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Record W4390813894 · doi:10.1002/eem2.12695

Template‐Induced Graphitic Nanodomains in Nitrogen‐Doped Carbons Enable High‐Performance Sodium‐Ion Capacitors

2024· article· en· W4390813894 on OpenAlexaff
Chun Li, Zihan Song, Minliang Liu, Enrico Lepre, Markus Antonietti, Junwu Zhu, Jian Liu, Yongsheng Fu, Nieves López‐Salas

Bibliographic record

VenueEnergy & environment materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersChina Scholarship CouncilMax-Planck-GesellschaftNational Natural Science Foundation of China
KeywordsMaterials scienceEnergy storageCarbon fibersMesoporous materialCapacitorNitrogenDopingChemical engineeringSodiumSupercapacitorIonNanotechnologyElectrodeVoltageChemistryOptoelectronicsComposite materialPower (physics)ElectrochemistryElectrical engineeringComposite numberOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Sodium‐ion capacitors (SICs) have great potential in energy storage due to their low cost, the abundance of Na, and the potential to deliver high energy and power simultaneously. This article demonstrates a template‐assisted method to induce graphitic nanodomains and micro‐mesopores into nitrogen‐doped carbons. This study elucidates that these graphitic nanodomains are beneficial for Na + storage. The obtained N‐doped carbon (As8Mg) electrode achieved a reversible capacity of 254 mA h g −1 at 0.1 A g −1 . Moreover, the As8Mg‐based SIC device achieves high combinations of power/energy densities (53 W kg −1 at 224 Wh kg −1 and 10 410 W kg −1 at 51 Wh kg −1 ) with outstanding cycle stability (99.7% retention over 600 cycles at 0.2 A g −1 ). Our findings provide insights into optimizing carbon's microstructure to boost sodium storage in the pseudocapacitive mode.

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 categoriesInsufficient payload (model declined to judge)
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 score0.006
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 spread0.182 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2024
Admission routes1
Has abstractyes

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