Carbon nanotubes-assisted solid-phase microextraction for the extraction of gasoline in fire debris samples

Gasoline is one of the most encountered ignitable liquids (IL) in fire debris analysis. The extraction of gasoline from fire debris samples presents challenges due to the complicated nature of multicomponent mixtures. This research work proposed a novel carbon nanotube-assisted solid phase microextraction (CNT-SPME) fiber coupled with gas chromatography and mass spectrometry (GC/MS) to determine gasoline residues for fire debris analysis. The CNT-SPME fiber was prepared by a sequential coating of polydopamine, epoxy, and CNTs on a stainless-steel wire. The extraction capabilities of the CNT-SPME fiber for gasoline and its major aromatic groups (xylenes, alkylbenzenes, indanes, and naphthalenes) from neat and spiked samples were promising, with linear dynamic ranges of 0.4-12.5 and 3.1-12.5 µg 20-mL^-1 headspace vial, respectively. The average relative standard deviations and accuracies for all concentration ranges in this work were lower than 15%. The relative recovery of the CNT-SPME fiber for all aromatic groups ranged from 28 ± 3% to 59 ± 2%. Additionally, the CNT-SPME fiber showed a higher selectivity for the naphthalenes group in gasoline, as indicated by the experimental outcome using a pulsed thermal desorption process of the extracts. We envision the nanomaterial-based SPME offers promising opportunities for extracting and detecting other ILs to support fire investigation.

Development of Crime Scene Intelligence Using a Hand-Held Raman Spectrometer and Transfer Learning

The classification of ignitable liquids, such as gasoline, is critical crime scene intelligence to assist arson investigations. Rapid field gasoline classification is challenging because the current forensic testing standard requires gas chromatography-mass spectrometry analysis of evidence in an accredited laboratory. In this work, we reported a new intelligent analytical platform for field identification and classification of gasoline evidence. A hand-held Raman spectrometer was utilized to collect Raman spectra of reference gasoline samples with various octane numbers. The Raman spectrum pattern was converted into image presentations by continuous wavelet transformation (CWT) to facilitate artificial intelligence development using the transfer learning technique. GoogLeNet, a pretrained convolutional neural network (CNN), was adapted to train the classification model. Six different classification models were also developed from the same data set using conventional machine learning algorithms to evaluate the performance of our new approach. The experimental results indicated that the pretrained CNN model developed by our new data workflow outperformed other models in several performance benchmarks, such as accuracy, precision, recall, F1, Cohen's Kappa, and Matthews correlation coefficient. When the transfer learning model was challenged with the data collected from weathered gasoline samples, the classifier could still offer 73 and 53% accuracy for 50 and 25% weathered gasoline samples, respectively. In conclusion, wavelet transforms combined with transfer learning successfully processed and classified complex Raman spectral data without feature engineering. We envision that this nondestructive, automated, and accurate platform will accelerate crime scene intelligence development based on evidence's chemical signatures detected by hand-held Raman spectrometers.

Development of magnetic carbon nanotubes for dispersive micro solid phase extraction of the cyanide metabolite, 2-aminothiazoline-4-carboxylic acid, in biological samples

2-aminothiazoline-4-carboxylic acid (ATCA) is a minor metabolite of cyanide and is suggested to be a promising biomarker for cyanide exposure due to its specificity to cyanide metabolism and its excellent short- and long-term stability during storage. In this study, magnetic carbon nanotubes, including magnetic multi-walled carbon nanotubes (Mag-MWCNT) and magnetic single-walled carbon nanotubes (Mag-SWCNT) were synthesized as a novel sorbent for dispersive micro solid phase extraction (d-μSPE) to extract ATCA from biological matrices. ATCA spiked deionized water samples with the addition of the isotopic internal standard (ATCA - ¹³C, ¹⁵N) were subjected to Mag-CNT/d-μSPE to confirm extraction efficiency of this new technique. The extracted ATCA was derivatized and quantitated using gas chromatography/mass spectrometry (GC/MS) analysis. The extraction parameters were optimized and a detection limits of 15 and 25 ng/mL were obtained for synthetic urine and bovine blood respectively with a linear dynamic range of 30-1000 ng/mL. The optimized Mag-CNT/d-μSPE method facilitated efficient extraction of ATCA using 2 mg of Mag-MWCNT with a 10-minute extraction time. The current assay was also found to be effective for the extraction of ATCA with average recoveries of 97.7 ± 4.0% (n = 9) and 96.5 ± 12.1% (n = 9) from synthetic urine and bovine blood respectively. The approach of using Mag-CNT to facilitate d-μSPE offered a novel alternative to extract ATCA from complex biological matrices.

Analysis of ethyl and methyl centralite vibrational spectra for mapping organic gunshot residues

Detection of ethyl and methyl centralites in gunshot residues is important in forensic science due to their limited contamination from environmental sources compared to other organic residues. However, the vibrational frequencies of centralites are little explored and their frequency assignments are incomplete. Herein, we investigated vibrational frequencies of centralites based on Density functional theory (DFT) to understand their vibrations. The simulated frequencies exhibit excellent agreement with the experimental data, and the detailed assignments are comprehensively elaborated. We also demonstrate that centralite particles could be detected through Raman imaging based on their fingerprints. This work is very important for the further vibrational studies in detecting and tracing centralites in gunshot residues.

The use of near-infrared photography to image fired bullets and cartridge cases

An imaging technique that is capable of reducing glare, reflection, and shadows can greatly assist the process of toolmarks comparison. In this work, a camera with near-infrared (near-IR) photographic capabilities was fitted with an IR filter, mounted to a stereomicroscope, and used to capture images of toolmarks on fired bullets and cartridge cases. Fluorescent, white light-emitting diode (LED), and halogen light sources were compared for use with the camera. Test-fired bullets and cartridge cases from different makes and models of firearms were photographed under either near-IR or visible light. With visual comparisons, near-IR images and visible light images were comparable. The use of near-IR photography did not reveal more details and could not effectively eliminate reflections and glare associated with visible light photography. Near-IR photography showed little advantages in manual examination of fired evidence when it was compared with visible light (regular) photography.