Quantum dots (QDs) are a main feature of nanotechnological applications. Nanoscience and nanotechnology cover a wide range from element, compounds and alloys in very broad applications including physical and chemical characterization for theoretical and experimental studies for less than 100 nm that exhibits size-dependent properties. A quite good quantity of works has been published on DQs within the past decade. The field of renewable energy has become one of the most active research areas within the nanotechnology and nanoscience community. New fundamental research is paralleled with original and inspired potential applications, including modeling, analysis and characterization for energy conversion and interfacing with living cells.
The focus of this book is synthesis and optical studies of self-assembled quantum dots (QDs) with particular consideration given to the capability to control nanoelectronics properties via structural modifications. Furthermore, it is devoted to new nanotechnology tools and technological procedures useful for the developed community of science, society, improved economy and best future. The newest addition is exploring the application of artificial intelligence (AI) techniques in synthesis and optimization of quantum dots. AI-driven design utilizes machine learning algorithms to predict the properties of synthesis condition-, composition- and size-based quantum dots. AI can achieve optimal conditions for desired optical and electronic characteristics. But the processing optimizes the synthesis of QDs. AI is recommended to improve functionality and uniformity of QDs. AI predict optical properties that bases on particle shape and size, enabling rapid screening of potential quantum dot configurations.
The focus of this book is synthesis and optical studies of self-assembled quantum dots (QDs) with particular consideration given to the capability to control nanoelectronics properties via structural modifications. Furthermore, it is devoted to new nanotechnology tools and technological procedures useful for the developed community of science, society, improved economy and best future. The newest addition is exploring the application of artificial intelligence (AI) techniques in synthesis and optimization of quantum dots. AI-driven design utilizes machine learning algorithms to predict the properties of synthesis condition-, composition- and size-based quantum dots. AI can achieve optimal conditions for desired optical and electronic characteristics. But the processing optimizes the synthesis of QDs. AI is recommended to improve functionality and uniformity of QDs. AI predict optical properties that bases on particle shape and size, enabling rapid screening of potential quantum dot configurations.
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