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Recent developments using malononitrile in ultrasound-assisted multicomponent synthesis of heterocycles

2023· review· en· W4390134822 on OpenAlexaff
Ramin Javahershenas, Sahand Nikzat

Bibliographic record

VenueUltrasonics Sonochemistry · 2023
Typereview
Languageen
FieldChemistry
TopicMulticomponent Synthesis of Heterocycles
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMalononitrileContext (archaeology)Scope (computer science)ReagentChemistryBiochemical engineeringReaction conditionsCombinatorial chemistryComputer scienceNanotechnologyOrganic chemistryMaterials scienceCatalysisEngineering

Abstract

fetched live from OpenAlex

Ultrasonic irradiation serves as a vigorous and environmentally sustainable approach for augmenting multicomponent reactions (MCRs), offering benefits such as thermal enhancement, agitation, and activation, among others. Malononitrile emerges as a versatile reagent in this context, participating in a myriad of MCRs to produce structurally diverse heterocyclic frameworks. This review encapsulates the critical role of malononitrile in the sonochemical multicomponent synthesis of these heterocyclic structures. The paper further delves into the biochemical and pharmacological implications of these heterocycles, elucidating their reaction mechanisms as well as delineating the method's scope and limitations. We furnish an overview of the merits and challenges inherent to this synthetic approach and offer insights for potential avenues in future research.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.108
GPT teacher head0.342
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations60
Published2023
Admission routes1
Has abstractyes

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