Guoqiang Li – författare

There have been many new developments since the first edition of this book was published back in 2015. These can be summarized as follows: integration of multiple properties into self-healing polymer materials, such as the shape memory effect and flame retardancy; beyond self-healing and the development of recyclable thermoset polymers; and the application of self-healing polymers in both 3D and 4D printing.

Recent Advances in Smart Self-healing Polymers and Composites, Second Edition provides a comprehensive introduction to the fascinating field of smart self-healing polymers and composites. All chapters are brought fully-up-to-date with the addition of six brand new contributions on the characterization of self-healing polymers, light-triggered self-healing, additive manufacturing, multifunctional thermoset polymers with self-healing ability, and recyclable thermoset polymers and 4D printing.� It is written for a large readership including not only R&D researchers from diverse backgrounds such as chemistry, materials science, aerospace, physics, and biological science, but also for graduate student working on self-healing technologies as well as their newly developed applications.

Features new chapters on characterization of self-healing polymers, light-triggered self-healing, additive manufacturing, multifunctional thermoset polymers with self-healing ability, recyclable thermoset polymers and 4D printing All chapters have been significantly updated from the previous edition Provides a grounding in all key areas of research to bring people up to speed with the latest developments

Gallium nitride (GaN) is a wide bandgap semiconductor with significant applications in optoelectronic and high-power electronic devices. The epitaxial growth of GaN on silicon (Si) substrates is of great interest due to the low cost, large wafer size, good thermal conductivity, and compatibility with established Si device technology. Gallium Nitride-Based Materials and Chips Epitaxially Grown on a Silicon Substrate reviews research on developing high-performance semiconductor materials and chips using gallium nitride-based materials. In particular the book, firstly, describes a novel, two-step growth method that combines low-temperature epitaxy by pulsed laser deposition (PLD) with high-temperature epitaxy by metal-organic chemical vapor deposition (MOCVD)/molecular beam epitaxy (MBE). This method effectively controls the stress of Si-based GaN epitaxial materials and significantly reduces the defect density of the epitaxial materials by three orders of magnitude. Secondly, in terms of chip structure design, the book describes a variety of Si-based GaN chips with novel heteroepitaxial structures, leading to the improved the efficiency of various chips such as light-emitting diode (LED) chips, high-electron-mobility transistors, Schottky diodes, photodetector chips, photoelectrochemical water-splitting chips, and Si-based GaN integrated chips. Finally, the book describes novel chip fabrication processes which greatly improve the performance and production efficiency of various chips by more than 30%.Reviews the developing high-performance semiconductor materials and chips using GaN or gallium nitride-based materials and chips epitaxially grown on a silicon substrateDiscusses techniques which effectively control the stress of the GaN epitaxial materials on Si substrates, leading to reduce the defect density of epitaxial materialsOutlines the fabrication of GaN chip structure epitaxial grown on Si substrate and modifications to several sets of key equipment for chip fabrication, thereby improving the production efficiency of various chips

Artificial Intelligence Assisted Structural Optimization explores the use of machine learning and correlation analysis within the forward design and inverse design frameworks to design and optimize lightweight load-bearing structures as well as mechanical metamaterials.Discussing both machine learning and design analysis in detail, this book enables readers to optimize their designs using a data-driven approach. This book discusses the basics of the materials utilized, for example, shape memory polymers, and the manufacturing approach employed, such as 3D or 4D printing. Additionally, the book discusses the use of forward design and inverse design frameworks to discover novel lattice unit cells and thin-walled cellular unit cells with enhanced mechanical and functional properties such as increased mechanical strength, heightened natural frequency, strengthened impact tolerance, and improved recovery stress. Inverse design methodologies using generative adversarial networks are proposed to further investigate and improve these structures. Detailed discussions on fingerprinting approaches, machine learning models, structure screening techniques, and typical Python codes are provided in the book.The book provides detailed guidance for both students and industry engineers to optimize their structural designs using machine learning.

Artificial Intelligence Assisted Structural Optimization explores the use of machine learning and correlation analysis within the forward design and inverse design frameworks to design and optimize lightweight load-bearing structures as well as mechanical metamaterials.Discussing both machine learning and design analysis in detail, this book enables readers to optimize their designs using a data-driven approach. This book discusses the basics of the materials utilized, for example, shape memory polymers, and the manufacturing approach employed, such as 3D or 4D printing. Additionally, the book discusses the use of forward design and inverse design frameworks to discover novel lattice unit cells and thin-walled cellular unit cells with enhanced mechanical and functional properties such as increased mechanical strength, heightened natural frequency, strengthened impact tolerance, and improved recovery stress. Inverse design methodologies using generative adversarial networks are proposed to further investigate and improve these structures. Detailed discussions on fingerprinting approaches, machine learning models, structure screening techniques, and typical Python codes are provided in the book.The book provides detailed guidance for both students and industry engineers to optimize their structural designs using machine learning.

