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Record W4296736489 · doi:10.1021/acsomega.2c03883

Exploring the Anionic Redox Chemistry in Cathode Materials for High-Energy-Density Sodium-Ion Batteries

2022· review· en· W4296736489 on OpenAlexafffund
Shoaib Muhammad, Venkataraman Thangadurai

Bibliographic record

VenueACS Omega · 2022
Typereview
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRedoxChemistryEnergy storageLithium (medication)IonOrganic radical batteryCathodeSodiumDegradation (telecommunications)NanotechnologyInorganic chemistryElectrodeElectrochemistryMaterials scienceOrganic chemistryComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Improving the energy and power densities of sodium-ion batteries is a prime challenge to establish this energy storage technology to be on par with state-of-the-art lithium-ion batteries. The energy density of the sodium-ion batteries is limited due to the lower redox potential of their electrode materials compared to that of the corresponding Li analogues; however, it can be overcome by triggering the anionic redox. Although anionic redox has received significant research interest, a clear understanding of the underlying mechanism for delivery of high capacity by utilizing anionic redox is still lacking. Formidable challenges associated with the utilization of anionic redox such as rapid material degradation, voltage fade, and oxygen release hinder its practical applications. Given the great potential of anionic redox chemistry for developing high-energy batteries, in this mini-review, the recent mechanistic understanding, electrode material degradation pathways including oxygen release, and strategies to trigger anionic redox are discussed. An overview of the existing potential and future research directions of sodium-ion batteries involving anionic reaction is provided at the end.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.074
GPT teacher head0.281
Teacher spread0.207 · 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.

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

Citations14
Published2022
Admission routes2
Has abstractyes

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