Department of Chemistry and Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai 200438, P. R. China.
School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, P. R. China.
ACS Appl Mater Interfaces. 2020 Jul 1;12(26):29549-29555. doi: 10.1021/acsami.0c01972. Epub 2020 Jun 16.
Development and comparison of the latent fingerprints (LFPs) are two major studies in detection and identification of LFPs, respectively. However, integrated research studies on both fluorescent materials for LFP development and digital-processing programs for LFP comparison are scarcely seen in the literature. In this work, highly efficient red-emissive carbon dots (R-CDs) are synthesized in one pot and mixed with starch to form R-CDs/starch phosphors. Such phosphors are comparable with various substrates and suitable for the typical powder dusting method to develop LFPs. The fluorescence images of the developed LFPs are handled with an artificial intelligence program. For the optimal sample, this program presents an excellent matching score of 93%, indicating that the developed sample has very high similarity with the standard control. Our results are significantly better than the benchmark obtained by the traditional method, and thus, both the R-CDs/starch phosphors and the digital processing program fit well for the practical applications.
发展和比较潜在指纹(LFPs)分别是 LFPs 检测和识别的两个主要研究方向。然而,在文献中很少见到同时针对 LFPs 发展的荧光材料和 LFPs 比较的数字处理程序的综合研究。在这项工作中,一锅法合成了高效红色发射的碳点(R-CDs)并与淀粉混合形成 R-CDs/淀粉荧光粉。这种荧光粉与各种基质具有可比性,适用于典型的粉末显影法来发展 LFPs。开发的 LFPs 的荧光图像由人工智能程序处理。对于最佳样本,该程序给出了 93%的优异匹配分数,表明开发的样本与标准对照具有非常高的相似度。我们的结果明显优于传统方法获得的基准,因此,R-CDs/淀粉荧光粉和数字处理程序都非常适合实际应用。
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