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Record W2107893028 · doi:10.1002/cssc.201301262

Construction of High‐Energy‐Density Supercapacitors from Pine‐Cone‐Derived High‐Surface‐Area Carbons

2014· article· en· W2107893028 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.

Bibliographic record

VenueChemSusChem · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsWestern University
Fundersnot available
KeywordsSupercapacitorCapacitanceMaterials scienceElectrodeSpecific surface areaChemical engineeringBiomass (ecology)Conifer conePetalCurrent densityComposite materialNanotechnologyChemistryOrganic chemistryBotanyCatalysisPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Very high surface area activated carbons (AC) are synthesized from pine cone petals by a chemical activation process and subsequently evaluated as an electrode material for supercapacitor applications in a nonaqueous medium. The maximum specific surface area of ∼3950 m(2) g(-1) is noted for the material treated with a 1:5 ratio of KOH to pine cone petals (PCC5), which is much higher than that reported for carbonaceous materials derived from various other biomass precursors. A symmetric supercapacitor is fabricated with PCC5 electrodes, and the results showed enhanced supercapacitive behavior with the highest energy density of ∼61 Wh kg(-1). Furthermore, outstanding cycling ability is evidenced for such a configuration, and ∼90 % of the initial specific capacitance after 20,000 cycles under harsh conditions was observed. This result revealed that the pine-cone-derived high-surface-area AC can be used effectively as a promising electrode material to construct high-energy-density supercapacitors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.194
Teacher spread0.181 · 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