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Record W2806946208 · doi:10.1002/adfm.201801323

Interface Design and Development of Coating Materials in Lithium–Sulfur Batteries

2018· article· en· W2806946208 on OpenAlex
Xia Li, Xueliang Sun

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Functional Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaWestern UniversityCanada Foundation for Innovation
KeywordsMaterials scienceCoatingEnergy storageInterface (matter)Lithium (medication)NanotechnologyElectrochemical energy storageFabricationElectrochemistryEngineering physicsSupercapacitorComposite materialElectrodeEngineering

Abstract

fetched live from OpenAlex

Abstract High‐energy Li‐S batteries have received extensive attention and are considered to be the most promising next‐generation electric energy storage devices beyond Li‐ion batteries. Interface design is an important direction to address challenges in the development of Li–S batteries. This review summarizes recently developed coatings and interlayer materials at various interfaces of Li–S batteries. In particular, advanced nanostructures and novel fabrication methods of coating and interlayer materials applied to Li–S batteries are highlighted. Furthermore, underlying mechanisms at the interfaces and electrochemical performance of the developed Li–S batteries are also discussed. Finally, existing challenges and the future development of interface design in high‐energy Li–S batteries are summarized and prospected.

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.

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 score0.013
Threshold uncertainty score0.885

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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