Mixed ionic electronic conductors(MIECs)have attracted increasing attention as anode materials for solid oxide fuel cells(SOFCs)and they hold great promise for lowering the operation temperature of SOFCs.However,there...Mixed ionic electronic conductors(MIECs)have attracted increasing attention as anode materials for solid oxide fuel cells(SOFCs)and they hold great promise for lowering the operation temperature of SOFCs.However,there has been a lack of understanding of the performance-limiting factors and guidelines for rational design of composite metal-MIEC electrodes.Using a newly-developed approach based on 3 D-tomography and electrochemical impedance spectroscopy,here for the first time we quantify the contribution of the dual-phase boundary(DPB)relative to the three-phase boundary(TPB)reaction pathway on real MIEC electrodes.A new design strategy is developed for Ni/gadolinium doped ceria(CGO)electrodes(a typical MIEC electrode)based on the quantitative analyses and a novel Ni/CGO fiber-matrix structure is proposed and fabricated by combining electrospinning and tape-casting methods using commercial powders.With only 11.5 vol%nickel,the designer Ni/CGO fiber-matrix electrode shows 32%and 67%lower polarization resistance than a nano-Ni impregnated CGO scaffold electrode and conventional cermet electrode respectively.The results in this paper demonstrate quantitatively using real electrode structures that enhancing DPB and hydrogen kinetics are more efficient strategies to enhance electrode performance than simply increasing TPB.展开更多
Heterotopic ossification(HO)is a consequence of traumatic bone and tissue damage,which occurs in 65%of military casualties with blast-associated amputations.However,the mechanisms behind blast-induced HO remain unclea...Heterotopic ossification(HO)is a consequence of traumatic bone and tissue damage,which occurs in 65%of military casualties with blast-associated amputations.However,the mechanisms behind blast-induced HO remain unclear.Animal models are used to study blast-induced HO,but developing such models is challenging,particularly in how to use a pure blast wave(primary blast)to induce limb fracture that then requires an amputation.Several studies,including our recent study,have developed platforms to induce limb fractures in rats with blast loading or a mixture of blast and impact loading.However,these models are limited by the survivability of the animal and repeatability of the model.In this study,we developed an improved platform,aiming to improve the animal's survivability and injury repeatability as well as focusing on primary blast only.The platform exposed only one limb of the rat to a blast wave while providing proper protection to the rest of the rat's body.We obtained very consistent fracture outcome in the tibia(location and pattern)in cadaveric rats with a large range of size and weight.Importantly,the rats did not obviously move during the test,where movement is a potential cause of uncontrolled injury.We further conducted parametric studies by varying the features of the design of the platform.These factors,such as how the limb is fixed and how the cavity through which the limb is placed is sealed,significantly affect the resulting injury.This platform and test setups enable well-controlled limb fracture induced directly by pure blast wave,which is the fundamental step towards a complete in vivo animal model for blast-induced HO induced by primary blast alone,excluding secondary and tertiary blast injury.In addition,the platform design and the findings presented here,particularly regarding the proper protection of the animal,have implications for future studies investigating localized blast injuries,such as blast induced brain and lung injuries.展开更多
Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accur...Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.展开更多
Perovskite solar cells have emerged as a promising technology for renewable energy generation.However,the successful integration of perovskite solar cells with energy storage devices to establish high-efficiency and l...Perovskite solar cells have emerged as a promising technology for renewable energy generation.However,the successful integration of perovskite solar cells with energy storage devices to establish high-efficiency and long-term stable photorechargeable systems remains a persistent challenge.Issues such as electrical mismatch and restricted integration levels contribute to elevated internal resistance,leading to suboptimal overall efficiency(η_(overall))within photorechargeable systems.Additionally,the compatibility of perovskite solar cells with electrolytes from energy storage devices poses another significant concern regarding their stability.To address these limitations,we demonstrate a highly integrated photorechargeable system that combines perovskite solar cells with a solid-state zinc-ion hybrid capacitor using a streamlined process.Our study employs a novel ultraviolet-cured ionogel electrolyte to prevent moisture-induced degradation of the perovskite layer in integrated photorechargeable system,enabling perovskite solar cells to achieve maximum power conversion efficiencies and facilitating the monolithic design of the system with minimal energy loss.By precisely matching voltages between the two modules and leveraging the superior energy storage efficiency,our integrated photorechargeable system achieves a remarkableηoverall of 10.01%while maintaining excellent cycling stability.This innovative design and the comprehensive investigations of the dynamic photocharging process in monolithic systems,not only offer a reliable and enduring power source but also provide guidelines for future development of self-power off-grid electronics.展开更多
