Research Projects

The development of the electronic nose (E-nose) and its various configurations is being taken place in diverse applications. The crucial role of E-nose is envisaged in future POC devices for breath analysis. The delay in disease diagnosis and inaccuracies has resulted in low medicine impact and treatment shortcoming. In rural areas, unavailability of the sophisticated medical infrastructure results in delayed diagnosis of the health issues. In order to overcome this, the government policies are mainly oriented to the development of cost effective, handheld, and easy to use devices. The implementation of E-nose for medical diagnosis will have significant contribution in early disease diagnosis. As per the ongoing research in E-nose for breath analysis, the critical challenges being faced are cross sensitivity, stability, and the interference of humidity. The detection of N-containing gaseous analytes such as TMA, TEA, NH₃, NO, and NO₂ is crucial. These gases are harmful to human respiratory systems and needs to be monitor below their safety limits. Among the harmful chemical species that releases from food and chemical industries, ammonia is one of them. The monitoring of TMA is important in seafood monitoring and health safety. The simultaneous detection of multiple gases using individual gas sensor faces the limitation and is being overcome by an array of multiple sensors in E-nose configuration. The use of multiple sensors offers the improved classification efficiency, as the information missed by one sensor can be compensate by the others. Herein, the selection of sensor materials contributes crucial role to realize the E-nose. The proposed work is focused on the deployment chemiresistive E-nose, towards the detection of N-containing analytes for environmental safety, seafood quality check, breath analysis, and food chains. The development of organic-inorganic nanohybrids based gas sensors using rGO, CNTs, conducting polymers, metal oxides and noble metal sensitizers will be caried out. The formation of nanohybrids with metal (Pd and Pt) and metal oxide (SnO₂) has been identified as a mean to impart the selectivity towards analytes. In the present work, emphasis will be given to the multivariate data analysis of the sensor array. The selection and implementation of the suitable machine learning algorithms bring the reliable E-nose device characteristics and reliable data banks/training data sets. The use of chemiresistive E-nose is a small-scale approach and relatively inexpensive and easy to use, thereby having potential to lead to the development of point-of-care solutions and can eliminate the requirement for sample storage and transportation. The developed sensor materials will be critically explored in the present work to overcome the current challenges being faced in E-nose. As per the requirements, the selected analytes will be also tested with vapors/VOCs to realize the E-nose for potential solutions.