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Record W3192367467 · doi:10.1039/d1ob01122g

Modern methods for the synthesis of perfluoroalkylated aromatics

2021· article· en· W3192367467 on OpenAlexafffund
Durbis J. Castillo‐Pazos, Juan D. Lasso, Chao‐Jun Li

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicFluorine in Organic Chemistry
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaE.B. Eddy Endowment FundCanada Research ChairsCanada Foundation for Innovation
KeywordsChemistryNanotechnologyEnvironmentally friendlyBiochemical engineeringCombinatorial chemistryOrganic chemistryEngineeringMaterials science

Abstract

fetched live from OpenAlex

Perfluoroalkyl-containing substances (PFAS), have become omnipresent materials in the modern world for both commercial and research applications. Compounds such as perfluoroalkylated arenes and heteroarenes have found uses in surfactants, lubricants, and flame retardants as a result of their astonishing chemical stability. Consequently, the synthesis of such compounds encompasses a large body of scientific articles and patents developed in the previous century. Most recent reviews on this subject have thus focused on summarizing this traditional literature, and have thereby spurred the development of a new wave of reaction manifolds employing modern synthesis principles. This new generation of methodologies focuses on the greener synthesis of perfluoroalkylated aromatic scaffolds, through the use of more efficient organometallic reactions, as well as by photochemical and electrochemical strategies. Herein, we will summarize this cohort of reactions while highlighting current challenges and future desirable outcomes for their environmentally friendly synthesis.

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)0.001
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.424
Teacher spread0.368 · 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

Citations35
Published2021
Admission routes2
Has abstractyes

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