JOURNAL ARTICLE

Forensic glass examinations—A review focused on elemental spectrochemical analysis.

  • Published In: Wiley Interdisciplinary Reviews: Forensic Science, 2023, v. 5, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Trejos, Tatiana 3 of 3

Abstract

Glass is a trace material commonly found at crime scenes that can provide valuable information at early investigative stages and during a trial. The forensic analysis of glass has steadily evolved since the 1970s with numerous technological advances in spectroscopy and spectrometry. This article presents an advanced review of the recent literature concerning the forensic examination of glass evidence and discusses the current state and future opportunities of the discipline. The review focuses on established elemental spectrochemical techniques, including Inductively Coupled Plasma methods (ICP), laser‐ablation, and x‐ray Fluorescence (XRF) methods, and newer applications using Laser Induced Breakdown Spectroscopy (LIBS), Raman Spectroscopy, and accelerator and reactor nuclear techniques. A brief overview of the transfer, persistence, interpretation, and analytical schemes implemented in the field is revised to provide context to the discussion of the capabilities and limitations of the current and emerging technology. This article is categorized under:Forensic Chemistry and Trace Evidence > Trace Evidence [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Wiley Interdisciplinary Reviews: Forensic Science. 2023/03, Vol. 5, Issue 2, p1
  • Document Type:Article
  • Subject Area:Applied Sciences
  • Publication Date:2023
  • ISSN:2573-9468
  • DOI:10.1002/wfs2.1476
  • Accession Number:162381975
  • 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.