Self-Monitoring Photoelectrochemical Sensing Platform Enabled by Dual-Channel Potential Resolution and PCA Outlier Recognition

Reliable photoelectrochemical (PEC) sensing remains challenging due to the vulnerability of single-channel systems to random disturbances and matrix interferences. Here, we report a potential-resolved dual-channel PEC platform that enables independent cathodic (CuBi₂O₄/CuO) and anodic (BiVO₄@TAFN) signal outputs. Dual-channel photocurrent data were statistically fused using Z-score normalization and principal component analysis (PCA), yielding the first principal component (PC1) as a unified quantitative metric with enhanced accuracy and robustness. More importantly, the second principal component (PC2) was exploited as an internal diagnostic index to automatically identify and exclude anomalous signals in real time, establishing a self-monitoring mechanism for data reliability assurance. Using hydrogen peroxide as a model analyte, the method achieved a wide dynamic range of 0.001-1000 μM, an ultralow detection limit of 1 nM, and satisfactory recoveries (95-104%) in HeLa cell lysates. This study introduces a chemometric strategy coupling dual-channel potential resolution with PCA-based outlier recognition, providing a generalizable paradigm for intelligent, self-validated PEC sensing in complex analytical environments.