Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics

Abstract

Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for real-time health monitoring. This review highlights recent advancements in breath-sensing technologies, with a focus on the innovative materials driving their enhanced sensitivity and selectivity. Polymers, carbon-based materials like graphene and carbon nanotubes, and metal oxides such as ZnO and SnO₂ have demonstrated significant potential in detecting biomarkers related to diseases including diabetes, liver/kidney dysfunction, asthma, and gut health. The structural and operational principles of these materials are examined, revealing how their unique properties contribute to the detection of key respiratory gases like acetone, ammonia (NH₃), hydrogen sulfide, and nitric oxide. The complexity of breath samples is addressed through the integration of machine learning (ML) algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), which optimize data interpretation and diagnostic accuracy. In addition to sensing VOCs, these devices are capable of monitoring parameters such as airflow, temperature, and humidity, essential for comprehensive breath analysis. This review also explores the expanding role of artificial intelligence (AI) in transforming wearable breath sensors into sophisticated tools for personalized health diagnostics, enabling real-time disease detection and monitoring. Together, advances in sensor materials and ML-based analytics present a promising platform for the future of individualized, noninvasive healthcare.