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Record W4309001239 · doi:10.1002/aenm.202203061

Ultrafast Manufacturing of Ultrafine Structure to Achieve An Energy Density of Over 120 Wh kg<sup>−1</sup> in Supercapacitors

2022· article· en· W4309001239 on OpenAlexaff
Jingchao Zhang, Jiawei Luo, Zhaoxin Guo, Zhedong Liu, Cunpeng Duan, Shuming Dou, Qunyao Yuan, Peng Liu, Kemeng Ji, Cuihua Zeng, Jie Xu, Wei‐Di Liu, Yanan Chen, Wenbin Hu

Bibliographic record

VenueAdvanced Energy Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsInstitute of Particle Physics
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceSupercapacitorElectrochemistryEnergy storageCarbon fibersPseudocapacitanceWettingChemical engineeringElectrodeActivated carbonNanotechnologyComposite materialComposite numberPhysical chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Supercapacitor (SC) is one of the most promising electrochemical energy‐storage devices. However, the practical application of SCs is limited by the low‐energy density. Herein, high‐temperature shock (HTS)‐derived ultrafine structure‐activated porous carbon (UAPC) with N, O functional groups is reported as high‐energy density SCs carbon. The process of ultrafast joule heating and cooling effectively transfers general‐purposed carbon into electrochemical‐activated carbon. The UAPC‐based SCs exhibit an energy density of up to 129 Wh kg −1 in EMIMBF 4 ionic liquid, which outperform almost all reported and commercial SCs (22 Wh kg −1 ). The outstanding electrochemical performance of UAPC is attributed to the ultrafine structure and N, O functional groups, which enlarges the surface area, improves the surface wettability of UAPC electrodes, and provides pseudocapacitance. The facile and efficient ultrafast‐processing strategy has opened up an unprecedented pathway for the application of low‐value carbon for the electrode design and application of SCs.

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 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 score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.218
Teacher spread0.210 · 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

Citations46
Published2022
Admission routes1
Has abstractyes

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