Platinum-based alloy nanoparticles are the most attractive catalysts for the oxygen reduction reaction at present,but an in-depth understanding of the relationship between their short-range structural information and ...Platinum-based alloy nanoparticles are the most attractive catalysts for the oxygen reduction reaction at present,but an in-depth understanding of the relationship between their short-range structural information and catalytic performance is still lacking.Herein,we present a synthetic strategy that uses transition-metal oxide-assisted thermal diffusion.PtCo/C catalysts with localized tetragonal distortion were obtained by controlling the thermal diffusion process of transition-metal elements.This localized structural distortion induced a significant strain effect on the nanoparticle surface,which further shortened the length of the Pt-Pt bond,improved the electronic state of the Pt surface,and enhanced the performance of the catalyst.PtCo/C catalysts with special short-range structures achieved excellent mass activity(2.27 Amg_(Pt)^(-1))and specific activity(3.34 A cm^(-2)).In addition,the localized tetragonal distortion-induced surface compression of the Pt skin improved the stability of the catalyst.The mass activity decreased by only 13% after 30,000 cycles.Enhanced catalyst activity and excellent durability have also been demonstrated in the proton exchange membrane fuel cell configuration.This study provides valuable insights into the development of advanced Pt-based nanocatalysts and paves the way for reducing noble-metal loading and increasing the catalytic activity and catalyst stability.展开更多
本文基于中国家庭追踪调查(China family panel studies,CFPS)的数据,针对股市上典型的"有限参与"现象,考察了教育背景在两个维度上对家庭股市参与概率的影响。通过Logit回归,本文发现:平均受教育年限越高及拥有专业背景的家...本文基于中国家庭追踪调查(China family panel studies,CFPS)的数据,针对股市上典型的"有限参与"现象,考察了教育背景在两个维度上对家庭股市参与概率的影响。通过Logit回归,本文发现:平均受教育年限越高及拥有专业背景的家庭,参与股市概率越大。进一步运用倾向性得分匹配法对专业背景的作用进行估计,本文发现:在家庭股票投资决策中,成员经管背景作用的大小与显著性受所在家庭整体教育水平的约束。主事者的决策地位随家庭整体受教育年限的提高而受到削弱,而家庭成员对金融知识的掌握也可能会因自身学历在家中占优与否相应地被高估或低估。基于研究结论与现实,笔者认为,在我国证券市场中,平均受教育年限越高的家庭共同决策的氛围更浓,一方面,学历更高与投资知识更专业的成员可能并不在金融参与决策上起支配作用,但另一方面,家庭也会因为某些成员的教育背景做出轻率的投资决定。针对教育背景伴随的欠理性投资行为以及完成"有限参与"向"积极参与"的转变,本文也提供了一些建议与意见。展开更多
The design of electrode materials with specific structures is considered a promising approach for improving the performance of lithium-ion batteries(LIBs).In this paper,FeO/CoO hollow nanocages coated with a N-doped c...The design of electrode materials with specific structures is considered a promising approach for improving the performance of lithium-ion batteries(LIBs).In this paper,FeO/CoO hollow nanocages coated with a N-doped carbon layer(FCO@NC)was prepared using Fe-Co-based Prussian blue analogs(PBA)as a precursor.During the synthesis,dopamine was the carbon and nitrogen source.The reducing atmosphere was assured via NH_3/Ar,which regulated the vacancies in the structure of FCO@NC as well as increased its conductivity.When used as anode materials for LIBs,the FCO@NC nanocages deliver a high reversible capacity of 774.89 mAh·g^(-1)at 0.3 A·g^(-1)after200 cycles with a capacity retention rate of 80.4%and426.76 mAh·g^(-1)after 500 cycles at a high current density of 1 A·g^(-1).It is demonstrated that the hollow nanocage structure can effectively enhance the cycle stability,and the heat treatment in NH_(3)/Ar atmosphere contributes to the oxygen vacancy content of the electrode materials,further facilitating its conductivity and electrochemical performance.展开更多
Proton exchange membrane fuel cells (PEMFCs) as energy conversion devices for hydrogen energy are crucial for achieving an eco-friendly society, but their cost and performance are still not satisfactory for large-scal...Proton exchange membrane fuel cells (PEMFCs) as energy conversion devices for hydrogen energy are crucial for achieving an eco-friendly society, but their cost and performance are still not satisfactory for large-scale commercialization. Multiple physical and chemical coupling processes occur simultaneously at different scales in PEMFCs. Hence, previous studies only focused on the optimization of different components in such a complex system separately. In addition, the traditional trial-and-error method is very inefficient for achieving the performance breakthrough goal. Machine learning (ML) is a tool from the data science field. Trained based on datasets built from experimental records or theoretical simulation models, ML models can mine patterns that are difficult to draw intuitively. ML models can greatly reduce the cost of experimental attempts by predicting the target output. Serving as surrogate models, the ML approach could also greatly reduce the computational cost of numerical simulations such as first-principle or multiphysics simulations. Related reports are currently trending, and ML has been proven able to speed up tasks in this field, such as predicting active electrocatalysts, optimizing membrane electrode assembly (MEA), designing efficient flow channels, and providing stack operation strategies. Therefore, this paper reviews the applications and contributions of ML aiming at optimizing PEMFC performance regarding its potential to bring a research paradigm revolution. In addition to introducing and summarizing information for newcomers who are interested in this emerging cross-cutting field, we also look forward to and propose several directions for future development.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.22278123).
