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Acta Mechanica Sinica 2023年第1期39卷 23-29页

作者：王林李二垒周宗彦张百成余艾冰 ARC Hub for Computational Particle Technology Department of Chemical & Biological Engineering Monash University Clayton VIC 3800 Australia Delft University of Technology Stevinweg 1 2628 CN Delft The Netherlands Jiangxi Provincial Key Laboratory for Simulation and Modelling of Particulate Systems Jiangxi University of Science and Technology Nanchang 330013 China Beijing Advanced Innovation Center Materials Genome Engineering Advanced Material & Technology Institute University of Science and Technology Beijing Beijing 100083 China

粉末床熔融增材制造技术已应用于功能梯度材料的制造.文献中报道了一种材料成分沿垂直于粉末铺粉方向变化的新设计.基于此设计,本工作采用离散单元数值模拟研究了两种密度差异较大的金属粉末在铺粉过程中颗粒的偏析情况.结果发现,在铺... 详细信息

粉末床熔融增材制造技术已应用于功能梯度材料的制造.文献中报道了一种材料成分沿垂直于粉末铺粉方向变化的新设计.基于此设计,本工作采用离散单元数值模拟研究了两种密度差异较大的金属粉末在铺粉过程中颗粒的偏析情况.结果发现,在铺粉过程中,较大密度粉末(重颗粒)一侧的铺粉量小于较小密度粉末(轻颗粒)的一侧的铺粉量.这种现象表明重颗粒从粉末密度大的一侧向粉末密度较小的一侧扩散.这种扩散受铺粉速度影响较大,铺粉速度越快,扩散越明显.但粉末层厚度对扩散影响不大.结果还表明,重颗粒与轻颗粒间的粒径差异影响扩散较大,减小重颗粒的粒径可能是控制颗粒偏析的一种方案.

关键词：材料成分颗粒偏析功能梯度材料离散单元金属粉末密度差异增材制造技术扩散影响

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Developing deep learning-based large-scale organic reaction classification model via sigma-profiles

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Green Chemical engineering 2025年第2期 181-192页

作者： Wenlong Wang Chenyang Xu Jian Du Lei Zhang State Key Laboratory of Fine Chemical Frontiers Science Center for Smart Materials Oriented Chemical EngineeringInstitute of Chemical Process Systems EngineeringSchool of Chemical EngineeringDalian University of Technology Department of Chemical and Biomolecular Engineering National University of Singapore

Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions, especially in the field of organic synthesis. With hundreds of thousands of reactions available for reference, one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics, which makes target-guided navigation in the vast chemical space possible. Although previous attempts that apply deep learning to reaction classification tasks have made substantial progress, developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open question. In this work, a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and autoencoder. Then, the model is trained under the open-source dataset USPTO＿TPL which contains recorded reactions of up to 1000 different types. The multi-classification accuracy of the model on the testing dataset is 99.382%, showing its great potential for practical use. Besides,a reaction similarity map is presented to correlate the reactions in the USPTO＿TPL dataset based on their sigmaprofile-based statistical features. Finally, representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.

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Ionic liquids for CO_(2) electrochemical reduction

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Chinese Journal of Chemical engineering 2021年第3期34卷 75-93页

作者： Fangfang Li Francesca Mocci Xiangping Zhang Xiaoyan Ji Aatto Laaksonen Energy Engineering Division of Energy ScienceLuleåUniversity of TechnologyLuleå97187Sweden Department of Chemical and Geological Sciences University of CagliariMonserrato 09042Italy CAS Key Laboratory of Green Process and Engineering Beijing Key Laboratory of Ionic Liquids Clean ProcessState Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China Division of Physical Chemistry Department of Materials and Environmental ChemistryArrhenius LaboratoryStockholm UniversityStockholm 10691Sweden Center of Advanced Research in Bionanocojugates and Biopolymers “Petru Poni”Institute of Macromolecular ChemistryIasi 700469Romania State Key Laboratory of Materials-Oriented and Chemical Engineering Nanjing Tech UniversityNanjing 211816China

