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Record W4384493136 · doi:10.1002/advs.202302932

Highly Conductive Polyoxanorbornene‐Based Polymer Electrolyte for Lithium‐Metal Batteries

2023· article· en· W4384493136 on OpenAlexfundno aff
So Young An, Xinsheng Wu, Yuqi Zhao, Tong Liu, Rongguan Yin, Jung Hyun Ahn, Lynn M. Walker, Jay Whitacre, Krzysztof Matyjaszewski

Bibliographic record

VenueAdvanced Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDivision of Materials ResearchCarnegie Mellon UniversityU.S. Department of EnergyNational Science Foundation
KeywordsLithium metalElectrolyteMaterials scienceLithium (medication)Electrical conductorPolymer electrolytesPolymerMetalChemical engineeringConductive polymerInorganic chemistryChemistryIonic conductivityMetallurgyComposite materialElectrode

Abstract

fetched live from OpenAlex

Abstract This present study illustrates the synthesis and preparation of polyoxanorbornene‐based bottlebrush polymers with poly(ethylene oxide) (PEO) side chains by ring‐opening metathesis polymerization for solid polymer electrolytes (SPE). In addition to the conductive PEO side chains, the polyoxanorbornene backbones may act as another ion conductor to further promote Li‐ion movement within the SPE matrix. These results suggest that these bottlebrush polymer electrolytes provide impressively high ionic conductivity of 7.12 × 10 −4 S cm −1 at room temperature and excellent electrochemical performance, including high‐rate capabilities and cycling stability when paired with a Li metal anode and a LiFePO 4 cathode. The new design paradigm, which has dual ionic conductive pathways, provides an unexplored avenue for inventing new SPEs and emphasizes the importance of molecular engineering to develop highly stable and conductive polymer electrolytes for lithium‐metal batteries (LMB).

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 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 score0.020
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2023
Admission routes1
Has abstractyes

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