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

Theoxidation state of an element significantly controls its toxicologicalimpacts on biological ecosystems. Therefore, design of robust sensingstrategies for multiplex detection of species with respect to theiroxidation states or bonding conditions, i.e., chemical speciation,is quite consequential. Chromium (Cr) species are known as the mostabundant inorganic groundwater pollutants and can be quite harmfulto human health depending on their oxidation states. In the presentstudy, a multicolorimetric probe based on silver-deposition-inducedcolor variations of gold nanorods (AuNRs) was designed for identificationand quantification of Cr species including Cr (III) and Cr (VI) (i.e.,CrO4 (2-) and Cr2O7 (2-)) in water samples. In fact, the presence ofCr species leads to inhibition of the silver metallization of AuNRsto various degrees depending on the concentration and identity ofthe analyte. This process is accompanied by the blue shift of thelongitudinal peak which results in sharp-contrast rainbow-like colorvariations, thereby providing great opportunity for highly accuratevisual detection. The gathered dataset was then statistically analyzedusing two pattern recognition and regression machine learning techniques.In particular, linear discriminant analysis was used as a classificationmethod to discriminate the unicomponent and mixture samples of Crspecies with 100% accuracy. Then, a well-known multivariate calibrationtechnique called partial least-squares regression was employed forquantitative analysis of Cr species. Responses were linearly relatedto Cr species concentrations over a wide range of 10.0-1000.0,1.0-200.0, and 1.0-200.0 & mu;mol L-1 with detection limits of 37.7, 8.7, and 2.9 & mu;mol L-1 for Cr3+, CrO4 (2-), and Cr2O7 (2-), respectively. The practicalapplicability of the multicolorimetric probe was successfully evaluatedby analyzing Cr species in several water specimens comprising tapwater, mineral water, river water, and seawater. Above all, the vividrainbow color tonality of the proposed assay further improves theaccuracy of the naked eye detection, making it a practical platformfor on-site monitoring of Cr contamination.

Automated summary

The oxidation state of an element is crucial for its toxicological impacts on biological ecosystems. Therefore, the development of robust sensing strategies for the detection of species based on their oxidation states or bonding conditions is of great importance. In this study, a multicolorimetric probe utilizing gold nanorods was designed for the identification and quantification of chromium species in water samples.