Abstract:Evidence of the involvement of a “cocktail”-type catalytic system in the alkyne and alkene hydrosilylation reaction in the presence of platinum on a carbon support is reported.
“…Even though Pt colloids were not visibly detected in hydrosilylation mixtures when using Pt−NHC complexes as pre‐catalysts, [10a,g,i] the observed slow formation of Pt colloids in our case, when reacting 3 ae and 4 a stoichiometrically, indicates the possible “cocktail” nature of the studied catalytic system. This is similar to the recently described system by Ananikov and co‐workers, [12c] involving multiple platinum active species (both nanoclusters/nanoparticles and molecular complexes). Unfortunately, in the case of our neat protocol utilizing [Pt(IPr*)(DMS)Cl 2 ], the contribution of platinum colloidal species to the overall reaction mechanism has proven challenging to assess.…”
Herein, we report the catalytic activity of a series of platinum(II) pre‐catalysts, bearing N‐heterocyclic carbene (NHC) ligands, in the alkene hydrosilylation reaction. Their structural and electronic properties are fully investigated using X‐ray diffraction analysis and nuclear magnetic resonance spectroscopy (NMR). Next, our study presents a structure‐activity relationship within this group of pre‐catalysts and gives mechanistic insights into the catalyst activation step. An exceptional catalytic performance of one of the complexes is observed, reaching a turnover number (TON) of 970 000 and a turnover frequency (TOF) of 40 417 h−1 at 1 ppm catalyst loading. Finally, an attractive solvent‐free and open‐to‐air alkene hydrosilylation protocol, featuring efficient platinum removal (reduction of residual Pt from 582 ppm to 5.8 ppm), is disclosed.
“…Even though Pt colloids were not visibly detected in hydrosilylation mixtures when using Pt−NHC complexes as pre‐catalysts, [10a,g,i] the observed slow formation of Pt colloids in our case, when reacting 3 ae and 4 a stoichiometrically, indicates the possible “cocktail” nature of the studied catalytic system. This is similar to the recently described system by Ananikov and co‐workers, [12c] involving multiple platinum active species (both nanoclusters/nanoparticles and molecular complexes). Unfortunately, in the case of our neat protocol utilizing [Pt(IPr*)(DMS)Cl 2 ], the contribution of platinum colloidal species to the overall reaction mechanism has proven challenging to assess.…”
Herein, we report the catalytic activity of a series of platinum(II) pre‐catalysts, bearing N‐heterocyclic carbene (NHC) ligands, in the alkene hydrosilylation reaction. Their structural and electronic properties are fully investigated using X‐ray diffraction analysis and nuclear magnetic resonance spectroscopy (NMR). Next, our study presents a structure‐activity relationship within this group of pre‐catalysts and gives mechanistic insights into the catalyst activation step. An exceptional catalytic performance of one of the complexes is observed, reaching a turnover number (TON) of 970 000 and a turnover frequency (TOF) of 40 417 h−1 at 1 ppm catalyst loading. Finally, an attractive solvent‐free and open‐to‐air alkene hydrosilylation protocol, featuring efficient platinum removal (reduction of residual Pt from 582 ppm to 5.8 ppm), is disclosed.
“…We suspect the proposal to mix these compounds arises as an artifact of making a combinatorial array (phactor) out of the popular Pd complex and ligand choices from the literature (ChatGPT). Nonetheless, the observation that this “cocktail” of ligands was the most productive result could be supported by related reports of “cocktail” catalysis in the Buchwald–Hartwig coupling. − …”
High-throughput experimentation is
a common practice
in the optimization
of chemical synthesis. Chemists design reaction arrays to optimize
the yield of couplings between building blocks. Popular reactions
used in pharmaceutical research include the amide coupling, Suzuki
coupling, and Buchwald–Hartwig coupling. We show how the artificial
intelligence (AI) language model ChatGPT can automatically formulate
reaction arrays for these common reactions based on the literature
corpus it was trained on. Critically, we showcase how ChatGPT results
can be directly translated into inputs for the management software
phactor, which enables automated execution and analysis of assays.
This workflow is experimentally demonstrated, with modest to excellent
yields of products obtained in each instance on the first attempt.
“…[58][59][60][61][62][63][64][65] First established for Pd complexes in CÀ C- [66] and CÀ N-coupling reactions, [67] the phenomenon was found to take place in a Pt-catalyzed hydrosilylation reaction. [68][69][70][71][72] Fast automatic prediction of 195 Pt shifts with an empirical (ML) scheme could greatly simplify the reaction monitoring in the case of dynamic catalysis. Third, it is important to note that the number of water-soluble Pt complexes is somewhat limited despite their importance in medicinal chemistry and catalysis.…”
Section: Introductionmentioning
confidence: 99%
“…Qualitative changes in the nature of catalytic centers in the course of the reaction (dynamic catalysis) and the formation of a “cocktail” of catalytic centers often occur [58–65] . First established for Pd complexes in C−C‐ [66] and C−N‐coupling reactions, [67] the phenomenon was found to take place in a Pt‐catalyzed hydrosilylation reaction [68–72] . Fast automatic prediction of 195 Pt shifts with an empirical (ML) scheme could greatly simplify the reaction monitoring in the case of dynamic catalysis.…”
Water-soluble Pt complexes are the key components in medicinal chemistry and catalysis. The well-known cisplatin family of anticancer drugs and industrial hydrosylilation catalysts are two leading examples. On the molecular level, the activity mechanisms of such complexes mostly involve changes in the Pt coordination sphere. Using 195 Pt NMR spectroscopy for operando monitoring would be a valuable tool for uncovering the activity mechanisms; however, reliable approaches for the rapid correlation of Pt complex structure with 195 Pt chemical shifts are very challenging and not available for everyday research practice. While NMR shielding is a response property, molecular 3D structure determines NMR spectra, as widely known, which allows us to build up 3D structure to 195 Pt chemical shift correlations. Accordingly, we present a new workflow for the determination of lowest-energy configurational/conformational isomers based on the GFN2-xTB semiempirical method and prediction of corresponding chemical shifts with a Machine Learning (ML) model tuned for Pt complexes. The workflow was designed for the prediction of 195 Pt chemical shifts of water-soluble Pt(II) and Pt(IV) anionic, neutral, and cationic complexes with halide, NO 2 À , (di)amino, and (di)carboxylate ligands with chemical shift values ranging from À 6293 to 7090 ppm. The model offered an accuracy (normalized root-mean-square deviation/RMSD) of 1.08 %/ 145.02 ppm on the held-out test set.
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