MétaCan
Menu
Back to cohort

Kinetics of the photoinduced dissociative reduction of the model alkyl peroxides di-tert-butyl peroxide and ascaridole

2012· article· en· W2170987824 on OpenAlexafffund
David C. Magri, Mark S. Workentin

Bibliographic record

VenueMediterranean Journal of Chemistry · 2012
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryPeroxideAlkylKineticsPhotochemistryMedicinal chemistryReduction (mathematics)Organic chemistry

Abstract

fetched live from OpenAlex

Rate constants for the reaction between excited singlet state aromatic donors and the dialkyl peroxide, di-tert-butyl peroxide (DTBP), and the bicyclic endoperoxide, ascaridole (ASC), were measured in acetonitrile using fluorescence quenching techniques. The rate constants, measured with 18 different aromatic donors by Stern-Volmer quenching, range from 9.2  109 to 4.4  106 M-1 s-1. Using accurately measured standard reduction potentials for the peroxides, the driving force for photoinduced electron transfer is predicted to be thermodynamically feasible over the entire range of excited donors ranging from 49 to 10 kcal mol-1 for ASC and 38 to 2 kcal mol-1 for DTBP. However, when the photoinduced kinetics are combined with previously measured electron transfer kinetics by homogeneous redox catalysis with ground state radical-anion donors, a smooth parabolic correlation for a dissociative electron transfer mechanism is not observed. Rather, a discontinuity is observed between the photochemical and electrochemical data sets with the rate constants with singlet excited states donors being over two orders of magnitude larger than the ground state kinetics at the same driving force. The discrepancy is examined considering the importance of attractive interaction between fragments in the dissociative photoinduced electron transfer reactions.

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 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 score0.581

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.232
Teacher spread0.216 · 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.

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

Citations1
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueMediterranean Journal of ChemistrySame topicAnalytical Chemistry and ChromatographyFrench-language works237,207