Dual-Chronoamperometry Drift Correction for Electrochemical Sensors

Area of Science:

• Electrochemistry
• Biosensing
• Biomarker Detection

Background:

• Accurate biomarker sensing is vital for disease diagnosis and personalized medicine.
• Signal drift from biofouling and instability hinders precise biomarker measurement.
• Existing methods struggle with reliability in real-world conditions.

Purpose of the Study:

• To develop a novel method for accurate and reliable biomarker detection despite signal drift.
• To address the challenges of biofouling and monolayer instability in biosensing.
• To enhance the sensitivity and reliability of chronoamperometry-based detection.

Main Methods:

• A continuous dual-chronoamperometry technique applying reference and test pulses.
• Faradaic current extraction to isolate target signals.
• Utilizing multilinear drift relationships to predict and correct signal drift.
• Application of a linear regression machine learning model for quantification.

Main Results:

• Demonstrated successful correction of signal drift in IFN-γ detection using a molecular pendulum.
• Validated broad applicability across various amperometry-based systems including DNA and aptamer sensors.
• Achieved reliable quantification of target concentrations using machine learning with dual-chronoamperometry data.

Conclusions:

• The novel dual-chronoamperometry method effectively corrects signal drift, enhancing biosensor accuracy.
• This technique offers broad applicability for various amperometric biosensing platforms.
• The method paves the way for real-time monitoring applications in diagnostics and personalized medicine.