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

Area of Science:

• Electrochemistry
• Analytical Chemistry
• Materials Science

Background:

• Single-channel photoelectrochemical (PEC) sensing faces challenges with reliability due to disturbances and interferences.
• Developing robust sensing platforms is crucial for accurate analysis in complex matrices.

Purpose of the Study:

• To develop a potential-resolved dual-channel PEC platform for enhanced sensing accuracy and reliability.
• To implement a self-monitoring mechanism for real-time data validation in PEC sensing.

Main Methods:

• Utilized a dual-channel PEC platform with independent cathodic (CuBi2O4/CuO) and anodic (BiVO4@TAFN) signal outputs.
• Applied Z-score normalization and principal component analysis (PCA) for statistical fusion of dual-channel photocurrent data.
• Exploited the second principal component (PC2) as an internal diagnostic index for outlier recognition.

Main Results:

• Achieved enhanced accuracy and robustness through statistical fusion, with PC1 as the unified quantitative metric.
• Demonstrated a self-monitoring mechanism using PC2 to automatically identify and exclude anomalous signals.
• Reported a wide dynamic range (0.001-1000 μM) and ultralow detection limit (1 nM) for hydrogen peroxide.
• Obtained satisfactory recoveries (95-104%) in complex biological samples (HeLa cell lysates).

Conclusions:

• The developed dual-channel PEC platform coupled with chemometric strategies offers a generalizable paradigm for intelligent, self-validated sensing.
• This approach significantly improves the reliability and accuracy of PEC sensing in complex analytical environments.
• The self-monitoring capability ensures data integrity and trustworthiness for critical applications.