Journal of Chemistry and Chemical Research
Journal of Chemistry and Chemical Research. 2025; 5: (3) ; 10.12208/j.jccr.20250079 .
总浏览量: 57
辽宁石油化工大学石油化工学院 辽宁抚顺
*通讯作者： 李鑫明,单位：辽宁石油化工大学石油化工学院 辽宁抚顺; ；
针对传统潜指纹荧光显影材料存在背景干扰大、环境兼容性不足及成本较高等问题，铜纳米簇（Copper Nanoclusters, CuNCs）因其原子级尺寸、可调控发光特性及良好生物相容性，逐渐成为潜指纹成像领域的重要研究方向。本文系统综述了模板法、电化学法、微乳液法和直接还原法在粒径控制、稳定性与可规模化制备方面的差异。在此基础上，归纳了金属核尺寸、表面配体及外部环境对CuNCs光学性能的调控规律，并结合典型研究实例，评价了其在背景抑制能力、显影分辨率及环境友好性方面相较于量子点、稀土材料和有机染料的优势与局限。进一步综述了 CuNCs 在生物成像及潜指纹显影中的最新应用进展。最后，基于当前研究现状提出通过异金属掺杂、聚集诱导发光调控及复合载体构建等策略提升综合性能，并展望其向低毒化、智能化和多物证协同检测方向发展的研究趋势，为绿色、高效的潜指纹显现技术提供参考。
Traditional fluorescent materials for latent fingerprint development face challenges including strong background interference, limited environmental compatibility, and high costs. Copper nanoclusters (CuNCs) have emerged as a significant research focus in latent fingerprint imaging due to their atomic-scale dimensions, tunable luminescence, and excellent biocompatibility. This review systematically compares template-based synthesis, electrochemical methods, microemulsion techniques, and direct reduction approaches regarding particle size control, stability, and scalability. Subsequently, it summarizes how metal core dimensions, surface ligands, and external environments regulate the optical properties of CuNCs. Through representative case studies, the advantages and limitations of CuNCs are evaluated against quantum dots, rare-earth materials, and organic dyes—particularly in background suppression, development resolution, and environmental friendliness. Recent advances in applying CuNCs to bioimaging and latent fingerprint visualization are also comprehensively reviewed. Finally, based on current research, strategies such as heterometallic doping, aggregation-induced emission modulation, and composite carrier construction are proposed to enhance overall performance. Future research trends toward reduced toxicity, intelligent systems, and multi-evidence collaborative detection are outlined, providing valuable references for developing green and efficient latent fingerprint technologies.
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