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Record W4283737578 · doi:10.1021/acs.jpcc.2c01900

Electrochemical Stability of ZnMn<sub>2</sub>O<sub>4</sub>: Understanding Zn-Ion Rechargeable Battery Capacity and Degradation

2022· article· en· W4283737578 on OpenAlexafffund
Oleg Rubel, Thuy Nguyen Thanh Tran, Storm Gourley, Sriram Anand, Andrew van Bommel, Brian D. Adams, Douglas G. Ivey, Drew Higgins

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of AlbertaMcMaster University
FundersMitacsCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsDegradation (telecommunications)ElectrochemistryBattery (electricity)Materials scienceChemistryComputer scienceElectrodeTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

We present a refined Mn–Zn–H2O Pourbaix diagram with the emphasis on parameters relevant for the Zn/MnO2 rechargeable cells. It maps out boundaries of electrochemical stability for MnO2, ZnMn2O4, ZnMn3O7, and MnOOH. The diagram helps to rationalize experimental observation on processes and phases occurring during charge/discharge, including the position of charge/discharge redox peaks and capacity fade observed in rechargeable aqueous Zn-ion batteries for stationary storage. The proposed Pourbaix diagram is validated by observing the pH-dependent transformation of electrolytic manganese dioxide to hetaerolite and chalcophanite during discharge and charge, respectively. Our results can guide the selection of operating conditions (the potential range and pH) for existing aqueous Zn/MnO2 rechargeable cells to maximise their longevity. In addition, the relation between electrochemical stability boundaries and operating conditions can be used as an additional design criterion in exploration of future cathode materials for aqueous rechargeable batteries.

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)0.000
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 score0.013
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.030
GPT teacher head0.235
Teacher spread0.205 · 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

Citations27
Published2022
Admission routes2
Has abstractyes

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