A Self-Powered Sensing Platform Based on the Janus Heterostructure for Machine Learning-Assisted Dual-Mode Detection...

Area of Science:

• Bioelectrochemical systems
• Nanomaterials science
• Biosensor technology

Background:

• Enzymatic biofuel cells face challenges in charge extraction due to interfacial energy level mismatch.
• Efficient self-powered sensing requires optimized electron transfer at bioelectrode interfaces.

Purpose of the Study:

• To develop a self-powered sensing platform for dual-mode detection of 17β-estradiol (E2).
• To engineer a Janus bioanode with a built-in electric field (BIEF) for enhanced charge extraction.
• To integrate the system with IoT and machine learning for robust real-time analysis.

Main Methods:

• Fabrication of a Janus heterointerface using Zn-O-Ti coordination for BIEF induction.
• Utilizing Density Functional Theory (DFT) to analyze energy level alignment for electron injection.
• Employing aptamer-triggered hybridization chain reaction for signal amplification and dual-mode output (electrochemical and optical).

Main Results:

• The Janus bioanode demonstrated favorable energy level alignment, promoting efficient electron injection and bioelectrocatalysis.
• The platform achieved intelligent dual-mode detection of E2.
• Real-time wireless readout via an IoT-connected voltmeter and improved analytical robustness using machine learning were demonstrated across various sample matrices.

Conclusions:

• Janus interfacial energy level engineering is a viable strategy for optimizing charge transfer in bioelectrochemical systems.
• The developed platform offers a robust and efficient solution for self-powered biosensing.
• This approach has broad implications for advancing enzymatic biofuel cell applications.