摘要：Rapid capacity decay and inferior kinetics are the vital issues of anodes in the conversion reaction for lithium-ion *** engineering can efficiently modulate the intrinsic properties of transition-metal oxide(TMO)-based electrode materials,but the effect of oxygen vacancies on electrode performance remains ***,abundant oxygen vacancies are in situ introduced into the lattice of different TMOs(e.g.,Co_(3)O_(4),Fe_(2)O_(3),and NiO)via a facile hydrothermal treatment combined with *** Co_(3)O_(4)as a typical example,results prove that the oxygen vacancies in Co_(3)O_(4−x)effectively accelerate charge transfer at the interface and significantly increase electrical conductivity and pseudocapacitance *** Li-ion diffusion coefficient of Co_(3)O_(4−x) is remarkably improved by two orders of magnitude compared with that of Co_(3)O_(4).Theoretical calculations reveal that Co_(3)O_(4−x)has a lower Li-insertion energy barrier and more density of states around the Fermi level than Co_(3)O_(4),which is favorable for ion and electron ***,TMOs with rich vacancies exhibit superior cycling performance and enhanced rate capability over their *** strategy regulating the reaction kinetics would provide inspiration for designing other TMObased electrodes for energy applications.

摘要：低品位热(<150℃)是一种丰富且广泛存在的可持续能源,但也存在分布弥散、利用率低的特点.因此,开发高效且经济的低品位热回收技术对于能源的利用有着重要的意义.近年来,一种新型的低成本低品位热富集体系——热电化学电池逐渐兴起,并引起研究者极大的兴趣.基于此,本综述详细总结了近十年来关于热电化学电池这一新兴能源富集体系的研究进展.本文首先介绍了热电化学电池的组成及工作原理,然后探讨了现有电池装置的主要设计类型;接着,针对如何提升电池体系的温度系数(Se)和功率密度(P),详细讨论了电解液和电极材料的研究进展;最后,介绍了电池体系在面向实际应用时的组装及设备集成方法,并展望了该研究领域未来所面临的机遇和挑战.

摘要：高熵材料(high-entropy material,HEM)是一类具有良好性能的新型材料,以其较好的催化潜力、耐腐蚀性能等特点受到广泛关注.传统的高熵催化剂研究大多局限于各自的知识体系,难以兼容合并,不利于更优异的催化剂的后续研发.机器学习(machine learning,ML)作为一种基于大数据集来建立数理模型、进行研究推理的新兴学科,正逐步成为人们重点关注的人工智能科学分支.通过机器学习建立大数据库可以有效改善传统的研究状况,使研究效率大为提高.机器学习能用于识别定量的组分-结构-性能关系,通过从历史数据中学习而无需通过显式编程来加速电催化剂的设计.对机器学习算法、高熵材料进行了介绍,并阐述了机器学习在设计高熵电催化剂中的应用,讨论了机器学习在高熵电催化剂筛选和预测方面的发展前景.