Hu Jiawang, Qian Hao, Han Sanyang, Zhang Ping, Lu Yuan
Department of Chemical Engineering, Tsinghua University, Beijing, 100084, People's Republic of China.
Key Laboratory of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084, People's Republic of China.
Nanomicro Lett. 2024 Aug 16;16(1):274. doi: 10.1007/s40820-024-01481-7.
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/TiCT 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 TiCT as well as photo excitation, the synthesized heterostructured complexes exhibited higher performance than pristine TiCT, with a response value 26% higher than that of pristine TiCT. 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.
冠心病（CHD）的早期无创诊断至关重要。然而，通过检测呼吸来实现准确的冠心病诊断具有挑战性。在这项工作中，采用自组装策略制备了具有高气敏性和光响应性的黑磷（BP）与二维碳化物和氮化物（MXene）的异质结构复合物。在光调制下制备了基于BP/TiCT的光激活虚拟传感器阵列（LAVSA），并进一步组装成即时气体传感平台（IGSP）。此外，引入了机器学习（ML）算法来帮助IGSP检测和识别呼吸样本信号以诊断冠心病。由于BP和TiCT的协同作用以及光激发，合成的异质结构复合物表现出比原始TiCT更高的性能，响应值比原始TiCT高26%。此外，借助模式识别算法，LAVSA成功检测并识别了15种与醇、酮、醛、酯和酸相关的气味分子。同时，在ML的辅助下，IGSP在检测45名健康人和冠心病患者的呼吸气味方面达到了69.2%的准确率。总之，设计并制造了一种用于冠心病无创诊断的即时、低成本且准确的原型，为诊断其他疾病和其他更复杂的应用场景提供了一种通用解决方案。
