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

2 days ago - Additionally, a thermal desorption/pyrolysis DART-TOFMS methodology was also evaluated to characterize the intact paint chips from the ve...
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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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Analytical Chemistry

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

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

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1

5 6

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3

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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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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

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

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

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

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

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

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

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

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

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

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

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

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techniques

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

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

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

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

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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

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

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Analytical Chemistry

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Introduction:

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

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

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

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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

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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

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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

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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

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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,

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

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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

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

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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

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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,

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

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

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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

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

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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

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

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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

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

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

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py-GCMS, in a shorter timeframe.

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

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

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

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

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

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

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Analytical Chemistry

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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

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

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

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

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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

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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

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

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methodology

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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

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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

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

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

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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

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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

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DART-TOFMS Methodology

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

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

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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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Analytical Chemistry

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temperature was required to pyrolyze and thermolytically breakdown the polymeric

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

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

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

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

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

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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

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duration of analysis.

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Py-GCMS Methodology

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

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

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

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

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

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

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

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

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

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

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

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

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temperature was held for an additional 10 minutes.

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Thermal desorption/pyrolysis DART-TOFMS Methodology

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The paint chips were characterized using the temperature gradient system ionRocket

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

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

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

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

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

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

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

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

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

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

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using MZmine 2.28.43

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Statistical Analysis

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

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

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

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Analytical Chemistry

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

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

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

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

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

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

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

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

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between groups of samples.

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Results & Discussion:

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

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

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

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

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

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

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

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

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

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

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

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similarity or dissimilarity between the paint samples.

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DART-TOFMS

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

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TOFMS was capable of thermolytically decomposing the polymeric macromolecules into the

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

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

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

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

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

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

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

243

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

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

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

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

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

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

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

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

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

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

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

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determine the inter-sample variability.

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Analytical Chemistry

258 259 260

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

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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

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

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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

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

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

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

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

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

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

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

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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,

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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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Analytical Chemistry

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

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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

Analytical Chemistry

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)

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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Small Small

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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,

317

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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319

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.

322

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

323

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

325

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

326

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

328

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

329

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

330

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

332

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

335

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

336

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

338

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

339

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

340

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

341

discriminatory information.

342

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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

348

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

350

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

351

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

352

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

353

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

354

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

355

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

356

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

357

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

358

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

359

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

360

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

361

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

362

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

363

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

367

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

370

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

371

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

372

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

373

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

374

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

375

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

376

these parts of the vehicle are finished differently.

377 378 379

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).

380

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

381

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

382

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

383

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

385

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

389

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

390

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

391

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.

396

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

397

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.

412

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:

420

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.

