A multi-university team has uncovered how atomic order and disorder in 2D MXene nanomaterials can be predicted and tailored using AI, enabled by advanced imaging analysis.
Key Details
- 1Research led by Drexel and Purdue Universities used imaging analysis to study 40 MXene materials, 30 of which are newly synthesized.
- 2Atomic order versus disorder in layered carbides depends on the number and composition of metal elements present.
- 3Dynamic secondary ion mass spectrometry (SIMS) was used to examine atomic arrangements layer by layer.
- 4The study establishes principles for predicting and synthesizing both ordered and high-entropy (random) atomic structures in these 2D materials.
- 5AI and computational modeling can now be better trained to design bespoke materials for technological applications.
Why It Matters
This discovery not only clarifies fundamental rules governing 2D materials' structures—central to imaging science—but also paves the way for faster AI-driven prediction and design of new materials, which could have significant downstream impact on imaging technology, device engineering, and high-performance radiology tools.
Source
EurekAlert
Related News
•EurekAlert
BraDiPho: New 3D AI Atlas Integrates Brain Dissections with MRI
Researchers have developed BraDiPho, a tool that merges ex-vivo photogrammetric brain dissection data with in-vivo MRI tractography using AI.
•EurekAlert
AI Maps Genetic Factors Shaping the Corpus Callosum via MRI Scans
USC researchers used AI to analyze MRI scans and uncover the genetic architecture of the brain's corpus callosum.
•EurekAlert
WashU Launches AI Imaging Center to Advance Precision Diagnostics
Washington University establishes a new center to develop AI-powered imaging tools for better diagnosis and precision medicine.