Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning.

ABSTRACT: In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled tr

SUBMITTER: Hollenbach JD 

PROVIDER: S-EPMC11297154 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature