MétaCan
Menu
Back to cohort
Record W4386939455 · doi:10.1039/d3ra05439j

A post-sulfonated one-pot synthesized magnetic cellulose nanocomposite for Knoevenagel and Thorpe–Ziegler reactions

2023· article· en· W4386939455 on OpenAlexaff
Mostafa Sayed, Abdelreheem Abdelfatah Saddik, Adel M. Kamal El‐Dean, Pedram Fatehi, Ahmed I. A. Soliman

Bibliographic record

VenueRSC Advances · 2023
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsLakehead University
FundersAssiut UniversityZhejiang University
KeywordsNanocompositeKnoevenagel condensationCellulosePolymer chemistryChemistryPolymer scienceChemical engineeringMaterials scienceOrganic chemistryNuclear chemistryComposite materialCatalysisEngineering

Abstract

fetched live from OpenAlex

oxidation or the formation of agglomerations without requiring the presence of capping agents, organic solvents, or an inert environment. The SMCNC catalyst was applied to activate the Knoevenagel condensation and the Thorpe-Ziegler reaction with determining the optimal reaction conditions. The presence of the SMCNC catalyst facilitated these transformations under green procedures, which enabled us to synthesize a new series of olefins and thienopyridines, and the yields of some isolated olefins and thienopyridines were up to 99% and 95%, respectively. Besides, the catalyst was stable for five cycles without a significant decrease in its reactivity, and the mechanistic routes of both reactions on the SMCNCs were postulated.

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 score0.005

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)0.0010.001

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRSC AdvancesSame topicNanomaterials for catalytic reactionsFrench-language works237,207