Carbon black is utilized as a conventional electrocatalyst support material for proton exchange membrane fuel cells. However, this support is prone to corrosion under oxidative and harsh environments, thus limiting th...Carbon black is utilized as a conventional electrocatalyst support material for proton exchange membrane fuel cells. However, this support is prone to corrosion under oxidative and harsh environments, thus limiting the durability of the fuel cells. Meanwhile, carbon corrosion would also weaken the linkage between Pt and the support material, which causes Pt agglomeration, and consequently, deterioration of the cell performance. To overcome the drawbacks of a Pt/C electrocatalyst, a hybrid support material comprising molybdenum disulfide and reduced graphene oxide is proposed and synthesized in this study to exploit the graphitic nature of graphene and the availability of the exposed edges of MoS2. TEM results show the uniform dispersion of Pt nanoparticles over the MoS2-rGO surface. Electrochemical measurements indicate higher ECSA retention and better ORR activity after 10000 potential cycles for Pt/MoS2-rGO as compared to Pt/C, demonstrating the improved durability for this hybrid support material.展开更多
In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and...In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells.展开更多
The reduced sealing difficulty of tubular solid oxide fuel cells(SOFCs)makes the stacking of tubular cell groups relatively easy,and the thermal stress constraints during stack operation are smaller,which helps the st...The reduced sealing difficulty of tubular solid oxide fuel cells(SOFCs)makes the stacking of tubular cell groups relatively easy,and the thermal stress constraints during stack operation are smaller,which helps the stack to operate stably for a long time.The special design of tubular SOFC structures can completely solve the problem of high-temperature sealing,especially in the design of multiple single-cell series integrated into one tube,where each cell tube is equivalent to a small electric stack,with unique characteristics of high voltage and low current output,which can significantly reduce the ohmic polarization loss of tubular cells.This paper provides an overview of typical tubular SOFC structural designs both domestically and internationally.Based on the geometric structure of tubular SOFCs,they can be divided into bamboo tubes,bamboo flat tubes,single-section tubes,and single-section flat tube structures.Meanwhile,this article provides an overview of commonly used materials and preparation methods for tubular SOFCs,including commonly used materials and preparation methods for support and functional layers,as well as a comparison of commonly used preparation methods for microtubule SOFCs,It introduced the three most important parts of building a fuel cell stack:manifold,current collector,and ceramic adhesive,and also provided a detailed introduction to the power generation systems of different tubular SOFCs,Finally,the development prospects of tubular SOFCs were discussed.展开更多
Developing platinum-group-metal(PGM)catalysts possessing strong metal-support interaction and controllable PGM size is urgent for the sluggish oxygen reduction reaction(ORR)in proton-exchange membrane fuel cells.Herei...Developing platinum-group-metal(PGM)catalysts possessing strong metal-support interaction and controllable PGM size is urgent for the sluggish oxygen reduction reaction(ORR)in proton-exchange membrane fuel cells.Herein,we propose an in-situ self-assembled reduction strategy to successfully induce highly-dispersed sub-3nm platinum nanoparticles(Pt NPs)to attach on resin-derived atomic Co coordinated by N-doped carbon substrate(Pt/Co_(SA)-N-C)for ORR.To be specific,the interfacial electron interaction effect,along with a highly robust Co_(SA)-N-C support endow the as-fabricated Pt/Co_(SA)-N-C catalyst with significantly enhanced catalytic properties,i.e.,a mass activity(MA)of 0.719 A/mgPt at 0.9 ViR-free and a reduction of 24.2%in MA after a 20,000-cycles test.Density functional theory(DFT)calculations demonstrate that the enhanced electron interaction between Pt and Co_(SA)-N-C support decreases the dband center of Pt,which is in favor of lowering the desorption energy of ^(*)OH on Pt/Co_(SA)-N-C surface and accelerating the formation of H_(2)O,thus enhance the instinct activity of ORR.Furthermore,the higher binding energy between Pt and Co_(SA)-N-C compared to Pt and C indicates that the migration of Pt has been suppressed,which theoretically explains the improved durability of Pt/Co_(SA)-N-C.Our work offers an enlightenment on constructing composite Pt-based catalysts with multiple active sites.展开更多