In this paper, a method to design bird-strike-resistant aircraft structures is presented and illustrated through examples. The focus is on bird strike experiments and simulations. The explicit finite element software ...In this paper, a method to design bird-strike-resistant aircraft structures is presented and illustrated through examples. The focus is on bird strike experiments and simulations. The explicit finite element software PAM-CRASH is employed to conduct bird strike simulations, and a coupled Smooth Particles Hydrodynamic（SPH） and Finite Element（FE） method is used to simulate the interaction between a bird and a target structure. The SPH method is explained, and an SPH bird model is established. Constitutive models for various structural materials, such as aluminum alloys, composite materials, honeycomb, and foam materials that are used in aircraft structures,are presented, and model parameters are identified by conducting various material tests. Good agreements between simulation results and experimental data suggest that the numerical model is capable of predicting the dynamic responses of various aircraft structures under a bird strike,and numerical simulation can be used as a tool to design bird-strike-resistant aircraft structures.展开更多
In this paper,a method sustaining system stability after decomposition is proposed.Based on the stability criterion derived from the energy function,a set of intelligent controllers is synthesized which is used to mai...In this paper,a method sustaining system stability after decomposition is proposed.Based on the stability criterion derived from the energy function,a set of intelligent controllers is synthesized which is used to maintain the stability of the system.The sustainable stability problem can be reformulated as a Linear Matrix Inequalities(LMI)problem.The key to guaranteeing the stability of the system as a whole is to find a common symmetrically positive definite matrix for all subsystems.Furthermore,the Evolved Bat Algorithm(EBA)is employed to replace the pole assignment method and the conventional mathematical methods for solving the LMI.The EBA is utilized to find feasible solutions in terms of the energy equation.The experimental results show that the EBA is capable of providing proper solutions,which satisfy the sustainability and stability criteria,after a short period of recursive computing.展开更多
With the increasing consumption of fossil fuels,proton exchange membrane fuel cells(PEMFCs)have attracted considerable attention as green and sustainable energy conversion devices.The slow kinetics of the cathodic oxy...With the increasing consumption of fossil fuels,proton exchange membrane fuel cells(PEMFCs)have attracted considerable attention as green and sustainable energy conversion devices.The slow kinetics of the cathodic oxygen reduction reaction(ORR)has a major impact on the performance of PEMFCs,and although platinum(Pt)can accelerate the reaction rate of the ORR,the scarcity and high cost of Pt resources still limit the development of PEMFCs.Therefore,the development of low-cost high-performance ORR catalysts is essential for the commercial application and development of PEMFCs.This paper reviews the research progress of researchers on Pt-based ORR catalysts in recent years,including Pt/C catalysts,Pt-based alloy catalysts,Pt-based intermetallic compounds,and Pt-based single-atom catalysts(SACs),with a focus on Pt-based alloy catalysts with different nanostructures.We described in detail the difficulties and solutions in the research process of various ORR catalysts and explained the principle of their activity enhancement with density functional theory(DFT).In addition,an outlook on the development of Pt-based catalysts is given,and reducing the amount of Pt used and improving the performance of catalysts are the directions to work on in the coming period.展开更多
The authors would like to make the following corrections to our published paper[1].The first author’s name is corrected from“Tcw Chen1”to“Tim Chen1”.The first affiliation address is corrected from“1 Department o...The authors would like to make the following corrections to our published paper[1].The first author’s name is corrected from“Tcw Chen1”to“Tim Chen1”.The first affiliation address is corrected from“1 Department of Electrical and Computer Engineering,North South University,Dhaka-1229,Bagladesh.”to“1 Faculty of Information Technology,Ton Duc Thang University,Ho Chi Minh City,Vietnam.”.展开更多
The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the ca...The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.展开更多
Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier.However,its production,especially green hydrogen generated from renewable sources,is hindered by low efficiency and limited yi...Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier.However,its production,especially green hydrogen generated from renewable sources,is hindered by low efficiency and limited yield,primarily due to the performance of the catalysts used.Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes.For instance,screening for effective catalysts still heavily relies on human-lab-work,a process that is time-consuming.Facing this critical challenge,machine learning(ML)emerges as a promising solution.ML,a core component of data mining and analysis that uses statistical algorithms without explicit instructions,can rationalize the design of catalysts through the use of big data,including DFT results.This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies,offering a more effective path to uncovering vital methodologies for catalyst development.This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years.It presents an overview of the existing ML algorithms,exploring their specific functionalities,benefits,and limitations.Besides,this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production,particularly through electrochemical and biological processes.展开更多
The recent Nature Water article,“To Solve Climate Change,We Need to Restore Our Sponge Planet,”by Kongjian Yu,Erica Gies,and Warren W.Wood[1],makes a compelling case for recalibrating climate strategies to prioritiz...The recent Nature Water article,“To Solve Climate Change,We Need to Restore Our Sponge Planet,”by Kongjian Yu,Erica Gies,and Warren W.Wood[1],makes a compelling case for recalibrating climate strategies to prioritize the water cycle alongside reducing carbon emissions.The authors highlight how human activities-agriculture,urbanization,and industrialization-have degraded 75%of the earth’s land,severely disrupting natural water systems.This degradation diminishes the planet’s capacity to regulate temperature through water vapor,cloud formation,and the hydrological cycle,further accelerating climate instability.展开更多
