Curcumin/kaolin composite for advanced latent fingerprint imaging with fluorescence quantification
Latent fingerprints (LFPs) are invisible impressions that need to be developed before being used for criminal investigation; however, existing fingerprint visualization techniques face challenges, such as complex preparation and poor contrast. To advance practical fingerprint detection, green-emissive micron-sized curcumin/kaolin composites were synthesized <i>via</i> a facile and cost-effective one-step physical cross-linking method, which exhibited unprecedented performance in developing diversified marks, including LFPs, knuckle prints, palm prints, and footprints, with clear three-level details on various substrates. Notably, the powders successfully developed LFPs that were aged for 30 days and even up to 100 days, meeting the stringent requirements for comprehensive forensic application. Afterward, a novel method, termed Fingerprint Fluorescence Intensity Ratio (FFIR), was developed to quantify the contrast between fingerprint signals and background noise and to compare the efficacy of full-color developing agents. Compared with the existing grayscale conversion strategy, the proposed FFIR method achieved tunable multi-color fingerprint image enhancement for the first time, which helped to eliminate background fluorescence interference and improved visual perception. The feasibility of FFIR and its sensitivity in tracking image capture parameters were demonstrated by the established mathematical model. Hence, the newly synthesized modified composites and the mathematical model-validated method demonstrate profound practical significance in comprehensive fingerprint imaging.
Authors
- Da-Wu Li
- Jianghua Zhang (ORCID: https://orcid.org/0000-0002-6734-3492)
- Jiujiang Wang (ORCID: https://orcid.org/0000-0001-8964-378X)
- Zijian Gao (ORCID: https://orcid.org/0000-0003-3864-738X)
- Jinke Han
Institutions
- Boston University (US)
- Hong Kong University of Science and Technology (HK)
- Criminal Investigation Police University of China (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- The Analyst
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1039/d4an01395f
- Citations
- 7
- Primary Topic
- Forensic Fingerprint Detection Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.68
Funders
- Natural Science Foundation of Liaoning Province
- Department of Education of Liaoning Province
- Ministry of Public Security of the People's Republic of China