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Record W2111366084 · doi:10.1002/chem.201501165

Isoprene Polymerization on Iron Nanoparticles Confined in Carbon Nanotubes

2015· article· en· W2111366084 on OpenAlexfundno aff
Xiaojian Li, Liping Zhang, Reasmey P. Tan, Pier‐Francesco Fazzini, Teresa Hungrı́a, Jérôme Durand, Sébastien Lachaize, Wen‐Hua Sun, Marc Respaud, Katerina Soulantica, Philippe Serp

Bibliographic record

VenueChemistry - A European Journal · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaResearch Nova ScotiaAgence Nationale de la Recherche
KeywordsIsopreneCarbon nanotubePolymerizationNanoparticleNanotechnologyMaterials scienceChemistryChemical engineeringOrganic chemistryCopolymerPolymerEngineering

Abstract

fetched live from OpenAlex

The confinement of air-protected metallic magnetic nanoparticles in the inner cavity of carbon nanotubes (CNTs) should offer an interesting perspective for biomedical applications or for controlling CNT alignment in composites. Because the direct confinement of polymer-precoated nanoparticles in CNTs could be restricted by diffusion limitations, we developed a process based on: 1) the confinement of iron nanoparticles surface-modified with an iron polymerization catalyst in the cavity of CNTs and 2) the polymerization of isoprene on the confined nanoparticles. The resulting material consists in CNT-confined iron nanoparticles coated with a polyisoprene air barrier. This approach constitutes a proof of concept for the development of smart materials for use in medicine or composites.

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)0.0010.000

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 head0.019
GPT teacher head0.232
Teacher spread0.212 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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Same venueChemistry - A European JournalSame topicCarbon Nanotubes in CompositesFrench-language works237,207