MétaCan
Menu
Back to cohort
Record W2771473781 · doi:10.1002/aenm.201702381

3D Porous Carbon Sheets with Multidirectional Ion Pathways for Fast and Durable Lithium–Sulfur Batteries

2017· article· en· W2771473781 on OpenAlexafffund
Gaoran Li, Wen Lei, Dan Luo, Ya‐Ping Deng, Deli Wang, Zhongwei Chen

Bibliographic record

VenueAdvanced Energy Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPolysulfideMaterials scienceSulfurPorosityElectrochemistryCarbon fibersLithium (medication)NanotechnologyEnergy storageIonElectrodeFaraday efficiencyChemical engineeringElectrolyteComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract In this work, unique porous carbon sheets (PCSs) are developed via a facile synthesis. The obtained PCS delivers long‐range conductive framework, abundant active interfaces, rich element doping, and notably a high inner porosity that builds up an admirable 3D network for multidirectional ion transfer. Such unique architecture and surface chemistry enable ultrafast sulfur electrochemistry as well as high‐efficiency inhibition on polysulfide shuttling via the dually physical and chemical sulfur confinement. The PCS‐based sulfur electrodes achieve superb rate capability up to 10 C, outstanding cyclability over 1000 cycles, and high areal capacity of 4.8 mA h cm −2 . This work offers an appealing model of material engineering for fast and reliable lithium–sulfur batteries, as well as guidance for rational structural design in extended energy storage and conversion systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations221
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced Energy MaterialsSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207