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Record W4415710955 · doi:10.1038/s41467-025-64612-7

Electroinitiated interfacial healing for external pressure-free solid-state sodium metal batteries

2025· article· en· W4415710955 on OpenAlexafffund
Tingzhou Yang, Siqi Qin, Shihui Gao, Xiaoen Wang, Dan Luo, Yu Shi, Qianyi Ma, Xinyu Zhang, Yongguang Zhang, Zhongwei Chen

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersSuzhou Institute of Nanotechnology, Chinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaChinese Academy of Sciences
KeywordsElectrolytePolymerizationCoatingClampingElectrowettingMetal

Abstract

fetched live from OpenAlex

Abstract Solid-state sodium metal batteries with inorganic electrolytes have long been heralded as candidates for post-lithium-ion batteries. However, challenges including interfacial instability and air sensitivity continue to impede their path to commercialization. Here, we propose an interfacial healing strategy for solid-state sodium metal batteries by utilizing an electroinitiated accelerated polymerization process facilitated by charged microdroplets to increase the polymerization rate by 21.4 times. We show that the charge-driven electrowetting enables efficient coating layers at interfaces, which impart prolonged air stability and preferentially fill voids and cracks, further constructing stable interfaces with improved compatibility and preventing dendrite-induced crack propagation. A higher critical current density of 6.8 mA cm −2 is achieved, and assembled cells exhibit prolonged cycling life at 1.0 C over 1000 cycles. In particular, the electroinitiated accelerated polymerization-assisted interfacial healing strategy enables Ah-level pouch cells to undergo stable long-term cycling without any clamping force, demonstrating the capabilities of solid-state batteries in 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 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: none
Teacher disagreement score0.851
Threshold uncertainty score0.633

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 science0.0010.000
Research integrity0.0000.001
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 head0.297
Teacher spread0.283 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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