Electrochemical reduction of CO_(2) is a novel research field towards a CO_(2)-neutral global economy and combating fast accelerating and disastrous climate changes while finding new solutions to store renewable energy in value-added chemicals and *** liquids(ILs),as medium and catalysts(or supporting part of catalysts)have been given wide attention in the electrochemical CO_(2) reduction reaction(CO_(2) RR)due to their unique advantages in lowering overpotential and improving the product selectivity,as well as their designable and tunable *** this review,we have summarized the recent progress of CO_(2) electro-reduction in IL-based electrolytes to produce higher-value *** then have highlighted the unique enhancing effect of ILs on CO_(2) RR as templates,precursors,and surface functional moieties of electrocatalytic ***,computational chemistry tools utilized to understand how the ILs facilitate the CO_(2) RR or to propose the reaction mechanisms,generated intermediates and products have been discussed.

关键词： Carbon dioxide Ionic liquids Electro-reduction Electrolyte Electrocatalytic material Computer simulation

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Light doping of tungsten into copper-platinum nanoalloys for boosting their electrocatalytic performance in methanol oxidation

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Nano Research Energy 2022年第2期1卷 194-200页

作者： Danye Liu Qing Zeng Chaoquan Hu Dong Chen Hui Liu Yongsheng Han Lin Xu Qingbo Zhang Jun Yang State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Nanjing IPE Institute of Green Manufacturing Industry Nanjing 211100China School of Chemistry and Materials Science Jiangsu Key Laboratory of New Power BatteriesJiangsu Collaborative Innovation Centre of Biomedical Functional MaterialsNanjing Normal UniversityNanjing 210023China Department of Bioengineering Rice UniversityHoustonTexas 77005USA

Coupling the bi-functional mechanism with compressive lattice strain might be an effective way to boost the electrocatalysis of platinum(Pt)-based nanoparticles for methanol oxidation reaction(MOR).This strategy weakens the chemisorption of poisoning CO-like intermediates generated during MOR on the active Pt sites by lowering their d-band *** this context,we herein report the synthesis of ternary copper-tungsten-platinum(CuWPt)nanoalloys with light doping of W element by simply co-reducing their precursors at elevated *** this ternary alloy system,the presence of only small amount of W element not only weakens the chemisorption of CO-like intermediates by lowering the Pt d-band center through compressive lattice strain,but also cleans the active Pt sites by“hydrogen spillover effect”,endowing the as-prepared CuWPt nanoalloys at an appropriate Cu/W/Pt ratio with good activity for *** specific,the ternary CuWPt alloy nanoparticles at a Cu/W/Pt molar ratio of 21/4/75 show a specific activity of 2.5 mA·cm^(−2)and a mass activity of 2.11 A·mg^(−1)with a better durability,outperforming those ternary CuWPt alloy nanoparticles at other Cu/W/Pt ratios,binary CuPt alloys and commercial Pt/C catalyst as well as a large number of reported Pt-based *** addition,a single direct methanol fuel cell(DMFC)assembled using ternary CuWPt nanoalloys as anodic catalysts shows a power density of 24.3 mW·cm^(−2)and an open-circle voltage of 0.6 V,also much higher than those of the single DMFC assembled from commercial Pt/C catalysts.

关键词： lattice strain d-band center electrocatalysis hydrogen spillover methanol oxidation reaction

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Core-shell Ag-Pt nanoparticles:A versatile platform for the synthesis of heterogeneous nanostructures towards catalyzing electrochemical reactions

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Chinese Chemical Letters 2021年第11期32卷 3288-3297页

作者： Danye Liu Niuwa Yang Qing Zeng Hui Liu Dong Chen Penglei Cui Lin Xu Chaoquan Hu Jun Yang State Key Laboratory of Multiphase Complex Systems and Center of Mesoscience Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Nanjing IPE Institute of Green Manufacturing Industiy Nanjing 211100China School of Chemistry and Materials Science Jiangsu Key Laboratoiy of New Power BatteriesNanjing Normal UniversityNanjing 210023China

