Differentiation of multilayered automotive coatings with Fourier transform infrared spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer

Purpose This study aims to using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer to identify different automotive coatings for forensic purpose. Design/methodology/approach Two four-layered samples in a hit-and-run case were compared layer by layer with three different methods. FTIR spectroscopy was used to primarily identify the organic and inorganic compositions. Raman spectrum and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS) were further used to complement the FTIR results. Findings Two weak and tiny peaks in one layer found between two samples by FTIR, Raman microscope and SEM-EDS verified the result of differences. The study used the three instruments in combination and found it's effective in sensing coatings, especially in the inorganic additives. Research limitations/implications Using these three instruments in combination is more accurate than individually in multilayered coating analysis for forensic purpose. Practical implications The three different instruments all present unique information on the composition, and provided similar and mutually verifiable results on the two samples. Originality/value With this method, scientists could identify and discriminate important coating evidences with tiny but characteristic differences.

Automated summary

This study aims to identify different automotive coatings for forensic purpose using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS). It was found that using these three methods in combination provides more accurate results in multilayered coating analysis for forensic purpose. All three methods provide unique information about the composition of the coatings and yield similar and mutually verifiable results.