Department of Biomedical Engineering, Izmir Katip Celebi University, 35620, Izmir, Turkey.
Department of Biomedical Engineering Graduate Program, Izmir Katip Celebi University, 35620, Izmir, Turkey.
Mikrochim Acta. 2022 Sep 6;189(10):373. doi: 10.1007/s00604-022-05474-4.
Peroxidase mimicking FeO@Chitosan (FeO@Chi) nanozyme was synthesized and used for high-sensitive enzyme-free colorimetric detection of HO. The nanozyme was characterized in comparison with FeO nanoparticles (NPs) using X-ray diffraction, Fourier-transform infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The catalytic performance of FeO@Chi nanozyme was first evaluated by UV-Vis spectroscopy using 3,3',5,5'-tetramethylbenzidine. Unlike FeONPs, FeO@Chi nanozyme exhibited an intrinsic peroxidase activity with a detection limit of 69 nM. Next, the nanozyme was applied to a microfluidic paper-based analytical device (µPAD) and colorimetric analysis was performed at varying concentrations of HO using a machine learning-based smartphone app called "Hi-perox Sens++ ." The app with machine learning classifiers made the system user-friendly as well as more robust and adaptive against variation in illumination and camera optics. In order to train various machine learning classifiers, the images of the µPADs were taken at 30 s and 10 min by four smartphone brands under seven different illuminations. According to the results, linear discriminant analysis exhibited the highest classification accuracy (98.7%) with phone-independent repeatability at t = 30 s and the accuracy was preserved for 10 min. The proposed system also showed excellent selectivity in the presence of various interfering molecules and good detection performance in tap water.
过氧化物酶模拟物 FeO@壳聚糖（FeO@Chi）纳米酶被合成并用于高灵敏度无酶比色检测 HO。通过 X 射线衍射、傅里叶变换红外光谱、动态光散射和热重分析对纳米酶与 FeO 纳米颗粒（NPs）进行了比较表征。首先通过使用 3,3',5,5'-四甲基联苯胺的紫外-可见光谱评估了 FeO@Chi 纳米酶的催化性能。与 FeONPs 不同，FeO@Chi 纳米酶表现出内在的过氧化物酶活性，检测限为 69 nM。接下来，将纳米酶应用于微流控纸基分析装置（µPAD），并使用基于机器学习的智能手机应用程序“Hi-perox Sens++”在不同浓度的 HO 下进行比色分析。带有机器学习分类器的应用程序使系统更加用户友好，并且对光照和相机光学的变化更加稳健和自适应。为了训练各种机器学习分类器，在七种不同光照下，通过四种智能手机品牌在 30 秒和 10 分钟时拍摄µPAD 的图像。结果表明，线性判别分析具有最高的分类准确性（98.7%），在 t=30 秒时具有与手机无关的可重复性，并且在 10 分钟内保持准确性。该系统在存在各种干扰分子的情况下也表现出出色的选择性，并且在自来水中具有良好的检测性能。
