JOURNAL ARTICLE
Nontoxic ammunition: Challenges and perspectives for GSR identification.
Published In: Wiley Interdisciplinary Reviews: Forensic Science, 2023, v. 5, n. 3. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Carneiro, Caroline R.; Lucena, Marcella A. M.; Santos‐Silva, Carolina; Weber, Ingrid T. 3 of 3
Abstract
Gunshot residues (GSR) are one of the most important forensic traces in crimes involving firearm discharge, being the conventional GSR characterized by containing spheroidal particles composed of Pb, Ba, and Sb. Over the years, a wide range of techniques has been applied and improved to characterize the conventional GSR, with deserved emphasis on scanning electron microscopy coupled with energy‐dispersive x‐ray spectroscopy (SEM/EDS). However, the introduction of nontoxic ammunition (NTA), or heavy metal‐free (HMF), on the market has triggered a search for new methodologies for GSR identification since methods based on the chemical and/or morphological profiles of conventional GSR are no longer appropriate for the analysis of this type of residues. This review highlights current methods and proposals for characterization of GSR from NTA, which involve either the search for characteristic chemical profiles or the insertion of selective markers in this new type of ammunition. This article is categorized under:Forensic Chemistry and Trace Evidence > Emerging Technologies and MethodsForensic Medicine > Imaging Modalities [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Wiley Interdisciplinary Reviews: Forensic Science. 2023/05, Vol. 5, Issue 3, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2023
- ISSN:2573-9468
- DOI:10.1002/wfs2.1477
- Accession Number:163670436
- Copyright Statement:Copyright of Wiley Interdisciplinary Reviews: Forensic Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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