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Record W4399462210 · doi:10.1002/smll.202309625

Leaf‐Inspired Hetero‐Structure WS <sub>2</sub> /WO <sub>3</sub> In Situ Embedded on Pomelo Peel Derived Carbon for Supercapacitor and Capacitive Deionization

2024· article· en· W4399462210 on OpenAlexaff
Yujing Zheng, Yue Lian, Jing Zhao, Yong Feng Hu, Huaihao Zhang

Bibliographic record

VenueSmall · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Saskatchewan
FundersNational Key Research and Development Program of ChinaPriority Academic Program Development of Jiangsu Higher Education InstitutionsYangzhou UniversityGovernment of Jiangsu Province
KeywordsPseudocapacitanceMaterials scienceSupercapacitorCapacitanceChemical engineeringCarbon fibersElectrochemistryCapacitive deionizationCarbonizationNanotechnologyComposite numberElectrodeComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract Composite materials have occupied a reliable position in electrochemical energy storage and conversion due to their double electric layer and pseudocapacitance. In this work, a leaf‐like heterostructure composite, obtained by peeling – carbonizing – in situ sulfuration/oxidation approach for the first time, is investigated as electrode material for electrochemical capacitance behavior. The thin and highly active transition metal WS 2 acts as an energetic “blade” to trap free ions, which are then transported across the material through a strong “tendon skeleton” WO 3 . The derived carbon PPC with a large aspect ratio holds up the overall leaf structure, also as a “warehouse” for ion storage, thus enhancing the conductivity and wettability of the material. The above three (WS 2 +WO 3 +PPC) synergistically provide outstanding double‐layer capacitance and pseudocapacitance. In particular, the vacancy defects, constructed at the heterogenous interface from WS 2 ‐WO 3 in situ growth, can still achieve superior ion absorption/desorption ability even under large current density and high concentration brackish solution.

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 categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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