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Heterogeneously Catalyzed Pechmann Condensation Employing the HFe(SO<sub>4</sub>)<sub>2</sub>.4H<sub>2</sub>O-Chitosan Nano-Composite: Ultrasound-Accelerated Green Synthesis of Coumarins

2021· article· en· W6920632081 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldChemistry
TopicMulticomponent Synthesis of Heterocycles
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsCatalysisCoumarinCondensationReaction conditionsCondensation reactionHeterogeneous catalysis

Abstract

fetched live from OpenAlex

A heterogeneous magnetic HFe(SO<sub>4</sub>)<sub>2</sub>.4H<sub>2</sub>O-chitosan nano-composite (<b>HFe(SO<sub>4</sub>)<sub>2</sub>.4H<sub>2</sub>O-Ch NCs</b>) was designed and synthesized through a facile and economical two-steps procedure. The synthesized <b>HFe(SO<sub>4</sub>)<sub>2</sub>.4H<sub>2</sub>O-Ch NCs</b> was identified by XRD, EDX, FT-IR, VSM, SEM, TEM, and TGA techniques. The resulting <b>HFe(SO<sub>4</sub>)<sub>2</sub>.4H<sub>2</sub>O-Ch NCs</b> was applied as an efficient nano-catalyst in the green synthesis of coumarin derivatives through pechmann condensation under solvent-free conditions at 100 °C, and also with ultrasonic-assisted at 70 °C. High yields of the products, short reaction times, and mild reaction conditions were perceived in both methods. The ability of detachment, recycling, and reuse of the nano-catalyst was investigated under 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.239
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