While the majority of the reports on toluene gas sensors are on rigid electrodes and based on composite materials, doping with additional noble metals, or a high temperature detection method, this work is the first demonstration of the vanadium carbide (V₂C) MXene based flexible and room-temperature (RT) toluene gas sensor. The V₂C MXene is synthesized by an HF etching route. The field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) images exhibit a typical accordion-like multilayered structure of the V₂C MXene, where the Fourier-transform infrared spectroscopy (FTIR), Raman, and X-ray photoelectron spectroscopy (XPS) data further ensured its successful growth. The V₂C (band gap of 3.9 eV) based flexible gas sensor employing a polyester substrate, displays good reproducibility, quick response/recovery time (14 s/34 s), long-term stability, good cross-selectivity, and a low detection limit of 47.85 ppb over the linear region of 5-200 ppm toluene at RT (27 ± 1 °C). The effect of relative humidity (RH) toward RT toluene gas sensing has also been investigated here. This sensor shows an excellent response of 775% at 200 ppm toluene, with brilliant selectivity toward toluene over six other hazardous gases. The sensor’s plentiful surface functional groups (−F, −OH, −O) and superior electrical characteristics are responsible for its enhanced performance. In light of this, the flexible and RT toluene gas sensor based on the V₂C MXene can be a smart way to fabricate the next-generation toluene gas sensors.

Breath biomarker detection has been a significant non-invasive approach for disease diagnosis. This method has significant potential for early diagnosis and accurate analysis of diseases. Emission from breath contains several volatile organic compounds. Among them, ammonia is a very commonly found VOC and mainly responsible for chronic kidney diseases. There exist several strategies to detect ammonia, however they demonstrate severe limitations such as cross-sensitivity and poor selectivity. This work demonstrates the synergistic effect of sensor functionalization and application of machine learning for selective detection of ammonia in the environment. The sensor exhibits high degree of selectivity towards ammonia owing to enormous hydroxyl groups contributed through curcumin. At 500 ppm ammonia, the sensor demonstrates 274% response and very high selectivity among seven volatile organic compounds. The machine learning models were trained with the help of sensor transients. Random Forest and CNN models were applied to predict the presence of ammonia in a mixture. Random Forest achieved 96.25% accuracy compared to 89% accuracy of CNN. Hence, Random Forest algorithms applied to curcumin functionalized reduced graphene oxide sensors can detect ammonia vapors with very high efficiency among a mixture of gases.