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Magnetically enhancing diffusion for dendrite-free and long-term stable lithium metal anodes

Green Energy & Environment 2022年第5期7卷 965-974页

作者： Yongxiu Chen Xiangyu Dou Kai Wang Yongsheng Han State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing100190China School of Metallurgy and Materials University of BirminghamBirminghamBT152TTUK

Lithium metal batteries are promising devices for the next-generation energy storage due to their ultrahigh theoretical specific capacity and extremely low electrochemical *** inherent problem is the formation of lithium dendrites in cycling,which has induced safety concerns for almost half a *** understanding the formation mechanism of branching structures,we propose to suppress lithium dendrites by adopting external magnetic fields to induce diffusion enhancement at the interface of the anode,thus attenuating concentration gradient there and reducing the driving force for the formation of dendritic *** diffusion coefficient of lithium ions is dependent on the strength of magnetic fields,confirming the effectiveness of magnetic fields in improving Li^(+) *** employing the magnetic field of0.8 T,the concentration gradients at the growth front becomes nearly half of the control case,which leads to a dendrite-free lithium deposition up to the high current density of 10 mA cm^(-2).Both the Cu|LiCoO_(2) batteries and the symmetric Li | Li coin cells show a long-term stable cycling at high current densities under the assistance of magnetic *** diffusion enhanced technique promises a facile and general approach to suppress dendritic structures in secondary batteries,which may help to develop quick charging strategies.

关键词： lithium diffusion cycling

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Geometric Probability on a Lattice with the 15th Type of Convex Pentagon as a Fundamental Region

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Acta Mathematica Sinica,English Series 2025年第4期41卷 1213-1230页

作者： Jiangfu Zhao Jun Jiang Hai Liu Department of Mathematics and Physics Fujian Jiangxia University College of Science Wuhan University of Science and TechnologyHubei Province Key Laboratory of Systems Science in Metallurgical Process National Engineering Research Center for E-learning Central China Normal University

In 2015,a group of mathematicians at the University of Washington,Bothell,discovered the 15th pentagon that can cover a plane, with no gaps and ***,research on its containment measure theory or geometric probability is *** this study,the Laplace extension of Buffon's problem is generalized to the case of the 15th *** the solving process,the explicit expressions for the generalized support function and containment function of this irregular pentagon are *** addition,the chord length distribution function and density function of random distance of this pentagon are obtained in terms of the containment function.

关键词： Buffon lattice containment function generalized support function Geometric probability tessellation

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Waste-to-X: Quantifying the enviro-economic performance of chemical production via municipal solid waste gasification

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Sustainable Production and Consumption 2025年 57卷 167-182页

作者： Ben Lyons Andrea Bernardi Benoît Chachuat The Sargent Centre for Process Systems Engineering Department of Chemical Engineering Imperial College London Exhibition Road SW7 2AZ London UK

Inadequate waste disposal methods currently employed around the globe are leading to substantial damage to both local human populations and the surrounding environment. Simultaneously, the chemical sector is facing pressure to shift from an unsustainable linear economy model and towards a more circular one. Municipal solid waste (MSW) gasification is a technology which is garnering increased attention as it offers a unified solution to both these issues. In this work, the economic and environmental performance of MSW gasification coupled with three chemical manufacturing routes (methanol, olefins via methanol-to-olefins, and ethanol) were compared to landfilling and incineration as a waste disposal method. Detailed process simulation is coupled with the monetisation of endpoint environmental impacts to determine the enviro-economic cost (EEC) of waste processing. Despite the high capital expenditure for all of the gasification routes, the break-even gate fees are competitive with both landfilling and incineration if the CO 2 produced is recovered and sent to storage (CCS). However, if the CO 2 is recycled and utilised (CCU), the break-even gate fees are significantly greater due to a higher green H 2 demand. Both methanol and MTO routes offer significant reductions in environmental impacts due to offsetting fossil-based chemical manufacturing. All gasification routes have predicted EECs lower than both landfilling and incineration, with methanol coupled with CCS resulting in the lowest overall EEC at −£0.21/kg MSW . Methanol production with CCU via MSW gasification is likely to be the most feasible option in the short term as it does not rely on the existence of CO 2 pipeline infrastructure, but it would still need policy support via higher landfilling and carbon taxes. Overall, this work highlights the potential environmental benefit in coupling chemical manufacture with MSW gasification.

