MXene/peptide biomimetic olfactory sensor array with machine learning for gas sensing

MXene-based gas sensors have demonstrated great potential for applications in gas detection, food preservation, and medical diagnostics. However, they still suffer from limitations in selectivity and sensitivity. In this study, to enhance the sensing performance of MXene-based gas sensors, a biomimetic olfactory sensing system was successfully developed by functionalizing MXene materials with specific peptides derived from odorant-binding proteins and integrating sensor arrays with machine learning algorithms for improved gas recognition. Experimental results showed that the introduction of olfactory-derived peptides significantly enhanced sensor performance, increasing the overall response by 2 ​times to 4 times compared to pristine MXene. Moreover, the system exhibited broad applicability with a detection range spanning from as low as 50 ​ppb to as high as 500 ​ppm. Based on this sensing system, we successfully achieved freshness detection of pork and distinguished between lung cancer patients and healthy individuals using breath analysis, achieving a classification accuracy of up to 94%. This study provides a promising foundation for future applications in food safety monitoring and noninvasive medical diagnostics.