Battery manufacturing holds great promise to build highperformance electrodes with fine-controlled microstructure,geometry and thickness.However,thick electrodes face concomitant challenge of the sluggish transport of...Battery manufacturing holds great promise to build highperformance electrodes with fine-controlled microstructure,geometry and thickness.However,thick electrodes face concomitant challenge of the sluggish transport of both electrons and Li ions.Here,we present a thick electrode with an aligned structure,as an alternative to achieve high-energy lithium-ion batteries.The freeze-drying process with the aid of gum binder and single-walled carbon nanotubes(SWCNT)is originally developed for preparing the LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2))(NCM811)-based aligned structured thick electrode as the representative cathode electrode material.The 1-mm-thick cathode with mass loading of 101 mg·cm^(-2) achieves a high specific capacity of 203.4 mAh·g^(-1).Moreover,the as-prepared ultra-thick electrodes with a high mass loading of 538 mg·cm^(-2) and high active material content of 99.5 wt%are successfully demonstrated,delivering an extremely high areal capacity of 93.4 mAh·cm^(-2),which represents a>30-times improvement compared to that of the commercial electrode.This design opens an effective avenue for greener,scalable and sustainable manufacturing processes toward energy storage devices and other related practical applications.展开更多
Graphite as a positive electrode material of dual ion batteries(DIBs)has attracted tremendous attentions for its advantages including low lost,high working voltage and high energy density.However,very few literatures ...Graphite as a positive electrode material of dual ion batteries(DIBs)has attracted tremendous attentions for its advantages including low lost,high working voltage and high energy density.However,very few literatures regarding to the real-time observation of anion intercalation behavior and surface evolution of graphite in DIBs have been reported.Herein,we use in situ atomic force microscope(AFM)to directly observe the intercalation/de-intercalation processes of PF6^-in graphite in real time.First,by measuring the change in the distance between graphene layers during intercalation,we found that PF6^-intercalates in one of every three graphite layers and the intercalation speed is measured to be 2μm-min^-1.Second,graphite will wrinke and suffer structural damnages at high voltages,along with severe electrolyte decomposition on the surface.These findings provide useful information for further optimizing the capacity and the stability of graphite anode in DIBs.展开更多
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices.This work implements a deep convolutional generative adversarial network(D...The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices.This work implements a deep convolutional generative adversarial network(DC-GAN)for generating realistic nphase microstructural data.The same network architecture is successfully applied to two very different three-phase microstructures:A lithium-ion battery cathode and a solid oxide fuel cell anode.A comparison between the real and synthetic data is performed in terms of the morphological properties(volume fraction,specific surface area,triple-phase boundary)and transport properties(relative diffusivity),as well as the two-point correlation function.The results show excellent agreement between datasets and they are also visually indistinguishable.By modifying the input to the generator,we show that it is possible to generate microstructure with periodic boundaries in all three directions.This has the potential to significantly reduce the simulated volume required to be considered“representative”and therefore massively reduce the computational cost of the electrochemical simulations necessary to predict the performance of a particular microstructure during optimisation.展开更多
The large-scale application of lithium metal batteries remains a challenge due to the hydrolysis of LiPF_(6),which can induce capacity fade and series of safety issues.Prof.Ma and his co-workers have designed a hydrop...The large-scale application of lithium metal batteries remains a challenge due to the hydrolysis of LiPF_(6),which can induce capacity fade and series of safety issues.Prof.Ma and his co-workers have designed a hydrophobic Li^(+)-solvated structure to obtain high performances lithium metal batteries.The specific functional groups of theadditive,hexafluoroisopropyl acrylate.展开更多
Effective management of lithium-ion batteries is a key enabler for a low carbon future,with applications including electric vehicles and grid scale energy storage.The lifetime of these devices depends greatly on the m...Effective management of lithium-ion batteries is a key enabler for a low carbon future,with applications including electric vehicles and grid scale energy storage.The lifetime of these devices depends greatly on the materials used,the system design and the operating conditions.This complexity has therefore made real-world control of battery systems challenging.However,with the recent advances in understanding battery degradation,modelling tools and diagnostics,there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin.In this cyber-physical system,there is a close interaction between a physical and digital embodiment of a battery,which enables smarter control and longer lifetime.This perspectives paper thus presents the state-of-the-art in battery modelling,in-vehicle diagnostic tools,data driven modelling approaches,and how these elements can be combined in a framework for creating a battery digital twin.The challenges,emerging techniques and perspective comments provided here,will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.展开更多
