Chemiluminescence-Derived Self-Powered Photoelectrochemical Immunoassay for Detecting a Low-Abundance Disease-Related Protein.

Early diagnosis of cancers relies on the sensitive detection of specific biomarkers, but most of the current testing methods are inaccessible to home healthcare due to cumbersome steps, prolonged testing time, and utilization of toxic and hazardous substances. Herein, we developed a portable self-powered photoelectrochemical (PEC) sensing platform for rapid detection of prostate-specific antigen (PSA, as a model disease-related protein) by integrating a self-powered photoelectric signal output system catalyzed with chemiluminescence-functionalized Au nanoparticles (AuNPs) and a phosphomolybdic acid (PMA)-based photochromic visualization platform. TiO2-g-C3N4-PMA photosensitive materials were first synthesized and functionalized on a sensor chip. The sensor consisted of filter paper modified with a photocatalytic material and a regional laser-etched FTO electrode as an alternative to a conventional PEC sensor with a glass-based electrode. The targeting system involved a monoclonal anti-PSA capture antibody-functionalized Fe3O4 magnetic bead (mAb1-MB) and a polyclonal anti-PSA antibody (pAb2)-N-(4-aminobutyl)-N-ethylisoluminol-AuNP (ABEI-AuNP). Based on the signal intensity of the chemiluminescent system, the photochromic device color changed from light yellow to heteropoly blue through the PMA photoelectric materials integrated into the electrode for visualization of the signal output. In addition, the electrical signal in the PEC system was amplified by a sandwich-type capacitor and readout on a handheld digital multimeter. Under optimum conditions, the sensor exhibited high sensitivity relative to PSA in the range of 0.01-50 ng mL-1 with a low detection limit of 6.25 pg mL-1. The flow-through chemiluminescence reactor with a semiautomatic injection device and magnetic separation was avoid of unstable light source intensity inherent in the chemiluminescence process. Therefore, our strategy provides a new horizon for point-of-care analysis and rapid cost-effective clinical diagnosis.