Computational discovery of a novel double transition metal nitride MXene and its applications as an anchoring and catalytic material in Li–Se batteries

Discovery of new DTM nitride MXene and its application as a catalyst in Li–Se battery systems.

Authors

Institutions

Publication Details

Journal
Journal of Materials Chemistry A
Published
2025-01-01
DOI
https://doi.org/10.1039/d5ta03099d
Citations
4
Primary Topic
MXene and MAX Phase Materials
Type
article
Field-Weighted Citation Impact
2.07

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computational discovery of a novel double transition metal nitride MXene and its applications as an anchoring and catalytic material in Li–Se batteries

Rafiuzzaman Pritom, Indrajit Nandi, Satyajit Mojumder, Md Mahbubul Islam et al.
4 citations
Journal of Materials Chemistry A
MXene and MAX Phase Materials
2.07
article

Computational discovery of a novel double transition metal nitride MXene and its applications as an anchoring and catalytic material in Li–Se batteries

Rafiuzzaman Pritom, Indrajit Nandi, Satyajit Mojumder, Md Mahbubul Islam, Md Shahriar Nahian, Rahul Jayan
article en
4 citations

Abstract

Discovery of new DTM nitride MXene and its application as a catalyst in Li–Se battery systems.

Journal of Materials Chemistry AVol. 13(29)
Los Alamos National Laboratory (US), Wayne State University (US), University of Illinois Chicago (US), Washington State University (US)
National Science Foundation, Division of Chemical, Bioengineering, Environmental, and Transport Systems
Openalex Percentile: Top 13%
MXene and MAX Phase Materials
2.07
AI Navigator

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.