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		题名	作者	年代	出处	被引量
	1	SnO_(2)/Graphene Composite with High Lithium Storage Capability for Lithium Rechargeable Batteries显示文摘SnO_(2)/graphene nanocomposites have been fabricated by a simple chemical method.In the fabrication process,the control of surface charge causes echinoid-like SnO_(2)nanoparticles to be formed and uniformly decorated on the graphene.The electrostatic attraction between a graphene nanosheet(GNS)and the echinoid-like SnO_(2)particles under controlled pH creates a unique nanostructure in which extremely small SnO_(2)particles are uniformly dispersed on the GNS.The SnO_(2)/graphene nanocomposite has been shown to perform as a high capacity anode with good cycling behavior in lithium rechargeable batteries.The anode retained a reversible capacity of 634 mA·h·g^(–1)with a coulombic efficiency of 98%after 50 cycles.The high reversibility can be attributed to the mechanical buffering by the GNS against the large volume change of SnO_(2)during delithiation/lithiation reactions.Furthermore,the power capability is significantly enhanced due to the nanostructure,which enables facile electron transport through the GNS and fast delithiation/lithiation reactions within the echinoid-like nano-SnO_(2).The route suggested here for the fabrication of SnO_(2)/graphene hybrid materials is a simple economical route for the preparation of other graphene-based hybrid materials which can be employed in many different fields.	Haegyeom Kim Sung-Wook Kim Young-Uk Park Hyeokjo Gwon Dong-Hwa Seo Yuhee Kim Kisuk Kang	2010	Nano Research2010，3，11：	8
	2	Nano-graphite platelet loaded with LiFePO4 nano particles used as the cathode in a high performance Li-ion battery显示文摘	Kim Haegyeom Kim Hyungsub Kim Sung Wook	2012	Sciverse Science Direct2012，5，：	1
	3	Superior Rechargeability and Efficiency of Lithium–Oxygen Batteries: Hierarchical Air Electrode Architecture Combined with a Soluble Catalyst显示文摘	Hee‐Dae Lim Hyelynn Song Jinsoo Kim Hyeokjo Gwon Youngjoon Bae Kyu‐Young Park Jihyun Hong Haegyeom Kim Taewoo Kim Yong Hyup Kim Xavier Lepró Raquel Ovalle‐Robles Ray H. Baughman Kisuk Kang	2014	Angew. Chem. Int. Ed2014，，15：	1
	4	Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification显示文摘Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron microscopy.Because ML models are fast once trained,there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness,which creates broad opportunities for rapid learning and information extraction from experiments.Here,we demonstrate such a capability with the development of autonomous and adaptive XRD.By coupling an ML algorithm with a physical diffractometer,this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases.We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times.The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer.Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.	Nathan J.Szymanski Christopher J.Bartel Yan Zeng Mouhamad Diallo Haegyeom Kim Gerbrand Ceder	2023	npj Computational Materials2023，，1：	0

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