Amity Institute of Click Chemistry Research and Studies, Amity University Uttar Pradesh, Noida, India.
Amity Institute of Forensic Sciences, Amity University Uttar Pradesh, Noida, India.
PLoS One. 2024 Jan 4;19(1):e0296270. doi: 10.1371/journal.pone.0296270. eCollection 2024.
Nowadays, it is fascinating to engineer waste biomass into functional valuable nanomaterials. We investigate the production of hetero-atom doped carbon quantum dots (N-S@MCDs) to address the adaptability constraint in green precursors concerning the contents of the green precursors i.e., Tagetes erecta (marigold extract). The successful formation of N-S@MCDs as described has been validated by distinct analytical characterizations. As synthesized N-S@MCDs successfully incorporated on corn-starch powder, providing a nano-carbogenic fingerprint powder composition (N-S@MCDs/corn-starch phosphors). N-S@MCDs imparts astounding color-tunability which enables highly fluorescent fingerprint pattern developed on different non-porous surfaces along with immediate visual enhancement under UV-light, revealing a bright sharp fingerprint, along with long-time preservation of developed fingerprints. The creation and comparison of latent fingerprints (LFPs) are two key research in the recognition and detection of LFPs, respectively. In this work, developed fingerprints are regulated with an artificial intelligence program. The optimum sample has a very high degree of similarity with the standard control, as shown by the program's good matching score (86.94%) for the optimal sample. Hence, our results far outperform the benchmark attained using the conventional method, making the N-S@MCDs/corn-starch phosphors and the digital processing program suitable for use in real-world scenarios.
如今,将废生物质工程化为具有功能的有价值的纳米材料是一件很有趣的事情。我们研究了杂原子掺杂碳量子点(N-S@MCDs)的生产,以解决绿色前体中绿色前体含量的适应性限制问题,即 Tagetes erecta(万寿菊提取物)。通过不同的分析特性验证了所描述的 N-S@MCDs 的成功形成。合成的 N-S@MCDs 成功地掺入到玉米淀粉粉末中,提供了一种纳米碳质指纹粉末成分(N-S@MCDs/corn-starch 荧光粉)。N-S@MCDs 赋予了令人惊讶的颜色可调性,使在不同非多孔表面上开发的高荧光指纹图案得以实现,同时在紫外光下立即增强视觉效果,显示出明亮锐利的指纹,以及开发后的指纹的长时间保存。潜指纹(LFPs)的创建和比较分别是 LFPs 识别和检测的两个关键研究。在这项工作中,开发的指纹由人工智能程序进行调节。最佳样本与标准对照具有非常高的相似度,程序对最佳样本的匹配得分(86.94%)非常好。因此,我们的结果远远超过了使用传统方法获得的基准,使 N-S@MCDs/corn-starch 荧光粉和数字处理程序适用于实际场景。
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