Machine learning assisted photocurrent wavelength classification in a self-powered broadband photoelectrochemical photodetector based on Bi2Te0.6Se2.4/MoSe2 heterojunctions

Abstract

Self-powered operation and broadband spectral adaptability are critical for the next generation of photodetectors, enabling sustainable performance and multispectral functionality. The broadband wavelength response capability can expand the application scenarios of a photodetector. However, achieving both broadband photoresponse and accurate wavelength recognition remains a significant challenge. Herein, we develop a self-powered photoelectrochemical photodetector based on Bi₂Te_0.6Se_2.4/MoSe₂ heterojunctions, capable of identifying 256, 365, 546, and 650 nm and simulated sunlight with the assistance of machine learning. To achieve precise energy band alignment with MoSe₂ and enhance the heterojunction interface, the selenium content in Bi₂Te_3-xSe_x and the mass ratio of the composite components were systematically optimized. These structural optimizations improve charge carrier separation and transport via band structure engineering and interfacial modulation, resulting in superior photoelectrochemical performance. By integrating material design with machine-learning-assisted wavelength discrimination, this study demonstrates a multidisciplinary approach to broadband detection and highlights the potential of such devices in optical communication, environmental monitoring, and multispectral imaging applications.