Heterogeneous nanostructures that are defined as a hybrid structure consisting of two or more nanoscale domains with distinct chemical compositions or physical characteristics have attracted intense efforts in recent *** this review,we focus on the introduction of a number of heterogeneous nanostructures derived using core-shell Ag-Pt nanoparticles as starting materials,including hollow,dimeric and composite structures and also highlight their application in catalyzing electrochemical reactions,e.g.,methanol oxidation reaction and oxygen reduction *** review not only shows the capability of core-shell Ag-Pt nanoparticles in producing various heterogeneous nanostructures as starting templates,but also highlights the structural design or electronic interaction that endows the heterogeneous nanostructures with enhanced catalytic properties either in methanol oxidation or in oxygen ***,we also make some perspectives for more heterogeneous nanostructures that may be prepared by using core-shell Ag-Pt particles or their derivatives so as to offer the readers the opportunities and challenges in this field.

关键词： Heterogeneous nanostructures Core-shell Electrochemical reaction Methanol oxidation reaction Oxygen reduction reaction

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Bacterial dimensions sensitively regulate surface diffusivity and residence time

arXiv

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arXiv 2025年

作者： Leishangthem, Premkumar Wang, Xuan Chen, Junan Yang, Shengqi Xu, Xinliang School of Physics and Optoelectronic Engineering Hainan University Haikou570228 China Complex Systems Division Beijing Computational Science Research Center Beijing100193 China Department of Physics Beijing Normal University Beijing100875 China School of Aerospace Engineering Tsinghua University Beijing100084 China

Run-and-tumble is a common but vital strategy that bacteria employ to explore environment suffused with boundaries, as well as to escape from entrapment. In this study we reveal how this strategy and the resulting dynamical behavior can be sensitively regulated by bacterial dimensions. Our results demonstrate that the logarithm of the surface residence time for bacteria with constant tumble bias is linearly related to a dimensionless parameter of bacterial intrinsic size characteristics, where a small variation in bacterial dimensions, which is natural in a suspension, reproduces well the experimentally observed large variation in bacterial residence time. Furthermore, our results predict that the optimal tumble bias corresponding to the maximum surface diffusivity depends strongly on bacterial dimensions, where the same small variation in bacterial dimensions gives rise to a strongly diversified optimal tumble bias and an order of magnitude change in surface diffusivity. © 2025, CC BY.

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Crystal Structures and Physicochemical Properties of Be2N and Mg2N as Electride Materials

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Physical Review Applied 2023年第3期19卷 034014-034014页

作者： Gui Wang Zhuang Ma Jing-Wen Jiang Jing-kai Yang Yi-Ling Sun Zheng-Fang Qian Pu Huang Peng Zhang Su-Huai Wei College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China Hebei Key Laboratory of Applied Chemistry College of Environmental and Chemical Engineering Yanshan University Qinhuangdao 066004 China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Computational Science Research Center Beijing 100193 China

Electrides are a special family of materials with exotic physical properties and various crystal structures in different dimensions. Among them, alkaline-earth metal nitrides (AE2N) have attracted enormous attention, since the synthesis of Ca2N as a two-dimensional (2D) electride. However, apart from Ca2N, the physicochemical properties of other AE2N are not well understood, and in some cases, their crystal structures remain unknown. To overcome these issues, here, we conduct systematic investigations into the crystal structures, electronic structures, and optical properties of two typical AE2N, i.e., Mg2N and Be2N, based on first-principles calculations. By symmetry analysis, we derive a crystal structure with R3¯m symmetry for Be2N, which is energetically more stable than all the other structures proposed previously. Our calculations show that R3¯m Be2N is a 2D electride with the anionic electrons located in the interstitial space, forming a 2D Kagome lattice. Moreover, distinct from traditional 2D electrides, it exhibits anomalous physicochemical properties, such as having a small interstitial space, a high work function, and a large cleavage energy, mainly due to the relatively strong bonding effect between anionic electrons and cationic framework. For Mg2N, on the other hand, we find that the most stable structure has R3m symmetry, which shows a total energy of 0.005 eV per formula unit lower than the previously predicted Cmcm structure. Moreover, different from R3¯m Be2N, R3m Mg2N is identified as a zero-dimensional (0D) electride with a semiconducting band structure, since its anionic electrons are confined in separated 0D cavities and cannot interact with each other. These studies thus provide a deeper understanding of the crystal structures and physicochemical properties of Be2N and Mg2N as potential electride materials.

