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Record W1971196452 · doi:10.1179/174329408x271363

Electrosynthesis of manganese oxide films

2008· article· en· W1971196452 on OpenAlexafffund
Junjun Wei, Igor Zhitomirsky

Bibliographic record

VenueSurface Engineering · 2008
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceElectrosynthesisManganeseManganese oxideMetallurgyOxideElectrochemistryElectrodeChemistry

Abstract

fetched live from OpenAlex

Nanostructured manganese oxide films for electrochemical supercapacitors were obtained by cathodic electrosynthesis from aqueous NaMnO4 solutions. The proposed deposition mechanism is based on cathodic reduction of MnO4− species. The amount of the deposited material has been controlled by the variation of deposition time at a constant current density. Obtained films were studied by scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction analysis, thermogravimetric and differential thermal analysis. It was found that the composition and microstructure of the films depend on the concentration of NaMnO4 in the solutions used for deposition. The films prepared from the 0˙02M NaMnO4 solutions were crack free and porous with typical pore size of about 100–200 nm. Cyclic voltammetry and chronopotentiometry data for the films tested in the 0˙1M Na2SO4 solutions showed capacitive behaviour in the voltage window of 0–1˙0 V versus standard calomel reference electrode. The highest specific capacitance of ∼220 F g−1 was obtained for 90 μg cm−2 films at a scan rate of 2 mV s−1 using Ni substrates. The capacitance decreased with increasing scan rate. The charge storage mechanism is discussed.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.011
GPT teacher head0.190
Teacher spread0.180 · 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

Citations26
Published2008
Admission routes2
Has abstractyes

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