Mn2+-activated NaYF4:Yb, Er red-emitting nanophosphors for accurate latent fingerprint identification by an Artificial Intelligence program

Abstract

The development and comparison of Latent Fingerprint Patterns (LFPs) constitute critical components of research in fingerprint detection and identification. However, the development of nanomaterials visualization and digital algorithms for LFPs analysis are mostly studied independently, resulting in inaccurate LFPs. This research synthesized a series of efficient red-emitting NaYF4:20 %Yb, 2 %Er, x%Mn (x = 0, 5, 10, 15, 20, 25, 30, 35, and 40) upconversion nanoparticles (UCNPs) for image development, which were combined with a digital algorithm for accurate LFP recognition. The eightfold increase in luminescence intensity of NaYF4:Yb, Er, 30 %Mn at 653 nm effectively avoids interference from biological tissue fluorescence. This improvement is credited to the efficient reverse energy transfer mechanism between Er3+ and Mn2+ ions, specifically the transition sequence 4S3/2(Er3+) → 4T1(Mn2+) → 4F9/2(Er3+). Furthermore, a MATLAB program was employed to process the fluorescent images of LFPs developed using the UCNPs nanophosphors, achieving a high matching score of 90.09 % for the optimized sample, significantly outperforming traditional benchmarks. These results demonstrate the superior effectiveness of the NaYF4:Yb, Er, 30 %Mn nanophosphor, combined with digital processing algorithms, for practical LFP recognition applications.