This book highlights the latest advances in AFM nano-manipulation research in the field of nanotechnology. There are numerous uncertainties in the AFM nano-manipulation environment, such as thermal drift, tip broadening effect, tip positioning errors and manipulation instability. This book proposes a method for estimating tip morphology using a blind modeling algorithm, which is the basis of the analysis of the influence of thermal drift on AFM scanning images, and also explains how the scanning image of AFM is reconstructed with better accuracy. Further, the book describes how the tip positioning errors caused by thermal drift and system nonlinearity can be corrected using the proposed landmark observation method, and also explores the tip path planning method in a complex environment. Lastly, it presents an AFM-based nano-manipulation platform to illustrate the effectiveness of the proposed method using theoretical research, such as tip positioning and virtual nano-hand.

Micro/nano-scale engineering-especially the design and implementation of ultra-fast and ultra-scale energy devices, sensors, and cellular and molecular systems-remains a daunting challenge. Modeling and control has played an essential role in many technological breakthroughs throughout the course of history. Therefore, the need for a practical guide to modeling and control for micro/nano-scale devices and systems has emerged. The first edited volume to address this rapidly growing field, Modeling and Control for Micro/Nano Devices and Systems gives control engineers, lab managers, high-tech researchers, and graduate students easy access to the expert contributors' cutting-edge knowledge of micro/nanotechnology, energy, and bio-systems. The editors offer an integrated view from theory to practice, covering diverse topics ranging from micro/nano-scale sensors to energy devices and control of biology systems in cellular and molecular levels. The book also features numerous case studies for modeling of micro/nano devices and systems, and explains how the models can be used for control and optimization purposes. Readers benefit from learning the latest modeling techniques for micro/nano-scale devices and systems, and then applying those techniques to their own research and development efforts.

Micro/nano-scale engineering-especially the design and implementation of ultra-fast and ultra-scale energy devices, sensors, and cellular and molecular systems-remains a daunting challenge. Modeling and control has played an essential role in many technological breakthroughs throughout the course of history. Therefore, the need for a practical guide to modeling and control for micro/nano-scale devices and systems has emerged. The first edited volume to address this rapidly growing field, Modeling and Control for Micro/Nano Devices and Systems gives control engineers, lab managers, high-tech researchers, and graduate students easy access to the expert contributors' cutting-edge knowledge of micro/nanotechnology, energy, and bio-systems. The editors offer an integrated view from theory to practice, covering diverse topics ranging from micro/nano-scale sensors to energy devices and control of biology systems in cellular and molecular levels. The book also features numerous case studies for modeling of micro/nano devices and systems, and explains how the models can be used for control and optimization purposes. Readers benefit from learning the latest modeling techniques for micro/nano-scale devices and systems, and then applying those techniques to their own research and development efforts.

This book highlights the latest advances in AFM nano-manipulation research in the field of nanotechnology. There are numerous uncertainties in the AFM nano-manipulation environment, such as thermal drift, tip broadening effect, tip positioning errors and manipulation instability. This book proposes a method for estimating tip morphology using a blind modeling algorithm, which is the basis of the analysis of the influence of thermal drift on AFM scanning images, and also explains how the scanning image of AFM is reconstructed with better accuracy. Further, the book describes how the tip positioning errors caused by thermal drift and system nonlinearity can be corrected using the proposed landmark observation method, and also explores the tip path planning method in a complex environment. Lastly, it presents an AFM-based nano-manipulation platform to illustrate the effectiveness of the proposed method using theoretical research, such as tip positioning and virtual nano-hand.

Nanophotonics has emerged as a major technology and applications domain, exploiting the interaction of light-emitting and light-sensing nanostructured materials. These devices are lightweight, highly efficient, low on power consumption, and are cost effective to produce. The authors of this book have been involved in pioneering work in manufacturing photonic devices from carbon nanotube (CNT) nanowires and provide a series of practical guidelines for their design and manufacture, using processes such as nano-robotic manipulation and assembly methods. They also introduce the design and operational principles of opto-electrical sensing devices at the nano scale. Thermal annealing and packaging processes are also covered, as key elements in a scalable manufacturing process. Examples of applications of different nanowire based photonic devices are presented. These include applications in the fields of electronics (e.g. FET, CNT Schotty diode) and solar energy.

This book offers a fundamental and comprehensive overview of nanomedicine from a systems engineering perspective, making it the first book in the field of quantitative nanomedicine based on systems theory. The book starts by introducing the concept of nanomedicine and provides basic mathematical modeling techniques that can be used to model nanoscale biomedical and biological systems. It then demonstrates how this idea can be used to model and analyze the central dogma of molecular biology, tumor growth and the immune system. Broad applications of the idea are further illustrated by Bayesian networks, multiscale and multiparadigm modeling and AFM engineering.