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Record W2991406478

Surface modifications of nanocarbon materials for electrochemical capacitors

2010· article· en· W2991406478 on OpenAlexfundno aff
Tahmina Akter

Bibliographic record

VenueTSpace (University of Toronto) · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsCapacitorMaterials scienceElectrochemistryNanotechnologyChemistryElectrodeEngineeringVoltageElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Multi-walled carbon nanotubes (MWCNTs) were successfully coated with two different pseudocapacitive polyoxometalates (POMs) (SiMo12O40-4 (SiMo12) and PMo12O40-3 (PMo12)) via “Layer-by-Layer” deposition. Even with merely a “single-layer” of POM, the modified nanotubes exhibited more than 2X increase in capacitance compared with that of bare nanotubes. To further improve their electrochemical performances, the deposition sequence of the POM layers was adjusted to form “alternate layer” coating to modify MWCNT. A synergistic effect on the capacitance and kinetics was observed with the alternate layer coatings. X-ray Photoelectron Spectroscopy (XPS) and Scanning Electron Microscopy (SEM) also proved the successful coating of POMs on MWCNTs. The potential-pH relationship provided important insights in terms of the deposition mechanism and suggested that the bottom layer close to the electrode substrate was the dominating layer in alternate layer coated MWCNT electrodes.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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