The human knee implant is computationally modelled in the mixed lubrication regime to investigate the tribological performance of the implant.This model includes the complex geometry of the implant components,unlike e...The human knee implant is computationally modelled in the mixed lubrication regime to investigate the tribological performance of the implant.This model includes the complex geometry of the implant components,unlike elliptical contact models that approximate this geometry.Film thickness and pressure results are presented for an ISO gait cycle to determine the lubrication regime present within the implant during its operation.It was found that it was possible for the lubrication regime to span between elastohydrodynamic,mixed and boundary lubrication depending on the operating conditions of the implant.It was observed that the tribological conditions present in one condyle were not necessarily representative of the other.Multiple points of contact were found within the same condyle,which cannot be computed by the elliptical contact solvers.This model can be used to balance forces in all directions,instead of only the normal loads,as often done in elliptical contact models.This work is an initial step towards understanding the role of the complex geometry in the tribological characteristics of the human knee implant when operating in physiological conditions.展开更多
Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silic...Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work;highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.展开更多
In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak...In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak shifting,broadening and splitting,leading to inaccurate physicochemical information,such as lattice parameters and crystallite sizes.Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness.It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic,eliminating the need for prior knowledge of the sample’s chemical composition.We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data,acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.展开更多
Cardiovascular diseases are a leading cause of death worldwide,and effective treatment for cardiac disease has been a research focal point.Although the development of new drugs and strategies has never ceased,the exis...Cardiovascular diseases are a leading cause of death worldwide,and effective treatment for cardiac disease has been a research focal point.Although the development of new drugs and strategies has never ceased,the existing drug development process relies primarily on rodent models such as mice,which have significant shortcomings in predicting human responses.Therefore,human-based in vitro cardiac tissue models are considered to simulate physiological and functional characteristics more effectively,advancing disease treatment and drug development.The microfluidic device simulates the physiological functions and pathological states of the human heart by culture,thereby reducing the need for animal experimentation and enhancing the efficiency and accuracy of the research.The basic framework of cardiac chips typically includes multiple functional units,effectively simulating different parts of the heart and allowing the observation of cardiac cell growth and responses under various drug treatments and disease conditions.To date,cardiac chips have demonstrated significant application value in drug development,toxicology testing,and the construction of cardiac disease models;they not only accelerate drug screening but also provide a new research platform for understanding cardiac diseases.In the future,with advancements in functionality,integration,and personalised medicine,cardiac chips will further simulate multiorgan systems,becoming vital tools for disease modelling and precision medicine.Here,we emphasised the development history of cardiac organ chips,highlighted the material selection and construction strategy of cardiac organ chip electrodes and hydrogels,introduced the current application scenarios of cardiac organ chips,and discussed the development opportunities and prospects for their of biomedical applications.展开更多
基金the financial support from EPSRC(EP/P024807/1,EP/M014045/1,EP/S000933/1 and EP/N009924/1)by the EPSRC energy storage for low carbon grids project(EP/K002252/1)+3 种基金the EPSRC Joint UK-India Clean Energy center(JUICE)(EP/P003605/1)the Integrated Development of Low-Carbon Energy Systems(IDLES)project(EP/R045518/1)the Innovate UK BAFTA project,the Innovate UK for Advanced Battery Lifetime Extension(ABLE)project for funding underthe China Scholarship Council。
文摘Mixed ionic electronic conductors(MIECs)have attracted increasing attention as anode materials for solid oxide fuel cells(SOFCs)and they hold great promise for lowering the operation temperature of SOFCs.However,there has been a lack of understanding of the performance-limiting factors and guidelines for rational design of composite metal-MIEC electrodes.Using a newly-developed approach based on 3 D-tomography and electrochemical impedance spectroscopy,here for the first time we quantify the contribution of the dual-phase boundary(DPB)relative to the three-phase boundary(TPB)reaction pathway on real MIEC electrodes.A new design strategy is developed for Ni/gadolinium doped ceria(CGO)electrodes(a typical MIEC electrode)based on the quantitative analyses and a novel Ni/CGO fiber-matrix structure is proposed and fabricated by combining electrospinning and tape-casting methods using commercial powders.With only 11.5 vol%nickel,the designer Ni/CGO fiber-matrix electrode shows 32%and 67%lower polarization resistance than a nano-Ni impregnated CGO scaffold electrode and conventional cermet electrode respectively.The results in this paper demonstrate quantitatively using real electrode structures that enhancing DPB and hydrogen kinetics are more efficient strategies to enhance electrode performance than simply increasing TPB.
