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Record W3041780010 · doi:10.1002/anie.202007031

General Formation of Macro‐/Mesoporous Nanoshells from Interfacial Assembly of Irregular Mesostructured Nanounits

2020· article· en· W3041780010 on OpenAlexaff
Guangrui Chen, Yuxing Yan, Jie Wang, Yong Sik Ok, Guiyuan Zhong, Yusuke Yamauchi

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsFuture Earth
FundersNational Natural Science Foundation of China
KeywordsNanoshellMesoporous materialMaterials scienceNanotechnologyNanomaterialsNanostructureCoatingPolymerMesoporous organosilicaSelf-assemblyNanoparticleChemical engineeringMesoporous silicaChemistryComposite materialOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Mesoporous core-shell nanostructures with controllable ultra-large open channels in their nanoshells are of great interest. However, soft template-directed cooperative assembly to mesoporous nanoshells with highly accessible pores larger than 30 nm, or even above 50 nm into macroporous range, remains a significant challenge. Herein we report a general approach for precisely tailored coating of hierarchically macro-/mesoporous polymer and carbon shells, possessing highly accessible radial channels with extremely wide pore size distribution from ca. 10 nm to ca. 200 nm, on diverse functional materials. This strategy creates opportunities to tailor the interfacial assembly of irregular mesostructured nanounits on core materials and generate various core-shell nanomaterials with controllable pore architectures. The obtained Fe,N-doped macro-/mesoporous carbon nanoshells show enhanced electrochemical performance for the oxygen reduction reaction in alkaline condition.

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 score0.009
Threshold uncertainty score0.835

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 communication0.0000.001
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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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

Citations64
Published2020
Admission routes1
Has abstractyes

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