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Record W4399785730 · doi:10.1002/anie.202407380

Single‐Anion Conductive Solid‐State Electrolytes with Hierarchical Ionic Highways for Flexible Zinc‐Air Battery

2024· article· en· W4399785730 on OpenAlexaff
Mi Xu, Rui Cao, Boying Hao, Dongdong Wang, Dan Luo, Haozhen Dou, Zhongwei Chen

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersChinese Academy of Sciences
KeywordsBattery (electricity)ElectrolyteZincElectrical conductorIonic bondingSolid-stateIonic conductivityMaterials scienceIonFast ion conductorInorganic chemistryChemical engineeringNanotechnologyChemistryEngineeringElectrodeEngineering physicsMetallurgyComposite materialOrganic chemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Flexible zinc‐air batteries are leading power sources for next‐generation smart wearable electronics. However, flexible zinc‐air batteries suffer from the highly‐corrosive safety risk and limited lifespan due to the absence of reliable solid‐state electrolytes (SSEs). Herein, a single‐anion conductive SSE with high‐safety is constructed by incorporating a highly amorphous dual‐cation ionomer into a robust hybrid matrix of functional carbon nanotubes and polyacrylamide polymer. The as‐fabricated SSE obtains dual‐penetrating ionomer‐polymer networks and hierarchical ionic highways, which contribute to mechanical robustness with 1200 % stretchability, decent water uptake and retention, and superhigh ion conductivity of 245 mS ⋅ cm −1 and good Zn anode reversibility. Remarkably, the flexible solid‐state zinc‐air batteries delivers a high specific capacity of 764 mAh ⋅ g −1 and peak power density of 152 mW ⋅ cm −2 as well as sustains excellent cycling stability for 1050 cycles (350 hours). This work offers a new paradigm of OH − conductors and broadens the definition and scope of OH − conductors.

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 score0.788
Threshold uncertainty score0.756

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 communication0.0000.001
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 head0.019
GPT teacher head0.277
Teacher spread0.259 · 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

Citations23
Published2024
Admission routes1
Has abstractyes

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