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Record W4321217302 · doi:10.3233/mgc-220085

Green synthesis of 2,4-dinitro-substituted bischalcones using bifunctional magnetic nanocatalyst

2023· article· en· W4321217302 on OpenAlexaff
Alireza Doroudi, Raziye Saeidi Rashk Oliaei, Layasadat Khorsandi, Mohammad Javad Tahmasebi Birgani, Amanollah Zarei Ahmady

Bibliographic record

VenueMain Group Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicSynthesis and biological activity
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChemistryBifunctionalCatalysisCombinatorial chemistryYield (engineering)OxideNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Flavonoids have many biological properties, such as anticancer activity. Chalcones, one of their subunits, attribute their biological activity to their enone part. The presence of dinitrophenyl group in bischalcone because of its radiosensitivity property is important. Radiosensitivity property reduces radiation time in cancer patients and reduces damage to their healthy tissues. In this regard, 2,4-dinitrophenyl bischalcones were synthesized. The presence of 2,4-dinitrobenzaldehyde as a fixed component in synthesis pathway, leads to a reduction in yield of synthesis by common catalysts. Therefore, in this study, for bis-chalone synthesis, we used Graphene Oxide/Fe 3 O 4 /L-Proline nanocomposite as a green recoverable bifunctional organocatalyst. This catalyst was recovered simply by applying an external magnet and reused for eight runs. In this research, chalcones and asymmetric bis-chalcones have been synthesized with diverse substitutes in high yields (78–97%). Also, short reaction times (10–82 min), and simple experimental procedures with easy work-up are advantages of the introduced procedure. The synthesized compounds were characterized by melting point and analytical techniques. The chemical structures of synthesized compounds were confirmed by means of IR, 1 HNMR, and 13 CNMR.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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)0.0030.000

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 head0.030
GPT teacher head0.229
Teacher spread0.200 · 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 teacher head, not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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