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Record W2025638947 · doi:10.1007/s11671-009-9518-0

Composite Electrodes for Electrochemical Supercapacitors

2010· article· en· W2025638947 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanoscale Research Letters · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsVale (Canada)McMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSupercapacitorHorizontal scan rateElectrodeNanofiberComposite numberManganeseElectrochemistryNanochemistryCapacitanceChemical engineeringComposite materialNanotechnologyCyclic voltammetryMetallurgyChemistry

Abstract

fetched live from OpenAlex

Manganese dioxide nanofibers with length ranged from 0.1 to 1 μm and a diameter of about 4-6 nm were prepared by a chemical precipitation method. Composite electrodes for electrochemical supercapacitors were fabricated by impregnation of the manganese dioxide nanofibers and multiwalled carbon nanotubes (MWCNT) into porous Ni plaque current collectors. Obtained composite electrodes, containing 85% of manganese dioxide and 15 mass% of MWCNT, as a conductive additive, with total mass loading of 7-15 mg cm-2, showed a capacitive behavior in 0.5-M Na2SO4 solutions. The decrease in stirring time during precipitation of the nanofibers resulted in reduced agglomeration and higher specific capacitance (SC). The highest SC of 185 F g-1 was obtained at a scan rate of 2 mV s-1 for mass loading of 7 mg cm-2. The SC decreased with increasing scan rate and increasing electrode mass.

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.

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)0.002
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 score0.005
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.313
Teacher spread0.285 · 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