two PCs. Additionally, the factor loadings 11 ACS Paragon Plus Environment


Page 12 of 27

264

for the relevant PCs were examined to identify variables (m/z) in the original data that have a

265

significant weighting on the PCs. The scores and loadings plots are presented in Figure 2. The

266

scores plot (left) revealed that samples F1104 and F1126 formed distinct non-overlapping

267

classes, while samples F1111 and F1177 attained very similar scores. The loadings plot (right)

268

for the first PC revealed strong positive correlations for the peaks at an m/z of 115.0760 and

269

143.0713 and significant negative correlations for the peaks with an m/z of 139.0731, 141.0896

270

and 153.0895. The positive correlations at 115.0760 and 143.0713 were indicative of the

271

protonated molecules [M+H]+ of caprolactone and glycidyl methacrylate. Samples F1111 and

272

F1177 were caprolactone modified acrylic enamels that contained a relatively large amount of

273

both components and thus attained large positive scores on PC1. The negative correlations for

274

the peaks at an m/z of 139.0731, 141.0896 and 153.0895 were characteristic for alkylated

275

melamine derivatives that cross-link the enamel. As only sample F1104 contained melamine in

276

its clear coat formulation, it is not surprising that these samples have substantial negative scores.

277

The factor loadings for PC2 also revealed strong positive correlations for caprolactone and

278

glycidyl methacrylate; however, positive correlations were also identified for peaks with an m/z

279

of 105.0687 and 113.0597. These peaks were characteristic for the protonated molecules [M+H]+

280

of styrene and the acrolein dimer respectively, both of which are common components in modern

281

automotive coatings. A significant negative correlation was observed for the peak with an m/z of

282

127.0758 was characteristic of allyl methacrylate. Samples that contained large amounts of

283

styrene and acrolein dimer (i.e. F1104, F1111 and F1177) attained large positive scores on PC2,

284

while the sample devoid of these components and contained a large amount of allyl methacrylate

285

(i.e. F1126) attained significant negative scores on PC2.


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286 287 288


Figure 2: (Left) 2-dimensional scores plot highlighting the distribution of the automotive clear coats based upon their corresponding DART-MS data; (Right) Factor loadings for the first two PCs.

289 290

Py-GCMS

291

The total ion chromatograms (TICs) obtained from py-GCMS of the automotive clear coats is

292

displayed in Figure 3. By examining Figure 3 it is clearly evident that much like the DART-MS

293

data, samples F1111 and F1177 provided near visually identical pyrograms. Table 2 provided

294

below lists all the significant components identified in the pyrograms. A large amount of styrene

295

was detectable in samples F1104, F1111 and F1177 (Rt ~ 6.70), but not present in sample F1126,

296

which coincided with the information obtained from the DART data. The main differences

297

between the pyrograms of these clear coatings was based on variations in the

298

acrylate/methacrylate monomers and additives identified. Samples F1111 and F1177 contained

299

detectable amounts of butyl acrylate and methacrylate, as well as substantial amounts of

300

camphene. Butyl acrylate and methacrylate were also identified in the DART mass spectral data,

301

but the relative intensities of the peaks attributable to these components were low in comparison

302

to the main components. Sample F1126 was comprised of substantial quantities of acrylic-based

303

monomers including; cyclohexyl methacrylate, 6-methylheptyl acrylate and octyl methacrylate,

304

as well as large amounts of cyclohexene and 2-ethylhexene additives. Only cyclohexyl

305

methacrylate was identified in the DART data for sample F1126, based on the presence of a peak

306

with an m/z of 169.1210 (Figure 1). On the other hand, sample F1104 was comprised of

307

substantial amounts of methyl and n-butyl methacrylate, and detectable amounts of hexyl and 13 ACS Paragon Plus Environment


308

octyl methacrylate. These components were identified in the DART data for sample F1104, but

309

the intensities of these peaks were diminished in comparison to other components.

310 311

Table 2: Table revealing the relative abundance of components identified from the pyrograms of the clear coats.

Compound

F1104

F1111

F1126

F1177

6.70

Styrene

Large

Large

Large

2.20

Methyl acrylate

Trace

6.80

n-Butyl acrylate

Trace

Small

Small

11.10

6-Methylheptyl acrylate

3.15 7.52 7.86 8.13 8.98 10.89 11.88 2.75 2.76 3.35 4.20 4.67 6.16 7.68 8.18 8.80 10.22 12.35

Methyl methacrylate n-Butyl methacrylate 2-Hydroxypropyl methacrylate Hexyl methacrylate Unknown Cyclohexyl methacrylate Octyl methacrylate Vinylcyclobutane Cyclohexene Ethenamine, N-methyleneToluene 2-Ethyl-1-hexene Ethylbenzene Camphene α-Methylstyrene 2-Ethylhexan-1-ol 2-Oxepanone Ethylidenecyclobutane

Moderate Moderate Moderate Small Small

Small

Moderate Moderate Large

Small

Small

Small Moderate

Moderate

Small

Moderate

Large Small

Small Small Small Small Small Moderate Small

Small Small Small

Other Additive(s)

Small Small Small Trace

Small Trace

Methacrylate(s)

Rt (min)

Acrylate(s)

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313 314

Figure 3: Representative pyrograms obtained from py-GCMS of the automotive clear coats.

315

Although melamine and alkylated melamine derivatives were identified in the DART mass

316

spectral data, they were not observed in the pyrograms without methylation.45 Conversely,

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various additives such as camphene and 2-ethyl-1-hexene were readily identifiable in the py-

318

GCMS data but not in the DART mass spectral data. While the majority of components were 15 ACS Paragon Plus Environment


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readily identifiable with both techniques, the fact that some components were unique to the

320

DART-TOFMS and py-GCMS infers that these methodologies may also afford complimentary

321

information.

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PCA performed on the significant variables identified in the pyrograms (Table 2) revealed that

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96.71 % of the variance in the dataset could be accounted for in the first two PCs. The factor

324

loadings for PC1, revealed a significant positive correlation with a peak with an Rt ~ 6.70

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minutes, which is characteristic for styrene and a significant negative correlation with a peak

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with an Rt ~ 11.88 minutes, indicative of octyl methacrylate. Samples that contained large

327

amounts of styrene in their formulation (i.e. F1104, F1111 and F1177) attained positive scores

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on PC1, while sample F1126 that did not contain styrene but contained a substantial amount of

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octyl methacrylate in its formulation obtained significant negative scores on PC1. The factor

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loadings for PC2 revealed two significant positive correlations with peaks that had retention

331

times of approximately 3.15 and 7.52 minutes, which were characteristic for methyl methacrylate

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and n-butyl methacrylate, respectively. A large negative correlation peak was also observed (Rt ~

333

7.68 min) which was indicative of the bicyclic monoterpene camphene. Sample F1104 had large

334

positive scores on PC2 based on the relatively large presence of methyl methacrylate and n-butyl

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methacrylate in the clear coat formulation. Conversely, samples F1111 and F1177 attained

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negative scores on PC2, due to the substantial amount of camphene in the clear coat samples. By

337

examining the scores plots presented in Figures 2 and 4, it is evident that the distribution of

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samples is almost identical, based on their DART spectra and pyrograms respectively. Although

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the intra-sample variability is slightly higher in the DART data based on the greater variance in

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scores, the similar structure in the datasets indicates that the two techniques provide comparable

341

discriminatory information.

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343 344 345

Figure 4: (Left) 2-dimensional scores plot depicted the variance in the pyrograms obtained from the automotive clear coats; (Right) Loadings plot for PC1 and PC2.

346 347

Thermal desorption/pyrolysis DART-TOFMS

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The thermal desorption and pyrolysis system ionRocket was interfaced to the DART-TOFMS to

349

investigate if any additional information could be elucidated from the temperature profiles

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afforded by this technique. An example thermal desorption plot from the full paint chips of

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sample F1104 is provided in Figure 5. The thermal desorption plots were highly reproducible

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and the intra-sample variability between replicates was relatively small, with only minimal

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variability in the time reproducibility attributable to minor inconsistencies in the sizes of the

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paint chips. The thermal desorption plots displayed in Figure 5, are base peak chronograms

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(BPCs) which reflects changes in the molecular base peak of the spectrum as a function of time,

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which in this instance is temperature dependent. By examining the mass spectral information

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from different temperature regions of the thermal desorption plot, we were able to visualize

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latent compounds from the clear coat that was not readily identifiable by the py-GCMS and

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DART-TOFMS protocols. For instance, upon examination of the low temperature mass spectral

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profile (i.e. 100-350 °C), the base peak (m/z - 101.0580) was identified as the binder methyl

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methacrylate. However, interestingly a peak was also observed with a m/z of 352.2385, which

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was indicative of a hydroxyphenylbenzotriazole ultraviolet (UV) absorber known as tinuvin 328.

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UV absorbers are utilized in only automotive clear coatings to protect the vehicle from

364

environmental degradation. It is important to note that this component was not observed when 17 ACS Paragon Plus Environment


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the clear coat from sample F1104 was characterized by py-GCMS and the DART-TOFMS

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protocol. In fact, information pertaining to the UV absorbers used in the automotive clear coat

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formulations can only typically by obtained upon analysis with UV microspectrophotometry.7, 46

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It is worth noting that as the thermal desorption/pyrolysis DART-TOFMS was performed on the

369

intact paint chips, there were also contributions from the underlying layers and not just the clear

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coats. This was reflected in observable differences in the thermal desorption plots from the clear

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coat and paint chip from sample F1126 provided in the supplementary information (Figure S1).

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Additionally, paint chips obtained from the bumper and the body (i.e. above the tire) of a 2011

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Toyota Matrix were characterized by thermal desorption/pyrolysis DART-TOFMS. As can be

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seen from Figure S2 in the supplementary information, there are visual differences in the thermal

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desorption plots from the bumper and vehicle body paint systems, reinforcing the notion that

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these parts of the vehicle are finished differently.

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Figure 5: Thermal desorption plots for sample F1104 in triplicate. Mass spectral data was acquired across three temperature profiles (i.e. 100-350 °C, 350-450 °C and 450-600 °C).

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Moreover, the results from thermal desorption/pyrolysis DART-TOFMS of the intact paint chips

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revealed that there was a clear and reproducible temperature dependence of pyrolysis products

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and paint components for each paint sample. It is important to note that while the total ion

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chronograms (TICs) were visually identical between the samples, the thermal desorption plots 18 ACS Paragon Plus Environment

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(i.e. BPCs) displayed in Figure 6, revealed marked differences in the profile of the four paint

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samples. Interestingly, even samples F1111 and F1177 which were obtained from vehicles from

386

the same manufacturer, model and manufacturing plant, still have visually different thermal

387

desorption plots (Figure 6). This is most likely attributable to the fact that thermal

388

desorption/pyrolysis DART-TOFMS was used to characterize the entire paint system, as

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components were detected in the mass spectral data that were characteristic from the underlying

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layers (i.e. basecoat and primer surfacer). Specifically, samples F1111 and F1177 have slightly

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different basecoat finishes, based on information provided by the FDLE, which may have

392

contributed to the difference observed in the BPCs.

393 394 395

Figure 6: Averaged thermal desorption plots highlighting the differences between the paint chips obtained from the four vehicles.

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One major advantage of the thermal desorption plots produced from thermal desorption/pyrolysis

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DART-TOFMS, is that they are reproducible but also very simplistic, allowing for comparisons

398

between samples to be made easily. The ease of interpretation is in stark contrast to the

399

pyrograms and DART mass spectral data, which can oftentimes be convoluted and very difficult

400

to interpret. This may aid in making questioned vs. known comparisons, and specifically in

401

demonstrating to a jury in a courtroom setting. Additionally, much like the DART-TOFMS



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402

protocol, samples can be characterized rapidly (i.e. 7 min), which may aid in casework due to the

403

high sample throughput potential.

404

Conclusion:

405

Preliminary results obtained has demonstrated that DART-TOFMS is capable of providing

406

comparable information to py-GCMS for the analysis of automotive paint coatings. In fact, in

407

some instances they provide complementary information. The main advantage of the DART-

408

TOFMS protocol is that it enables sample analysis to be completed within 2-3 minutes, while a

409

standard py-GCMS method could take up to an hour (including a bake method). Subsequently,

410

the DART-TOFMS has shown to provide comparable sample discrimination to py-GCMS in a

411

fraction of the time, which could potentially provide an avenue for forensic casework.

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Additionally, thermal desorption/pyrolysis DART-TOFMS has shown the capability to

413

characterize paint specimens and visualize latent compounds not observed by the other two

414

protocols. This technique has demonstrated, albeit in this limited sample set, that the thermal

415

desorption plots are highly reproducible, with minimal intra-sample variability but a large inter-

416

sample variability. Consequently, a large validation study is currently being undertaken to

417

investigate the discriminating capabilities of the DART protocols, in comparison to standard

418

techniques such as FTIR spectroscopy and py-GCMS.

419

Acknowledgements:

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The authors would like to acknowledge Chikako Takei at BioChromato for providing the

421

ionRocket used in this research. Additionally, the authors acknowledge Kristen Taylor for

422

assisting in the analysis of the clear coats by py-GCMS. This research has been funded by the

423

Lucas Research Grant from the Forensic Sciences Foundation.

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For TOC only

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Figures Widths

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Figure 1: 2 column

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Figure 2: 1 column

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Figure 3: 2 column

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Figure 4: 1 column

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Figure 5: 2 column

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Figure 6: 1 column

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Table 1: 1 column

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Table 2: 2 column

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Figure 1: Representative peak centroided high resolution DART mass spectra obtained from the clear coats for the four black vehicles. 164x190mm (150 x 150 DPI)

ACS Paragon Plus Environment


Figure 2: (Left) 2-dimensional scores plot highlighting the distribution of the automotive clear coats based upon their corresponding DART-MS data; (Right) Factor loadings for the first two PCs. 179x79mm (150 x 150 DPI)


Page 26 of 27

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Figure 3: Representative pyrograms obtained from py-GCMS of the automotive clear coats. 166x191mm (150 x 150 DPI)


DART-MS: A New Analytical Technique for Forensic Paint Analysis

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