Amorphous materials represent a promising platform for advancing CO_(2)electrochemical reduction due to their inherently diverse coordination environments.In this study,we demonstrate computationally the superior perf...Amorphous materials represent a promising platform for advancing CO_(2)electrochemical reduction due to their inherently diverse coordination environments.In this study,we demonstrate computationally the superior performance of amorphous CuNi alloys for CO_(2)electrochemical reduction.By integrating machine learning forcefields for efficient structure generation and density functional theory for subsequent structural refinement and property calculations,we reveal the potential of these disordered systems to outperform their crystalline counterparts.Machine learning forcefields can generate a bulk structure containing a mixture of Cu and Ni atoms,resulting in enhanced catalytic performance.Effective screening of the amorphous surfaces is used to identify undercoordinated Cu and Ni sites in the amorphous structure to synergistically promote selective CO production and favor ethanol formation over ethylene via the stabilization of the*COCHO intermediate,resulting in significantly lower Gibbs free energy changes compared to the crystalline counterpart.The varying atomic coordination environments on amorphous surfaces promote both C–C bond formation and subsequent proton-electron transfer,leading to ethanol formation.These findings demonstrate the superior catalytic performance of amorphous CuNi,highlighting its potential for efficient and selective electroreduction of CO_(2).展开更多
Background:Motor adaptation relies on error-based learning for accurate movements in changing environ-ments.However,the neurophysiological mechanisms driving individual differences in performance are unclear.Transcran...Background:Motor adaptation relies on error-based learning for accurate movements in changing environ-ments.However,the neurophysiological mechanisms driving individual differences in performance are unclear.Transcranial magnetic stimulation(TMS)-evoked potential can provide a direct measure of cortical excitability.Objective:To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield.Methods:A group of 15 right-handed healthy participants(mean age 23 years)performed a robot-mediated forcefield perturbation task.There were two conditions:unperturbed non-adaptation and perturbed adapta-tion.TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex(left M1).Electroencephalographic(EEG)activity was continuously recorded,and cortical excitability was measured by TMS-evoked potential(TEP).Motor learning was quantified by the motor learning index.Results:Larger error-related negativity(ERN)in fronto-central regions was associated with improved motor per-formance as measured by a reduction in trajectory errors.Baseline TEP N100 peak amplitude predicted motor learning(P=0.005),which was significantly attenuated relative to baseline(P=0.0018)following motor adap-tation.Conclusions:ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation.Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex.TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.展开更多
To investigate the effect of void defects on the shock response of hexanitrohexaazaisowurtzitane(CL-20)co-crystals,shock responses of CL-20 co-crystals with energetic materials ligands trinitrotoluene(TNT),1,3-dinitro...To investigate the effect of void defects on the shock response of hexanitrohexaazaisowurtzitane(CL-20)co-crystals,shock responses of CL-20 co-crystals with energetic materials ligands trinitrotoluene(TNT),1,3-dinitrobenzene(DNB),solvents ligands dimethyl carbonate(DMC) and gamma-butyrolactone(GBL)with void were simulated,using molecular dynamics method and reactive force field.It is found that the CL-20 co-crystals with void defects will form hot spots when impacted,significantly affecting the decomposition of molecules around the void.The degree of molecular fragmentation is relatively low under the reflection velocity of 2 km/s,and the main reactions are the formation of dimer and the shedding of nitro groups.The existence of voids reduces the safety of CL-20 co-crystals,which induced the sensitivity of energetic co-crystals CL-20/TNT and CL-20/DNB to increase more significantly.Detonation has occurred under the reflection velocity of 4 km/s,energetic co-crystals are easier to polymerize than solvent co-crystals,and are not obviously affected by voids.The results show that the energy of the wave decreases after sweeping over the void,which reduces the chemical reaction frequency downstream of the void and affects the detonation performance,especially the solvent co-crystals.展开更多
Molecular modeling methods have been applied to the structural characterization of the interaction between chiral metal complexes [Co(phen)2dppz]3+ (where phen = 1, 10-phenanthroline, dppz = dipyrido[3,2-a: 2’, 3’-c...Molecular modeling methods have been applied to the structural characterization of the interaction between chiral metal complexes [Co(phen)2dppz]3+ (where phen = 1, 10-phenanthroline, dppz = dipyrido[3,2-a: 2’, 3’-c]phenazine) and the oligonucleotide (B-DNA fragment). The natures of two kinds of the binding modes, which are currently intense controversy, have been explored. Barton proposed that there is enantio-selective DMA binding by the octahedral complexes and intercalative access by these complexes from the major groove; but Norden suggested that both enantiomers bind extremely strongly to DNA from the minor groove without any noticeable enantio-selectivity. Our results support and extend structural models based upon Norden’s studies, and conflict with Barton’s model.展开更多
