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Record W2228972629 · doi:10.1021/jacs.5b12277

Probing Stereoselectivity in Ring-Opening Metathesis Polymerization Mediated by Cyclometalated Ruthenium-Based Catalysts: A Combined Experimental and Computational Study

2016· article· en· W2228972629 on OpenAlexfundno aff
Lauren E. Rosebrugh, Tonia S. Ahmed, Vanessa M. Marx, J. B. Hartung, Peng Liu, Jaybree Lopez, K. N. Houk, Robert H. Grubbs

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsnot available
FundersDivision of ChemistryNational Institute of General Medical SciencesOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaNational Science FoundationGovernment of CanadaU.S. Department of Defense
KeywordsROMPStereocenterChemistryMetathesisCarbeneRutheniumPolymerizationIsomerizationCatalysisStereoselectivityRing-opening metathesis polymerisationTacticityPolymer chemistryPhotochemistryPolymerOrganic chemistryEnantioselective synthesis

Abstract

fetched live from OpenAlex

The microstructures of polymers produced by ring-opening metathesis polymerization (ROMP) with cyclometalated Ru-carbene metathesis catalysts were investigated. A strong bias for a cis,syndiotactic microstructure with minimal head-to-tail bias was observed. In instances where trans errors were introduced, it was determined that these regions were also syndiotactic. Furthermore, hypothetical reaction intermediates and transition structures were analyzed computationally. Combined experimental and computational data support a reaction mechanism in which cis,syndio-selectivity is a result of stereogenic metal control, while microstructural errors are predominantly due to alkylidene isomerization via rotation about the Ru═C double bond.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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

Citations47
Published2016
Admission routes1
Has abstractyes

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