Data science enables X-ray vision

David A Santos; Binbin Lin; Justin L Andrews

doi:10.1016/j.patter.2022.100660

Data science enables X-ray vision

Patterns (N Y). 2022 Dec 9;3(12):100660. doi: 10.1016/j.patter.2022.100660.

Authors

David A Santos^{1

2}, Binbin Lin³, Justin L Andrews^{1

2}

Affiliations

¹ Department of Chemistry, Texas A&M University, College Station, TX 77843-3255, USA.
² Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843-3255, USA.
³ Institute of Materials Science, Mechanics of Functional Materials, Technische Universität Darmstadt, Otto-Berndt-Str. 3, 64287 Darmstadt, Germany.

Abstract

In their recent publication in Patterns, the authors proposed a novel workflow to derive compositional and stress maps for positive electrode materials of Li-ion batteries from hyperspectral X-ray imaging data. They describe their interdisciplinary collaboration, the elements that sustain such collaborations, and their effect on the flourishing of the domain and data science.

Publication types

News