Nanomaterial-enabled breath sensors for disease diagnosis: Sensing mechanisms, selectivity strategies, and clinical translation

Exhaled breath has recently been identified as a promising non-invasive sample for disease diagnosis, as it reflects the biological metabolic compositional profile of volatile organic compounds (VOCs). Breath-based assays combined with advanced material-based chemical sensors yield a fast, non-invasive, and reproducible technique for diagnosing various diseases. This review highlights potentially emerging developments in the use of nanomaterials in breath sensors. Metal- and metal oxide-based nanostructures, carbon-based conducting polymers, metal-organic and covalent-organic frameworks, and nanoarchitectures for breath sensing were discussed. In addition, Artificial intelligence (AI) and machine learning (ML) have expanded the application of breath sensors by extending pattern recognition to dynamic pattern detection and automated classification of disease with high accuracy. Advancements in wearable, flexible-sensor modules, and wireless and self-powered capabilities are driving respiratory breath sensors toward personalized medicine and in-home and on-demand applications. Therefore, this review presents the current accomplishments, the outstanding gaps, and the potential course of development for viable, portable, and smart breath sensors. The field of respiratory diagnostics has the potential to provide breakthrough insights into future health practices through practical interdisciplinary cooperation among materials science, sensor technology, and data science.