Metal nanoaggregates can simultaneously enhance the activity and stability of Fe-N-C catalysts in proton-exchange-membrane fuel cells(PEMFC).Previous studies on the relevant mechanism have focused on the direct intera...Metal nanoaggregates can simultaneously enhance the activity and stability of Fe-N-C catalysts in proton-exchange-membrane fuel cells(PEMFC).Previous studies on the relevant mechanism have focused on the direct interaction between FeN_(4)active sites and metal nanoaggregates.However,the role of carbon support that hosts metal nanoaggregates and active sites has been overlooked.Here,a Fe-N-C catalyst encapsulating inactive gold nanoparticles is prepared as a model catalyst to investigate the electronic tuning of Au nanoparticles(NPs)towards the carbon support.Au NPs donate electrons to carbon support,making it rich inπelectrons,which reduces the work function and regulates the electronic configuration of the FeN_(4)sites for an enhanced ORR activity.Meanwhile,the electron-rich carbon support can mitigate the electron depletion of FeN_(4)sites caused by carbon support oxidation,thereby preserving its high activity.The yield and accumulation of H_(2)O_(2)are thus alleviated,which delays the oxidation of the catalyst and benefits the stability.Due to the electron-rich carbon support,the composite catalyst achieves a top-level peak power density of 0.74 W/cm^(2) in a 1.5 bar H_(2)-air PEMFC,as well as the improved stability.This work elucidates the key role of carbon support in the performance enhancement of the FeN-C/metal nanoaggregate composite catalysts for fuel cell application.展开更多
Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, vari...Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.展开更多
Platinum nanoparticles supported on carbons（Pt/C,60%,mass fraction） electrocatalysts for direct methanol fuel cell（DMFC） were prepared by citrate-stabilized method with different reductants and carbon supports.The...Platinum nanoparticles supported on carbons（Pt/C,60%,mass fraction） electrocatalysts for direct methanol fuel cell（DMFC） were prepared by citrate-stabilized method with different reductants and carbon supports.The catalysts were characterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM） and cyclic voltammetry（CV）.It is found that the size of Pt nanoparticles on carbon is controllable by citrate addition and reductant optimization,and the form of carbon support has a great influence on electrocatalytic activity of catalysts.The citrate-stabilized Pt nanoparticles supported on BP2000 carbon,which was reduced by formaldehyde,exhibit the best performance with about 2 nm in diameter and 66.46 m2/g（Pt） in electrocatalytic active surface（EAS） area.Test on single DMFC with 60%（mass fraction） Pt/BP2000 as cathode electrocatalyst showed maximum power density at 78.8 mW/cm2.展开更多
基金financially aided by the National Key R&D Program of China(2016YFB0101201)the National Natural Science Foundation of China(21706158,21533005)~~
文摘Carbon black is utilized as a conventional electrocatalyst support material for proton exchange membrane fuel cells. However, this support is prone to corrosion under oxidative and harsh environments, thus limiting the durability of the fuel cells. Meanwhile, carbon corrosion would also weaken the linkage between Pt and the support material, which causes Pt agglomeration, and consequently, deterioration of the cell performance. To overcome the drawbacks of a Pt/C electrocatalyst, a hybrid support material comprising molybdenum disulfide and reduced graphene oxide is proposed and synthesized in this study to exploit the graphitic nature of graphene and the availability of the exposed edges of MoS2. TEM results show the uniform dispersion of Pt nanoparticles over the MoS2-rGO surface. Electrochemical measurements indicate higher ECSA retention and better ORR activity after 10000 potential cycles for Pt/MoS2-rGO as compared to Pt/C, demonstrating the improved durability for this hybrid support material.
文摘In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells.
基金financially supported by the National Key Research and Development Program of China (No.2021YFB4001400)。
文摘The reduced sealing difficulty of tubular solid oxide fuel cells(SOFCs)makes the stacking of tubular cell groups relatively easy,and the thermal stress constraints during stack operation are smaller,which helps the stack to operate stably for a long time.The special design of tubular SOFC structures can completely solve the problem of high-temperature sealing,especially in the design of multiple single-cell series integrated into one tube,where each cell tube is equivalent to a small electric stack,with unique characteristics of high voltage and low current output,which can significantly reduce the ohmic polarization loss of tubular cells.This paper provides an overview of typical tubular SOFC structural designs both domestically and internationally.Based on the geometric structure of tubular SOFCs,they can be divided into bamboo tubes,bamboo flat tubes,single-section tubes,and single-section flat tube structures.Meanwhile,this article provides an overview of commonly used materials and preparation methods for tubular SOFCs,including commonly used materials and preparation methods for support and functional layers,as well as a comparison of commonly used preparation methods for microtubule SOFCs,It introduced the three most important parts of building a fuel cell stack:manifold,current collector,and ceramic adhesive,and also provided a detailed introduction to the power generation systems of different tubular SOFCs,Finally,the development prospects of tubular SOFCs were discussed.
