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Published on: September 19, 2017
Energy-Decoupled Photoelectrochemical and Pressure Dual-Mode Biosensors for Machine-Learning-Assisted Immunoassay
Jinxin Liu1,2, Zhen Yang1, Xingxing Meng1,3
1Anhui Laboratory of Functional Coordinated Complexes for Materials Chemistry and Application, School of Chemical and Environmental Engineering, Anhui Polytechnic University, Wuhu241000, P. R. China.
This study introduces a novel dual-mode biosensor for ultrasensitive cardiac troponin I (cTnI) detection. The innovative platform combines photoelectrochemical sensing and catalysis-induced pressure transduction for reliable, self-validated results in point-of-care diagnostics.
Area of Science:
- * Biosensing and Nanomaterials
- * Analytical Chemistry
- * Biomedical Engineering
Background:
- * Conventional biosensors face challenges in reliable and ultrasensitive detection of cardiac troponin I (cTnI) due to signal interference and limited self-validation.
- * There is a need for advanced diagnostic tools that offer enhanced accuracy and reliability for cardiac biomarker detection.
Purpose of the Study:
- * To develop an orthogonal dual-mode biosensing strategy for energy-decoupled signal generation and data-level cross-validation.
- * To achieve ultrasensitive and reliable detection of cardiac troponin I (cTnI).
- * To create a portable and smart sensing platform for point-of-care testing (POCT).
Main Methods:
- * Synthesis of a multifunctional plasmonic Z-scheme probe (CdS@CdIn2S4/AuPt) for cTnI recognition.
- * Integration of photoelectrochemical (PEC) sensing with catalysis-induced pressure transduction (CIPT) for dual-signal output.
- * Fabrication of 3D-printed portable devices and application of machine learning (ML)-assisted data analysis for signal acquisition and fusion.
Main Results:
- * The dual-mode platform demonstrated high sensitivity for cTnI detection with limits of detection of 41.94 fg·mL-1 (PEC) and 164.80 fg·mL-1 (CIPT).
- * Energy-decoupled signal generation from distinct pathways (interfacial charge transfer and bulk gas expansion) enhanced reliability.
- * Machine learning-assisted data fusion and partial least-squares (PLS) model enabled cross-channel validation, achieving high prediction correlation (R2 > 0.9972) and accurate recovery rates (98.74%-102.69%) in human serum.
Conclusions:
- * The developed orthogonal dual-mode biosensing strategy offers a robust and reliable method for cTnI detection.
- * The energy-decoupled system and ML-assisted cross-validation significantly improve sensing accuracy and self-validation capabilities.
- * This innovative platform holds promise for advancing point-of-care testing applications in cardiac diagnostics.
