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Record W4367678639 · doi:10.1002/elsa.202200013

Catalytic conversion of polysulfides by atomic layer deposition derived titanium nitride for high‐performance lithium‐sulfur batteries

2023· article· en· W4367678639 on OpenAlexaff
Ameer Nizami, Sixu Deng, Ruying Li, Xia Li, Xueliang Sun

Bibliographic record

VenueElectrochemical Science Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern UniversityConcordia University
Fundersnot available
KeywordsAtomic layer depositionTinSulfurTitanium nitrideLithium–sulfur batteryMaterials scienceCatalysisChemical engineeringCathodeCarbon fibersNitrideTitaniumElectrochemistryNucleationInorganic chemistryLayer (electronics)NanotechnologyComposite numberElectrodeChemistryMetallurgyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Lithium‐Sulfur (Li‐S) batteries as the next‐generation battery system have an ultrahigh theoretical energy density. However, the limited conversion of polysulfides in sulfur cathodes deteriorates the performance of Li‐S batteries. In this study, we develop a novel titanium nitride (TiN) catalyst for sulfur cathodes via atomic layer deposition (ALD). The synthesized ALD‐TiN catalyst shows controllable ultrafine particle size (<2 nm) and uniform distribution at the nanoscale in the carbon matrix. Combined with electrochemical analysis and multiple post‐characterization techniques, ALD‐TiN demonstrates an excellent catalytic effect to facilitate the nucleation and deposition of Li 2 S, which effectively suppresses the dissolution and shuttle of polysulfides. The as‐prepared sulfur cathodes, with the assistance of TiN catalyst, exhibit excellent cycling performance at a high rate (4 C) and deliver 200% higher discharge capacity than the pristine Sulfur‐pristine porous carbon composite cathodes.

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 score0.006
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.217
Teacher spread0.212 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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