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Record W4416568224 · doi:10.1149/ma2025-026927mtgabs

<i>(Invited)</i> Eutectic Electrolytes for Enhanced MnO₂ Deposition/Dissolution in Electrode-Free Batteries

2025· article· W4416568224 on OpenAlexaff
Linda F. Nazar, Jingrui Li, Bo Liu, Chang Li, Yuzhang Li

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFaraday efficiencyElectrolyteEutectic systemCathodeEnergy storageEnergy densityNanoarchitectures for lithium-ion batteriesBattery (electricity)

Abstract

fetched live from OpenAlex

Aqueous metal batteries (ZMBs) are emerging as a promising next-generation energy storage technology for large-scale grid applications. Among these, Zn 2+ /Zn ||MnO 2 /Mn 2+ batteries operating via an electrodeposition/dissolution mechanism, offer high discharge voltage and excellent theoretical capacity. However, strong acid electrolytes – a requirement for reversible MnO 2 /Mn 2+ conversion –induce poor Zn reversibility with corrosion. In this work, we present a global approach that encompasses a wide range of eutectic hybrid aqueous-organic electrolytes. These electrolytes exhibit different mechanisms for interrupting the water-bonding network, thereby controlling HER and OER and directing the MnO 2 morphology, which will be discussed in this presentation. The optimal electrolyte systems not only enable highly reversible, long-term MnO 2 deposition/dissolution at the cathode but also stable plating/stripping of the Zn anode. Efficient Zn stripping/plating was demonstrated in Zn||Cu cells and high coulombic efficiency was achieved in electrodeless Zn 2+ /Zn || MnO 2 /Mn 2+ cells up to a thousand cycles with viable capacities. This study demonstrates the feasibility of extending the electrodeposition/dissolution mechanism to systems without external acid addition and with a simple design, advancing the development of zinc-manganese batteries with enhanced energy density and efficiency.

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)0.005
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 score0.153
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.262
Teacher spread0.253 · 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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