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
MIT Introduces Interactive AI System for Fast Medical Image Annotation
MIT researchers have developed MultiverSeg, an interactive AI tool enabling efficient, user-driven segmentation of biomedical image datasets without prior model training.
•EurekAlert
Study Finds Gaps in FDA Safety Reporting for AI Medical Devices
A study highlights insufficient standardized safety and efficacy assessments for FDA-cleared AI/ML medical devices.
•EurekAlert
MSK Research: Proton Therapy, AI Pain Summaries, and Tumor Metastasis Insights
MSK Cancer Center research advances include proton therapy for leptomeningeal metastasis and evaluating large language models for cancer pain summary.