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Record W4288066772 · doi:10.1002/cssc.202200999

Electrolytes for Multivalent Metal‐Ion Batteries: Current Status and Future Prospect

2022· review· en· W4288066772 on OpenAlexaff
Shu Zhang, Tao Long, Hao-Ze Zhang, Qing‐Yuan Zhao, Feng Zhang, Xiongwei Wu, Xian‐Xiang Zeng

Bibliographic record

VenueChemSusChem · 2022
Typereview
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of ChinaEducation Department of Hunan Province
KeywordsElectrolyteNanotechnologyElectrochemistryCurrent (fluid)Fast ion conductorMaterials scienceElectrodeChemistryEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Electrochemical energy storage has experienced unprecedented advancements in recent years and extensive discussions and reviews on the progress of multivalent metal-ion batteries have been made mainly from the aspect of electrode materials, but relatively little work comprehensively discusses and provides an outlook on the development of electrolytes in these systems. Under this circumstance, this Review will initially introduce different types of electrolytes in current multivalent metal-ion batteries and explain the basic ion conduction mechanisms, preparation methods, and pros and cons. On this basis, we will discuss in detail the research and development of electrolytes for multivalent metal-ion batteries in recent years, and finally, critical challenges and prospects for the application of electrolytes in multivalent metal-ion batteries will be put forward.

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)0.000
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.046
GPT teacher head0.336
Teacher spread0.289 · 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

Citations31
Published2022
Admission routes1
Has abstractyes

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