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Record W4392354784 · doi:10.1021/acscatal.4c00249

DalPhos on Demand: Facile Ligand Generation Enables New Ligand Discovery and Expedient Catalyst Screening

2024· article· en· W4392354784 on OpenAlexafffund
Joshua W. M. MacMillan, Ryan T. McGuire, Adam M. McMahon, Timothy S. Anderson, Katherine N. Robertson, Mark Stradiotto

Bibliographic record

VenueACS Catalysis · 2024
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsSaint Mary's UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLigand (biochemistry)Combinatorial chemistryReagentPhosphineCatalysisArylChemistryScope (computer science)Organic chemistryComputer scienceReceptor

Abstract

fetched live from OpenAlex

DalPhos/Ni-based catalysts have emerged as top performers in C–N and C–O cross-couplings. Expedient means of generating such ligands would facilitate the discovery of effective DalPhos ligand variants as well as accelerate reaction development processes for end users. A protocol for generating structurally varied phosphine- and phosphonite-type DalPhos ligands from a single ligand precursor upon treatment with commercial reagents and without the need for chromatographic purification is disclosed. The formation of DalPhos ligands via this divergent synthetic strategy was exploited in the expedited screening of representative Ni-catalyzed C–N and C–O cross-couplings, leading to the identification of the DalPhos ligand variants (i.e., BnPAd-DalPhos, L4, and OAdPAd-DalPhos, L9 ) that, in turn, were carried forward for reaction scope analysis in challenging cross-couplings of fluoroalkylamines, by use of prepared (DalPhos)Ni(aryl)Cl precatalyst complexes. The reported methodology offers a user-friendly means of generating DalPhos variants in reaction development.

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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0010.001
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 head0.022
GPT teacher head0.265
Teacher spread0.242 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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