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Record W3116851298 · doi:10.1021/acsomega.0c05499

Ruthenium-Catalyzed [2 + 2] versus Homo Diels–Alder [2 + 2 + 2] Cycloadditions of Norbornadiene and Disubstituted Alkynes: A DFT Study

2020· article· en· W3116851298 on OpenAlexafffund
Austin Pounder, Leanne D. Chen, William Tam

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldChemistry
TopicCatalytic Alkyne Reactions
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNorbornadieneCycloadditionCationic polymerizationChemistryDensity functional theoryRutheniumCatalysisReactivity (psychology)Medicinal chemistryComputational chemistryPhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The ruthenium-catalyzed [2 + 2] and homo Diels-Alder [2 + 2 + 2] cycloadditions of norbornadiene with disubstituted alkynes are investigated using density functional theory (DFT). These DFT calculations provide a mechanistic explanation for observed reactivity trends with different functional groups. Alkynyl phosphonates and norbornadiene form the [2 + 2 + 2] cycloadduct, while other functionalized alkynes afford the respective [2 + 2] cycloadduct, in excellent agreement with experimental results. The computational studies on the potential energy profiles of the cycloadditions show that the rate-determining step for the [2 + 2] cycloaddition is the final reductive elimination step, but the overall rate for the [2 + 2 + 2] cycloaddition is controlled by the initial oxidative cyclization. Two distinct mechanistic pathways for the [2 + 2 + 2] cycloaddition, cationic and neutral, are characterized and reveal that Cp*RuCl(COD) energetically prefers the cationic pathway.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.255
Teacher spread0.226 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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