Enhanced Down-Conversion Emission, High-Level Security, and Advanced Latent Fingerprint Visualization in La₂Zr₂O₇:Er³⁺ Nanophosphor through Surface Modification and Deep Learning Analysis

This study explores surface modifications on Er3+-activated La2Zr2O7 nanophosphors (NPs) through solution combustion, revealing a narrower band gap (3.1 eV) in surface-modified NPs. Under UV excitation (378 nm), these nanophosphors emit green light at 550 nm, notably enhanced by oleic acid (OA) modification, offering the potential for white light-emitting diode applications. Due to enhanced energy transfer processes, OA-modified NPs show 98.6% color purity and improved quantum efficiency (68.7%). Additionally, OA-modified NPs exhibit promise for UV-based forensic applications. This research also showcases the YOLOv8 algorithm's robustness (mAP@50 of 0.78 for bifurcation, 0.72 for ridge-end) in fingerprint detection, aiding forensics. The deep learning method supports feature-matching fingerprint detection and advanced identification techniques. These findings offer insights into nanophosphor modification's optical impact and advanced fingerprint identification methods, contributing to lighting technology and forensic applications.