MRS Meetings and Events

 

DS04.07.07 2023 MRS Fall Meeting

A Convergence of Fast Sintering, Grain Growth Analysis, High Throughput Measurements, and Data Driven Computer Models to Develop New Solid-State Sodium-Ion Battery Materials

When and Where

Nov 28, 2023
8:00pm - 10:00pm

Hynes, Level 1, Hall A

Presenter

Co-Author(s)

Michael Thuis1,Seyed Arefpour2,Bryson Clifford2,Sufend Liu2,Ramanuja Saravanan2,Shuo Wang2,Alicia Koenig3,Chris Marvel3,Martin Harmer3,Liangbing Hu2,Yifei Mo2,Sossina Haile1

Northwestern University1,University of Maryland2,Lehigh University3

Abstract

Michael Thuis1,Seyed Arefpour2,Bryson Clifford2,Sufend Liu2,Ramanuja Saravanan2,Shuo Wang2,Alicia Koenig3,Chris Marvel3,Martin Harmer3,Liangbing Hu2,Yifei Mo2,Sossina Haile1

Northwestern University1,University of Maryland2,Lehigh University3
Rapidly increasing demand for electric vehicles is straining the supply of battery materials. This need could be met with improved batteries using solid-state electrolytes and alternative battery chemistries. The ADDD-Ions collaborative project is working to combine high throughput electrochemical measurements and data analysis with computational models to predict and develop new sodium-ion solid-state battery materials, in particular electrolytes. These methods are paired with an ultra-fast high-temperature synthesis (UHS) method allowing access to previously theoretical structures with simplified manufacturing steps. Scanning transmission electron microscopy (STEM) analysis is used to understand the unique grain growth and microstructure of these materials under fast sintering conditions. Bulk pellets are made using traditional and UHS methods to compare processing effects on the properties of solid-state electrolytes. Thin film samples are used to probe the chemical compositional and structure effects on the ionic conductivity of these materials.

Keywords

scanning transmission electron microscopy (STEM) | sintering

Symposium Organizers

Andrew Detor, GE Research
Jason Hattrick-Simpers, University of Toronto
Yangang Liang, Pacific Northwest National Laboratory
Doris Segets, University of Duisburg-Essen

Symposium Support

Bronze
Cohere

Session Chairs

Jason Hattrick-Simpers
Yangang Liang
Michael Thuis

In this Session

DS03.07.05
WITHDRAWN (NO SHOW) 12.13.2023 DS03.07.05 Optimizing 2.8 Micron Emission in Er:YLF Q-Switched Lasers

DS04.07.01
Unraveling the Mechanisms of Stability in CoxMo70-xFe10Ni10Cu10 High Entropy Alloys via Physically Interpretable Graph Neural Networks

DS04.07.02
Autoencoder Based on Graph and Recurrent Neural Networks and Application to Property Prediction

DS04.07.03
Chemical State Analysis Assisted Combinatorial Exploration of New Phase Spaces: Application to Ternary Zn-M-N Nitrides and Synthesis of Wurtzite Zn2TaN3.

DS04.07.04
Data-Driven Doping for Semiconductors: Identifying Top Dopant Candidates for Complex Crystals

DS04.07.05
Optimizing Active Learning in Materials Discovery Through a Holistic Pruning Strategy for NN-based Agents

DS04.07.06
Hydrogen Absorption and Diffusion in High Entropy Alloys: Insights from DFT and Machine Learning

DS04.07.07
A Convergence of Fast Sintering, Grain Growth Analysis, High Throughput Measurements, and Data Driven Computer Models to Develop New Solid-State Sodium-Ion Battery Materials

DS04.07.08
A Unified Theory Quantifying How Lattice Dynamics Facilitate Proton Transport in Various Ternary-Oxide Phases

DS04.07.09
Machine Learning Prediction of Heat Capacity for Solid Mixtures of Pseudo-Binary Oxides

View More »

Publishing Alliance

MRS publishes with Springer Nature