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Record W3204518381 · doi:10.1002/celc.202100946

Carbon Nanospheres with High Intra‐ and Inter‐Sphere Porosities for High‐Rate Energy‐Storage Applications

2021· article· en· W3204518381 on OpenAlexaff
Xudong Liu, Xinling Wang, Ximeng Zhang, Bahareh Raisi, Jalal Rahmatinejad, Zhibin Ye

Bibliographic record

VenueChemElectroChem · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsConcordia UniversityLaurentian University
Fundersnot available
KeywordsAnodeMaterials scienceCapacitanceEnergy storagePorosityLithium (medication)DiffusionCapacitorSupercapacitorCarbon fibersElectrodeIonChemical engineeringKinetic energyNanotechnologyComposite materialComposite numberChemistryVoltageElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Kinetic problems restrict the applications of carbon‐based materials in energy‐storage systems at high currents. Herein, small carbon nanospheres (5 and 20 nm in average diameter) featured with high intra‐sphere micro‐/meso‐porosity and inter‐sphere meso‐/macro‐porosity are demonstrated as high‐rate anode materials for lithium‐ion batteries (capacity retention: 42.3 % at 1 A g −1 relative to 0.05 A g −1 ), enabling rapid lithium‐ion diffusion with shortened diffusion lengths. Additionally, the rapid lithium‐ion response is also verified with their superior capacitance retention at high currents as electrode materials for electrical double layer capacitors (capacitance retention: 69.2 % at 100 A g −1 relative to 0.5 A g −1 ). Our results confirm the optimum energy‐storage performance of this class of nanocarbons with hierarchical micro‐/meso‐/macro‐porous structures.

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 score0.086
Threshold uncertainty score0.853

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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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