基金partially funded by EPSRC (EP/T022213/1, EP/W032260/1 and EP/P020194/1) via our membership of the UK’s HEC Materials Chemistry Consortium, which is funded by EPSRC (EP/L000202)part of the “Advancing Solid Interface and Lubricants by First Principles Material Design (SLIDE)” project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 865633)
文摘Amorphous materials represent a promising platform for advancing CO_(2)electrochemical reduction due to their inherently diverse coordination environments.In this study,we demonstrate computationally the superior performance of amorphous CuNi alloys for CO_(2)electrochemical reduction.By integrating machine learning forcefields for efficient structure generation and density functional theory for subsequent structural refinement and property calculations,we reveal the potential of these disordered systems to outperform their crystalline counterparts.Machine learning forcefields can generate a bulk structure containing a mixture of Cu and Ni atoms,resulting in enhanced catalytic performance.Effective screening of the amorphous surfaces is used to identify undercoordinated Cu and Ni sites in the amorphous structure to synergistically promote selective CO production and favor ethanol formation over ethylene via the stabilization of the*COCHO intermediate,resulting in significantly lower Gibbs free energy changes compared to the crystalline counterpart.The varying atomic coordination environments on amorphous surfaces promote both C–C bond formation and subsequent proton-electron transfer,leading to ethanol formation.These findings demonstrate the superior catalytic performance of amorphous CuNi,highlighting its potential for efficient and selective electroreduction of CO_(2).
基金supported by a University of East London Excellence PhD scholarship to MT and in part from a Medical Research Council grant to CF(grant number G0802594).
文摘Background:Motor adaptation relies on error-based learning for accurate movements in changing environ-ments.However,the neurophysiological mechanisms driving individual differences in performance are unclear.Transcranial magnetic stimulation(TMS)-evoked potential can provide a direct measure of cortical excitability.Objective:To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield.Methods:A group of 15 right-handed healthy participants(mean age 23 years)performed a robot-mediated forcefield perturbation task.There were two conditions:unperturbed non-adaptation and perturbed adapta-tion.TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex(left M1).Electroencephalographic(EEG)activity was continuously recorded,and cortical excitability was measured by TMS-evoked potential(TEP).Motor learning was quantified by the motor learning index.Results:Larger error-related negativity(ERN)in fronto-central regions was associated with improved motor per-formance as measured by a reduction in trajectory errors.Baseline TEP N100 peak amplitude predicted motor learning(P=0.005),which was significantly attenuated relative to baseline(P=0.0018)following motor adap-tation.Conclusions:ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation.Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex.TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.
基金supported by the National Natural Science Foundation of China (22275018)the Project of State Key Laboratory of Explosion Science and Technology (Beijing Institute of Technology)(Grant No.QNKT20-04)。
文摘To investigate the effect of void defects on the shock response of hexanitrohexaazaisowurtzitane(CL-20)co-crystals,shock responses of CL-20 co-crystals with energetic materials ligands trinitrotoluene(TNT),1,3-dinitrobenzene(DNB),solvents ligands dimethyl carbonate(DMC) and gamma-butyrolactone(GBL)with void were simulated,using molecular dynamics method and reactive force field.It is found that the CL-20 co-crystals with void defects will form hot spots when impacted,significantly affecting the decomposition of molecules around the void.The degree of molecular fragmentation is relatively low under the reflection velocity of 2 km/s,and the main reactions are the formation of dimer and the shedding of nitro groups.The existence of voids reduces the safety of CL-20 co-crystals,which induced the sensitivity of energetic co-crystals CL-20/TNT and CL-20/DNB to increase more significantly.Detonation has occurred under the reflection velocity of 4 km/s,energetic co-crystals are easier to polymerize than solvent co-crystals,and are not obviously affected by voids.The results show that the energy of the wave decreases after sweeping over the void,which reduces the chemical reaction frequency downstream of the void and affects the detonation performance,especially the solvent co-crystals.
文摘Molecular modeling methods have been applied to the structural characterization of the interaction between chiral metal complexes [Co(phen)2dppz]3+ (where phen = 1, 10-phenanthroline, dppz = dipyrido[3,2-a: 2’, 3’-c]phenazine) and the oligonucleotide (B-DNA fragment). The natures of two kinds of the binding modes, which are currently intense controversy, have been explored. Barton proposed that there is enantio-selective DMA binding by the octahedral complexes and intercalative access by these complexes from the major groove; but Norden suggested that both enantiomers bind extremely strongly to DNA from the minor groove without any noticeable enantio-selectivity. Our results support and extend structural models based upon Norden’s studies, and conflict with Barton’s model.
