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Record W4317584016 · doi:10.1002/adfm.202212607

Recent Advances in Mn‐Rich Layered Materials for Sodium‐Ion Batteries

2023· article· en· W4317584016 on OpenAlexafffund
Kuan Wang, Haoxiang Zhuo, Jiantao Wang, Fanny Poon, Xueliang Sun, Biwei Xiao

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
FundersNational Postdoctoral Program for Innovative TalentsGeneral Research Institute for Nonferrous MetalsNatural Sciences and Engineering Research Council of CanadaUniversity of Science and Technology BeijingChinese Academy of SciencesNational Natural Science Foundation of ChinaCanada Foundation for InnovationInstitut national de la recherche scientifique
KeywordsMaterials scienceLithium (medication)IonNanotechnologySodiumPhase (matter)MetallurgyChemistry

Abstract

fetched live from OpenAlex

Abstract Branded with low cost and a high degree of safety, with an ambitious aim of substituting lithium‐ion batteries in many fields, sodium‐ion batteries have received fervid attention in recent years after being dormant for decades. Layered materials are a major focus of study owing to the extensive experience already gained in lithium‐ion batteries, and the pursuit of a Mn‐rich composition is critical to reduce the cost while retaining the performance. This review provides a timely update of the recent progress of Mn‐rich layered materials for sodium‐ion batteries based on the understandings of the phase forming principles, structure transformation upon cycling and charge compensation mechanisms and discusses potential ambiguities in the pursuit of high‐performance materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.263
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations116
Published2023
Admission routes2
Has abstractyes

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