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Electrochemical Activation of Mn<sub>3</sub>O<sub>4</sub> (Hausmannite) for a Rechargeable Aqueous Zn/Mn-Oxide Battery for Energy Storage Applications

2019· article· en· W2965035659 on OpenAlexaff
Ivan Stoševski, Arman Bonakdarpour, Sharon Ting Voon, David P. Wilkinson

Bibliographic record

Venue2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectrochemistryAqueous solutionManganeseOxideMaterials sciencePhysicsChemistryPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Aqueous zinc/manganese dioxide batteries are excellent candidates for stationary energy storage applications due to several advantages, including low cost, the earth-abundance of Zn and Mn-oxides, high theoretical volumetric and specific capacity. The hausmannite phase of manganese oxide (Mn3O4) has been studied for rechargeable near-neutral (2 M ZnSO4) zinc-manganese oxide battery applications. Electrochemical investigation in coin cell hardware reveals that Mn3O4activation occurs during the initial ≈ 45 cycles, after which maximum capacity was achieved. More than 65% of its maximum capacity was retained for more than 800 charge/discharge cycles showing excellent reversibility of the material. XRD analysis shows no phase change of Mn3O4during the cycling. Identification and characterization of new Mn-oxide polymorphs, which lead to improved battery performance, are a critical contribution to the field of energy storage. With further improvements, this battery chemistry and its variations, have the potential to meet the requirements for grid-level storage applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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