Hierarchically porous Co@N-doped carbon fiber assembled by MOF-derived hollow polyhedrons enables effective electronic/mass transport:An advanced 1D oxygen reduction catalyst for Zn-air battery

Developing advanced oxygen reduction reaction(ORR)electrocatalysts with rapid mass/electron transport as well as conducting relevant kinetics investigations is essential for energy technologies,but both still face ongoing challenges.Herein,a facile approach was reported for achieving the highly dispersed Co nanoparticles anchored hierarchically porous N-doped carbon fibers(Co@N-HPCFs),which were assembled by core-shell MOFs-derived hollow polyhedrons.Notably,the unique one-dimensional(1D)carbon fibers with hierarchical porosity can effectively improve the exposure of active sites and facilitate the electron transfer and mass transfer,resulting in the enhanced reaction kinetics.As a result,the ORR performance of the optimal Co@N-HPCF catalysts remarkably outperforms that of commercial Pt/C in alkaline solution,reaching a limited diffusion current density(J)of 5.85 m A cm^(-2)and a half-wave potential(E_(1/2))of 0.831 V.Particularly,the prepared Co@N-HPCF catalysts can be used as an excellent air-cathode for liquid/solid-state Zn-air batteries,exhibiting great potentiality in portable/wearable energy devices.Furthermore,the reaction kinetic during ORR process is deeply explored by finite element simulation,so as to intuitively grasp the kinetic control region,diffusion control region,and mixing control region of the ORR process,and accurately obtain the relevant kinetic parameters.This work offers an effective strategy and a reliable theoretical basis for the engineering of first-class ORR electrocatalysts with fast electronic/mass transport.

Electrochemical hydrogen-storage capacity of graphene can achieve a carbon-hydrogen atomic ratio of 1:1

As a promising hydrogen-storage material,graphene is expected to have a theoretical capacity of 7.7 wt%,which means a carbon-hydrogen atomic ratio of 1:1.However,it has not been demonstrated yet by experiment,and the aim of the U.S.Department of Energy is to achieve 5.5 wt%in 2025.We designed a spatially-confined electrochemical system and found that the storage capacity of hydrogen adatoms on single layer graphene(SLG)is as high as 7.3 wt%,which indicates a carbon-hydrogen atomic ratio of 1:1 by considering the sp^(3) defects of SLG.First,SLG was deposited on a large-area polycrystalline platinum(Pt)foil by chemical vapor deposition(CVD);then,a micropipette with reference electrode,counter electrode and electrolyte solution inside was impacted on the SLG/Pt foil(the working electrode)to construct the spatially-confined electrochemical system.The SLG-uncovered Pt atoms act as the catalytic sites to convert protons(H^(+))to hydrogen adatoms(H_(ad)),which then spill over and are chemically adsorbed on SLG through surface diffusion during the cathodic scan.Because the electrode processes are reversible,the H_(ad) amount can be measured by the anodic stripping charge.This is the first experimental evidence for the theoretically expected hydrogen-storage capacity on graphene at ambient environment,especially by using H+rather than hydrogen gas(H_(2))as the hydrogen source,which is of significance for the practical utilization of hydrogen energy.

Chemical-element-distribution-mediated deformation partitioning and its control mechanical behavior in high-entropy alloys

The chemical element distributions always strongly affect the deformation mechanisms and mechanical properties of alloying materials.However,the detailed atomic origin still remains unknown in highentropy alloys(HEAs)with a stable random solid solution.Here,considering the effect of elemental fluctuation distribution,the deformation behavior and mechanical response of the widely-studied equimolar random Co Cr Fe Mn Ni HEA are investigated by atomic simulations combined with machine learning and micro-pillar compression experiments.The elemental anisotropy factor is proposed,and then used to evaluate the chemical element distribution.The experimental and simulation results show that the local variations of chemical compositions exist and play a critical role in the deformation partitioning and mechanical properties.The high strength and good plasticity of HEAs are obtained via tuning the chemical element distributions,and the optimal elemental anisotropy factor ranges from 2.9 to 3 using machine learning.This trend can be attributed to the cooperative mechanisms depending on the local variational composition:massive partial dislocation multiplication at an initial stage of plastic deformation,and the inhibition of localized shear banding via the nucleation of deformation twinning at a later stage.Using the new insights gained here,it would be possible to create new metallic alloys with superior properties through thermal-mechanical treatment to tailoring the chemical element distribution.

Machine learning guided appraisal and exploration of phase design for high entropy alloys

High entropy alloys(HEAs)and compositionally complex alloys(CCAs)have recently attracted great research interest because of their remarkable mechanical and physical properties.Although many useful HEAs or CCAs were reported,the rules of phase design,if there are any,which could guide alloy screening are still an open issue.In this work,we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning(ML)algorithms.Based on the artificial neural network algorithm,we were able to derive and extract a sensitivity matrix from the ML modeling,which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase,such as solid solution,intermetallic,or amorphous phase.Furthermore,we explored the use of an extended set of new design parameters,which had not been considered before,for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule,we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system.The outcomes of our experiments agree reasonably well with our predictions,which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs.

机器学习原子运动揭示金属玻璃塑性起源:从热塑性到超声塑性

金属玻璃具有无序的原子排列,但其结构与动力学并非各处均匀.许多研究证实金属玻璃的结构与动态不均匀性对于其塑性机制至关重要.金属玻璃的"缺陷"被视为结构上疏松排布、动力学上积极响应外界刺激的区域.但迄今仍未建立明确的结构-性能关系来甄别金属玻璃中的类液缺陷.本文中,我们基于模拟原子运动轨迹并结合机器学习提出了一种不依赖于静态结构特征的缺陷.利用k近邻机器学习模型分析并预测了不同温度下的原子运动行为,建立了温度类标签-原子运动特征映射关系.应用这个"机器学习温度"参数理解金属玻璃在应力下的塑性流,识别类液区原子.类液区的演化揭示了金属玻璃塑性的动态起源(包括热塑性和超声塑性),展示了应力诱发的非均匀性和原子局域环境的关联,为热塑性成型和超声加工提供了新见解.