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Record W4412508976 · doi:10.1002/smll.202504133

Synergistic Modulation of Cationic Preferential Adsorption and Anionic Solvation Structure Reconstruction for Enhanced Stability of Zinc Anode

2025· article· en· W4412508976 on OpenAlexaff
Pengfei Mao, Hongxing Wang, Lantao Liu, Weiwei Pang, Yiming Li, Sasha Omanovic, Huaihe Song, Shuhui Sun, Xiaohong Chen

Bibliographic record

VenueSmall · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsInstitut National de la Recherche ScientifiqueMcGill University
FundersNational Natural Science Foundation of China
KeywordsCationic polymerizationAdsorptionSolvationZincAnodeInorganic chemistryChemical engineeringMaterials scienceChemistryPolymer chemistryElectrodeIonOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Rechargeable aqueous zinc‐ion batteries (AZIBs) with low cost and high safety are promising for energy storage. However, challenges such as the hydrogen evolution reaction, corrosion, and dendrite growth diminish the stability and reversibility of the zinc anode. Herein, zirconium oxychloride is used as an electrolyte additive to address these issues via synergistic modulation. Experimental and theoretical results reveal that the cations (ZrO 2+ ) preferentially anchor to the Zn anode, forming a water‐poor electrical double layer that alters zinc ion migration pathways and restrains side reactions. Meanwhile, the anions (Cl − ) enter the solvation‐sheath structure of zinc ions, reconstruct the hydrogen‐bond network of the electrolyte, and weaken water reactivity, eliminating dendrite growth and promoting anticorrosion behavior. Consequently, the Zn||Zn symmetric cell confers a lifespan of 1800 h at 3 mA cm −2 for 1 mAh cm −2 . Zn||Cu half‐cells maintain a high coulombic efficiency of 99.8% after 1900 cycles. When matched with NaV 3 O 8 ·1.5H 2 O (NVO) cathode, the Zn||NVO full‐cell achieves a capacity retention of 77% at 5.0 A g −1 after 1000 cycles. This work provides a solution for developing high‐performance AZIBs.

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: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.326

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)0.0000.000

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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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