MétaCan
Menu
Back to cohort
Record W2134142595 · doi:10.1039/c4ta02007c

Tailoring interactions of carbon and sulfur in Li–S battery cathodes: significant effects of carbon–heteroatom bonds

2014· article· en· W2134142595 on OpenAlexaff
Xia Li, Xifei Li, Mohammad Norouzi Banis, Biqiong Wang, Andrew Lushington, Xiaoyu Cui, Ruying Li, Tsun‐Kong Sham, Xueliang Sun

Bibliographic record

VenueJournal of Materials Chemistry A · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsCanadian Light Source (Canada)Western University
Fundersnot available
KeywordsSulfurHeteroatomCarbon fibersCathodeElectrochemistryCarbon blackOxygenX-ray photoelectron spectroscopyNitrogenChemistryInorganic chemistryBattery (electricity)Materials scienceChemical engineeringElectrodeOrganic chemistryPhysical chemistryComposite numberComposite materialRing (chemistry)

Abstract

fetched live from OpenAlex

In this study, effects of carbon–heteroatom bonds on sulfur cathodes were investigated. A series of carbon black substrates were prepared using various treatments to introduce nitrogen or oxygen surface species. Our results indicated that nitrogen-doped carbon black significantly improved the electrochemical performance of sulfur cathode materials. Synchrotron-based XPS revealed that the defect sites of nitrogen-doped carbon are favorable for the discharge product deposition, leading to a high utilization and reversibility of sulfur cathodes. Our studies also found that the introduction of oxygen functional groups results in deteriorated performance of Li–sulfur batteries due to the reduced conductivity and unwanted side reactions occurring between sulfur and surface oxygen species.

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 score0.001
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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

Citations82
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Materials Chemistry ASame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207