Energy-Decoupled Photoelectrochemical and Pressure Dual-Mode Biosensors for Machine-Learning-Assisted Immunoassay

Abstract

Reliable and ultrasensitive detection of cardiac troponin I (cTnI) remains challenging due to the signal interference and limited self-validation capability of conventional biosensors. Herein, we report an orthogonal dual-mode biosensing strategy that integrates photoelectrochemical (PEC) sensing with catalysis-induced pressure transduction (CIPT) to achieve energy-decoupled signal generation and data-level cross-validation. Specifically, a multifunctional plasmonic Z-scheme probe, CdS@CdIn2S4/AuPt, was first synthesized to trigger in situ assembly of ZnO-based dual Z-scheme heterostructures upon cTnI recognition. This configuration simultaneously amplifies the photocurrent via enhanced charge separation and catalyzes the hydrolysis of ammonia borane to produce a macroscopic pressure change. These two outputs originate from distinct energy conversion pathways─interfacial charge transfer versus bulk gas expansion─thereby establishing an energy-decoupled dual-signal system. 3D-printed portable devices were further fabricated for signal acquisition, and a machine learning (ML)-assisted data analysis was employed for multivariate data fusion. The smart sensing platform exhibits high sensitivity for cTnI with limits of detection of 41.94 fg·mL–1 (PEC) and 164.80 fg·mL–1 (CIPT). The partial least-squares (PLS) model enables cross-channel validation, achieving a high prediction correlation (R2 > 0.9972) and improving recovery rates (98.74%–102.69%) in human serum. This work offers a robust and reliable pathway for the construction of energy-decoupled sensing platform for point-of care testing applications.