Aditi Barua
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Also affiliated: Chittagong University of Engineering & Technology (2021); The University of Texas at El Paso (2014)
Research Areas
Biography and Research Information
OverviewAI-generated summary
Aditi Barua's research focuses on materials science, particularly the development and application of advanced carbon materials for energy storage devices and environmental remediation. Her work has investigated metal-organic framework (MOF)-derived carbons, exploring their potential for high-performance supercapacitors and CO2 capture. She has examined the synergistic effects of doping, such as oxygen and nitrogen co-doping, and the role of pore structure in enhancing material performance. Barua has also explored the theoretical underpinnings of membership functions used in fuzzy logic systems and investigated patterns in while loop specifications. Additionally, her research has touched upon the influence of environmental factors on animal coloration and growth, specifically in guppies.
Barua has published 15 papers, accumulating 268 citations and an h-index of 5. She has collaborated with Shweta Dabetwar and H. Jones, both from the University of Arkansas at Little Rock, on shared publications. Her recent work in 2020-2023 indicates ongoing activity in her research areas.
Metrics
- h-index: 2
- Publications: 10
- Citations: 147
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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