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Record W4311098089 · doi:10.1002/smsc.202200032

Waxberry‐Shaped Ordered Mesoporous P‐TiO<sub>2−<i>x</i></sub> Microspheres as High‐Performance Cathodes for Lithium–Sulfur Batteries

2022· article· en· W4311098089 on OpenAlexaff
Wenna Zhang, Yuanmei Xu, Jiabing Liu, Yebao Li, Eser Metin Akinoglu, Yaojie Zhu, Yongguang Zhang, Xin Wang, Zhongwei Chen

Bibliographic record

VenueSmall Science · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersHigher Education Discipline Innovation ProjectGuangdong Science and Technology DepartmentNatural Science Foundation of Hebei ProvinceNatural Science Foundation of Guangdong Province
KeywordsPolysulfideMesoporous materialSulfurCathodeMaterials scienceLithium (medication)DissolutionChemical engineeringElectrodeConductivityImpurityNanotechnologyCatalysisChemistryElectrolyte

Abstract

fetched live from OpenAlex

As a candidate for a new generation of inexpensive and high‐performance energy storage systems, lithium–sulfur (Li–S) batteries have attracted widespread research. However, the development and application of Li–S batteries are limited by severe polysulfide dissolution and slow reaction kinetics. Herein, a type of ordered mesoporous P‐TiO2−x microsphere with a waxberry‐like shape as the sulfur host material for Li–S batteries is put forward, which combines the radially arranged mesoporous structure with oxygen defects in the mesoporous framework. In addition, the introduction of phosphorus impurities greatly improves the conductivity of the sulfur electrode, enhances electron mobility, and promotes the interaction between the sulfur species and P‐TiO2−x microspheres. Finally, S/P‐TiO2−x cathodes have achieved a high capacity of 1174.9 mAh g−1 at 0.2C and stable cycling (the average capacity attenuation is only 0.086% per cycle at 1C after 600 cycles).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2022
Admission routes1
Has abstractyes

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