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Record W2905327836 · doi:10.1002/adsc.201801571

<i>De novo</i> Design of Organic Photocatalysts: Bithiophene Derivatives for the Visible‐light Induced C−H Functionalization of Heteroarenes

2018· article· en· W2905327836 on OpenAlexfundno aff
Cecilia Bottecchia, Raúl Martín, Irini Abdiaj, Ettore Crovini, Jesús Alcázar, J. Orduna, María‐Jesús Blesa, José R. Carrillo, Pilar Prieto, Timothy Noël

Bibliographic record

VenueAdvanced Synthesis & Catalysis · 2018
Typearticle
Languageen
FieldChemistry
TopicRadical Photochemical Reactions
Canadian institutionsnot available
FundersMinisterio de Economía y CompetitividadJunta de Comunidades de Castilla-La ManchaGobierno de AragónEuropean CommissionMountain Equipment Co-operative
KeywordsChemistrySurface modificationPhotocatalysisPhotoredox catalysisCatalysisMicroreactorVisible spectrumOrganic synthesisAbsorption (acoustics)Combinatorial chemistryPhotochemistryRedoxFlow chemistryOrganic chemistryPhysical chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Herein, we report the de novo synthesis and characterization of a series of substituted bithiophene derivatives as novel and inexpensive organic photocatalysts. DFT calculations were used to predict a priori their absorption spectra and redox potentials, which were then confirmed with empirical data. The photocatalytic activity of this novel class of organic photoredox catalyst was demonstrated in two visible‐light mediated strategies for the C−H functionalization of heteroarenes. The implementation of these strategies in a continuous‐flow photo‐microreactor afforded moderate to excellent yields within few minutes of reaction time. Due to their straightforward synthesis, low cost and good photocatalytic properties we believe that the proposed bithiophene derivatives could be employed as a new class of organic photoredox catalysts. magnified image

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 score0.002

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)0.0010.000

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 head0.021
GPT teacher head0.265
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

Citations50
Published2018
Admission routes1
Has abstractyes

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