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Record W3129803506 · doi:10.1002/elsa.202100001

N,S‐Codoped hollow carbon dodecahedron/sulfides composites enabling high‐performance lithium‐ion intercalation

2021· article· en· W3129803506 on OpenAlexafffund
Lu Chen, Zhi Chen, Xudong Liu, Zhibin Ye, Xiaolei Wang

Bibliographic record

VenueElectrochemical Science Advances · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of AlbertaConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnodeMaterials scienceDodecahedronBimetalCarbon fibersEnergy storageNanostructureNanocrystalLithium (medication)Chemical engineeringNanotechnologyBattery (electricity)Intercalation (chemistry)ElectrodeComposite materialInorganic chemistryComposite numberChemistry

Abstract

fetched live from OpenAlex

Abstract Lithium‐ion batteries are the predominant energy storage devices for portable electronic devices and hold great promise for renewable energies and sustainability. High capacity and long‐life anode materials are highly desired for the next‐generation Li‐ion battery with high energy density. Herein, a high‐performance anode electrode constructed with cobalt and zinc sulfides nanocrystals embedded within a nitrogen and sulfur co‐doped porous carbon is successfully designed bimetal‐organic frameworks as the precursor. Benefiting from synergistic effects of bimetal sulfides, the unique rhombohedral dodecahedral nanostructure with rough surface area and N,S‐codoped carbon matrix, such an anode material presents superior initial reversible capacity of 938.2 mA h g −1 with a high‐capacity retention of 65.6% after 100 cycles at 150 mA/g. The effective nanostructure design is expected to open a venue to construct high‐performance materials for energy and environment applications.

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 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 score0.004
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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