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Record W4416596064 · doi:10.1149/ma2025-02663085mtgabs

Interfacial Mechanisms Driving Enhanced MnO₂ Deposition/Dissolution in Electrode-Free Batteries

2025· article· W4416596064 on OpenAlexaff
Jingrui Li, Chang Li, Bo Liu, Yuzhang Li, Linda F. Nazar

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrolyteFaraday efficiencyBattery (electricity)AnodeCathodeEutectic systemElectrochemistryEnergy storage

Abstract

fetched live from OpenAlex

Aqueous zinc-manganese batteries operating via MnO 2 electrodeposition/dissolution mechanisms offer promising energy storage capabilities for grid-scale applications. However, they are traditionally realized by acids in electrolytes that compromise zinc anode stability. In this presentation, we discuss the interfacial phenomena enabling high-performance Zn 2+ /Zn||MnO 2 /Mn 2+ batteries through eutectic aqueous-organic electrolytes without external acid addition. The presentation will focus on how these eutectic electrolytes alter the local environment at electrode interfaces, creating localized pH gradients that influence critical electrochemical processes including proton transport and gas evolution suppression. We also demonstrate connections between electrolyte composition and the morphology and phases of deposited MnO 2 . Collectively, these interfacial phenomena significantly improve the battery discharge performance. By understanding and controlling these interfacial factors, our optimized electrolyte system simultaneously enhances MnO 2 /Mn 2+ reversibility at the cathode while promoting stable zinc cycling at the anode. Our battery achieves high coulombic efficiency for extended cycling without external acid addition, advancing zinc-manganese battery development through rational electrolyte design.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.180
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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