Research Projects

Flexible electronics using nanomaterials hold immense prospects in gas sensing technology. With the advent of flexible electronics, sensors have become thin, low-cost, with large sensing area, lightweight, wearable, flexible and transparent. Demand for printed sensors in IoT world is growing continuously and stimulated the development of sensors in detecting toxic gases in a mixed environment. Next-generation smart sensors will be critical to monitor healthcare, food quality, security, environment, machine operation, and would influence human life in multidimensions. Considering ever-increasing industrialization and urbanization, timely and accurate detection of toxic gases in the environment is essential. In this regard, developing scalable and printable ultralow gas sensors are critical for human life. Few areas they find applications are public/domestic safety, industrial processes, monitoring environmental pollution, and air quality. Among the various types of gas sensors, metal oxide (MOx)-based chemiresistive gas sensors are attractive due to their ease of fabrication/ operation and miniaturization. Though they can be fabricated with tailored nanostructures, but need to significantly improve their sensitivity, particularly at lower gas concentrations. Meanwhile, pristine reduced graphene oxide (rGO) gas sensors can detect gases at ultralow concentrations with good thermal stability, but suffer from a long response and recovery times. Hence, to shorten the response time and enhance the sensitivity, this proposal aims towards developing ultralow sensors by complementing unique properties of both rGO and MOx by making their composite. Emphasis is on developing printable ultralow sensors capable of detecting in multi-gas environment. This aspect of the sensors is critical considering the real-life situation and need for scalablity/sensitivity that must be an integral part of a sensor for commercial application. In this regard, proposed work will develop innovative methods to make nanocompsites of rGO/MOx via printing method. Lastly, a database is generated using the experiments conducted to gain rational insight on the effect of morphology, temperature, ink formulation used for printing, mixed gas environment, physicochemical properties on the efficiency, response time and sensitivity during the gas sensing process. This data will be used to develop a machine learning (ML) model to predict the appropriate concentration and types of gases in mixed environment. Alternatively, this insight would also be useful in determining a correlation between the physicochemical properties of the materials with sensor performance, which will help in developing better sensor materials. Further, the printed gas senor will be deployed at ambient and outdoor conditions to evaluate the efficiency and robustness. It will be conducted for 1 year to check the stability/sensitivity of the developed printed sensor, making a step closer towards the commercialization.