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Record W2467970661 · doi:10.1021/acsomega.6b00058

Library of Cationic Organic Dyes for Visible-Light-Driven Photoredox Transformations

2016· article· en· W2467970661 on OpenAlexafffund
Spencer P. Pitre, Christopher D. McTiernan, J. C. Scaiano

Bibliographic record

VenueACS Omega · 2016
Typearticle
Languageen
FieldChemistry
TopicRadical Photochemical Reactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsPhotoredox catalysisCationic polymerizationVisible spectrumChemistryCatalysisPhotochemistryElectrochemistryHalogenationCombinatorial chemistryPhotocatalysisMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Organic dyes can be excellent catalysts for photoredox chemistry, offering low price, low toxicity, and an exceptional range of available materials. Their use has been limited because in comparison to their transition-metal catalysts the spectroscopic, kinetic, and electrochemical information available is far more limited. To remediate this situation, we have determined the necessary data for 14 readily available dyes with excellent potential as photoredox catalysts. We have also demonstrated the utility of these dyes through visible-light-mediated reductive dehalogenation and Aza-Henry reactions. We envision that this collection of data will lead to an increase in the use of cationic dyes in photoredox processes because users will find the necessary information readily available.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.234
Teacher spread0.225 · 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

Citations118
Published2016
Admission routes2
Has abstractyes

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