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Record W2789705881 · doi:10.1002/adma.201705590

Revisiting the Role of Polysulfides in Lithium–Sulfur Batteries

2018· review· en· W2789705881 on OpenAlexafffund
Gaoran Li, Shun Wang, Yining Zhang, Matthew Li, Zhongwei Chen, Jun Lü

Bibliographic record

VenueAdvanced Materials · 2018
Typereview
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Zhejiang ProvinceArgonne National LaboratoryOffice of Energy EfficiencyWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaUniversity of ChicagoNational Natural Science Foundation of ChinaOffice of ScienceUniversity of WaterlooOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsPolysulfideSulfurLithium (medication)ElectrochemistryLithium–sulfur batteryMaterials scienceNanotechnologyChemistryElectrodeMetallurgyMedicinePhysical chemistry

Abstract

fetched live from OpenAlex

, where n = 2-8) play a critical role in both mechanistic understanding and performance improvement of lithium-sulfur batteries. The rational management of polysulfides is of profound significance for high-efficiency sulfur electrochemistry. Here, the key roles of polysulfides are discussed, with regard to their status, behavior, and their correspondingimpact on the lithium-sulfur system. Two schools of thoughts for polysulfide management are proposed, their advantages and disadvantages are compared, and future developments are discussed.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations694
Published2018
Admission routes2
Has abstractyes

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