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"Yin, Jian"
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Consolidation analyses of soils
When stresses are applied to saturated soil, deformation will occur as water in voids is squeezed out. Consolidation Analyses of Soils focuses on the consolidation of fully saturated soils. The book follows a classic approach by beginning with one-dimensional constitutive relations of soils and one-dimensional consolidation. It then moves on to analytical solutions to several one-dimensional consolidation problems and one-dimensional finite strain consolidation. The authors also present a finite element method for consolidation analysis of one-dimensional problems, analytical solutions to consolidation of soil with vertical drains, and a finite difference method for consolidation analysis of one-dimensional problems. Simplified methods for consolidation analysis of soils exhibiting creep are introduced and applied to different cases. Three-dimensional consolidation equations and solutions of typical three-dimensional consolidation problems are covered, as well as simplified finite element consolidation analysis of soils with vertical drain and finite element method for three-dimensional consolidation problems. The book is unique in that it covers both classic solutions and state-of-the-art work in consolidation analyses of soils. Authors Jian-Hua Yin is Chair Professor of Soil Mechanics in the Department of Civil and Environmental Engineering at The Hong Kong Polytechnic University. Guofu Zhu is a Professor in the Department of Engineering Structures and Mechanics at Wuhan University of Technology, China.
Book
Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor,
μ
/
t
, where
μ
and
t
are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs
0.4
La
0.6
Mn
0.25
Co
0.75
O
3
, Cs
0.3
La
0.7
NiO
3
, SrNi
0.75
Co
0.25
O
3
, and Sr
0.25
Ba
0.75
NiO
3
, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.
Symbolic regression holds big promise for guiding materials design, yet its application in materials science is still limited. Here the authors use symbolic regression to introduce an activity descriptor predicting new oxide perovskites with improved oxygen evolution activity as corroborated by experimental validation.
Journal Article
تعرف على 5000 عام من تاريخ الصين : نحو قراءة ميسرة لتاريخ الصين
يضم الكتاب قصص من تاريخ الصين القديم ذلك البلد الذي يعد من أقدم حضارات العالم التي يعود تاريخها إلى ما قبل خمسة آلاف عام حيث يجمع الكتاب بين المعرفة والمتعة والإبداع ويحتوي على نصوص من معارف تاريخية وجزءا خاصا بالثقافة يعمل على توسيع الأفق المعرفي للقراء ويعتمد الكتاب في طرح أفكاره بطريقة قص الحكايات كأسلوب لسرد الأحداث التاريخية كما يحتوي على مجموعة هائلة من الصور مما يزيد من شغف القارئ.
BOOK
Crystal structure prediction by combining graph network and optimization algorithm
Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph network (GN) is employed to establish a correlation model between the crystal structure and formation enthalpies at the given database, and an optimization algorithm (OA) is used to accelerate the search for crystal structure with lowest formation enthalpy. The framework of the utilized approach (a database + a GN model + an optimization algorithm) is flexible. We implemented two benchmark databases,
i.e
., the open quantum materials database (OQMD) and Matbench (MatB), and three OAs,
i.e
., random searching (RAS), particle-swarm optimization (PSO) and Bayesian optimization (BO), that can predict crystal structures at a given number of atoms in a periodic cell. The comparative studies show that the GN model trained on MatB combined with BO,
i.e
., GN(MatB)-BO, exhibit the best performance for predicting crystal structures of 29 typical compounds with a computational cost three orders of magnitude less than that required for conventional approaches screening structures through density functional theory calculation. The flexible framework in combination with a materials database, a graph network, and an optimization algorithm may open new avenues for data-driven crystal structural predictions.
Predicting crystal structure prior to experimental synthesis is highly desirable. Here the authors propose a machine-learning framework combining graph network and optimization algorithms for crystal structure prediction, which is about three orders of magnitude faster than DFT-based approach.
Journal Article
Cs2AgBiBr6 single-crystal X-ray detectors with a low detection limit
Sensitive X-ray detection is crucial for medical diagnosis, industrial inspection and scientific research. The recently described hybrid lead halide perovskites have demonstrated low-cost fabrication and outstanding performance for direct X-ray detection, but they all contain toxic Pb in a soluble form. Here, we report sensitive X-ray detectors using solution-processed double perovskite Cs
2
AgBiBr
6
single crystals. Through thermal annealing and surface treatment, we largely eliminate Ag
+
/Bi
3+
disordering and improve the crystal resistivity, resulting in a detector with a minimum detectable dose rate as low as 59.7 nGy
air
s
−1
, comparable to the latest record of 0.036 μGy
air
s
−1
using CH
3
NH
3
PbBr
3
single crystals. Suppressed ion migration in Cs
2
AgBiBr
6
permits relatively large external bias, guaranteeing efficient charge collection without a substantial increase in noise current and thus enabling the low detection limit.
Double perovskite Cs
2
AgBiBr
6
single crystals are used to make a sensitive X-ray detector. The device exhibits a high sensitivity of 105 µC Gy
air
−1
cm
−2
and a low detection limit of 59.7 nGy
air
s
−1
, and demonstrates long-term operational stability.
Journal Article
Intellectual capital, digital transformation and firms’ financial performance: Evidence from ecological protection and environmental governance industry in China
As the pace of enterprise digital transformation accelerates, intellectual capital (IC) has become a core driving force of gaining market competitive advantages and enhancing value creation capabilities. The paper aims to investigate the impact of IC and its components on financial performance of Chinese ecological protection and environmental governance companies during 2018–2021. In addition, the moderating effect of digital transformation between them is examined. IC is measured by the modified value added intellectual coefficient (MVAIC) model, and the measurement of digital transformation is based on text mining. The results suggest that IC can improve firm financial performance, especially during COVID-19. Physical capital, human capital (HC), and relational capital (RC) positively affect financial performance, while structural and innovation capitals have no significant impact. In addition, digital transformation strengthens the positive relationship between IC and its two elements (HC and RC) and financial performance. Heterogeneous analysis finds that the relationship between RC and innovation capital and financial performance is positive before COVID-19, and it is not significant during COVID-19. For highly leveraged companies, structural capital negatively affects financial performance, and RC has a positive impact. These impacts are not significant for low leveraged companies. This paper provides some new insights for managers who seek new ways to improve firm performance in the process of digital transformation.
Journal Article
Understanding Defects in Perovskite Solar Cells through Computation: Current Knowledge and Future Challenge
Lead halide perovskites with superior optoelectrical properties are emerging as a class of excellent materials for applications in solar cells and light‐emitting devices. However, perovskite films often exhibit abundant intrinsic defects, which can limit the efficiency of perovskite‐based optoelectronic devices by acting as carrier recombination centers. Thus, an understanding of defect chemistry in lead halide perovskites assumes a prominent role in further advancing the exploitation of perovskites, which, to a large extent, is performed by relying on first‐principles calculations. However, the complex defect structure, strong anharmonicity, and soft lattice of lead halide perovskites pose challenges to defect studies. In this perspective, on the basis of briefly reviewing the current knowledge concerning computational studies on defects, this work concentrates on addressing the unsolved problems and proposing possible research directions in future. This perspective particularly emphasizes the indispensability of developing advanced approaches for deeply understanding the nature of defects and conducting data‐driven defect research for designing reasonable strategies to further improve the performance of perovskite applications. Finally, this work highlights that theoretical studies should pay more attention to establishing close and clear links with experimental investigations to provide useful insights to the scientific and industrial communities. A deep understanding of defect chemistry in lead‐halide perovskites is vitally important. On the basis of reviewing current knowledge concerning computational studies about defects, controversial problems concerning the role of defects on carrier recombination and phase degradation, as well as possible research directions are proposed, with emphasizing the construction of close links between theoretical and experimental investigations via machine learning.
Journal Article
ROS: Executioner of regulating cell death in spinal cord injury
The damage to the central nervous system and dysfunction of the body caused by spinal cord injury (SCI) are extremely severe. The pathological process of SCI is accompanied by inflammation and injury to nerve cells. Current evidence suggests that oxidative stress, resulting from an increase in the production of reactive oxygen species (ROS) and an imbalance in its clearance, plays a significant role in the secondary damage during SCI. The transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) is a crucial regulatory molecule for cellular redox. This review summarizes recent advancements in the regulation of ROS-Nrf2 signaling and focuses on the interaction between ROS and the regulation of different modes of neuronal cell death after SCI, such as apoptosis, autophagy, pyroptosis, and ferroptosis. Furthermore, we highlight the pathways through which materials science, including exosomes, hydrogels, and nanomaterials, can alleviate SCI by modulating ROS production and clearance. This review provides valuable insights and directions for reducing neuronal cell death and alleviating SCI through the regulation of ROS and oxidative stress.
Journal Article
Exploring the Impact of Board Size on ESG Controversies: New Evidence from China
This study aims to investigate the impact of board size on environmental, social, and governance (ESG) controversies using data from Chinese-listed companies during 2007–2022. In addition, we explore the moderating effects of female participation on corporate boards, board age, financing constraints, and internal control. ESG controversies are measured by an ESG controversies score from the LSEG Workspace, and fixed effects models are used to perform the analysis. The results show that larger boards can lead to more ESG controversies in China. This impact is greater in non-manufacturing, heavily polluted, and non-high-tech industries, in state-owned enterprises, eastern regions, and non-foreign-funded companies. Additionally, women on boards and internal control weaken the impact of board size on ESG controversies, while financing constraints strengthen this impact. The moderating effect of board age is not significant. The findings can help Chinese-listed companies improve their ESG performance and achieve sustainable development through strengthening corporate governance.
Journal Article
Self-compensation in arsenic doping of CdTe
Efficient p-type doping in CdTe has remained a critical challenge for decades, limiting the performance of CdTe-based semiconductor devices. Arsenic is a promising p-type dopant; however, reproducible doping with high concentration is difficult and carrier lifetime is low. We systematically studied defect structures in As-doped CdTe using high-purity single crystal wafers to investigate the mechanisms that limit p-type doping. Two As-doped CdTe with varying acceptor density and two undoped CdTe were grown in Cd-rich and Te-rich environments. The defect structures were investigated by thermoelectric-effect spectroscopy (TEES), and first-principles calculations were used for identifying and assigning the experimentally observed defects. Measurements revealed activation of As is very low in both As-doped samples with very short lifetimes indicating strong compensation and the presence of significant carrier trapping defects. Defect studies suggest two acceptors and one donor level were introduced by As doping with activation energies at ~88 meV, ~293 meV and ~377 meV. In particular, the peak shown at ~162 K in the TEES spectra is very prominent in both As-doped samples, indicating a signature of AX-center donors. The AX-centers are believed to be responsible for most of the compensation because of their low formation energy and very prominent peak intensity in TEES spectra.
Journal Article