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Record W3121091342 · doi:10.1002/anie.202011493

Strain Engineering of a MXene/CNT Hierarchical Porous Hollow Microsphere Electrocatalyst for a High‐Efficiency Lithium Polysulfide Conversion Process

2021· article· en· W3121091342 on OpenAlexaff
Xin Wang, Dan Luo, Jiayi Wang, Zhenghao Sun, Guoliang Cui, Yuxuan Chen, Tong Wang, Lirong Zheng, Yan Zhao, Lingling Shui, Guofu Zhou, Krzysztof Kempa, Yongguang Zhang, Zhongwei Chen

Bibliographic record

VenueAngewandte Chemie International Edition · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPolysulfideMaterials scienceElectrocatalystChemical engineeringPorosityComposite numberNanotechnologyCatalysisNanosheetElectrochemistryElectrodeComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Tensile‐strained Mxene/carbon nanotube (CNT) porous microspheres were developed as an electrocatalyst for the lithium polysulfide (LiPS) redox reaction. The internal stress on the surface results in lattice distortion with expanding Ti−Ti bonds, endowing the Mxene nanosheet with abundant active sites and regulating the d‐band center of Ti atoms upshifted closer to the Fermi level, leading to strengthened LiPS adsorbability and accelerated catalytic conversion. The macroporous framework offers uniformed sulfur distribution, potent sulfur immobilization, and large surface area. The composite interwoven by CNT tentacle enhances conductivity and prevents the restacking of Mxene sheets. This combination of tensile strain effect and hierarchical architecture design results in smooth and favorable trapping–diffusion–conversion of LiPS on the interface. The Li‐S battery exhibits an initial capacity of 1451 mAh g−1 at 0.2 C, rate capability up to 8 C, and prolonged cycle life.

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 score0.002

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)0.0000.000

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 head0.248
Teacher spread0.239 · 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

Citations279
Published2021
Admission routes1
Has abstractyes

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