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Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence
Xiang-Yang Dong1, Xiao-Qing Niu1, Zheng-Yong Zhang2
1Department of Chemistry and Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai 200438, P. R. China.
ACS Applied Materials & Interfaces
|June 17, 2020
Summary
Highly efficient red-emissive carbon dots (R-CDs) mixed with starch create phosphors for developing latent fingerprints (LFPs). An AI program analyzes LFP images, achieving a 93% match score for superior forensic identification.
Area of Science:
- Forensic Science
- Materials Science
- Nanotechnology
Background:
- Latent fingerprint (LFP) development and comparison are crucial in forensics.
- Integrated research on fluorescent materials for LFP development and digital comparison programs is limited.
Purpose of the Study:
- To synthesize novel red-emissive carbon dots (R-CDs) for latent fingerprint development.
- To develop and evaluate an AI-driven digital processing program for latent fingerprint comparison.
- To integrate LFP development and comparison into a single, efficient workflow.
Main Methods:
- One-pot synthesis of highly efficient red-emissive carbon dots (R-CDs).
- Formation of R-CDs/starch phosphors for latent fingerprint development via powder dusting.
- Application of an artificial intelligence (AI) program for analyzing fluorescence images of developed LFPs.
Main Results:
- R-CDs/starch phosphors demonstrated suitability for developing LFPs on various substrates.
- The AI program achieved an excellent matching score of 93% for the optimal sample.
- Developed method significantly outperformed traditional latent fingerprint analysis techniques.
Conclusions:
- R-CDs/starch phosphors offer a promising material for latent fingerprint development.
- The AI-based digital processing program enhances latent fingerprint comparison accuracy and efficiency.
- The integrated approach shows strong potential for practical forensic applications.
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