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Record W4390231765 · doi:10.1002/sus2.177

Lithium sulfide: a promising prelithiation agent for high‐performance lithium‐ion batteries

2023· article· en· W4390231765 on OpenAlex
Junkang Huang, Weifeng Li, Wenli Zhang, Bixia Lin, Yang Wang, Siu Wing Or, Shuhui Sun, Zhenyu Xing

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSusMat · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsLithium (medication)CathodeEnergy densityElectrolyteMaterials scienceElectronicsComputer scienceNanotechnologyElectrical engineeringEngineering physicsChemistryEngineeringElectrode

Abstract

fetched live from OpenAlex

Abstract Lithium‐ion batteries are widely used in portable electronics and electric vehicles due to their high energy density, stable cycle life, and low self‐discharge. However, irreversible lithium loss during the formation of the solid electrolyte interface greatly impairs energy density and cyclability. To compensate for the lithium loss, introducing an external lithium source, that is, a prelithiation agent, is an effective strategy to solve the above problems. Compared with other prelithiation strategies, cathode prelithiation is more cost‐effective with simpler operation. Among various cathode prelithiation agents, we first systematically summarize the recent progress of Li 2 S‐based prelithiation agents, and then propose some novel strategies to tackle the current challenges. This review provides a comprehensive understanding of Li 2 S‐based prelithiation agents and new research directions in the future.

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.

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 score0.206
Threshold uncertainty score0.836

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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