关键词： Municipal solid waste Gasification Techno-economic analysis Life-cycle assessment Monetisation Circular economy

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Large-area magnetic skin for multi-point andmulti-scale tactile sensingwith superresolution

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npj Flexible Electronics 2024年第1期8卷 475-486页

作者： Hao Hu Chengqian Zhang Xinyi Lai Huangzhe Dai Chengfeng Pan Haonan Sun Daofan Tang Zhezai Hu Jianzhong Fu Tiefeng Li Peng Zhao The State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical EngineeringZhejiang UniversityHangzhou310027China The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province College of Mechanical EngineeringZhejiang UniversityHangzhou310027China Center for X-Mechanics Department of Engineering MechanicsZhejiang University310027 HangzhouChina School of Electrical Engineering Zhejiang University310027 HangzhouChina

The advancements in tactile sensor technology have found wide-ranging applications in robotic fields,resulting in remarkable achievements in object manipulation and overall human-machine ***,the widespread availability of high-resolution tactile skins remains limited,due to the challenges of incorporating large-sized,robust sensing units and increased wiring *** approach to achieve high-resolution and robust tactile skins is to integrate a limited number of sensor units(taxels)into a flexible surface material and leverage signal processing techniques to achieve super-resolution ***,we present a magnetic skin consisting of multi-direction magnetized flexible films and a contactless Hall sensor *** key features of the proposed sensor include the specific magnetization arrangement,K-Nearest Neighbors(KNN)clustering algorithm and convolutional neural network(CNN)model for signal *** only an array of 4*4 taxels,our magnetic skin is capable of achieving super-resolution perception over an area of 48400mm^(2),with an average localization error of 1.2 *** employing neural network algorithms to decouple the multidimensional signals,the skin can achieve multi-point and multi-scale *** also demonstrate the promising potentials of the proposed sensor in intelligent control,by simultaneously controlling two vehicles with trajectory mapping on the magnetic skin.

关键词： resolution magnetic network

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control-INFORMED REINFORCEMENT LEARNING FOR CHEMICAL processES

arXiv

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arXiv 2024年

作者： Bloor, Maximilian Ahmed, Akhil Kotecha, Niki Mercangöz, Mehmet Tsay, Calvin Del Rio Chanona, Ehecactl Antonio Sargent Centre for Process Systems Engineering Department of Chemical Engineering Imperial College London London United Kingdom

This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of deep reinforcement learning (RL) policies. The proposed approach augments deep RL agents with a PID controller layer, incorporating prior knowledge from control theory into the learning process. CIRL improves performance and robustness by combining the best of both worlds: the disturbance-rejection and setpoint-tracking capabilities of PID control and the nonlinear modeling capacity of deep RL. Simulation studies conducted on a continuously stirred tank reactor system demonstrate the improved performance of CIRL compared to both conventional model-free deep RL and static PID controllers. CIRL exhibits better setpoint-tracking ability, particularly when generalizing to trajectories outside the training distribution, suggesting enhanced generalization capabilities. Furthermore, the embedded prior control knowledge within the CIRL policy improves its robustness to unobserved system disturbances. The control-informed RL framework combines the strengths of classical control and reinforcement learning to develop sample-efficient and robust deep reinforcement learning algorithms, with potential applications in complex industrial systems. © 2024, CC BY-SA.

关键词： Deep reinforcement learning

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Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and process Design

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engineering 2021年第9期7卷 1231-1238页

作者： Teng Zhou Rafiqul Gani Kai Sundmacher Process Systems Engineering Max Planck Institute for Dynamics of Complex Technical SystemsMagdeburg D-39106Germany Process Systems Engineering Otto-von-Guericke University MagdeburgMagdeburg D-39106Germany PSE for SPEED Co. Ltd.Allerod DK 3450Denmark Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea

The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable *** process materials lie at the heart of this ***,new advanced materials are found empirically or through trial-and-error *** theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and *** to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design *** this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually *** modeling provides a promising option to tackle such complex design *** hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic *** article highlights the significance of hybrid modeling in multiscale material and process *** generic design methodology is first *** important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering *** each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is *** remarks are provided at the end,and current limitations and future opportunities are pointed out.

