Biomaterials and tissue engineering in traumatic brain injury:novel perspectives on promoting neural regeneration

Traumatic brain injury is a serious medical condition that can be attributed to falls, motor vehicle accidents, sports injuries and acts of violence, causing a series of neural injuries and neuropsychiatric symptoms. However, limited accessibility to the injury sites, complicated histological and anatomical structure, intricate cellular and extracellular milieu, lack of regenerative capacity in the native cells, vast variety of damage routes, and the insufficient time available for treatment have restricted the widespread application of several therapeutic methods in cases of central nervous system injury. Tissue engineering and regenerative medicine have emerged as innovative approaches in the field of nerve regeneration. By combining biomaterials, stem cells, and growth factors, these approaches have provided a platform for developing effective treatments for neural injuries, which can offer the potential to restore neural function, improve patient outcomes, and reduce the need for drugs and invasive surgical procedures. Biomaterials have shown advantages in promoting neural development, inhibiting glial scar formation, and providing a suitable biomimetic neural microenvironment, which makes their application promising in the field of neural regeneration. For instance, bioactive scaffolds loaded with stem cells can provide a biocompatible and biodegradable milieu. Furthermore, stem cells-derived exosomes combine the advantages of stem cells, avoid the risk of immune rejection, cooperate with biomaterials to enhance their biological functions, and exert stable functions, thereby inducing angiogenesis and neural regeneration in patients with traumatic brain injury and promoting the recovery of brain function. Unfortunately, biomaterials have shown positive effects in the laboratory, but when similar materials are used in clinical studies of human central nervous system regeneration, their efficacy is unsatisfactory. Here, we review the characteristics and properties of various bioactive materials, followed by the introduction of applications based on biochemistry and cell molecules, and discuss the emerging role of biomaterials in promoting neural regeneration. Further, we summarize the adaptive biomaterials infused with exosomes produced from stem cells and stem cells themselves for the treatment of traumatic brain injury. Finally, we present the main limitations of biomaterials for the treatment of traumatic brain injury and offer insights into their future potential.

Molecular Dynamics (MD) Applications in Materials Science and Engineering and Nanotechnology

Molecular dynamics (MD) is a computer simulation technique that helps to explore the behavior and properties of molecules and atoms. MD has been used in research and development in many spaces, including materials science and engineering and nanotechnology. MD has been proven useful in topics like the nano-engineering of construction materials, correcting graphene planar defects, studying self-assembling bio-materials, and the densification, consolidation, and sintering of nanocrystalline materials.

Knowledge-reused transfer learning for molecular and materials science

Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molecular and materials design, synthesis, and application has revolutionized the field of molecular and materials science, allowing researchers to gain a deeper understanding of material properties and behaviors,leading to the development of new materials that are more efficient and reliable. However, the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of conventional machine learning(ML) approaches. Knowledgereused transfer learning(TL) methods are expected to break this dilemma. The application of TL lowers the data requirements for model training, which makes TL stand out in researches addressing data quality issues. In this review, we summarize recent progress in TL related to molecular and materials. We focus on the application of TL methods for the discovery of advanced molecules/materials, particularly, the construction of TL frameworks for different systems, and how TL can enhance the performance of models. In addition, the challenges of TL are also discussed.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

Exploration of Ideological and Political Materials for the Course of Inorganic and Analytical Chemistry for Environmental and Ecological Engineering Major

Integrating ideological and political theories teaching into the whole process of classroom teaching construction is a new requirement for implementing the fundamental task of cultivating people by virtue and playing the role of collaborative education.In order to realize the seamless integration of inorganic and analytical chemistry courses and ideological and political education,this paper summarizes the current situation of ideological and political research on inorganic and analytical chemistry courses in three major databases in China(VIP,CNKI and Wanfang),and sorts out the knowledge points,ideological and political elements and educational goals according to the content of the course chapters,to provide a basic guarantee for the ideological and political education construction of the course.

