Enhancing macromolecular composite films: The synergy of lifting and developing latent fingermarks
Source abstract
Abstract
Fingermarks are among the most dependable and frequently detected forms of evidence at crime scenes, providing cost-effective, rapid, and definitive means of binary identification, either confirming or excluding a match. This study investigates the development of an all-in-one solution for the collection, transportation, preservation, and enhancement of latent fingermarks by utilizing a specialized composite film. To achieve this, a naturally abundant, eco-friendly, and chemically stable polymer-cellulose-is employed alongside silicon dioxide (SiO2), a noteworthy reinforcement nanoparticle, and three recent chemicals used in forensic fingermark analysis: 3-aminocrotonitrile (AKN), diaminomaleonitrile (DAM), and methacrylonitrile (MAN). Moreover, the study utilizes both pristine and modified silica alongside three additional compounds to systematically investigate the impact of chemical modifications and synergistic interactions in composite films. Fourier-Transform Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD) and Scanning Electron Microscopy with EnergyDispersive X-ray Spectroscopy (SEM-EDX) analysis are used to investigate composite films. The films were applied to fingermarks aged for 1, 3, 5, 7, and 14 days on soda-lime-silicate glass, polystyrene (PS) plastic, aluminum foil (98.5-99.5 % purity), processed Scots pine wood, and steel plate surfaces. The findings indicated that composite films offered a viable approach for a comprehensive fingermark examination solution. Notably, films containing modified silica exhibited superior performance compared to those with pristine silica, with AKN demonstrating the most effective enhancement among the three nitriles examined.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Forensic Fingerprint Detection Methods · Digital and Cyber Forensics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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