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Record W2809472441 · doi:10.1007/s41918-018-0010-3

Structural Design of Lithium–Sulfur Batteries: From Fundamental Research to Practical Application

2018· article· en· W2809472441 on OpenAlex

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

VenueElectrochemical Energy Reviews · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersYouth Innovation Promotion AssociationDalian Institute of Chemical PhysicsNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationNational Natural Science Foundation of ChinaYouth Innovation Promotion Association of the Chinese Academy of SciencesChina Scholarship Council
KeywordsCommercializationLithium (medication)Energy densityProcess engineeringEnergy storageElectrolyteGravimetric analysisComputer scienceEnvironmental scienceEngineeringEngineering physicsChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Lithium–sulfur (Li–S) batteries have been considered as one of the most promising energy storage devices that have the potential to deliver energy densities that supersede that of state-of-the-art lithium ion batteries. Due to their high theoretical energy density and cost-effectiveness, Li–S batteries have received great attention and have made great progress in the last few years. However, the insurmountable gap between fundamental research and practical application is still a major stumbling block that has hindered the commercialization of Li–S batteries. This review provides insight from an engineering point of view to discuss the reasonable structural design and parameters for the application of Li–S batteries. Firstly, a systematic analysis of various parameters (sulfur loading, electrolyte/sulfur ( E / S ) ratio, discharge capacity, discharge voltage, Li excess percentage, sulfur content, etc.) that influence the gravimetric energy density, volumetric energy density and cost is investigated. Through comparing and analyzing the statistical information collected from recent Li–S publications to find the shortcomings of Li–S technology, we supply potential strategies aimed at addressing the major issues that are still needed to be overcome. Finally, potential future directions and prospects in the engineering of Li–S batteries are discussed. Graphical Abstract

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: none
Teacher disagreement score0.642
Threshold uncertainty score0.558

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 head0.058
GPT teacher head0.337
Teacher spread0.279 · 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