MétaCan
Menu
Back to cohort
Record W2504714180 · doi:10.1149/07524.0041ecst

Resolving the Effects of Surface Area and Porosity on the Capacitance of Activated Carbon

2017· article· en· W2504714180 on OpenAlexaff
Jocelyn E. Zuliani, Donald W. Kirk, Charles Q. Jia

Bibliographic record

VenueECS Transactions · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapacitanceMicroporous materialMaterials sciencePorositySpecific surface areaMesoporous materialCapacitorCarbon fibersBulk densitySupercapacitorAnalytical Chemistry (journal)Composite materialElectrodeChemistryChromatographyElectrical engineeringSoil science

Abstract

fetched live from OpenAlex

One strategy to improve the energy density of electrochemical double-layer capacitors is to maximize the capacitance per unit surface area. However, due to the intercoupled relationship between surface area and pore size distribution, the effect of pore size on capacitance remains unclear. In this study, two samples prepared from the same raw material, with similar specific surface areas and chemical compositions, but different pore size distributions are compared in order to investigate the effects of pore size on capacitance. The results demonstrate that in the microporous sample, the SSA-normalized capacitance is 15.1 µF cm-2, while the mesoporous sample has an SSA-normalized capacitance of 13.2 µF cm-2 for all charging rates. However, when normalized to mass, the enhanced micropore capacitance may be masked due to variations in a sample's bulk density. Therefore, these results demonstrate in broad pore size distribution activated carbon samples, the SSA-normalized capacitance is enhanced in microporous materials.

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 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)0.000
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 score0.025
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

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 head0.018
GPT teacher head0.224
Teacher spread0.206 · 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 teacher head, 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueECS TransactionsSame topicSupercapacitor Materials and FabricationFrench-language works237,207