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 : Recent advances in nanomedicine: Sensors, implants, artificial intelligence, saving the environment, human studies, and more
Thomas J Webster, Hebei University of Technology, China
Title : Harnessing the unique transport properties of InAs nanowires for single molecule level sensing
Harry E Ruda, University of Toronto, Canada
Title : Success in developing CVD graphene coating on mild steel: A disruptive approach to remarkable/durable corrosion resistance
Raman Singh, Monash University, Australia
Title : Photonic metasurfaces in azobenzene materials
Ribal Georges Sabat, Royal Military College of Canada, Canada
Title : Advances in sustainable stimuli-responsive nanoscale platforms for biomedical applications
Manuela Cedrun Morales, CNR NANOTEC, Italy
Title : Using CuO polycrystalline nanofilms as sensor for small organic molecules
Paulo Cesar De Morais, Catholic University of Brasilia, Brazil