Integrating nanotechnology with deep learning for engineering, healthcare, and environmental solutions: A review
Year: 2026
Type: article
Abstract: Combining nanotechnology and deep learning methods is a leading-edge frontier in engineering solutions, with unparalleled prospects for innovation and growth. The synergistic combination blends the scale and precision of nano-materials with the predictive capability and resilience of artificial intelligence. Deep learning methods can process enormous amounts of data collected by nano-scale sensors, designing and optimizing materials at the atomic level. This integration makes possible the creation of self-healing smart nano-materials, highly efficient energy storage devices, and ultra-sensitive healthcare diagnostic tools. Additionally, deep learning methods can expedite the discovery of new nanostructures and their uses with much less time and expense compared to conventional experimental approaches. As this multidisciplinary area continues to grow, it holds the potential to transform numerous industries, such as electronics, healthcare, environmental science, and energy technology, towards more efficient, sustainable, and smart engineering solutions.
Authors: Riaz Israni, Jaydeep Parmar, Mehul Chauhan, Hina Maniya, Brij Pal Singh, Harsh Purohit, Jignesh Kamdar
Institutions: RK University, Chandigarh University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7125434285
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