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Record W3019948594 · doi:10.3389/fchem.2020.00309

β-FeOOH Interlayer With Abundant Oxygen Vacancy Toward Boosting Catalytic Effect for Lithium Sulfur Batteries

2020· article· en· W3019948594 on OpenAlexaff
Yingying Li, Xifei Li, Youchen Hao, Alibek Kakimov, Dejun Li, Qian Sun, Liang Kou, Zhanyuan Tian, Le Shao, Cheng Zhang, Jiujun Zhang, Xueliang Sun

Bibliographic record

VenueFrontiers in Chemistry · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersNatural Science Basic Research Program of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsCatalysisSulfurElectrocatalystChemical engineeringOxygenVacancy defectBoosting (machine learning)Composite numberMaterials scienceChemistryLithium (medication)Inorganic chemistryNanotechnologyElectrochemistryElectrodeMetallurgyOrganic chemistryComposite materialPhysical chemistryCrystallographyComputer science

Abstract

fetched live from OpenAlex

Due to the shuttle effect and low conductivity of sulfur (S), it has been challenging to 2 realize the application of lithium-sulfur (Li-S) batteries with high performance and long cyclability. In this study, a high catalytic active CNTs@FeOOH composite is introduced as a functional interlayer for Li-S batteries. Interestingly, the existence of oxygen vacancy in FeOOH functions electrocatalyst and promotes the catalytic conversion of intercepted lithium polysulfides (LiPS). As a result, the optimized CNTs@FeOOH interlayer contributed to a high reversible capacity of 556 mAh g -1 at 3200 mA g -1 over 350 cycles. This study demonstrates that enhanced catalytic effect can accelerate conversion efficiency of polysulfides, which is beneficial of boosting high performance Li-S batteries.

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 score0.114
Threshold uncertainty score0.897

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 head0.009
GPT teacher head0.197
Teacher spread0.189 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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