Machine Learning-Assisted Colorimetric Assay Based on Au@Ag Nanorods for Chromium Speciation

Abstract

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 4 2− and Cr 2 O 7 2−) 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 −1 with detection limits of 37.7, 8.7, and 2.9 μmol L −1 for Cr 3+ , CrO 4 2− , and Cr 2 O 7 2−, 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.

Formation of plasmonic core/shell nanorods through ammonia-mediated dissolution of silver(i)oxide for ammonia monitoring

“…Therefore, AuNRs are very promising for applications as signal transducers in the development of sensors and biosensors. 23–26 In general, AuNRs are used in sensor design based on various mechanisms, including etching and formation/growth. 27 In growth-based methods, the analyte directly or indirectly reduces metal ions and forms core/shell nanoparticles.…”

Synthesis of Au@Ag core-shell nanorods with tunable optical properties

“…These core-shell nanorods can also be used in sensing and catalysis applications. The enhanced plasmonic properties resulting from the integration of the silver promote the sensitive detection of trace analytes in diverse samples [8][9][10][11][12][13][14]. Besides, the unique optical properties of nanorods make the design of a selective catalytic system more feasible, which improves the efficiency of chemical transformations [15].…”

Recent advances and trends in innovative biosensor‐based devices for heavy metal ion detection in food

“…Currently, various nanomaterials (e.g., metal-organic frameworks [MOFs], metal oxides, quantum dots [QDs], conductive polymers, and graphene-based materials) with excellent biocompatibility, chemical stability, and environmental friendliness have been rapidly developed and attracted growing interest (Liang et al, 2023;Liu et al, 2017;Manikandan et al, 2023;Yang et al, 2023). With suitable optical properties, superior catalytic efficiency, remarkable conductivity, abundant active sites, and high specific surface area of nanomaterials, extensive efficient and reliable biosensors have been constructed for monitoring the residual levels of HMIs in food-related matrices (Manikandan et al, 2023;Orouji et al, 2023;Tang, Chen, et al, 2023). Following the development of optical biosensors in recent decades, from the initial chelation reaction sensors to the later distance-induced localized surface plasmon resonance (LSPR) sensors and current widely reported enzyme-mimetic reaction sensors, they have demonstrated significant applications for HMIs detection (Dai et al, 2022;Fan, He, et al, 2021;Na et al, 2016).…”