In this book, the self-healing of composite structures with shape memory polymer as either matrix or embedded suture is systematically discussed. Self-healing has been well known in biological systems for many years: a typical example is the self-healing of human skin. Whilst a minor wound can be self-closed by blood clotting, a deep and wide cut needs external help by suturing. Inspired by this observation, this book proposes a two-step close-then-heal (CTH) scheme for healing wide-opened cracks in composite structures–by constrained shape recovery first, followed by molecular healing. It is demonstrated that the CTH scheme can heal wide-opened structural cracks repeatedly, efficiently, timely, and molecularly. It is believed that self-healing represents the next-generation technology and will become an engineering reality in the near future.  The book consists of both fundamental background and practical skills for implementing the CTH scheme, with additional focus on understanding strain memory versus stress memory and healing efficiency evaluation under various fracture modes. Potential applications to civil engineering structures, including sealant for bridge decks and concrete pavements, and rutting resistant asphalt pavements, are also explored.  This book will help readers to understand this emerging field, and to establish a framework for new innovation in this direction.Key features: explores potential applications of shape memory polymers in civil engineering structures, which is believed to be unique within the literaturebalanced testing and mathematical modeling, useful for both academic researchers and practitionersthe self-healing scheme is based on physical change of polymers and is written in an easy to understand style for engineering professionals without a strong background in chemistry

Recent Advances in Smart Self-Healing Polymers and Composites examines the advances made in smart materials over the last few decades and their significant applications in aerospace, automotive, civil, mechanical, medical, and communication engineering fields.�

Based on a thorough review of the literature, the book identifies "smart self-healing polymers and composites� as one of the most popular, challenging, and promising areas of research.

Readers will find valuable information compiled by a large pool of researchers who not only studied the latest datasets, but also reached out to leading contributors for insights and forward-thinking analogies.

Examines the advances made in smart materials over the last few decades Presents significant applications in aerospace, automotive, civil, mechanical, medical, and communication engineering fields Compiled by a large pool of researchers who not only studied the latest datasets, but also reached out to leading contributors for insights and forward-thinking analogies

Presents the findings on behaviours of steel structural components in a fire, such as the catenary actions of restrained steel beams, the design methods for restrained steel columns, and the membrane actions of concrete floor slabs with steel decks. The book is of interest to architects, code officials, building designers and fire fighters.

Advanced Analysis and Design for Fire Safety of Steel Structures systematically presents the latest findings on behaviours of steel structural components in a fire, such as the catenary actions of restrained steel beams, the design methods for restrained steel columns, and the membrane actions of concrete floor slabs with steel decks. Using a systematic description of structural fire safety engineering principles, the authors illustrate the important difference between behaviours of an isolated structural element and the restrained component in a complete structure under fire conditions.The book will be an essential resource for structural engineers who wish to improve their understanding of steel buildings exposed to fires. It is also an ideal textbook for introductory courses in fire safety for master’s degree programs in structural engineering, and is excellent reading material for final-year undergraduate students in civil engineering and fire safety engineering. Furthermore, it successfully bridges the information gap between fire safety engineers, structural engineers and building inspectors, and will be of significant interest to architects, code officials, building designers and fire fighters.Dr. Guoqiang Li is a Professor at the College of Civil Engineering of Tongji University, China; Dr. Peijun Wang is an Associate Professor at the School of Civil Engineering of Shandong University, China.

This thesis combines advanced femtosecond laser micro/nanofabrication technologies and frontier bionic design principles to prepare diverse biomimetic micro/nanostructures to realize their functions. By studying the formation mechanism of the micro/nanostructures, the author identifies various artificial structural colors, three-dimensional micro/nanocage arrays, and fish-scale inspired microcone arrays in different processing environments. Multiple functions such as enhanced antireflection, hydrophobicity, and underwater superoleophobicity are achieved by precisely adjusting laser-machining parameters. This novel design and method have extensive potential applications in the context of new colorizing technologies, microfluidics, microsensors, and biomedicine.