Native Point Defect Measurement and Manipulation in ZnO Nanostructures

This review presents recent research advances in measuring native point defects in ZnO nanostructures, establishing how these defects affect nanoscale electronic properties, and developing new techniques to manipulate these defects to control nano- and micro- wire electronic properties. From spatially-resolved cathodoluminescence spectroscopy, we now know that electrically-active native point defects are present inside, as well as at the surfaces of, ZnO and other semiconductor nanostructures. These defects within nanowires and at their metal interfaces can dominate electrical contact properties, yet they are sensitive to manipulation by chemical interactions, energy beams, as well as applied electrical fields. Non-uniform defect distributions are common among semiconductors, and their effects are magnified in semiconductor nanostructures so that their electronic effects are significant. The ability to measure native point defects directly on a nanoscale and manipulate their spatial distributions by multiple techniques presents exciting possibilities for future ZnO nanoscale electronics

Native Point Defect Measurement and Manipulation in ZnO Nanostructures

This review presents recent research advances in measuring native point defects in ZnO nanostructures, establishing how these defects affect nanoscale electronic properties, and developing new techniques to manipulate these defects to control nano- and micro- wire electronic properties. From spatially-resolved cathodoluminescence spectroscopy, we now know that electrically-active native point defects are present inside, as well as at the surfaces of, ZnO and other semiconductor nanostructures. These defects within nanowires and at their metal interfaces can dominate electrical contact properties, yet they are sensitive to manipulation by chemical interactions, energy beams, as well as applied electrical fields. Non-uniform defect distributions are common among semiconductors, and their effects are magnified in semiconductor nanostructures so that their electronic effects are significant. The ability to measure native point defects directly on a nanoscale and manipulate their spatial distributions by multiple techniques presents exciting possibilities for future ZnO nanoscale electronics

Native Point Defect Measurement and Manipulation in ZnO Nanostructures

This review presents recent research advances in measuring native point defects in ZnO nanostructures, establishing how these defects affect nanoscale electronic properties, and developing new techniques to manipulate these defects to control nano- and micro- wire electronic properties. From spatially-resolved cathodoluminescence spectroscopy, we now know that electrically-active native point defects are present inside, as well as at the surfaces of, ZnO and other semiconductor nanostructures. These defects within nanowires and at their metal interfaces can dominate electrical contact properties, yet they are sensitive to manipulation by chemical interactions, energy beams, as well as applied electrical fields. Non-uniform defect distributions are common among semiconductors, and their effects are magnified in semiconductor nanostructures so that their electronic effects are significant. The ability to measure native point defects directly on a nanoscale and manipulate their spatial distributions by multiple techniques presents exciting possibilities for future ZnO nanoscale electronics

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages