MétaCan
Menu
Back to cohort
Record W4391645441 · doi:10.26434/chemrxiv-2024-mbbfv

Understanding the Mechanism of Triplet-Triplet Energy Transfer in the Photocatalytic [2+2] Cycloaddition: Insights from Quantum Chemical Modeling

2024· preprint· en· W4391645441 on OpenAlexaff
Eunji Lee, Hyejin Moon, Jiyong Park, Mu‐Hyun Baik

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsKootenay Association for Science & Technology
FundersInstitute for Basic Science
KeywordsMechanism (biology)PhotocatalysisCycloadditionEnergy transferPhotochemistryQuantum chemicalChemistryQuantumChemical physicsPhysicsQuantum mechanicsOrganic chemistryCatalysisMolecule

Abstract

fetched live from OpenAlex

This study investigates the mechanism of the [2+2] photocycloaddition reaction of 3-(but-3-enyl)oxyquinolone using a chiral xanthone-containing triplet sensitizer. Quantum chemical computer models were utilized to examine the substrate-catalyst encounter complex structures, which were classified into syn- and anti-adducts. The photoactivation steps of the substrate were analyzed based on the Marcus equation of electron transfer, including intersystem crossings (ISC) and outer sphere triplet-triplet energy transfer (TTEnT). Our results show that the calculated rates of ISC are comparable for the two adducts, while the computed rates of TTEnT differ between them due to the orbital overlap between the donor and acceptor sites. After the TTEnT, a stereospecific cyclization occurs, completing the catalytic cycle. We propose a novel strategy to improve stereoselectivity by exploiting the intrinsic difference in TTEnT rates between the two encounter complex isomers.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.252
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueChemRxivSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207