Using 2D-to-3D deep learning models to quantify the 3D representativity of a 2D image for quantification of battery electrode transport properties directly from SEM data
Institutions
National Laboratory of the Rockies (US)
Publication Details
Journal
Zenodo (CERN European Organization for Nuclear Research)
Using 2D-to-3D deep learning models to quantify the 3D representativity of a 2D image for quantification of battery electrode transport properties directly from SEM data
Journal
Zenodo (CERN European Organization for Nuclear Research)
Topics
Advanced Battery Technologies Research
article
Using 2D-to-3D deep learning models to quantify the 3D representativity of a 2D image for quantification of battery electrode transport properties directly from SEM data
article en
Abstract
No abstract available for this paper.
Publication Tag
Zenodo (CERN European Organization for Nuclear Research)
Ask Laika to Summarize, Analyze, and Connect papers live on the map.
Summarize Papers & Methodologies
Extract key findings, datasets, and comparative methods across publications.
Benchmark Rankings & Visual Analytics
Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.
Connect Distant Disciplines
Bridge topological clusters on the map to find hidden collaborative intersections.
Using 2D-to-3D deep learning models to quantify the 3D representativity of a 2D image for quantification of battery electrode transport properties directly from SEM data · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS