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Machine Learning System To Monitor Hg2+ and Sulfide Using a Polychromatic Fluorescence-Colorimetric Paper Sensor
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2023-02-07 , DOI: 10.1021/acsami.2c16565 Zhiwei Lu 1 , Maoting Chen 1 , Tao Liu 2 , Chun Wu 1 , Mengmeng Sun 1 , Gehong Su 1 , Xianxiang Wang 1 , Yanying Wang 1 , Huadong Yin 3 , Xinguang Zhou 4 , Jianshan Ye 5 , Yizhong Shen 6 , Hanbing Rao 1
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2023-02-07 , DOI: 10.1021/acsami.2c16565 Zhiwei Lu 1 , Maoting Chen 1 , Tao Liu 2 , Chun Wu 1 , Mengmeng Sun 1 , Gehong Su 1 , Xianxiang Wang 1 , Yanying Wang 1 , Huadong Yin 3 , Xinguang Zhou 4 , Jianshan Ye 5 , Yizhong Shen 6 , Hanbing Rao 1
Affiliation
An optical monitoring device combining a smartphone with a polychromatic ratiometric fluorescence-colorimetric paper sensor was developed to detect Hg2+ and S2– in water and seafood. This monitoring included the detection of food deterioration and was made possible by processing the sensing data with a machine learning algorithm. The polychromatic fluorescence sensor was composed of blue fluorescent carbon quantum dots (CDs) (BU-CDs) and green and red fluorescent CdZnTe quantum dots (QDs) (named GN-QDs and RD-QDs, respectively). The experimental results and density functional theory (DFT) prove that the incorporation of Zn can improve the stability and quantum yield of CdZnTe QDs. According to the dynamic and static quenching mechanisms, GN-QDs and RD-QDs were quenched by Hg2+ and sulfide, respectively, but BU-CDs were not sensitive to them. The system colors change from green to red to blue as the concentration of the two detectors rises, and the limits of detection (LOD) were 0.002 and 1.488 μM, respectively. Meanwhile, the probe was combined with the hydrogel to construct a visual sensing intelligent test strip, which realized the monitoring of food freshness. In addition, a smartphone device assisted by multiple machine learning methods was used to text Hg2+ and sulfide in real samples. It can be concluded that the fabulous stability, sensitivity, and practicality exhibited by this sensing mechanism give it unlimited potential for assessing the contents of toxic and hazardous substances Hg2+ and sulfide.
中文翻译:
使用多色荧光比色纸传感器监测 Hg2+ 和硫化物的机器学习系统
开发了一种将智能手机与多色比率荧光比色纸传感器相结合的光学监测设备,用于检测水和海鲜中的 Hg 2+和 S 2– 。这种监测包括检测食品变质,并通过使用机器学习算法处理传感数据来实现。多色荧光传感器由蓝色荧光碳量子点(CD)(BU-CD)和绿色和红色荧光CdZnTe量子点(QD)(分别称为GN-QD和RD-QD)组成。实验结果和密度泛函理论(DFT)证明Zn的掺入可以提高CdZnTe量子点的稳定性和量子产率。根据动态和静态猝灭机制,GN-QDs和RD-QDs分别被Hg 2+和硫化物猝灭,但BU-CDs对此不敏感。随着两个检测器浓度的升高,系统颜色从绿色变为红色到蓝色,检测限 (LOD) 分别为 0.002 和 1.488 μM。同时,将探针与水凝胶结合,构建视觉传感智能试纸条,实现食品新鲜度的监测。此外,在多种机器学习方法的辅助下,还使用智能手机设备对真实样品中的 Hg 2+和硫化物进行了文本分析。可以得出结论,这种传感机制所表现出的出色的稳定性、灵敏度和实用性使其在评估有毒有害物质Hg 2+和硫化物的含量方面具有无限的潜力。
更新日期:2023-02-07
中文翻译:
使用多色荧光比色纸传感器监测 Hg2+ 和硫化物的机器学习系统
开发了一种将智能手机与多色比率荧光比色纸传感器相结合的光学监测设备,用于检测水和海鲜中的 Hg 2+和 S 2– 。这种监测包括检测食品变质,并通过使用机器学习算法处理传感数据来实现。多色荧光传感器由蓝色荧光碳量子点(CD)(BU-CD)和绿色和红色荧光CdZnTe量子点(QD)(分别称为GN-QD和RD-QD)组成。实验结果和密度泛函理论(DFT)证明Zn的掺入可以提高CdZnTe量子点的稳定性和量子产率。根据动态和静态猝灭机制,GN-QDs和RD-QDs分别被Hg 2+和硫化物猝灭,但BU-CDs对此不敏感。随着两个检测器浓度的升高,系统颜色从绿色变为红色到蓝色,检测限 (LOD) 分别为 0.002 和 1.488 μM。同时,将探针与水凝胶结合,构建视觉传感智能试纸条,实现食品新鲜度的监测。此外,在多种机器学习方法的辅助下,还使用智能手机设备对真实样品中的 Hg 2+和硫化物进行了文本分析。可以得出结论,这种传感机制所表现出的出色的稳定性、灵敏度和实用性使其在评估有毒有害物质Hg 2+和硫化物的含量方面具有无限的潜力。