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Record W2293063949 · doi:10.1021/acs.macromol.6b00021

Employing Gradient Copolymer To Achieve Gel Polymer Electrolytes with High Ionic Conductivity

2016· article· en· W2293063949 on OpenAlexaff
Zhenan Zheng, Xiang Gao, Yingwu Luo, Shiping Zhu

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsCopolymerElectrolyteIonic conductivityPolymerPolymer chemistryConductivityMaterials scienceMonomerPolymerizationChemical engineeringIonic bondingAcrylateStyreneLithium (medication)ChemistryIonElectrodeComposite materialOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

A new type of polymer electrolyte based on gradient copolymers is proposed to resolve the problem of low ionic conductivity faced by polymer electrolytes. A series of random, block, and gradient copolymers of styrene (St) and methyl acrylate (MA) are synthesized by a living radical polymerization method and employed as polymer electrolytes for lithium-ion batteries. The gradient copolymer has gradual change in the monomeric composition along polymer chains. There is no abrupt composition change between the adjacent blocks. This design minimizes possible sharp domain boundaries and generates highly conductive smooth ion pathways. The gradient copolymer gel electrolyte filled with a regular ester electrolyte gives the highest ionic conductivity of 1.20 × 10 –3 S cm –1 at room temperature, which meets the requirement of practical applications.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 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

Citations33
Published2016
Admission routes1
Has abstractyes

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