AI and Machine Learning in Nanotechnology are accelerating the development of new materials and applications by enabling better simulations, predictions, and optimization processes. AI-driven algorithms are used to analyze vast amounts of data, helping researchers identify the most promising nanomaterials for specific applications, whether in medicine, energy storage, or electronics. Machine learning techniques also enable the design of nanomaterials with customized properties, enhancing their performance and efficiency. Additionally, AI is improving the manufacturing processes for nanomaterials, making them more scalable and cost-effective. The integration of AI and machine learning with nanotechnology is unlocking new possibilities for creating smarter, more efficient solutions across various industries.
Title : 40,000 implants in humans and no failure: The impact of nanomedicine
Thomas J Webster, Hebei University of Technology, China
Title : Cellulose-derived biochar modified with iron oxide and ZnO nanoparticles by a novel one-step pyrolytic method for removal of emerging contaminants from water
Rashad Al Gaashani, Hamad Bin Khalifa University, Qatar
Title : Harnessing the unique properties of engineered nanostructures for sensing
Harry Ruda, University of Toronto, Canada
Title : Circumventing challenges in developing CVD graphene on steels for extraordinary and durable corrosion resistance
Raman Singh, Monash University, Australia
Title : Nano DAP augments productivity, phosphorus use efficiency, and profitability of spring wheat in India
Binaya Kumar Parida, Coromandel International Ltd, India
Title : Lipid nanoparticles formulations: From bench scale to industrial scale
Mohammad A Obeid, RAK Medical and Health Sciences University, United Arab Emirates