关键词： Crystal structure Electronic structure Optoelectronics Metals Nitrides Semiconductor compounds Two-dimensional electron system Density functional calculations

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Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models

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Fundamental Research 2024年第4期4卷 890-897页

作者： Chenyang Shuai Bu Zhao Xi Chen Jianguo Liu Chunmiao Zheng Shen Qu Jian-Ping Zou Ming Xu School of Management Science and Real Estate Chongqing UniversityChongqing 400044China bSchool for Environment and Sustainability University of MichiganAnn ArborMI 489United Stats Michigan Institute for Computational Discovery&Engineering University of MichiganAnn ArborMI 48109United States College of Economics and Management Southwest UniversityChongqing 400044China Center for Systems Integration and Sustainability Department of Fisheries and WildlifeMichigan State UniversityEast LansingMI 48824United States Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Management and Economics Beijing Institute of TechnologyBeijing 100081China Center for Energy&Environmental Policy Research Beijing Institute of TechnologyBejing 100081China Key Laboratory of Jiangxi Province for Persistent Plutants Control and Resources Recycle Nanchang Hangkong UniversityNanchang 330063China Department of Civil and Environmental Engineering University of MichiganAnn ArborMI 48109United States

The COvID-19 pandemic has posed severe threats to global sustainable ***,a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals(SDGs)is still *** research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine,random forest,and extreme gradient *** results show that the overall SDG performance declined by 7.7%in 2020 at the global scale,with 12 socioeconomic SDG performance decreasing by 3.0%-22.3%and 4 environmental SDG performance increasing by 1.6%-9.2%.By 2024,the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories,while extra time will be gained for 4 environment-related ***,the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies,and the latter will recover more quickly to be closer to their pre-covID-19 trajectories by ***-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental *** results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.

关键词： Sustainable development goals COVID-19 Machine learning Global development Sustainable development assessment

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Enhancement of Near-infrared Light Absorption by Nanoimprinted Light Trapping Structure Implemented into Si Heterojunction Solar Cells 52

Enhancement of Near-infrared Light Absorption by Nanoimprint...

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52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Kurokawa, Yasuyoshi Iseki, Yoshiko Gotoh, Kazuhiro Miyamoto, Satoru Ozaki, Ryo Nakamura, Kyotaro Ohshita, Yoshio Usami, Noritaka Niigata University Dept. Electrical and Information Engineering Niigata950-2181 Japan Intereisciplinaty Research Center for Carbon-Neutral Technologies Niigata University Niigata950-2181 Japan Toyota Technological Institute Dept. Advanced Science and Technology Nagoya468-8511 Japan Nagoya University Dept. Materials Process Engineering Nagoya464-8603 Japan Institute of Materials and Systems for Sustainability Nagoya University Nagoya464-8601 Japan Institutes of Innovation for Future Society Nagoya University Nagoya464-8601 Japan

ISBN: (纸本)9781665464260

Lightweight and flexible PV modules are required to apply PV to buildings and vehicles. One way to realize lightweight and flexible PV is to make the silicon substrate thinner. However, thinner substrates result in lower near-infrared light absorption. Although light trapping structure (LTS) can increase light absorption, conventional alkali-etched pyramid textures are not specialized for near-infrared light and are insufficient to improve near-infrared light absorption. Therefore, in this study, a honeycomb LTS was implemented into the rear side of Si heterojunction (SHJ) solar cells by a nanoimprinting method, which enables us to prepare submicron-sized LTS on solar cells over a large area. As a result, in the near-infrared range of more than 1000 nm, the reflectance of the sample with honeycomb LTS was reduced compared to that without the LTS. The reflectance at 1050 nm was reduced from 59.2 to 41.5%. The external quantum efficiency (EQE) in the near-infrared range of more than 1000 nm was also increased by the LTS, leading to the improvement of the short-circuit current density (Jsc) by 0.02 mA/cm2 © 2024 IEEE.

关键词： Honeycomb structures

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