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Highlights: Photoresponsive black phosphorus (BP)/Ti3C2Tx composites were synthesized by a self-assembly strategy. Enhanced gas sensitive property was achieved by visible light modulation. Light activated virtual sensor array was fabricated based on BP/Ti3C2Tx composite. Diagnosis of coronary heart disease was achieved with the help of machine learning. Early non-invasive diagnosis of coronary heart disease (CHD) is critical. However, it is challenging to achieve accurate CHD diagnosis via detecting breath. In this work, heterostructured complexes of black phosphorus (BP) and two-dimensional carbide and nitride (MXene) with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy. A light-activated virtual sensor array (LAVSA) based on BP/Ti3C2Tx was prepared under photomodulation and further assembled into an instant gas sensing platform (IGSP). In addition, a machine learning (ML) algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD. Due to the synergistic effect of BP and Ti3C2Tx as well as photo excitation, the synthesized heterostructured complexes exhibited higher performance than pristine Ti3C2Tx, with a response value 26% higher than that of pristine Ti3C2Tx. In addition, with the help of a pattern recognition algorithm, LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols, ketones, aldehydes, esters, and acids. Meanwhile, with the assistance of ML, the IGSP achieved 69.2% accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients. In conclusion, an immediate, low-cost, and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD, which provided a generalized solution for diagnosing other diseases and other more complex application scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] • MXene/perovskite-based gas-sensitive material library was built. • Bionic sensor array with enhanced performance was built on the material library. • Instant detection platform for human identification was fabricated. • Machine learning algorithms were introduced to assist with human identification. • This study provides general solution for human identification in instant scenarios. Human identification is crucial in many fields. However, it is challenging to achieve instant and accurate human identification through body odors. In this work, an in-situ growth strategy was utilized to establish a gas-sensitive nanocomposite material library with MXene and perovskite. A plug-and-play bionic sensor array module based on the material library was manufactured and assembled into an instant detection platform (IDP). Machine learning (ML) algorithms were introduced to assist IDP in identifying human odors. Due to its unique Schottky Barrier structure, the material library exhibited higher performance and responded 37 % ∼ 70 % higher than initial MXene. IDP was applied to detect the odors of volunteers' breaths and clothes with an accuracy rate of 69.2 % and 51.1 %, respectively, with the assistance of ML. In all, an instant, convenient, and accurate human identification prototype machine was fabricated, providing a general solution for more complex application scenarios. [ABSTRACT FROM AUTHOR]