Department of Chemistry, Sharif University of Technology, Tehran 111559516, Iran.
Department of Nanotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education, and Extension Organization (AREEO), Karaj 3135933151, Iran.
Anal Chem. 2023 Jul 4;95(26):10110-10118. doi: 10.1021/acs.analchem.3c01904. Epub 2023 Jun 19.
The oxidation state of an element significantly controls its toxicological impacts on biological ecosystems. Therefore, design of robust sensing strategies for multiplex detection of species with respect to their oxidation states or bonding conditions, i.e., chemical speciation, is quite consequential. Chromium (Cr) species are known as the most abundant inorganic groundwater pollutants and can be quite harmful to human health depending on their oxidation states. In the present study, a multicolorimetric probe based on silver-deposition-induced color variations of gold nanorods (AuNRs) was designed for identification and quantification of Cr species including Cr (III) and Cr (VI) (i.e., CrO and CrO) in water samples. In fact, the presence of Cr species leads to inhibition of the silver metallization of AuNRs to various degrees depending on the concentration and identity of the analyte. This process is accompanied by the blue shift of the longitudinal peak which results in sharp-contrast rainbow-like color variations, thereby providing great opportunity for highly accurate visual detection. The gathered dataset was then statistically analyzed using two pattern recognition and regression machine learning techniques. In particular, linear discriminant analysis was used as a classification method to discriminate the unicomponent and mixture samples of Cr species with 100% accuracy. Then, a well-known multivariate calibration technique called partial least-squares regression was employed for quantitative analysis of Cr species. Responses were linearly related to Cr species concentrations over a wide range of 10.0-1000.0, 1.0-200.0, and 1.0-200.0 μmol L with detection limits of 37.7, 8.7, and 2.9 μmol L for Cr, CrO, and CrO, respectively. The practical applicability of the multicolorimetric probe was successfully evaluated by analyzing Cr species in several water specimens comprising tap water, mineral water, river water, and seawater. Above all, the vivid rainbow color tonality of the proposed assay further improves the accuracy of the naked eye detection, making it a practical platform for on-site monitoring of Cr contamination.
元素的氧化态显著控制其对生物生态系统的毒理学影响。因此，设计稳健的传感策略来对物种进行多元检测，以了解其氧化态或键合状态，即化学形态，是非常重要的。铬（Cr）物种是最丰富的无机地下水污染物之一，其毒性取决于其氧化态。在本研究中，设计了一种基于银沉积诱导金纳米棒（AuNRs）颜色变化的多色探针，用于识别和定量水样中的 Cr 物种，包括 Cr（III）和 Cr（VI）（即 CrO 和 CrO）。事实上，Cr 物种的存在会导致 AuNRs 的银金属化程度不同程度的抑制，这取决于分析物的浓度和种类。这一过程伴随着纵向峰的蓝移，导致鲜明的彩虹色变化，从而为高度准确的目视检测提供了巨大机会。然后使用两种模式识别和回归机器学习技术对收集到的数据进行统计分析。具体来说，线性判别分析被用作分类方法，以 100%的准确率区分 Cr 物种的单一组分和混合物样品。然后，采用一种称为偏最小二乘回归的多元校正技术对 Cr 物种进行定量分析。响应与 Cr 物种浓度在很宽的范围内呈线性关系，范围为 10.0-1000.0、1.0-200.0 和 1.0-200.0 μmol L，检测限分别为 37.7、8.7 和 2.9 μmol L，用于 Cr、CrO 和 CrO。该多色探针的实际适用性通过分析包含自来水、矿泉水、河水和海水在内的几种水样中的 Cr 物种得到了成功评估。最重要的是，所提出的测定方法生动的彩虹色调进一步提高了肉眼检测的准确性，使其成为现场监测 Cr 污染的实用平台。
