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Record W4207002566 · doi:10.1021/acscatal.1c04983

Polycationic Rh–JosiPhos Polymers Supported on Phosphotungstic Acid/Al<sub>2</sub>O<sub>3</sub> by Multiple Electrostatic Attractions

2022· article· en· W4207002566 on OpenAlexafffund
Prabin Nepal, Suneth Kalapugama, Michael Shevlin, John R. Naber, Louis‐Charles Campeau, Cristofer Pezzetta, Armando Carlone, Christopher J. Cobley, Steven H. Bergens

Bibliographic record

VenueACS Catalysis · 2022
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of Alberta
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsROMPPhosphotungstic acidCationic polymerizationNorborneneMonomerChemistryPolymer chemistryPolymerMetathesisCycloocteneCatalysisRing-opening metathesis polymerisationCopolymerOlefin fiberOrganic chemistryPolymerization

Abstract

fetched live from OpenAlex

Cationic Rh–JosiPhos complexes were tethered to crowded, ring-opening olefin metathesis (ROMP)-active norbornene groups. These monomers underwent facile, alternating ROMP (alt-ROMP) with cyclooctene as the co-monomer catalyzed by RuCl 2 (═CHPh)(PCy 3 ) 2 to form linear, polycationic copolymers. The polymers were readily absorbed by phosphotungstic acid (PTA) on Al 2 O 3 via multiple electrostatic attractions between the cationic Rh centers in the polymer and the PTA anions on the Al 2 O 3 support. The enantioselective hydrogenation of dimethyl itaconate occurred with 100% conversion, 5000 turnovers per run, with up to 96% ee, and without significant Rh leaching over 10 reuses of the catalyst.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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Same venueACS CatalysisSame topicAsymmetric Hydrogenation and CatalysisFrench-language works237,207