Structural reconstruction of electrocatalysts plays a pivotal role in catalytic performances for CO_(2)reduction reaction(CO_(2)RR),whereas the behavior is by far superficially understood.Here,we report that CO_(2)acc...Structural reconstruction of electrocatalysts plays a pivotal role in catalytic performances for CO_(2)reduction reaction(CO_(2)RR),whereas the behavior is by far superficially understood.Here,we report that CO_(2)accessibility results in a universal self-adaptive structural reconstruction from Cu_(2)O to Cu@CuxO composites,ending with feeding gas-dependent microstructures and catalytic performances.The CO_(2)-rich atmosphere favors reconstruction for CO_(2)RR,whereas the CO_(2)-deficient one prefers that for hydrogen evolution reaction.With the assistance of spectroscopic analysis and theoretical calculations,we uncover a CO_(2)-induced passivation behavior by identifying a reductionresistant but catalytic active Cu(I)-rich amorphous layer stabilized by*CO intermediates.Additionally,we find extra CO production is indispensable for the robust production of C2H4.An inverse correlation between durability and FECO/FEC2H4 is disclosed,suggesting that the selfstabilization process involving the absorption of*CO intermediates on Cu(I)sites is essential for durable electrolysis.Guided by this insight,we design hollow Cu_(2)O nanospheres for durable and selective CO_(2)RR electrolysis in producing C2H4.Our work recognizes the previously overlooked passivation reconstruction and self-stabilizing behavior and highlights the critical role of the local atmosphere in modulating reconstruction and catalytic processes.展开更多
利用热驱动制冷循环回收压缩余热,将制取冷量用于冷却各级空压机入口空气,从而实现压缩余热“自利用”,是降低空压机组能耗,促进空分装备低耗化的有效途径之一。本文主要构建了基于有机朗肯–蒸气压缩制冷的空压流程(ORVC-ACS)和基于吸...利用热驱动制冷循环回收压缩余热,将制取冷量用于冷却各级空压机入口空气,从而实现压缩余热“自利用”,是降低空压机组能耗,促进空分装备低耗化的有效途径之一。本文主要构建了基于有机朗肯–蒸气压缩制冷的空压流程(ORVC-ACS)和基于吸收式制冷的空压流程(ARS-ACS)的热力学模型,对比分析了环境温、湿度和冷冻水出口温度对两种压缩余热自利用系统的性能影响。结果显示,当气候条件发生波动时,采用ORVC回收压缩余热可产生更高的节能效益,节能率可达3.5%,而ARS-ACS的节能率仅为2.6%。此外,高冷冻水输送温度更有利于ORVC-ACS的节能效果,六万等级空分系统的节能量最高可达854 k W。展开更多
近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分...近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分析维度高、尺度跨度大以及局限性科研实验制约大规模跨学科科研活动的瓶颈问题,促进科学研究迈向以“平台协作”为主要特征的新模式。分析了AI4S的国际态势,梳理了当前我国农业数字化发展现状及现实困境,将文献、统计数据、调研案例分析相结合,提出推动AI4S赋能我国农业发展的实践路径。AI4S将成为撬动农业生产从“看天、看地、看庄稼”的传统模式向智能感知、智能决策、可视化管理等模式转变的强力引擎,推动科学研究从单打独斗的“小农作坊模式”迈向“安卓模式”的平台科研。在此平台上,科研人员共享算力、模型、算法、数据库和知识库等基础设施,围绕农业全产业链全生命周期研发应用,通过“滚雪球效应”加速科研创新和成果应用。利用AI技术赋能农业生产数字化、网络化和智能化,为支撑理论-实验的在线迭代,还需要完善高质量农业科学数据资源体系、适度超前推进AI关键技术与基础设施、优化新范式下的交叉创新科研生态、加强农业数据安全监管、制定完善的配套政策和激励机制等措施来打通数据壁垒,推动AI+农业落地,从源头强化农业科技创新,推动农业强国建设。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.22479097)the Shanghai Science and Technology Committee(Grant No.23ZR1433000)the National High-Level Talent Program for Young Scholars,the Start-up Fund(F.S.)from Shanghai Jiao Tong University.
文摘Structural reconstruction of electrocatalysts plays a pivotal role in catalytic performances for CO_(2)reduction reaction(CO_(2)RR),whereas the behavior is by far superficially understood.Here,we report that CO_(2)accessibility results in a universal self-adaptive structural reconstruction from Cu_(2)O to Cu@CuxO composites,ending with feeding gas-dependent microstructures and catalytic performances.The CO_(2)-rich atmosphere favors reconstruction for CO_(2)RR,whereas the CO_(2)-deficient one prefers that for hydrogen evolution reaction.With the assistance of spectroscopic analysis and theoretical calculations,we uncover a CO_(2)-induced passivation behavior by identifying a reductionresistant but catalytic active Cu(I)-rich amorphous layer stabilized by*CO intermediates.Additionally,we find extra CO production is indispensable for the robust production of C2H4.An inverse correlation between durability and FECO/FEC2H4 is disclosed,suggesting that the selfstabilization process involving the absorption of*CO intermediates on Cu(I)sites is essential for durable electrolysis.Guided by this insight,we design hollow Cu_(2)O nanospheres for durable and selective CO_(2)RR electrolysis in producing C2H4.Our work recognizes the previously overlooked passivation reconstruction and self-stabilizing behavior and highlights the critical role of the local atmosphere in modulating reconstruction and catalytic processes.
基金国家重点研发计划项目(No.2021YFE0112500)国家自然科学基金项目(No.52106029)funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement(No.101007976)
文摘利用热驱动制冷循环回收压缩余热,将制取冷量用于冷却各级空压机入口空气,从而实现压缩余热“自利用”,是降低空压机组能耗,促进空分装备低耗化的有效途径之一。本文主要构建了基于有机朗肯–蒸气压缩制冷的空压流程(ORVC-ACS)和基于吸收式制冷的空压流程(ARS-ACS)的热力学模型,对比分析了环境温、湿度和冷冻水出口温度对两种压缩余热自利用系统的性能影响。结果显示,当气候条件发生波动时,采用ORVC回收压缩余热可产生更高的节能效益,节能率可达3.5%,而ARS-ACS的节能率仅为2.6%。此外,高冷冻水输送温度更有利于ORVC-ACS的节能效果,六万等级空分系统的节能量最高可达854 k W。
文摘近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分析维度高、尺度跨度大以及局限性科研实验制约大规模跨学科科研活动的瓶颈问题,促进科学研究迈向以“平台协作”为主要特征的新模式。分析了AI4S的国际态势,梳理了当前我国农业数字化发展现状及现实困境,将文献、统计数据、调研案例分析相结合,提出推动AI4S赋能我国农业发展的实践路径。AI4S将成为撬动农业生产从“看天、看地、看庄稼”的传统模式向智能感知、智能决策、可视化管理等模式转变的强力引擎,推动科学研究从单打独斗的“小农作坊模式”迈向“安卓模式”的平台科研。在此平台上,科研人员共享算力、模型、算法、数据库和知识库等基础设施,围绕农业全产业链全生命周期研发应用,通过“滚雪球效应”加速科研创新和成果应用。利用AI技术赋能农业生产数字化、网络化和智能化,为支撑理论-实验的在线迭代,还需要完善高质量农业科学数据资源体系、适度超前推进AI关键技术与基础设施、优化新范式下的交叉创新科研生态、加强农业数据安全监管、制定完善的配套政策和激励机制等措施来打通数据壁垒,推动AI+农业落地,从源头强化农业科技创新,推动农业强国建设。
