High selectivity and sensitivity through nanoparticle sensors for cleanroom CO 2 detection.

Clean room facilities are becoming more popular in both academic and industry settings, including low-and middle-income countries. This has led to an increased demand for cost-effective gas sensors to monitor air quality. Here we have developed a gas sensor using CoNiO 2 nanoparticles through a cost-effective combustion method. The sensitivity and selectivity of the sensor towards CO 2 were influenced by the structure of the nanoparticles, which were affected by the reducing agent (biofuels) used during synthesis. Among all reducing agents, urea found to yield highly crystalline and uniformly distributed CoNiO 2 nanoparticles, which when developed into sensors showed high sensitivity (limit of detection: 200 ppm) and selectivity for the detection of CO 2 gas in the presence of common interfering volatile organic compounds observed in cleanroom facilities including ammonia, formaldehyde, acetone, toluene, ethanol, isopropanol and methanol. In addition, the urea-mediated nanoparticle-based sensors exhibited room temperature operation, high stability, prompt response and recovery rates, and excellent reproducibility. Consequently, the synthesis approach to nanoparticle-based, energy efficient and affordable sensors represent a benchmark for CO 2 sensing in cleanroom settings.&#xD.