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Record W4284682803 · doi:10.1002/adfm.202204778

Heterogeneous Nanodomain Electrolytes for Ultra‐Long‐Life All‐Solid‐State Lithium‐Metal Batteries

2022· article· en· W4284682803 on OpenAlexaff
Leixin Yang, Dan Luo, Yun Zheng, Tingzhou Yang, Qianyi Ma, Yihang Nie, Haozhen Dou, Yongguang Zhang, Rong Huang, Aiping Yu, Lingling Shui, Xin Wang, Zhongwei Chen

Bibliographic record

VenueAdvanced Functional Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersChina Postdoctoral Science Foundation
KeywordsMaterials scienceElectrolyteIonic conductivityLithium (medication)Fast ion conductorElectrical conductorLithium metalChemical engineeringConductivityDeposition (geology)NanotechnologyPolymerComposite materialElectrode

Abstract

fetched live from OpenAlex

Abstract Solid polymer electrolytes exhibit huge advantages but are hindered by insufficient mechanical strength and ionic conductivity in the applications of all‐solid‐state lithium‐metal batteries (ASSLBs). Herein, poly(ether‐block‐amide) (Pebax) strategies to construct heterogeneous nanodomain electrolytes (HNEs) for ultra‐long‐life ASSLBs are introduced. Pebax HNEs forms conductive nanodomains via phase separation, exhibiting interconnected and high Li + conductive features. Compared with conventional PEO‐based electrolytes, the Pebax HNEs with controllable size and order can facilitate rapid Li + transport with steerable transport channels, further enhancing the Li + conductivity and inducing the uniform Li + deposition. Furthermore, the obtained thin and dense hybrid SEI layer with potent mechanical strength can synergistically suppress the dendrite growth, and the as‐prepared ASSLBs exhibit a satisfactory capacity with a tiny capacity reduction of 0.013% per cycle over 1500 cycles. This work provides a brand‐new insight to construct a conductive structure in electrolytes for high‐performance ASSLBs.

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), Insufficient payload (model declined to judge)
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 score0.086
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations65
Published2022
Admission routes1
Has abstractyes

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