Aditi Barua
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Researcher
Also affiliated: Chittagong University of Engineering & Technology (2021); Indian Institute of Science Education and Research Kolkata (2020); The University of Texas at El Paso (2014); Indian Institute of Science Education and Research Mohali (2020); Indian Institute of Science Education and Research, Bhopal (2020–2023); Dublin Business School (2024); Indian Institute of Science Education and Research Berhampur (2020); Indian Institute of Science Education and Research, Tirupati (2020); Bhopal Memorial Hospital & Research Centre (2020); Chattogram Veterinary and Animal Sciences University (2021)
Faculty Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Aditi Barua's research focuses on the development and application of advanced materials for energy storage and environmental remediation. She has investigated metal-organic framework (MOF) derived ultramicroporous carbon materials, particularly examining the synergistic effects of oxygen and nitrogen co-doping for enhanced stability in solid-state supercapacitors. Barua has also studied the role of nitrogen doping and pore volume in these materials for carbon dioxide capture. Her work extends to artificial intelligence and infrared thermography for identifying damage in composite materials, such as wind turbine blades. Additionally, Barua has explored the use of integrated graphene-MOF nanostructures for high-performance supercapacitors at commercial scale mass loading. Her research interests also encompass robotic systems and the influence of background color on guppy coloration.
Metrics
- h-index: 5
- Publications: 15
- Citations: 268
Selected Publications
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Optimization of Experimental Infrared Image Dataset for the Identification of Barely Visible Damage in Wind Turbine Blade Samples Using Transfer Learning (2025)
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Identification and Localization of Areas of Damage in Composite Materials Using Infrared Thermography and Artificial Intelligence (2024)
Collaboration Network
Top Collaborators
- Identification and Localization of Areas of Damage in Composite Materials Using Infrared Thermography and Artificial Intelligence
- Optimization of Experimental Infrared Image Dataset for the Identification of Barely Visible Damage in Wind Turbine Blade Samples Using Transfer Learning
- Optimization of Experimental Infrared Image Dataset for the Identification of Barely Visible Damage in Wind Turbine Blade Samples Using Transfer Learning
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