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Record W3041237420 · doi:10.1002/anie.202007063

Universal Access to Two‐Dimensional Mesoporous Heterostructures by Micelle‐Directed Interfacial Assembly

2020· article· en· W3041237420 on OpenAlexaff
Jie Wang, Zhi Chang, Bing Ding, Tao Li, Gaoliang Yang, Zhibin Pang, Teruyuki Nakato, Miharu Eguchi, Yong‐Mook Kang, Jongbeom Na, Bu Yuan Guan, Yusuke Yamauchi

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsFuture Earth
FundersJapan Society for the Promotion of ScienceAustralian Research CouncilNatural Science Foundation of Jiangsu Province
KeywordsMesoporous materialMaterials scienceHeterojunctionNanotechnologyMonolayerNanosheetCarbon fibersMicelleGrapheneComposite numberChemical engineeringChemistryComposite materialCatalysisOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Two-dimensional (2D) mesoporous heterostructures combining ultrathin nanosheet morphology, periodic porous surface structures, and diverse hybrid compositions have become increasingly important for renewable energy storage and electronics. However, it remains a great challenge to develop a universal method to prepare 2D mesoporous heterostructures. Herein, we report a composite-micelle-directed interfacial assembly method to synthesize heterostructures of an ultrathin 2D material covered with mesoporous monolayers assembled on both sides. To demonstrate the concept, we first fabricated a new sandwichlike carbon@MXene@carbon mesoporous heterostructure through the self-assembly of exfoliated MXene nanosheets and block copolymer F127/melamine-formaldehyde resin composite micelles and subsequent thermal treatment. Finally, we demonstrate that the carbon@MXene@carbon mesoporous heterostructured nanosheets manifest remarkably enhanced electrochemical performance as a cathode material for lithium-sulfur 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 score0.094
Threshold uncertainty score0.755

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 head0.014
GPT teacher head0.247
Teacher spread0.234 · 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

Citations94
Published2020
Admission routes1
Has abstractyes

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