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Record W2914779866 · doi:10.3390/catal9020140

Palladium Comprising Dicationic Bipyridinium Supported Periodic Mesoporous Organosilica (PMO): Pd@Bipy–PMO as an Efficient Hybrid Catalyst for Suzuki–Miyaura Cross-Coupling Reaction in Water

2019· article· en· W2914779866 on OpenAlexaff
Arefeh Ahadi, Sadegh Rostamnia, Paria Panahi, Lee D. Wilson, Qingshan Kong, Zengjian An, Mohammadreza Shokouhimehr

Bibliographic record

VenueCatalysts · 2019
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsUniversity of Saskatchewan
FundersNatural Science Foundation of Shandong Province
KeywordsCatalysisPalladiumMesoporous materialIonic liquidFourier transform infrared spectroscopyDesorptionMaterials scienceChemical engineeringMesoporous organosilicaAdsorptionHeterogeneous catalysisInorganic chemistryChemistryMesoporous silicaPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we developed a novel catalysts consisting of periodic mesoporous organosilica functionalized with bipyridinium ionic liquid supported palladium. The physiochemical properties of the hybrid catalyst were investigated using Fourier transform infrared spectroscopy, small angle X–ray powder diffraction, field emission scanning electron microscope, transmission electron microscope, nitrogen adsorption–desorption analyses, and atomic absorption spectroscopy. The stabilized Pd species inside the mesochannels provided good catalytic efficiency for the Suzuki–Miyaura coupling reactions in water. The activity of the designed catalysts retained for several consecutive recycle runs. The stability, recoverability, and reusability of the designed heterogeneous catalyst were also studied under various reaction conditions.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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

Citations70
Published2019
Admission routes1
Has abstractyes

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