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Record W4317914929 · doi:10.26434/chemrxiv-2023-lrg06

Competitive Isomerization and Catalyst Decomposition During Ring-Closing Metathesis

2023· preprint· en· W4317914929 on OpenAlexafffund
Charles Killeen, Jie Liu, Harmen S. Zijlstra, Florian Maaß, James Piers, Reid B. Adams, Allen G. Oliver, J. Scott McIndoe

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsIsomerizationCatalysisChemistryYield (engineering)CarbeneMetathesisSalt metathesis reactionElectrospray ionizationPhotochemistryCombinatorial chemistryOrganic chemistryIonMaterials sciencePolymerization

Abstract

fetched live from OpenAlex

Ring-closing metathesis (RCM) is an elegant means of forming cyclic structural elements in both simple and complex molecules. Mechanistically, the reaction cycle is well understood, though subtle details concerning the fate of the catalyst and the appearance of yield-reducing by-products remain to be fully deciphered. We applied real-time analysis using electrospray ionization mass spectrometry (ESI-MS) to probe the RCM reaction, including studying the dynamics of all charged species in the reaction mixture and investigating the nature of the by-products formed. The catalyst of choice was Grubbs’ second-generation catalyst. The principal findings included the fact that for slower reactions, by-products appeared that differed in mass from the starting material and product by increments of CH2; that isomerization reactions were responsible for these by-products; and that the catalyst decomposes to form charged products including [ClPCy3]+, [HPCy3]+, and the imidazolinium salt of the N-heterocyclic carbene (NHC) ligand. In cases where RCM is slow, isomerization reactions play a disproportionate part in affecting yield of the desired product.

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 categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.027
GPT teacher head0.297
Teacher spread0.269 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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