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Record W4389097517 · doi:10.1002/aenm.202302876

Designing Polymer Electrolytes via Ring‐Opening Polymerization for Advanced Lithium Batteries

2023· article· en· W4389097517 on OpenAlexaff
Shi Wang, Lei Zhang, Qinghui Zeng, Jiazhu Guan, Haiqi Gao, Liaoyun Zhang, Zhong Jin, Wen‐Yong Lai, Qian Wang

Bibliographic record

VenueAdvanced Energy Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Jiangsu ProvincePriority Academic Program Development of Jiangsu Higher Education InstitutionsNanjing University of Posts and TelecommunicationsProject 333 of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsPolymerizationCationic polymerizationMaterials scienceRing-opening polymerizationPolymerNanotechnologyAnionic addition polymerizationPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Replacing liquid electrolytes with solid‐state polymer electrolytes (SPEs) can solve the safety hazards of Li metal batteries (LMBs) while increasing their energy density. However, there has been limited success so far in preparing advanced SPEs with controllable molecular structure and chemical composition, posing great obstacles to further promoting its application in LMBs. Recently, ring‐opening polymerization (ROP), including cationic ROP, anionic ROP, and ring‐opening metathesis polymerization, has become a dazzling new star in achieving SPEs due to its mild polymerization conditions and controllable chemical composition (molecular structure, functional group), etc. Besides, there is no small molecule released during the polymerization process, which means reduced interfacial side reaction. Hence, in this review, the merits of ROP in preparing SPEs and its mechanism as well as interfering factors, etc are evaluated from the perspective of synthetic chemistry. Furthermore, the review focuses on outlining the existing cases related to ROP as much as possible and summarize them from different ring structures (from triple ring to multivariate ring) and polymerization methods, hoping to provide a comprehensive understanding and serve as strategic guidance for designing high‐performance SPEs. Challenges and opportunities regarding this burgeoning field are also discussed 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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 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

Citations70
Published2023
Admission routes1
Has abstractyes

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