基金the auspices of the Royal British Legion Centre for Blast Injury Studies at Imperial College Londonthe financial support of the Royal British Legion。
文摘Heterotopic ossification(HO)is a consequence of traumatic bone and tissue damage,which occurs in 65%of military casualties with blast-associated amputations.However,the mechanisms behind blast-induced HO remain unclear.Animal models are used to study blast-induced HO,but developing such models is challenging,particularly in how to use a pure blast wave(primary blast)to induce limb fracture that then requires an amputation.Several studies,including our recent study,have developed platforms to induce limb fractures in rats with blast loading or a mixture of blast and impact loading.However,these models are limited by the survivability of the animal and repeatability of the model.In this study,we developed an improved platform,aiming to improve the animal's survivability and injury repeatability as well as focusing on primary blast only.The platform exposed only one limb of the rat to a blast wave while providing proper protection to the rest of the rat's body.We obtained very consistent fracture outcome in the tibia(location and pattern)in cadaveric rats with a large range of size and weight.Importantly,the rats did not obviously move during the test,where movement is a potential cause of uncontrolled injury.We further conducted parametric studies by varying the features of the design of the platform.These factors,such as how the limb is fixed and how the cavity through which the limb is placed is sealed,significantly affect the resulting injury.This platform and test setups enable well-controlled limb fracture induced directly by pure blast wave,which is the fundamental step towards a complete in vivo animal model for blast-induced HO induced by primary blast alone,excluding secondary and tertiary blast injury.In addition,the platform design and the findings presented here,particularly regarding the proper protection of the animal,have implications for future studies investigating localized blast injuries,such as blast induced brain and lung injuries.
基金financially supported by the National Natural Science Foundation of China(No.52102470)。
文摘Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.
基金the UK Engineering and Physical Sciences Research Council(EPSRC)Standard Research(EP/V027131/1)EPSRC New Investigator Award(2018+6 种基金EP/R043272/1)Newton Advanced Fel owship(192097)for financial supportEPSRC New Investigator Award(EP/V002260/1)National Measurement System of the UK Department of Business,Energy&Industrial Strategythe China Scholarship Council(CSC,no.201808370197)for financial supportCSC(no.202007040033)for financial supportCSC(no.201908310074)for financial support
文摘Perovskite solar cells have emerged as a promising technology for renewable energy generation.However,the successful integration of perovskite solar cells with energy storage devices to establish high-efficiency and long-term stable photorechargeable systems remains a persistent challenge.Issues such as electrical mismatch and restricted integration levels contribute to elevated internal resistance,leading to suboptimal overall efficiency(η_(overall))within photorechargeable systems.Additionally,the compatibility of perovskite solar cells with electrolytes from energy storage devices poses another significant concern regarding their stability.To address these limitations,we demonstrate a highly integrated photorechargeable system that combines perovskite solar cells with a solid-state zinc-ion hybrid capacitor using a streamlined process.Our study employs a novel ultraviolet-cured ionogel electrolyte to prevent moisture-induced degradation of the perovskite layer in integrated photorechargeable system,enabling perovskite solar cells to achieve maximum power conversion efficiencies and facilitating the monolithic design of the system with minimal energy loss.By precisely matching voltages between the two modules and leveraging the superior energy storage efficiency,our integrated photorechargeable system achieves a remarkableηoverall of 10.01%while maintaining excellent cycling stability.This innovative design and the comprehensive investigations of the dynamic photocharging process in monolithic systems,not only offer a reliable and enduring power source but also provide guidelines for future development of self-power off-grid electronics.
