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Record W2904203626 · doi:10.1016/j.nanoen.2018.12.020

Designing a highly efficient polysulfide conversion catalyst with paramontroseite for high-performance and long-life lithium-sulfur batteries

2018· article· en· W2904203626 on OpenAlexafffund
Sizhe Wang, Jiaxuan Liao, Xiaofei Yang, Jianneng Liang, Qian Sun, Jianwen Liang, Feipeng Zhao, Alicia Koo, Fanpeng Kong, Yao Yao, Xuejie Gao, Mengqiang Wu, Shize Yang, Ruying Li, Xueliang Sun

Bibliographic record

VenueNano Energy · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersLanzhou Institute of Chemical Physics, Chinese Academy of SciencesBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaOffice of ScienceDepartment of Science and Technology of Sichuan ProvinceCanada Foundation for InnovationChina Scholarship CouncilHarbin Institute of TechnologyCanada Research ChairsUniversity of ManchesterCummings FoundationChesapeake Research ConsortiumWestern UniversityNational Natural Science Foundation of ChinaInstitut national de la recherche scientifiqueSichuan Provincial Youth Science and Technology FundUniversity of British ColumbiaU.S. Department of Energy
KeywordsPolysulfideCathodeMaterials scienceSulfurCatalysisCarbon nanotubeNanoparticleChemical engineeringNanotubeNanotechnologyElectrodeChemistryElectrolyteOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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

Citations214
Published2018
Admission routes2
Has abstractno

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