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Record W7117504131 · doi:10.1021/acscatal.5c07745

Electronic and Steric Effects of the ProPhos Ligand Family for Nickel-Catalyzed Suzuki–Miyaura Coupling of Heteroaromatics

2025· article· en· W7117504131 on OpenAlexfundno aff
Jin Yang, Hengyuan Zhao, Johnathan E. Schultz, Steven R. Wisniewski, Eric M. Simmons, Tianning Diao

Bibliographic record

VenueACS Catalysis · 2025
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthYork UniversityYeshiva UniversityBristol-Myers SquibbBristol-Myers Squibb Foundation
KeywordsSteric effectsCatalysisLinkerDenticityArylPhosphine

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Nickel-catalyzed Suzuki–Miyaura coupling (Ni-SMC) reactions offer a cost-effective approach for active pharmaceutical ingredient (API) synthesis but remain constrained by their limited compatibility with heteroaromatic substrates and the need for high catalyst loadings. Our laboratory recently developed the ProPhos ligand, which significantly addresses these challenges. In this study, we systematically investigate the electronic and steric effects of the ProPhos ligand framework. Evaluation of over 20 ProPhos derivatives, alongside representative monodentate and bidentate phosphine ligands, reveals that the beneficial influence of the tethered hydroxyl group is general. Other nucleophilic substituents were less effective, and variations in the three-carbon linker length compromised the catalytic efficiency. Increased steric bulk on phosphorus decreased reactivity, whereas electron-donating substituents such as para -tolyl enhanced performance; in contrast, replacing aryl groups with cyclohexyl groups proved detrimental. Finally, the application of the optimized ligand, ProPhos*, to a range of previously challenging substrates demonstrated the robustness and efficiency of Ni-SMC at low catalyst loading.

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 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 score0.720

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.241
Teacher spread0.235 · 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.

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
Published2025
Admission routes1
Has abstractyes

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