Professional Cognitive Education Model for Agricultural Water Conservancy Engineering under the Background of New Agricultural Science

The construction of new agricultural science requires the use of modern scientific and technological means to transform and enhance current agricultural related majors.The agricultural water conservancy engineering major,with its inherent disciplinary advantages,plays an indispensable and important role in the construction of new agricultural science.In recent years,the lack of professional cognitive education has gradually become a significant problem in the training of talents in agricultural water conservancy engineering.Therefore,this paper deeply analyzes the problems and reasons faced by professional cognitive education,and proposes specific educational strategies for several key aspects such as enrollment promotion,freshman enrollment education,construction of teacher team,combination of scientific research and teaching,and strengthening professional cognition through competition activities.It aims to provide reference for improving the quality of professional cognitive education and exploring effective ways.

从力学的基础性谈工程科学的科研与教学——以《Journal of Ocean Engineering and Science》为例

力学是一门重要的基础学科,也是工程科学的先导和基础。科技期刊是科研成果的载体和服务科研的平台。本文以《Journal of Ocean Engineering and Science》为例,通过分析该期刊刊文的统计信息及与力学相关的文章,揭示了在海洋工程科学研究中涉及的基础学科主要是力学和数学以及两学科的重要性,探讨了力学等基础学科对工程科学的科研与教学方面的推动作用,进而结合该期刊刊文中有关力学问题的研究,讨论并提出了科技期刊促进科研与教学的建议。

Science and reflections: With some thoughts to young applied scientists and engineers

I provide some science and reflections from my experiences working in geophysics,along with connections to computational and data sciences,including recent developments in machine learning.I highlight several individuals and groups who have influenced me,both through direct collaborations as well as from ideas and insights that I have learned from.While my reflections are rooted in geophysics,they should also be relevant to other computational scientific and engineering fields.I also provide some thoughts for young,applied scientists and engineers.

Creative Economy and Materials Science & Engineering

New materials are fundamental to the growth,security,and quality of life of human being sand open doors to technologies in civil,chemical,nuclear,aeronautical,mechanical,biomedical,and electrical engineering.Creative companies use multiple materials in the development of their activities,such as solid stone,fiber glass,concrete,and glass reinforced concrete,for example.Based on bibliographic research,the article examines the synergy between materials science&engineering and creative economy.The main argument indicates that this synergy creates solutions and functionalities that add value to existing products and allow the development of new products with competitive advantages.It may also contribute to the preservation of cultural values and promote sustainability.

Materials Science-Technology and Intelligent Engineering

This paper analyses the peculiar acting mechanism of artificial neural network (ANN) tech, and explores the great immediate significence for the intelligent sci-tech (IST) to research and develop the nano-tech.

MatChat: A large language model and application service platform for materials science

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

Introduction to the Special Issue on Advances in Neutrosophic and Plithogenic Sets for Engineering and Sciences:Theory,Models,and Applications

Recently,research on uncertainty modeling has been progressing rapidly,and many essential and breakthrough studies have already been done.There are various ways to handle these uncertainties,such as fuzzy and intuitionistic fuzzy sets.Although these concepts can take incomplete information in various real-world issues,they cannot address all types of uncertainty,such as indeterminate and inconsistent information.The neutrosophic theory founded by Florentin Smarandache in 1998 constitutes a further generalization of fuzzy set,intuitionistic fuzzy set,picture fuzzy set,Pythagorean fuzzy set,spherical fuzzy set,etc.Since then,this logic has been applied in various science and engineering domains.

Integrated Computational Materials Engineering for the Development and Design of High Modulus Al Alloys

Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys design and development,which enables the design and development of Al alloys to upgrade from traditional empirical to the integration of compositionprocess-structure-mechanical property,thus greatly accelerating its development speed and reducing its development cost.This study combines calculation of phase diagram(CALPHAD),Finite element calculations,first principle calculations,and microstructure characterization methods to predict and regulate the formation and structure of composite precipitates from the design of highmodulus Al alloy compositions and optimize the casting process parameters to inhibit the formation of micropore defects in the casting process,and the final tensile strength of Al alloys reaches420 MPa and Young's modulus reaches more than 88 GPa,which achieves the design goal of the high strength and modulus Al alloys,and establishes a new mode of the design and development of the strength/modulus Al alloys.

