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Adelaide M. Nolan

Researcher at University of Maryland, College Park

Publications -  23
Citations -  2059

Adelaide M. Nolan is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Lithium & Fast ion conductor. The author has an hindex of 13, co-authored 21 publications receiving 904 citations.

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Garnet-Type Solid-State Electrolytes: Materials, Interfaces, and Batteries.

TL;DR: Garnet-type electrolyte has been considered one of the most promising and important solid-state electrolytes for batteries with potential benefits in energy density, electrochemical stability, high temperature stability, and safety, and this Review will survey recent development of garnet- type LLZO electrolytes.
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Lithium Chlorides and Bromides as Promising Solid-State Chemistries for Fast Ion Conductors with Good Electrochemical Stability

TL;DR: This study used first principles computation to investigate the Li-ion diffusion, electrochemical stability, and interface stability of chloride and bromide materials and elucidated the origin of their high ionic conductivities and good electrochemical stabilities.
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Computation-Accelerated Design of Materials and Interfaces for All-Solid-State Lithium-Ion Batteries

TL;DR: In this paper, the authors present an overview of recently developed computation techniques and their applications in understanding and advancing materials and interfaces in all-solid-state batteries, and highlight the computational studies in the design and discovery of new solid electrolyte materials and outline design guidelines for solid electrolytes and their interfaces.
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Lithium-Graphite Paste: An Interface Compatible Anode for Solid-State Batteries.

TL;DR: The present work demonstrates a promising strategy to develop ceramic-compatible lithium metal-based anodes and hence low-impedance ASSBs and shows a dramatic modification in wettability with garnet SSE.
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Unsupervised discovery of solid-state lithium ion conductors.

TL;DR: The authors combine unsupervised learning techniques and molecular dynamics simulations to discover new compounds with high Li-ion conductivity, demonstrating the capability of unsuper supervised learning for discovering materials over a wide materials space with limited property data.