A smartphone-integrated colorimetric sensor array based on chiral carbon dot nanozymes for rapid biothiol...

The discrimination of structurally similar biothiols remains a critical challenge in clinical diagnostics, as conventional nanozyme-based sensor arrays often suffer from limited signal diversity and poor discriminative ability. To address this, a colorimetric sensor array using two rationally designed chiral carbon dots (CDs) was constructed. The role of chirality was primarily reflected in regulating the catalytic heterogeneity and enriching the multi-channel response diversity rather than direct enantioselective recognition. The two chiral CDs possess markedly distinct oxidase-like and laccase-like activities. This disparity in catalytic activity effectively doubles the number of response channels, thereby enriching the cross-reactive signal patterns. The CDs catalyzed the oxidation of three chromogenic substrates, namely 3,3',5,5'-tetramethylbenzidine (TMB), 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and the 4-aminoantipyrine/2,4-dichlorophenol (4-AP/2,4-DP) coupling system in the absence of H₂O₂, producing distinct colorimetric responses. The addition of thiols elicits a differential inhibition of this activity in a degree-dependent manner, thereby inducing characteristic changes in the colorimetric readout. The developed array enabled accurate simultaneous identification and quantitative analysis of four thiols, including glutathione (GSH), L-cysteine (Cys), homocysteine (Hcy), and thioglycolic acid (TGA), by extracting RGB values using a smartphone color picker app and combining them with principal component analysis (PCA), hierarchical cluster analysis (HCA), and supervised machine learning. The k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) classifiers achieved overall classification accuracies ranging from 95.83% to 97.22%, which further confirmed the robustness and reliability of the proposed sensing strategy. PCA score plots showed well-separated clusters for all analytes over the concentration range of 1-100 μM, while HCA further confirmed 100% classification accuracy. Furthermore, the first principal component (PC1) exhibited good linear correlation with GSH and TGA concentrations in a wide linear range of 1-50 μM, and the detection limit for all thiols was 1 μM. Successful application in serum and urine samples highlights its potential for smartphone-based clinical disease diagnosis.