基金supported by Natural Science Foundation of China (No.11472225)
文摘In this paper, a method to design bird-strike-resistant aircraft structures is presented and illustrated through examples. The focus is on bird strike experiments and simulations. The explicit finite element software PAM-CRASH is employed to conduct bird strike simulations, and a coupled Smooth Particles Hydrodynamic（SPH） and Finite Element（FE） method is used to simulate the interaction between a bird and a target structure. The SPH method is explained, and an SPH bird model is established. Constitutive models for various structural materials, such as aluminum alloys, composite materials, honeycomb, and foam materials that are used in aircraft structures,are presented, and model parameters are identified by conducting various material tests. Good agreements between simulation results and experimental data suggest that the numerical model is capable of predicting the dynamic responses of various aircraft structures under a bird strike,and numerical simulation can be used as a tool to design bird-strike-resistant aircraft structures.
文摘In this paper,a method sustaining system stability after decomposition is proposed.Based on the stability criterion derived from the energy function,a set of intelligent controllers is synthesized which is used to maintain the stability of the system.The sustainable stability problem can be reformulated as a Linear Matrix Inequalities(LMI)problem.The key to guaranteeing the stability of the system as a whole is to find a common symmetrically positive definite matrix for all subsystems.Furthermore,the Evolved Bat Algorithm(EBA)is employed to replace the pole assignment method and the conventional mathematical methods for solving the LMI.The EBA is utilized to find feasible solutions in terms of the energy equation.The experimental results show that the EBA is capable of providing proper solutions,which satisfy the sustainability and stability criteria,after a short period of recursive computing.
基金supported by CITIC Dameng Mining Industries Limited-Guangxi University Joint Research Institute of Manganese Resources Utilization and Advanced Materials Technology,Guangxi University-CITIC Dameng Mining Industries Limited Joint Base of Postgraduate Cultivation,and State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structuresthe National Natural Science Foundation of China(Nos.11364003 and 52102470)+1 种基金Guangxi Innovation Driven Development Project Grant(Nos.AA17204100 and AA18118052)the Natural Science Foundation of Guangxi Province(No.2018GXNSFAA138186)。
文摘With the increasing consumption of fossil fuels,proton exchange membrane fuel cells(PEMFCs)have attracted considerable attention as green and sustainable energy conversion devices.The slow kinetics of the cathodic oxygen reduction reaction(ORR)has a major impact on the performance of PEMFCs,and although platinum(Pt)can accelerate the reaction rate of the ORR,the scarcity and high cost of Pt resources still limit the development of PEMFCs.Therefore,the development of low-cost high-performance ORR catalysts is essential for the commercial application and development of PEMFCs.This paper reviews the research progress of researchers on Pt-based ORR catalysts in recent years,including Pt/C catalysts,Pt-based alloy catalysts,Pt-based intermetallic compounds,and Pt-based single-atom catalysts(SACs),with a focus on Pt-based alloy catalysts with different nanostructures.We described in detail the difficulties and solutions in the research process of various ORR catalysts and explained the principle of their activity enhancement with density functional theory(DFT).In addition,an outlook on the development of Pt-based catalysts is given,and reducing the amount of Pt used and improving the performance of catalysts are the directions to work on in the coming period.
文摘The authors would like to make the following corrections to our published paper[1].The first author’s name is corrected from“Tcw Chen1”to“Tim Chen1”.The first affiliation address is corrected from“1 Department of Electrical and Computer Engineering,North South University,Dhaka-1229,Bagladesh.”to“1 Faculty of Information Technology,Ton Duc Thang University,Ho Chi Minh City,Vietnam.”.
基金support of the Henry Royce Institute for advanced materials through the Materials Challenge Accelerator Programme(MCAP)funded from a grant provided by the Engineering and Physical Sciences Research Council(EP/X527257/1).
文摘The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.
基金support from Energy Innovation Centre,Warwick Manufacturing Group at the University of Warwick.
文摘Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier.However,its production,especially green hydrogen generated from renewable sources,is hindered by low efficiency and limited yield,primarily due to the performance of the catalysts used.Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes.For instance,screening for effective catalysts still heavily relies on human-lab-work,a process that is time-consuming.Facing this critical challenge,machine learning(ML)emerges as a promising solution.ML,a core component of data mining and analysis that uses statistical algorithms without explicit instructions,can rationalize the design of catalysts through the use of big data,including DFT results.This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies,offering a more effective path to uncovering vital methodologies for catalyst development.This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years.It presents an overview of the existing ML algorithms,exploring their specific functionalities,benefits,and limitations.Besides,this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production,particularly through electrochemical and biological processes.
文摘The recent Nature Water article,“To Solve Climate Change,We Need to Restore Our Sponge Planet,”by Kongjian Yu,Erica Gies,and Warren W.Wood[1],makes a compelling case for recalibrating climate strategies to prioritize the water cycle alongside reducing carbon emissions.The authors highlight how human activities-agriculture,urbanization,and industrialization-have degraded 75%of the earth’s land,severely disrupting natural water systems.This degradation diminishes the planet’s capacity to regulate temperature through water vapor,cloud formation,and the hydrological cycle,further accelerating climate instability.
基金This study was financially supported by the National Key Research and Development Program of China(No.2016YFB0100300)the National Natural Science Foundation of China(Nos.U1864213 and 51871113)the Key Project of Scientific Research Plan of Colleges and Universities in Xinjiang(No.XJEDU2018I015).
文摘Battery manufacturing holds great promise to build highperformance electrodes with fine-controlled microstructure,geometry and thickness.However,thick electrodes face concomitant challenge of the sluggish transport of both electrons and Li ions.Here,we present a thick electrode with an aligned structure,as an alternative to achieve high-energy lithium-ion batteries.The freeze-drying process with the aid of gum binder and single-walled carbon nanotubes(SWCNT)is originally developed for preparing the LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2))(NCM811)-based aligned structured thick electrode as the representative cathode electrode material.The 1-mm-thick cathode with mass loading of 101 mg·cm^(-2) achieves a high specific capacity of 203.4 mAh·g^(-1).Moreover,the as-prepared ultra-thick electrodes with a high mass loading of 538 mg·cm^(-2) and high active material content of 99.5 wt%are successfully demonstrated,delivering an extremely high areal capacity of 93.4 mAh·cm^(-2),which represents a>30-times improvement compared to that of the commercial electrode.This design opens an effective avenue for greener,scalable and sustainable manufacturing processes toward energy storage devices and other related practical applications.
基金This research was financially supported by Soft Science Research Project of Guangdong Province(No.2017B030301013)the Shenzhen Science and Technology Research(Nos.CYJ20170818085823773 and ZDSYS201707281026184).
文摘Graphite as a positive electrode material of dual ion batteries(DIBs)has attracted tremendous attentions for its advantages including low lost,high working voltage and high energy density.However,very few literatures regarding to the real-time observation of anion intercalation behavior and surface evolution of graphite in DIBs have been reported.Herein,we use in situ atomic force microscope(AFM)to directly observe the intercalation/de-intercalation processes of PF6^-in graphite in real time.First,by measuring the change in the distance between graphene layers during intercalation,we found that PF6^-intercalates in one of every three graphite layers and the intercalation speed is measured to be 2μm-min^-1.Second,graphite will wrinke and suffer structural damnages at high voltages,along with severe electrolyte decomposition on the surface.These findings provide useful information for further optimizing the capacity and the stability of graphite anode in DIBs.
基金This work was supported by funding from both the CONACYT-SENER fund and the EPSRC Faraday Institution Multi-Scale Modelling project(https://faraday.ac.uk/,EP/S003053/1,grant number FIRG003).
文摘The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices.This work implements a deep convolutional generative adversarial network(DC-GAN)for generating realistic nphase microstructural data.The same network architecture is successfully applied to two very different three-phase microstructures:A lithium-ion battery cathode and a solid oxide fuel cell anode.A comparison between the real and synthetic data is performed in terms of the morphological properties(volume fraction,specific surface area,triple-phase boundary)and transport properties(relative diffusivity),as well as the two-point correlation function.The results show excellent agreement between datasets and they are also visually indistinguishable.By modifying the input to the generator,we show that it is possible to generate microstructure with periodic boundaries in all three directions.This has the potential to significantly reduce the simulated volume required to be considered“representative”and therefore massively reduce the computational cost of the electrochemical simulations necessary to predict the performance of a particular microstructure during optimisation.
基金financially supported by the National Natural Science Foundation of China(No.52102470)。
文摘The large-scale application of lithium metal batteries remains a challenge due to the hydrolysis of LiPF_(6),which can induce capacity fade and series of safety issues.Prof.Ma and his co-workers have designed a hydrophobic Li^(+)-solvated structure to obtain high performances lithium metal batteries.The specific functional groups of theadditive,hexafluoroisopropyl acrylate.
