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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Innovations in Energy Materials: Unveiling Future Possibilities of Computational Modelling and Atomically Controlled Experiments
Presentation Title Local Thermal Conductivity Imaging and Modelling to Guide Microstructure Engineering in Energy Materials
Author(s) Eleonora Isotta, Christina Scheu, G. Jeffrey Snyder, Oluwaseyi Balogun
On-Site Speaker (Planned) Eleonora Isotta
Abstract Scope Engineering microstructural defects offers superior control over transport properties, critically affecting the performance of energy materials. Despite the relevance, we lack a clear understanding of how individual microstructures modulate microscale transport owing to the scarcity of local investigations. In this work, we illustrate recent efforts in developing structure-property relations for individual microstructural defects based on microscale thermal conductivity imaging. Experimental observations in thermoelectric SnTe and photovoltaic silicon reveal a thermal conductivity suppression in the vicinity of grain boundaries, localized within a few microns. Furthermore, not all boundaries behave the same: misorientation angle, lattice symmetry, interface roughness and morphology are found to strongly correlate with the effective thermal boundary resistance. Finally, semi-empirical models of the thermal conductivity profile around a boundary are developed, based on mean free path suppression functions. This advancement can improve understanding of carrier-defect interactions, enabling the rational engineering of microstructures for superior performance in energy and electronics.
Proceedings Inclusion? Planned:
Keywords Characterization, Energy Conversion and Storage, Thin Films and Interfaces

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Development of kinetic lattice Monte Carlo model to study ionic diffusion at misfit dislocations in oxide heterostructures
Exploring Ultra-Stable Green Rust Compositions for Green Energy Catalysis
From Prediction to Experimental Realization of Ferroelectric Wurtzite AlN-Based Alloys
Local Thermal Conductivity Imaging and Modelling to Guide Microstructure Engineering in Energy Materials
Machine Learned Multiphysics Modeling: Enhancing Uniform Distribution of Low-Energy Lithium-Ion Transport Channels in Solid Electrolyte Interphase of Electrodes
Magnetic Metasurfaces for sustainable Information and Communication technologies
Nanomaterial and nanostructure physics for thermoelectric performance enhancement
Nanoscale design of 3D anode and high effective catalysis for high performance Aluminum-air batteries
Optimization of CO2 Reduction Reaction Using Nanoporous Copper Catalysts through Machine Learning-Driven Process Parameter Modeling
Quantum-Assisted Machine Learning Analysis of Silicon-Based Anodes for Lithium Batteries: Thermodynamics, Structural Insights, and Lithium Diffusion. Identifying Challenges and Exploring Novel Candidates
Reaching new frontiers to for superconductors using pulsed high magnetic fields
Resonant Ultrasound Spectroscopy for Rapid Down Selection, Elastic Property Determination, and Model Validation in High-Entropy Materials
Specialized Machine Learning Interatomic Potential to assess Self-Healing at a W Grain Boundary
Starrydata2: an Open Platform for Materials Data Curated from Literature
Structure Low Dimensionality and Lone-Pair Stereochemical Activity: the Key to Low Thermal Conductivity in sulfides
The Exploration of FeNiMoW-based alloys for High Value Magnetic Materials
The Magic and Myths of Machine Learning in Materials Science
Two-dimensional oxides: structural modulation and energy storage applications
Unraveling the Effects of Dislocations on Ferroelectric Behavior by Molecular Dynamics Simulations
Ab Initio Models for the Prediction of Corrosion-Passivation Behavior in Aqueous Media

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