文摘Platinum-based alloy nanoparticles are the most attractive catalysts for the oxygen reduction reaction at present,but an in-depth understanding of the relationship between their short-range structural information and catalytic performance is still lacking.Herein,we present a synthetic strategy that uses transition-metal oxide-assisted thermal diffusion.PtCo/C catalysts with localized tetragonal distortion were obtained by controlling the thermal diffusion process of transition-metal elements.This localized structural distortion induced a significant strain effect on the nanoparticle surface,which further shortened the length of the Pt-Pt bond,improved the electronic state of the Pt surface,and enhanced the performance of the catalyst.PtCo/C catalysts with special short-range structures achieved excellent mass activity(2.27 Amg_(Pt)^(-1))and specific activity(3.34 A cm^(-2)).In addition,the localized tetragonal distortion-induced surface compression of the Pt skin improved the stability of the catalyst.The mass activity decreased by only 13% after 30,000 cycles.Enhanced catalyst activity and excellent durability have also been demonstrated in the proton exchange membrane fuel cell configuration.This study provides valuable insights into the development of advanced Pt-based nanocatalysts and paves the way for reducing noble-metal loading and increasing the catalytic activity and catalyst stability.
文摘本文基于中国家庭追踪调查(China family panel studies,CFPS)的数据,针对股市上典型的"有限参与"现象,考察了教育背景在两个维度上对家庭股市参与概率的影响。通过Logit回归,本文发现:平均受教育年限越高及拥有专业背景的家庭,参与股市概率越大。进一步运用倾向性得分匹配法对专业背景的作用进行估计,本文发现:在家庭股票投资决策中,成员经管背景作用的大小与显著性受所在家庭整体教育水平的约束。主事者的决策地位随家庭整体受教育年限的提高而受到削弱,而家庭成员对金融知识的掌握也可能会因自身学历在家中占优与否相应地被高估或低估。基于研究结论与现实,笔者认为,在我国证券市场中,平均受教育年限越高的家庭共同决策的氛围更浓,一方面,学历更高与投资知识更专业的成员可能并不在金融参与决策上起支配作用,但另一方面,家庭也会因为某些成员的教育背景做出轻率的投资决定。针对教育背景伴随的欠理性投资行为以及完成"有限参与"向"积极参与"的转变,本文也提供了一些建议与意见。
基金financially supported by the National Natural Science Foundation of China (No.52274294)the Fundamental Research Funds for the Central Universities (No.N2124007-1)。
文摘The design of electrode materials with specific structures is considered a promising approach for improving the performance of lithium-ion batteries(LIBs).In this paper,FeO/CoO hollow nanocages coated with a N-doped carbon layer(FCO@NC)was prepared using Fe-Co-based Prussian blue analogs(PBA)as a precursor.During the synthesis,dopamine was the carbon and nitrogen source.The reducing atmosphere was assured via NH_3/Ar,which regulated the vacancies in the structure of FCO@NC as well as increased its conductivity.When used as anode materials for LIBs,the FCO@NC nanocages deliver a high reversible capacity of 774.89 mAh·g^(-1)at 0.3 A·g^(-1)after200 cycles with a capacity retention rate of 80.4%and426.76 mAh·g^(-1)after 500 cycles at a high current density of 1 A·g^(-1).It is demonstrated that the hollow nanocage structure can effectively enhance the cycle stability,and the heat treatment in NH_(3)/Ar atmosphere contributes to the oxygen vacancy content of the electrode materials,further facilitating its conductivity and electrochemical performance.
基金supported by National Key R&D Plan of China[2019YFB1504503]Interdisciplinary Innovation Program of North China Electric Power University.
文摘Proton exchange membrane fuel cells (PEMFCs) as energy conversion devices for hydrogen energy are crucial for achieving an eco-friendly society, but their cost and performance are still not satisfactory for large-scale commercialization. Multiple physical and chemical coupling processes occur simultaneously at different scales in PEMFCs. Hence, previous studies only focused on the optimization of different components in such a complex system separately. In addition, the traditional trial-and-error method is very inefficient for achieving the performance breakthrough goal. Machine learning (ML) is a tool from the data science field. Trained based on datasets built from experimental records or theoretical simulation models, ML models can mine patterns that are difficult to draw intuitively. ML models can greatly reduce the cost of experimental attempts by predicting the target output. Serving as surrogate models, the ML approach could also greatly reduce the computational cost of numerical simulations such as first-principle or multiphysics simulations. Related reports are currently trending, and ML has been proven able to speed up tasks in this field, such as predicting active electrocatalysts, optimizing membrane electrode assembly (MEA), designing efficient flow channels, and providing stack operation strategies. Therefore, this paper reviews the applications and contributions of ML aiming at optimizing PEMFC performance regarding its potential to bring a research paradigm revolution. In addition to introducing and summarizing information for newcomers who are interested in this emerging cross-cutting field, we also look forward to and propose several directions for future development.
