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
AI Model Predicts Growth Spurts from Pediatric Neck X-rays for Orthodontics
Korean researchers developed an AI system (ARNet-v2) that predicts children's growth spurts from neck X-rays to enhance orthodontic treatment planning.
•EurekAlert
Imaging Reveals Skull Changes and Immune Impact in Glioblastoma
Advanced imaging uncovers that glioblastoma affects the skull and immune system, not just the brain.
•EurekAlert
AI-Based CT Analysis Predicts Outcomes in Fibrotic Lung Disease
AI analysis of one-year CT changes predicts disease progression and survival in fibrotic interstitial lung disease.