基金financially supported by the Natural Science Foundation of China(Nos.22169005,22209186,22068009 and 22262006)the Science and Technology Support Project of Guizhou Provincial Science and Technology Department(Nos.ZK[2023]050 and[2023]403)+2 种基金the Open Project of Institute of Dualcarbon and New Energy Technology Innovation and Development of Guizhou Province(No.DCRE-2023-06)Youth Innovation Promotion Association,CAS(No.2023343)Self-deployed Projects of Ganjiang Innovation Academy,CAS(No.E355F006).
文摘Developing platinum-group-metal(PGM)catalysts possessing strong metal-support interaction and controllable PGM size is urgent for the sluggish oxygen reduction reaction(ORR)in proton-exchange membrane fuel cells.Herein,we propose an in-situ self-assembled reduction strategy to successfully induce highly-dispersed sub-3nm platinum nanoparticles(Pt NPs)to attach on resin-derived atomic Co coordinated by N-doped carbon substrate(Pt/Co_(SA)-N-C)for ORR.To be specific,the interfacial electron interaction effect,along with a highly robust Co_(SA)-N-C support endow the as-fabricated Pt/Co_(SA)-N-C catalyst with significantly enhanced catalytic properties,i.e.,a mass activity(MA)of 0.719 A/mgPt at 0.9 ViR-free and a reduction of 24.2%in MA after a 20,000-cycles test.Density functional theory(DFT)calculations demonstrate that the enhanced electron interaction between Pt and Co_(SA)-N-C support decreases the dband center of Pt,which is in favor of lowering the desorption energy of ^(*)OH on Pt/Co_(SA)-N-C surface and accelerating the formation of H_(2)O,thus enhance the instinct activity of ORR.Furthermore,the higher binding energy between Pt and Co_(SA)-N-C compared to Pt and C indicates that the migration of Pt has been suppressed,which theoretically explains the improved durability of Pt/Co_(SA)-N-C.Our work offers an enlightenment on constructing composite Pt-based catalysts with multiple active sites.
基金supported by the Natural Science Foundation of Beijing Municipality (Z200012)the National Natural Science Foundation of China (U21A20328,22225903)the National Key Research and Development Program of China (2021YFB4000601)。
文摘Metal nanoaggregates can simultaneously enhance the activity and stability of Fe-N-C catalysts in proton-exchange-membrane fuel cells(PEMFC).Previous studies on the relevant mechanism have focused on the direct interaction between FeN_(4)active sites and metal nanoaggregates.However,the role of carbon support that hosts metal nanoaggregates and active sites has been overlooked.Here,a Fe-N-C catalyst encapsulating inactive gold nanoparticles is prepared as a model catalyst to investigate the electronic tuning of Au nanoparticles(NPs)towards the carbon support.Au NPs donate electrons to carbon support,making it rich inπelectrons,which reduces the work function and regulates the electronic configuration of the FeN_(4)sites for an enhanced ORR activity.Meanwhile,the electron-rich carbon support can mitigate the electron depletion of FeN_(4)sites caused by carbon support oxidation,thereby preserving its high activity.The yield and accumulation of H_(2)O_(2)are thus alleviated,which delays the oxidation of the catalyst and benefits the stability.Due to the electron-rich carbon support,the composite catalyst achieves a top-level peak power density of 0.74 W/cm^(2) in a 1.5 bar H_(2)-air PEMFC,as well as the improved stability.This work elucidates the key role of carbon support in the performance enhancement of the FeN-C/metal nanoaggregate composite catalysts for fuel cell application.
基金support from Canadian Urban Transit Research and Innovation Consortium(CUTRIC)via Project Number 160028Natural Sciences and Engineering Research Council of Canada(NSERC)via a Discovery Grant。
文摘Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.
基金Project(50573041)supported by the National Natural Science Foundation of China
文摘Platinum nanoparticles supported on carbons（Pt/C,60%,mass fraction） electrocatalysts for direct methanol fuel cell（DMFC） were prepared by citrate-stabilized method with different reductants and carbon supports.The catalysts were characterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM） and cyclic voltammetry（CV）.It is found that the size of Pt nanoparticles on carbon is controllable by citrate addition and reductant optimization,and the form of carbon support has a great influence on electrocatalytic activity of catalysts.The citrate-stabilized Pt nanoparticles supported on BP2000 carbon,which was reduced by formaldehyde,exhibit the best performance with about 2 nm in diameter and 66.46 m2/g（Pt） in electrocatalytic active surface（EAS） area.Test on single DMFC with 60%（mass fraction） Pt/BP2000 as cathode electrocatalyst showed maximum power density at 78.8 mW/cm2.
