Research Projects

Harvesting surrounding mechanical energy to operate healthcare units is a unique idea to achieve self-powered systems. Development of triboelectric nanogenerators (TENG) is found to be promising for converting random mechanical energy to electrical signals. Apart from power generation, the influence of operating conditions and chemical environment on TENG output enables the system to work as a self-powered sensor. Since triboelectrification is not limited to solid-solid interface, the liquid-solid (L-S) interface is fascinating for power generation as well as sensing of the liquid characteristics. This feature opens up the possibility for the development of self-powered biosensors while considering the triboelectrification occurring due to the interaction of a liquid and a solid surface. Thus, L-S TENG configuration can be an efficient technique to monitor different biomarkers present in human biofluid. In contrast, the implementation of Artificial Intelligence (AI) or Machine Learning (ML) techniques to analyze the L-S TENG data can be a promising approach to attain great accuracy in classification and recognition of different levels of biomarkers present in biofluids under investigation. While sensing of different biomarkers through L-S TENG is quite promising to develop low cost, self-powered sensors, it is quite challenging to achieve satisfactory signal strength while operating with different biofluids. Deployment of functionalized active layer, modification of surface hydrophobicity and electrode geometry are the key aspects to investigate to achieve adequate sensing resolution for any particular analyte. Beyond signal generation, a big challenge lies in proper classification and recognition of the analytes when two or three components vary simultaneously. Thus, assistance of ML analysis may be useful to analyze the complex signals generated in this process. Depending on these particular issues, the objectives of the proposal are broadly categorized in three sections: development of functionalized active layer and optimization of L-S TENG architecture; sensing of biomarkers with optimized L-S TENG; ML assisted analysis of L-S TENG output. For developing the functionalized active layer, polymers (PVA, FEP, PVDF etc.) with modified functional groups will be tested for L-S TENG application. Similarly, nanostructures of metal oxide (ZnO, TiO2), 2D materials (graphene, g-C3N4 etc.) functionalized with -OH, -COOH, -NH/NH2 groups will also be deployed in this purpose. Next, L-S TENG architecture will be modified with varying TENG configuration. Especially, surface curvature and electrode arrangement will be varied to optimize the output. Simulated biofluids will be tested with the optimized L-S TENG to sense biomarkers like Na, K, glucose, Uric acid etc. Following that the generated output will be analysed through ML techniques like Linear and Logistic Regression, Support Vector Machines, Random Forrest, Neural Network etc. Special emphasis will be made to AI techniques such as Joint Embedding Predictive Architecture (JEPA) to reveal the hidden state of the cause and effect of the liquid-solid interface from the generated data. Finally, miniaturized proof-of-concept device of L-S TENG based sensor will be developed by integrating microcontroller unit to generate and transmit data to a remote location to be analysed by ML techniques. The exploration of different functionalized materials as active layers may add new information about the charge transfer process between impacting liquid and the solid. In addition, deployment of AI techniques can contribute a lot in deepening the understanding about triboelectrification at liquid-solid interface. Overall, the experimental findings and theoretical knowledge of the proposed research work may pave a path to the development of low cost, self-powered, smart biosensors for real time detection of Na/K ratio, glucose, Creatinine, Uric acid etc. from sweat/urine samples of users.