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Record W4380301849 · doi:10.1139/cjc-2023-0012

Development of carbon materials for sulfur cathodes in inorganic-based solid-state lithium–sulfur batteries

2023· article· en· W4380301849 on OpenAlexafffundvenue
Yang Zhao, Qihang Yu, Anna Thinphang-Nga, Xia Li

Bibliographic record

VenueCanadian Journal of Chemistry · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia UniversityCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsCathodeSulfurCarbon fibersLithium (medication)NanotechnologyChemistryMaterials scienceComposite materialOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Inorganic-based solid-state lithium–sulfur batteries (SSLSBs) with high energy density and high safety have attracted wide attention as they are one of the most promising energy storage devices to meet future market requirements. However, the development of SSLSBs faces various challenges due to the unreasonable structural design in sulfur cathodes. Carbon is one of the indispensable components in sulfur cathodes. The rational design of carbon materials becomes an important strategy to address the challenges in sulfur cathodes. This review summarizes recent literature about the design and application of carbon materials for sulfur cathodes in inorganic-based SSLSBs. It starts with the introduction of different carbon materials from zero-dimensional to three-dimensional carbon materials. Particularly, this review paper highlights the structural design of carbon materials and the cathode fabrication methods, toward improving the conductivity of cathodes, buffering volume changes in cathodes, reducing interfacial resistance among cathode components, and increasing the mass loading of active materials. Finally, the existing challenges and promising solutions for carbon materials in the cathodes are discussed and proposed.

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 score0.002
Threshold uncertainty score0.581

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 head0.013
GPT teacher head0.222
Teacher spread0.208 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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