MétaCan
Menu
Back to cohort
Record W2787472869 · doi:10.1039/c7cy02588b

Pd nanoparticles immobilized on carbon nanotubes with a polyaniline coaxial coating for the Heck reaction: coating thickness as the key factor influencing the efficiency and stability of the catalyst

2018· article· en· W2787472869 on OpenAlexfundno aff
Rui Yu, Rui Liu, Jie Deng, Maofei Ran, Ning Wang, Wei Chu, Zhiwei He, Zheng Du, Chengfa Jiang, Wenjing Sun

Bibliographic record

VenueCatalysis Science & Technology · 2018
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsnot available
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of ChinaUniversity of WindsorNational Science Foundation
KeywordsCoatingCatalysisHeck reactionPolyanilineMaterials scienceCarbon nanotubeChemical engineeringLayer (electronics)ChitosanNanoparticleCoaxialNanotechnologyPolymer chemistryChemistryComposite materialOrganic chemistryPalladiumPolymerPolymerization

Abstract

fetched live from OpenAlex

Pd/PANI@CNTs were synthesized using a low-cost and simple method. The thickness of the PANI layer is the key in determining the stability of the catalyst in the Heck reaction.

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)0.0000.000
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCatalysis Science & TechnologySame topicCatalytic Cross-Coupling ReactionsFrench-language works237,207