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Record W2997804731 · doi:10.1002/batt.201900150

Electrodeposited Manganese Oxide on Carbon Paper for Zinc‐Ion Battery Cathodes

2019· article· en· W2997804731 on OpenAlexafffund
Arjun Dhiman, Douglas G. Ivey

Bibliographic record

VenueBatteries & Supercaps · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsX-ray photoelectron spectroscopyMaterials scienceCathodeRaman spectroscopyElectrochemistryBattery (electricity)OxideManganeseIntercalation (chemistry)ElectrodeChemical engineeringScanning electron microscopeCarbon fibersInorganic chemistryMetallurgyChemistryComposite materialComposite number

Abstract

fetched live from OpenAlex

Abstract Nano‐crystalline, flake‐like Mn oxide was electrodeposited onto carbon paper (CP) using a pulsed electrodeposition technique. The electrodeposited Mn oxide was identified as Mn3O4 through a combination of X‐ray photoelectron spectroscopy (XPS), X‐ray diffraction (XRD), and Raman spectroscopy. The Mn3O4 on CP was used as a cathode for Zn‐ion batteries (ZIBs) and showed excellent cyclability at a current density of 1 A g−1 with a capacity retention of 139 % after 200 cycles. Electron microscopy was used to characterize the microstructural changes of the cathode at various stages during discharge/charge cycling. This work in combination with the electrochemical results suggests a two‐step reaction mechanism involving both intercalation and conversion reactions. The results demonstrate that electrodeposition of the cathode material is a simple, quick, and potentially scalable electrode synthesis method for producing high performing Zn‐ion electrodes without the use of binders or other additives.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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

Citations44
Published2019
Admission routes2
Has abstractyes

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