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Nitrogen, oxygen and sulfur co-doped hierarchical porous carbon toward high performance supercapacitor by dual activation strategy

2025· article· en· W4409289057 on OpenAlexaff
Huyan Shen, Fang Song, Yanyu Li, Hongjie Li, Yiyan Zhu, Shenmin Zhu, Keryn Lian, Yao Li, Di Zhang

Bibliographic record

VenueJournal of Power Sources · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of China Stem Cell and Translational ResearchNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsSupercapacitorSulfurDual (grammatical number)Carbon fibersNitrogenOxygenPorosityDopingChemical engineeringMaterials scienceInorganic chemistryDual roleChemistryNanotechnologyCapacitanceElectrodeComposite numberOptoelectronicsOrganic chemistryComposite materialCombinatorial chemistryEngineering

Abstract

fetched live from OpenAlex

Pore structure modulation and heteroatom doping are two very important strategies for designing high performance porous carbon materials. Herein, we prepare nitrogen, oxygen and sulfur co-doped hierarchical porous carbon electrode by a scalable ZnO-KOH dual activation treatment of CMC-LS-urea aerogel (CMC: sodium carboxymethyl cellulose; LS: sodium lignosulfonate). The achieved porous carbon material possesses a high specific surface area (2898.2 m 2 g −1 ), hierarchical porous structure (V meso = 0.43 cm 3 g −1 , V micro = 0.93 cm 3 g −1 ) and rich heteroatom contents (O: 17.4 at%; N: 5.96 at% and S: 0.59 at%). The optimal sample demonstrates an exceptional specific capacitance of 342.5 F g −1 at the current density of 0.1 A g −1 and a high capacitance retention rate of 93.7 % after 30000 cycles in 6 M KOH electrolyte. Furthermore, the assembled symmetric capacitors exhibit a high energy density of 33 Wh kg −1 in 1 M TEABF 4 /AN organic electrolyte. This method also can be applied to other natural polymers precursors system for highly efficient and environmentally friendly energy storage.

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)0.001
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 score0.028
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations12
Published2025
Admission routes1
Has abstractyes

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