关键词： Data-driven Surrogate model Machine learning Hybrid modeling Material design process optimization

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MXene Key Composites:A New Arena for Gas Sensors

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Nano-Micro Letters 2024年第10期16卷 350-391页

作者： Yitong Wang Yuhua Wang Min Jian Qinting Jiang Xifei Li Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan University of Science and TechnologyWuhan 430081People's Republic of China Key Materials and Components of Electrical Vehicles for Overseas Expertise Introduction Center for Discipline Innovation Institute of Advanced Electrochemical Energy and School of Materials Science and EngineeringXi'an University of TechnologyXi'an 710048People's Republic of China College of Materials Science and Engineering Fuzhou UniversityFuzhou 350108FujianPeople's Republic of China

With the development of science and technology,the scale of industrial production continues to grow,and the types and quantities of gas raw materials used in industrial production and produced during the production process are also constantly *** gases include flammable and explosive gases,and even contain toxic ***,it is very important and necessary for gas sensors to detect and monitor these gases quickly and *** recent years,a new two-dimensional material called MXene has attracted widespread attention in various *** abundant surface functional groups and sites,excellent current conductivity,tunable surface chemistry,and outstanding stability make them promising for gas sensor *** the birth of MXene materials,researchers have utilized the efficient and convenient solution etching preparation,high flexibility,and easily functionalize MXene with other materials to pre-pare composites for gas *** has opened a new chapter in high-performance gas sensing materials and provided a new approach for advanced sensor ***,previous reviews on MXene-based composite materials in gas sensing only focused on the performance of gas sensing,without systematically explaining the gas sensing mechanisms generated by different gases,as well as summarizing and predicting the advantages and disadvantages of MXene-based composite *** article reviews the latest progress in the application of MXene-based composite materials in gas ***,a brief summary was given of the commonly used methods for preparing gas sens-ing device structures,followed by an introduction to the key attributes of MXene related to gas sensing *** article focuses on the performance of MXene-based composite materials used for gas sensing,such as MXene/graphene,MXene/Metal oxide,MXene/Transition metal sulfides(TMDs),MXene/Metal-organic framework(MOF),MXene/*** summarizes the advantages and disad

关键词： MXene Compound material Gas sensor Gas sensitive preparation Gas sensitivity performance

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Simulation Analysis and Model Modification of the LWR Traffic Flow Theory

Simulation Analysis and Model Modification of the LWR Traffi...

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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Yang Chen Jun Fu Anton Selivanov State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Automatic Control and Systems Engineering University of Sheffield UK

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The surge in urbanization and ensuing traffic congestion underscores the imperative for contemporary traffic flow control models. This paper explores the Lighthill-Whitham-Richards (LWR) theory in the macroscopic domain of traffic flow control. It evaluates the accuracy and mathematical logic of simulation performance of related models, namely Greenshields, Newell-Daganzo, Greenberg, Underwood, and California. This paper employs thorough simulations that utilize a holistic three-dimensional(3D) analysis based on time, location, and density. It provides a comprehensive comparison and discusses specific features of each model. Additionally, a modification to the macroscopic model is proposed. The modified model overcomes the shortcomings of performing well only in some specific short ranges and achieves a full range of application effects. The study offers valuable guidance for real-life scenario selections and applications in the traffic domain.

关键词： Analytical models Accuracy Urban areas Transportation Predictive models Mathematical models Numerical models

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A Decomposition Approach to Characterizing Feasibility in Acyclic Multi-Unit processes ⁎

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IFAC-PapersOnLine 2024年第14期58卷 216-221页

作者： Max Mowbray Cleo Kontoravdi Nilay Shah Benoît Chachuat The Sargent Centre for Process Systems Engineering Department of Chemical Engineering Imperial College London London SW7 2AZ United Kingdom

The ability to certify feasibility in process design and process operations is crucial in many applications. This includes quality-by-design in pharmaceutical manufacturing, where a key element is the characterization of the design space to better understand the links between materials, processes and products. Sampling-based approaches are versatile but they are cursed by dimensionality, which currently limits their application to problems in a few process variables only. We propose a decomposition approach that enables feasibility characterization for nominal settings of uncertain parameters in acyclic multi-unit processes by sampling. Our methodology leverages problem structure to decompose unit-wise, and deploys surrogate models to couple the resultant subproblems. We demonstrate it on a serial, batch chemical reactor network. In future research, we will extend this framework to cyclic multi-unit processes and the presence of parameter uncertainty.

关键词： process design Nested sampling Quality-by-design Design space Network structure Machine learning

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Solvent Screening for Separation processes Using Machine Learning and High-Throughput Technologies

Chem and Bio Engineering

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Chem and Bio engineering 2025年第4期2卷 210-228页

作者： Edaugal, Justin P. Zhang, Difan Liu, Dupeng Glezakou, Vassiliki-Alexandra Sun, Ning Advanced Biofuels and Bioproducts Process Development Unit Biological Systems and Engineering Division Lawrence Berkeley National Laboratory EmeryvilleCA94608 United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Chemical Sciences Division Oak Ridge National Laboratory Oak RidgeTN37830 United States

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes. © 2025 The Authors. Co-published by Zhejiang University and American Chemical Society.

关键词： Chemical industry

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