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Record W4307404732 · doi:10.1002/cjce.24735

Dual‐template synthesis of hierarchical porous carbon and its application in electrical double‐layer capacitors

2022· article· en· W4307404732 on OpenAlexvenueno aff
Liwen Xu, Xiaodan Yuan, Chengjun Gu, Jing Ding, Zi-sheng Lin, Junguang Meng, Changqi Liu, Xinye Wang, Changsheng Bu, Jubing Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceSupercapacitorCarbonizationCapacitanceChemical engineeringElectrolyteElectrochemistryMesoporous materialSpecific surface areaPorosityCarbon fibersElectrodeComposite materialScanning electron microscopeChemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Abstract Hierarchical porous carbons (HPCs) with tailored pore structure were fabricated via direct carbonization of a precursor consisting of agar and acetates in an inert atmosphere. The agar served as the carbon source and curing agent, while potassium acetate and calcium acetate were used to create micropores and mesopores, respectively. The morphology and structural features of the HPCs were characterized, and the electrochemical properties were estimated in a three‐electrode system with 6 M KOH as the electrolyte. The resulting HPCs possess a developed pore structure and concentrated pore size distribution (focused at 0.4 and 4 nm), and exhibit typical electrical double‐layer capacitive behaviour. Specially, the HCP‐2 owns a large specific surface area of 1441.65 m 2 /g and excellent electrochemical properties, including a favourable specific capacitance (235 F/g at 1 A/g), good rate capability (capacitance retention rate of 88.1% at 20 A/g), and impressive long‐term cycling stability (90.6% retention of initial capacitance after 3000 cycles).

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 score0.002

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 head0.011
GPT teacher head0.194
Teacher spread0.183 · 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
Published2022
Admission routes1
Has abstractyes

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