Cambridge EnerTech’s

Next-Generation Battery Research
( 次世代电池研究 )

Advances in Chemical, Material, and Electrochemical Engineering

2020年3月31日~4月1日


Have lithium-ion batteries (LIBs) reached their technical limit? A revolutionary paradigm is required to design new stable anode, cathode, and electrolyte chemistries and engineer separator materials to provide LIBs with higher energy, higher power, longer lifetime, and superior safety. Coordinated efforts in fundamental research and advanced engineering are needed to effectively combine new materials, electrode architectures, and manufacturing technologies.

Preliminary Agenda

INCREASING ENERGY DENSITY: CATHODES

FEATURED PRESENTATION: Cobalt-Free Li-Excess Disordered Rocksalt Cathodes with High Energy Density

Gerbrand Ceder, PhD, Daniel M. Tellep Distinguished Professor of Engineering, University of California, Berkeley; Senior Faculty Scientist, Lawrence Berkeley National Lab

FEATURED PRESENTATION: Vanadium Disulfide Flakes with Nanolayered Titanium Disulfide Coating as Cathode Materials in Lithium-Ion Batteries

Nikhil Koratkar, PhD, Clark and Crossan Chair Professor, Mechanical Engineering and Materials Science and Engineering, Rensselaer Polytechnic Institute

Enhancing Oxygen Stability in Low-Cobalt Layered Oxide Cathode Materials by Three-Dimensional Targeted Doping

Huolin Xin, PhD, Assistant Professor, Department of Physics and Astronomy, University of California, Irvine

Improvements to Disordered Rock-Salt Li-Excess Cathode Materials

Dee Strand, PhD, Chief Scientific Officer, Technology, Wildcat Discovery Technologies

INCREASING ENERGY DENSITY: ANODES

High Performance Anodes with High Energy and Power Density

Yufeng Lu, PhD, Professor, Chemical Engineering, UCLA

Lithium Sulfur Battery Case Studies

Mark Crittenden, PhD, Head, Battery Development and Integration, OXIS Energy

INCREASING ENERGY DENSITY: MATERIALS

Atomic Layer Deposition Made Ultra-Thin Coatings for Li-Ion Battery Components

Anil Mane, PhD, Principal Materials Science Engineer, Applied Materials Division, Argonne National Laboratory

COMPREHENDING THE COMPLEXITIES OF LI-IONS: MACHINE LEARNING

Identification of 11 New Solid Li-Ion Conductors with Promise for Battery Applications Using Data Science Approaches for Small Data Sets

Evan Reed, PhD, Associate Professor, Materials Science and Engineering, Stanford University

Development of Machine Learning Models for the Simulation of Complex Battery Materials with Non-Crystalline Structures

Nongnuch Artrith, PhD, Research Scientist, Chemical Engineering, Columbia University

* 活动内容有可能不事先告知作更动及调整。

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