State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, P. R. China.
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China.
ACS Appl Mater Interfaces. 2020 Mar 18;12(11):13494-13502. doi: 10.1021/acsami.9b22251. Epub 2020 Mar 5.
Fingerprint formed through lifted papillary ridges is considered the best reference for personal identification. However, the currently available latent fingerprint (LFP) images often suffer from poor resolution, have a low degree of information, and require multifarious steps for identification. Herein, an individual Cloud-based fingerprint operation platform has been designed and fabricated to achieve high-definition LFPs analysis by using CsPbBr perovskite nanocrystals (NCs) as eikonogen. Moreover, since CsPbBr NCs have a special response to some fingerprint-associated amino acids, the proposed platform can be further used to detect metabolites on LFPs. Consequently, in virtue of Cloud computing and artificial intelligence (AI), this study has demonstrated a champion platform to realize the whole LFP identification analysis. In a double-blind simulative crime game, the enhanced LFP images can be easily obtained and used to lock the suspect accurately within one second on a smartphone, which can help investigators track the criminal clue and handle cases efficiently.
通过提起乳突脊形成的指纹被认为是个人身份识别的最佳参考。然而，目前可用的潜伏指纹 (LFP) 图像通常分辨率较差，信息量低，并且需要多种识别步骤。在此，设计并制造了一种基于云的个人指纹操作平台，通过使用 CsPbBr 钙钛矿纳米晶体 (NCs) 作为模板来实现高清晰度 LFPs 分析。此外，由于 CsPbBr NCs 对一些与指纹相关的氨基酸有特殊的响应，因此所提出的平台可以进一步用于检测 LFPs 上的代谢物。因此，借助云计算和人工智能 (AI)，本研究展示了一个冠军平台，实现了整个 LFP 识别分析。在一个双盲模拟犯罪游戏中，可以轻松获得增强的 LFP 图像，并在智能手机上在一秒钟内准确锁定嫌疑人，这有助于调查人员跟踪犯罪线索并高效处理案件。