424 425 426 427

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Bell, S., Forensic Chemistry. Pearson Prentice Hall; New Jersey, 2006. Toyota, Quad Coat Meet the Challenge of Four-Stage Toyota & Lexus Colors. Pros, C., Ed. 2016. Bender, L., Chemistry/Trace/Paint and Coating: Automotive Paint. In Encyclopedia of Forensic Sciences, Siegel, J. A.; Saukko, P. J., Eds. Academic Press: London, 2013; pp 257-264. Scientific Working Group for Materials Analysis (SWGMAT), Forensic Sci. Comm. 1999, 1 (2). ASTM E-1610-18, Standard guide for forensic paint examination. ASTM International: Pennsylvania, 2018. Kopchick, K. A.; Bommarito, C. R., J. Forensic Sci. 2006, 51 (2), 340-343. Liszewski, E. A.; Lewis, S. W.; Siegel, J. A.; Goodpaster, J. V., Appl. Spectrosc. 2010, 64 (10), 11221125. Trzcińska, B.; Zięba-Palus, J.; Kościelniak, P., Anal. Lett. 2013, 46 (8), 1267-1277. Beauchaine, J. P.; Peterman, J. W.; Rosenthal, R. J., Microchim. Acta 1988, 94 (1-6), 133-138. Maric, M.; van Bronswijk, W.; Lewis, S. W.; Pitts, K., Anal. Methods 2012, 4 (9), 2687-2693. Zięba-Palus, J.; Michalska, A.; Wesełucha-Birczyńska, A., J. Mol. Struct. 2011, 993 (1–3), 134-141. Bell, S. E. J.; Stewart, S. P.; Armstrong, W. J., Raman spectroscopy for forensic analysis of household and automotive paints. In Infrared and Raman Spectroscopy in Forensic Science, Chalmers, J. M.; Edwards, H. G. M.; Hargreaves, M. D., Eds. John Wiley & Sons, Ltd: West Sussex, 2012; pp 121-135. Massonnet, G.; Stoecklein, W., Sci. Justice 1999, 39 (3), 181-187. Maric, M.; Van Bronswijk, W.; Pitts, K.; Lewis, S. W., J. Raman Spectrosc. 2016, 47 (8), 948-955. Burke, P.; Curry, C. J.; Davies, L. M.; Cousins, D. R., Forensic Sci. Int. 1985, 28 (3–4), 201-219. Fukuda, K., Forensic Sci. Int. 1985, 29 (3–4), 227-236. Burns, D. T.; Doolan, K. P., Anal. Chim. Acta 2005, 539 (1–2), 157-164. Plage, B.; Berg, A.; Luhn, S., Forensic Sci. Int. 2008, 177 (2–3), 146-152. Zięba-Palus, J.; Zadora, G.; Milczarek, J. M.; Kościelniak, P., J. Chromatogr. A 2008, 1179 (1), 41-46. De Nolf, W.; Janssens, K., Surf. Interface Anal. 2010, 42 (5), 411-418. McNorton, S. C.; Nutter, G. W.; Siegel, J. A., J. Forensic Sci. 2008, 53 (1), 116-124. Nishiwaki, Y.; Watanabe, S.; Shimoda, O.; Saito, Y.; Nakanishi, T.; Terada, Y.; Ninomiya, T.; Nakai, I., J. Forensic Sci. 2009, 54 (3), 564-570. Muehlethaler, C.; Gueissaz, L.; Massonnet, G., Chemistry/Trace/Paint and Coating: Forensic Paint Analysis. In Encyclopedia of Forensic Sciences, Siegel, J. A.; Saukko, P. J., Eds. Academic Press: London, 2013; pp 265-272. Lavine, B. K.; Fasasi, A.; Mirjankar, N.; Sandercock, M., Talanta 2014, 119, 331-340. Lavine, B. K.; Mirjankar, N.; Ryland, S.; Sandercock, M., Talanta 2011, 87, 46-52. Zięba-Palus, J.; Zadora, G.; Milczarek, J. M., J. Chromatogr. A 2008, 1179 (1), 47-58. Burns, D. T.; Doolan, K. P., Anal. Chim. Acta 2006, 571 (1), 25-29. Wampler, T. P.; Bishea, G. A.; Simonsick, W. J., J. Anal. Appl. Pyrolysis 1997, 40–41, 79-89. Lesiak, A. D.; Adams, K. J.; Domin, M. A.; Henck, C.; Shepard, J. R. E., Drug Test. Anal. 2014, 6 (7-8), 788-796. Steiner, R. R.; Larson, R. L., J. Forensic Sci. 2009, 54 (3), 617-622. Bennett, M. J.; Steiner, R. R., J. Forensic Sci. 2009, 54 (2), 370-375. Grange, A. H.; Sovocool, G. W., Rapid Comm. Mass Spectrom. 2011, 25 (9), 1271-1281. Houlgrave, S.; LaPorte, G. M.; Stephens, J. C.; Wilson, J. L., J. Forensic Sci. 2013, 58 (3), 813-821. Jones, R. W.; Cody, R. B.; McClelland, J. F., J. Forensic Sci. 2006, 51 (4), 915-918. Jones, R. W.; McClelland, J. F., Forensic Sci. Int. 2013, 231 (1), 73-81. Sisco, E.; Dake, J.; Bridge, C., Forensic Sci. Int. 2013, 232 (1), 160-168.

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37. Nilles, J. M.; Connell, T. R.; Stokes, S. T.; Dupont Durst, H., Propellants Explos. Pyrotech. 2010, 35 (5), 446-451. 38. Clemons, K.; Dake, J.; Sisco, E.; Verbeck, G. F., Forensic Sci. Int. 2013, 231 (1), 98-101. 39. Musah, R. A.; Cody, R. B.; Dane, A. J.; Vuong, A. L.; Shepard, J. R. E., Rapid Comm. Mass Spectrom. 2012, 26 (9), 1039-1046. 40. Maric, M.; Harvey, L.; Tomcsak, M.; Solano, A.; Bridge, C., Rapid Comm. Mass Spectrom. 2017, 31 (12), 1014-1022. 41. Nilles, J. M.; Connell, T. R.; Durst, H. D., Anal. Chem. 2009, 81 (16), 6744-6749. 42. Chen, T.-H.; Wu, S.-P., Forensic Sci. Int. 2018, 277, 179-187. 43. Pluskal, T.; Castillo, S.; Villar-Briones, A.; Orešič, M., BMC Bioinformatics 2010, 11 (1), 395. 44. Smylie, W. T., Vehicle identification. In Forensic Investigation of Stolen-Rcovered and Other CrimeRelated Vehicles, Stauffer, E.; Bonfant, M., Eds. Elsevier: Massachusetts, 2006; pp 127-176. 45. Leidl, M.; Schwarzinger, C., J. Anal. Appl. Pyrolysis 2005, 74 (1-2), 200-203. 46. Stoecklein, W.; Fujiwara, H., Sci. Justice 1999, 39 (3), 188-195.

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Analytical Chemistry

For TOC only

491 492

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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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500 501

Table 1: 1 column

502

Table 2: 2 column

503 504

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Analytical Chemistry

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)

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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)

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Analytical Chemistry

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

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