MétaCan
Menu
Back to cohort
Record W4411010461 · doi:10.1149/1945-7111/ade0ed

Suppression of Transition Metal Dissolution in Doped NMC 811 Cathode Active Materials, a Combinatorial Study

2025· article· en· W4411010461 on OpenAlexfundno aff
Alexander S. Hebert, Nooshin Zeinali Galabi, Dae Hyun Kim, Eric McCalla

Bibliographic record

VenueJournal of The Electrochemical Society · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDissolutionCathodeDopingTransition metalMaterials scienceMetalInorganic chemistryChemistryChemical engineeringMetallurgyOptoelectronicsPhysical chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC 811) is a cathode active material of great interest due to its high capacity and lower cost compared to high Co-content layered oxides. However, it suffers from poor cyclability, in part due to Ni migration into the Li layer and surface decomposition, with transition metal dissolution further hindering long-term performance. One method of improving the cathode stability is doping the material with metal atoms. In this work, we systematically test 56 dopants simultaneously in order to further reduce the Co content and investigate the impact on structure and performance. We find that many dopants integrate into the layered structure and several dopants increase cyclability and dramatically reduce metal dissolution from the cathode. We found that Cs and Na boosted discharge capacity by 3 and 5%, respectively and that Fe and Ti reduced metal dissolution by up to 80%. This study is the first of its kind to compare such a large number of dopants in high nickel layered oxides to deepen our understanding of their relative impact and provide a framework for more efficient material exploration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.257
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicExtraction and Separation ProcessesFrench-language works237,207