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Record W2333741640 · doi:10.1021/nn402994m

General Synthesis of Discrete Mesoporous Carbon Microspheres through a Confined Self-Assembly Process in Inverse Opals

2013· article· en· W2333741640 on OpenAlexaff
Zhenkun Sun, Yong Liu, Bin Li, Jing Wei, Minghong Wang, Qin Yue, Yonghui Deng, Serge Kaliaguine, Dongyuan Zhao

Bibliographic record

VenueACS Nano · 2013
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMesoporous materialMaterials scienceCarbon fibersAdsorptionMacroporeParticle sizeChemical engineeringNanotechnologyParticle (ecology)Specific surface areaMesoporous silicaMicrosphereNanoparticleComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A general confined coassembly process has been demonstrated to produce discrete uniform mesoporous carbon microspheres with 0.8-1 μm particle size using 3-D-ordered macroporous silica as the template. The obtained mesoporous carbon microspheres (MC-MSs) have uniform and discrete spherical morphology, variable symmetry (hexagonal p6mm or cubic Im3m) of mesostructures, high specific surface areas (500-1100 m(2)/g), large pore volumes (0.6-2.0 cm(3)/g), and highly accessible large mesopores (7-10.3 nm). The particle size of the carbon microspheres can be easily tuned by simply using templates with different macropore sizes. It was found that the smaller MC-MSs (330 nm) with higher surface-to-volume ratio tend to shape into an integral monolithic MC-MS matrix and larger MC-MSs (>800 nm) with lower surface-to-volume ratio to discrete spherical morphology. This feature is attributed to the difference in shrinkage behavior of mesoporous carbon spheres confined in the macropores caused by the interaction between the silica wall and carbon microspheres. Adsorption experiments indicate that the cobalt-based nanoparticle-incorporated mesoporous carbon microspheres exhibit excellent size selectivity for protein adsorption in a complex solution and good magnetic separability for easy recycling.

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)0.0010.000
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)0.0010.001

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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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

Citations80
Published2013
Admission routes1
Has abstractyes

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Same venueACS NanoSame topicNanomaterials for catalytic reactionsFrench-language works237,207