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SECM for imaging and detection of latent fingerprints
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Zhang, Meiqin and Girault, Hubert H. (2009) SECM for imaging and detection of latent fingerprints. Analyst, Vol.134 (No.1). pp. 25-30. doi:10.1039/b815336a
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Official URL: http://dx.doi.org/10.1039/b815336a
Abstract
Fingerprint analysis is one of the most important methods used for personal identification of evidence found at a crime scene for forensic purposes. Using scanning electrochemical microscopy (SECM) imaging, researchers can visualize the ultrastructure of human fingerprints on wet porous and nonporous surfaces by combining with silver-staining or multi-metal-deposition (MMD) technology. SECM allows investigators to image chemical activities of fingerprint surfaces with an impressively high resolution, such as the third level valuable information for confirming an identification. This methodology takes a significant advantage of the high sensitivity of SECM towards the small variation of electrochemical reaction rates at the substrate surface. In this review, we highlight the recent breakthroughs in ultrasensitive imaging and detection of latent fingerprints with a special focus on a novel application of SECM. We will also discuss our perspectives on future research directions.
| Item Type: | Journal Item | ||||
|---|---|---|---|---|---|
| Subjects: | Q Science > QD Chemistry | ||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry | ||||
| Journal or Publication Title: | Analyst | ||||
| Publisher: | Royal Society of Chemistry | ||||
| ISSN: | 0003-2654 | ||||
| Official Date: | 2009 | ||||
| Dates: |
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| Volume: | Vol.134 | ||||
| Number: | No.1 | ||||
| Number of Pages: | 6 | ||||
| Page Range: | pp. 25-30 | ||||
| DOI: | 10.1039/b815336a | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | Ecole Polytechnique Federale de Lausanne, European Commission | ||||
| Grant number: | 040126 |
Data sourced from Thomson Reuters' Web of Knowledge
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