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Record W2019713669 · doi:10.5539/apr.v7n2p93

Desiccated Coconut Residue Based Activated Carbon as an Electrode Material for Electric Double Layer Capacitor

2015· article· en· W2019713669 on OpenAlexvenueno aff
Mohd Adib Yahya, C.W. Zanariah C.W. Ngah, M. A. Hashim, Musa Ahmad, Mohd Ambar Yarmo

Bibliographic record

VenueApplied Physics Research · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
FundersUniversiti Sains Islam Malaysia
KeywordsSupercapacitorMaterials scienceActivated carbonMicrocrystallineCapacitanceCyclic voltammetrySpecific surface areaElectrochemistryElectric double-layer capacitorElectrolyteAdsorptionChemical engineeringPorosityElectrodeDouble-layer capacitanceComposite materialOrganic chemistryChemistryDielectric spectroscopy

Abstract

fetched live from OpenAlex

Activated carbon derived from desiccated coconut residue was treated with sodium hydroxide (NaOH) and analyzed for its supercapacitor performance. The sample was then characterized by N2 adsorption at -196°C, Energy dispersive x-ray (EDX) analysis and X-ray diffraction (XRD) in order to investigate its surface area, porosity and microcrystalline properties. Specific surface area (SSA) was found to be 1394.79 m2/g with high microporosity of 76.92 %. Electrochemical double-layer capacitance was studied by cyclic voltammetry with potential window of 1V. The presence of high microporosity properties affects the supercapacitor performance due to lack of accessibility of the electrolyte into the activated carbon pores. The calculated specific capacitance was found to be 42 F/g.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.129
GPT teacher head0.364
Teacher spread0.234 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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