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Record W2081083197 · doi:10.1002/cjce.21979

Nanofibrillar alginic acid‐derived hierarchical porous carbon supercapacitors

2014· article· en· W2081083197 on OpenAlexaffvenue
Sy‐Thang Ho, Thanh‐Dinh Nguyen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversité Laval
FundersNational Foundation for Science and Technology Development
KeywordsAlginic acidMaterials scienceChemical engineeringMesoporous materialCarbon fibersAqueous solutionSupercapacitorAcetic acidPorositySodium alginateNanotechnologyChemistryOrganic chemistrySodiumElectrochemistryComposite materialComposite numberCatalysis

Abstract

fetched live from OpenAlex

A versatile route has been demonstrated for the preparation of hierarchical porous carbon films derived from nanofibrillar alginic acid extracted from brown seaweeds. An acetic acid‐sodium acetate buffer was used to adjust pH of the nanofibrillar alginic acid gels, giving rise to a stable aqueous homogeneous suspension at the concentration of 5.0 wt% and pH ∼4.5. A crack‐free nanofibrillar alginic acid film with a diameter of several centimeters was cast from the suspension. Self‐condensing silica around nanofibrillar alginic acid formed silica‐alginic acid composites that were carbonised and then etched the silica to generate mesoporous carbon films. The aligned mesochannel carbon was originated from the hierarchical parallel organisation of the self‐assembled nanofibrillar alginic acid. The introduction of mesoporosity into the carbon structure afforded the high‐surface‐area mesoporous carbon film‐based supercapacitor that exhibits an ultrahigh‐rate capability and highly electrical capacitance.

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)0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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