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Record W4396832227 · doi:10.1002/eem2.12735

Amphoteric Supramolecular Nanofiber Separator for High‐Performance Sodium‐Ion Batteries

2024· article· en· W4396832227 on OpenAlexaff
Yuping Zhang, Hongzhi Zheng, Xing Tong, Hao Zhuo, Yang Wu, Yuling Chen, Ge Shi, Zehong Chen, Linxin Zhong, Xinwen Peng

Bibliographic record

VenueEnergy & environment materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersBasic and Applied Basic Research Foundation of Guangdong Province
KeywordsNanofiberSeparator (oil production)Supramolecular chemistryIonMaterials scienceSodiumChemical engineeringNanotechnologyChemistryOrganic chemistryEngineeringMoleculeMetallurgyPhysics

Abstract

fetched live from OpenAlex

The separator is an essential component of sodium‐ion batteries (SIBs) to determine their electrochemical performances. However, the separator with high mechanical strength, good electrolyte wettability and excellent electrochemical performance remains an open challenge. Herein, a new separator consisting of amphoteric nanofibers with abundant functional groups was fabricated through supramolecular assembly of natural polymers for SIB. The uniform nanoporous structure, remarkable mechanical properties and abundant functional groups (e.g. −COOH, −NH 2 and −OH) endow the separator with lower dissolution activation energy and higher ion migration numbers. These metrics enable the separator to lower the barrier for desolvation of Na + , accelerate the migration of Na + , and generate more stable solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI). The battery assembled with the amphoteric nanofiber separator shows higher specific capacity and better stability than that assembled with glass fiber (GF) separator.

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 score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.000

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 head0.006
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 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

Citations28
Published2024
Admission routes1
Has abstractyes

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