基金This work was kindly supported by:the EPSRC Faraday Insti-tution Multi-Scale Modelling Project(EP/S003053/1,grant number FIRG003)the EPSRC Joint UK-India Clean Energy centre(JUICE)(EP/P003605/1)+1 种基金the EPSRC Integrated Development of Low-Carbon Energy Systems(IDLES)project(EP/R045518/1)National Science Foundation of China(No.U1864213).
文摘Effective management of lithium-ion batteries is a key enabler for a low carbon future,with applications including electric vehicles and grid scale energy storage.The lifetime of these devices depends greatly on the materials used,the system design and the operating conditions.This complexity has therefore made real-world control of battery systems challenging.However,with the recent advances in understanding battery degradation,modelling tools and diagnostics,there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin.In this cyber-physical system,there is a close interaction between a physical and digital embodiment of a battery,which enables smarter control and longer lifetime.This perspectives paper thus presents the state-of-the-art in battery modelling,in-vehicle diagnostic tools,data driven modelling approaches,and how these elements can be combined in a framework for creating a battery digital twin.The challenges,emerging techniques and perspective comments provided here,will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.
基金Imperial College Research FellowshipEngineering and Physical Sciences Research Council,Grant/Award Number:EP/N509486/1。
文摘The human knee implant is computationally modelled in the mixed lubrication regime to investigate the tribological performance of the implant.This model includes the complex geometry of the implant components,unlike elliptical contact models that approximate this geometry.Film thickness and pressure results are presented for an ISO gait cycle to determine the lubrication regime present within the implant during its operation.It was found that it was possible for the lubrication regime to span between elastohydrodynamic,mixed and boundary lubrication depending on the operating conditions of the implant.It was observed that the tribological conditions present in one condyle were not necessarily representative of the other.Multiple points of contact were found within the same condyle,which cannot be computed by the elliptical contact solvers.This model can be used to balance forces in all directions,instead of only the normal loads,as often done in elliptical contact models.This work is an initial step towards understanding the role of the complex geometry in the tribological characteristics of the human knee implant when operating in physiological conditions.
基金supported by the EPSRC Impact Acceleration Award(EP/X52556X/1)the Faraday Institution's Industrial Fellowship(FIIF-013)+2 种基金the EPSRC Faraday Institution's Multi-Scale Modelling Project(EP/S003053/1,grant number FIRG003)the EPSRC Joint UK-India Clean Energy Center(JUICE)(EP/P003605/1)the EPSRC Integrated Development of Low-Carbon Energy Systems(IDLES)project(EP/R045518/1).
文摘Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work;highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.
基金funding through the Innovate UK Analysis for Innovators(A4i)programme(Project No.106003)Parts of this research were carried out at PETRA III.A.V.acknowledges financial support from the Royal Society as a Royal Society Industry Fellow(IF\R2\222059).
文摘In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak shifting,broadening and splitting,leading to inaccurate physicochemical information,such as lattice parameters and crystallite sizes.Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness.It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic,eliminating the need for prior knowledge of the sample’s chemical composition.We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data,acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.
基金supported by the National Natural Science Foundation of China(Nos.32430057,U21A20173,32201083 and 32071355)the Guangdong Basic and Applied Basic Research Foundation(No.2023B1515120055).
文摘Cardiovascular diseases are a leading cause of death worldwide,and effective treatment for cardiac disease has been a research focal point.Although the development of new drugs and strategies has never ceased,the existing drug development process relies primarily on rodent models such as mice,which have significant shortcomings in predicting human responses.Therefore,human-based in vitro cardiac tissue models are considered to simulate physiological and functional characteristics more effectively,advancing disease treatment and drug development.The microfluidic device simulates the physiological functions and pathological states of the human heart by culture,thereby reducing the need for animal experimentation and enhancing the efficiency and accuracy of the research.The basic framework of cardiac chips typically includes multiple functional units,effectively simulating different parts of the heart and allowing the observation of cardiac cell growth and responses under various drug treatments and disease conditions.To date,cardiac chips have demonstrated significant application value in drug development,toxicology testing,and the construction of cardiac disease models;they not only accelerate drug screening but also provide a new research platform for understanding cardiac diseases.In the future,with advancements in functionality,integration,and personalised medicine,cardiac chips will further simulate multiorgan systems,becoming vital tools for disease modelling and precision medicine.Here,we emphasised the development history of cardiac organ chips,highlighted the material selection and construction strategy of cardiac organ chip electrodes and hydrogels,introduced the current application scenarios of cardiac organ chips,and discussed the development opportunities and prospects for their of biomedical applications.