Development of advanced electron tomography in materials science based on TEM and STEM

The recent developments of electron tomography（ET） based on transmission electron microscopy（TEM） and scanning transmission electron microscopy（STEM） in the field of materials science were introduced. The various types of ET based on TEM as well as STEM were described in detail, which included bright-field（BF）-TEM tomography, dark-field（DF）-TEM tomography, weak-beam dark-field（WBDF）-TEM tomography, annular dark-field（ADF）-TEM tomography, energy-filtered transmission electron microscopy（EFTEM） tomography, high-angle annular dark-field（HAADF）-STEM tomography, ADF-STEM tomography, incoherent bright field（IBF）-STEM tomography, electron energy loss spectroscopy（EELS）-STEM tomography and X-ray energy dispersive spectrometry（XEDS）-STEM tomography, and so on. The optimized tilt series such as dual-axis tilt tomography, on-axis tilt tomography, conical tilt tomography and equally-sloped tomography（EST） were reported. The advanced reconstruction algorithms, such as discrete algebraic reconstruction technique（DART）, compressed sensing（CS） algorithm and EST were overviewed. At last, the development tendency of ET in materials science was presented.

Application of machine learning in perovskite materials and devices:A review

Metal-halide hybrid perovskite materials are excellent candidates for solar cells and photoelectric devices.In recent years,machine learning(ML)techniques have developed rapidly in many fields and provided ideas for material discovery and design.ML can be applied to discover new materials quickly and effectively,with significant savings in resources and time compared with traditional experiments and density functional theory(DFT)calculations.In this review,we present the application of ML in per-ovskites and briefly review the recent works in the field of ML-assisted perovskite design.Firstly,the advantages of perovskites in solar cells and the merits of ML applied to perovskites are discussed.Secondly,the workflow of ML in perovskite design and some basic ML algorithms are introduced.Thirdly,the applications of ML in predicting various properties of perovskite materials and devices are reviewed.Finally,we propose some prospects for the future development of this field.The rapid devel-opment of ML technology will largely promote the process of materials science,and ML will become an increasingly popular method for predicting the target properties of materials and devices.

ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions

Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.

Two-Dimensional Materials for Highly Efficient and Stable Perovskite Solar Cells

Perovskite solar cells(PSCs)offer low costs and high power conversion efficiency.However,the lack of long-term stability,primarily stemming from the interfacial defects and the sus-ceptible metal electrodes,hinders their practical application.In the past few years,two-dimensional(2D)materials(e.g.,graphene and its derivatives,transitional metal dichalcogenides,MXenes,and black phosphorus)have been identified as a promising solution to solving these problems because of their dangling bond-free surfaces,layer-dependent electronic band structures,tunable functional groups,and inherent compactness.Here,recent progress of 2D material toward efficient and stable PSCs is summarized,including its role as both interface materials and electrodes.We discuss their beneficial effects on perovskite growth,energy level alignment,defect passivation,as well as blocking external stimulus.In particular,the unique properties of 2D materials to form van der Waals heterojunction at the bottom interface are emphasized.Finally,perspectives on the further development of PSCs using 2D materials are provided,such as designing high-quality van der Waals heterojunction,enhancing the uniformity and coverage of 2D nanosheets,and developing new 2D materials-based electrodes.

Recent Progress in Space Science and Applications on Chinese Space Station in 2022–2024

Chinese Space Station(CSS)has been fully deployed by the end of 2022,and the facility has entered into the application and development phase.It has conducted scientific research projects in various fields,such as space life science and biotechnology,space materials science,microgravity fundamental physics,fluid physics,combustion science,space new technologies,and applications.In this review,we introduce the progress of CSS development and provide an overview of the research conducted in Chinese Space Station and the recent scientific findings in several typical research fields.Such compelling findings mainly concern the rapid solidification of ultra-high temperature alloy melts,dynamics of fluid transport in space,gravity scaling law of boiling heat transfer,vibration fluidization phenomenon of particulate matter,cold atom interferometer technology under high microgravity and related equivalence principle testing,the full life cycle of rice under microgravity and so forth.Furthermore,the planned scientific research and corresponding prospects of Chinese space station in the next few years are presented.