Evidence map›Paper›PMID 40681954›Full record

ReviewForensic science, medicine, and pathology2025

Colorimetric methods using gold nanoparticles for forensic investigations.

S D Anudevi, K Kumar Ebenezar, Shoba Narayan

Abstract readReview
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In one paragraph

Review in Forensic science, medicine, and pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

S D AnudeviFaculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamilnadu, 603103, India.
K Kumar EbenezarCollege of Allied Health Sciences, Faculty of Medicine & Health Science, SRM Institute of Science & Technology, Trichy - Chennai Highways, Irungalur, Tamilnadu, 621105, India.
Shoba NarayanFaculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamilnadu, 603103, India. shobulu@gmail.com.ORCID http://orcid.org/0000-0003-0019-0388

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gold nanoparticles (GNPs) are increasingly employed in biosensing and forensic applications due to its unique optical properties, especially surface plasmon resonance (SPR). This property enables GNPs to undergo significant color changes in response to alterations in particle size, shape, and aggregation, making them ideal for sensitive colorimetric assays. These assays can detect analytes such as heavy metals (e.g., mercury, lead) and biomolecules without requiring complex instrumentation. The biocompatibility and functionalization versatility of GNPs enhance its application in forensic science, particularly for detecting toxic metals in biological samples. This review paper provides details regarding colorimetric methods using GNPs for forensic investigations using aggregation and ant aggregation-based detection of analytes using several ligands tagged with GNPs. This review is also focused on forensic toxicology, drug analysis, DNA profiling, latent fingerprint visualization, pesticide residue detection, and identification of explosive and gunshot residues. Forensic analysis of pesticides, such as organophosphates, carbamates, and pyrethroids, is effectively achieved using GNP-based colorimetric sensors, which detect compounds through visible color changes. Overall, the review emphasizes the critical role of GNP-assisted colorimetric methods in modern forensic investigations and its potential to revolutionize field-based analytical approaches.

Indexed as

ColorimetryForensic SciencesGoldMetal NanoparticlesDNA FingerprintingForensic ToxicologyHumansGold

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.