Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Self-Driving Laboratories for Chemistry and Materials Science.

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Active Site Descriptors from 95Mo NMR Signatures of Silica-Supported Mo-Based Olefin Metathesis Catalysts.

The olefin metathesis activity of silica-supported molybdenum oxides depends strongly on metal loading and preparation conditions, indicating that the nature and/or amounts of the active sites vary across compositionally similar catalysts. This is illustrated by comparing Mo-based (pre)catalysts prepared by impregnation (2.5-15.6 wt % Mo) and a model material (2.3 wt % Mo) synthesized via surface organometallic chemistry (SOMC). Analyses of FTIR, UV-vis, and Mo K-edge X-ray absorption spectra show that these (pre)catalysts are composed predominantly of similar isolated Mo dioxo sites. However, they exhibit different reaction properties in both liquid and gas-phase olefin metathesis with the SOMC-derived catalyst outperforming a classical catalyst of a similar Mo loading by ×1.5-2.0. Notably, solid-state 95Mo NMR analyses leveraging state-of-the-art high-field (28.2 T) measurement conditions resolve four distinct surface Mo dioxo sites with distributions that depend on the (pre)catalyst preparation methods. The intensity of a specific deshielded 95Mo NMR signal, which is most prominent in the SOMC-derived catalyst, is linked to reducibility and catalytic activity. First-principles calculations show that 95Mo NMR parameters directly manifest the local strain and coordination environment: acute (SiO-Mo(O)2-OSi) angles and low coordination numbers at Mo lead to highly deshielded 95Mo chemical shifts and small quadrupolar coupling constants, respectively. Natural chemical shift analyses relate the 95Mo NMR signature of strained species to low LUMO energies, which is consistent with their high reducibility and corresponding reactivity. The 95Mo chemical shifts of supported Mo dioxo sites are thus linked to their specific electronic structures, providing a powerful descriptor for their propensity toward reduction and formation of active sites.

Elucidation of radical- and oxygenate-driven paths in zeolite-catalysed conversion of methanol and methyl chloride to hydrocarbons.

Understanding hydrocarbon generation in the zeolite-catalysed conversions of methanol and methyl chloride requires advanced spectroscopic approaches to distinguish the complex mechanisms governing C-C bond formation, chain growth and the deposition of carbonaceous species. Here operando photoelectron photoion coincidence (PEPICO) spectroscopy enables the isomer-selective identification of pathways to hydrocarbons of up to C14 in size, providing direct experimental evidence of methyl radicals in both reactions and ketene in the methanol-to-hydrocarbons reaction. Both routes converge to C5 molecules that transform into aromatics. Operando PEPICO highlights distinctions in the prevalence of coke precursors, which is supported by electron paramagnetic resonance measurements, providing evidence of differences in the representative molecular structure, density and distribution of accumulated carbonaceous species. Radical-driven pathways in the methyl chloride-to-hydrocarbons reaction(s) accelerate the formation of extended aromatic systems, leading to fast deactivation. By contrast, the generation of alkylated species through oxygenate-driven pathways in the methanol-to-hydrocarbons reaction extends the catalyst lifetime. The findings demonstrate the potential of the presented methods to provide valuable mechanistic insights into complex reaction networks.

Cerebrospinal fluid fatty acids in glucocerebrosidase-associated Parkinson's disease.

BACKGROUND: Heterozygous mutations in the glucocerebrosidase gene lead to an increased risk for and to more severe alpha-synuclein-associated pathology in Parkinson's disease. As both glucocerebrosidase and alpha-synuclein interact with fatty acids, we hypothesized that cerebrospinal fluid fatty acid levels are altered in these Parkinson's disease patients. METHODS: Cerebrospinal fluid levels of 13 fatty acids in 8 Parkinson's disease patients with a heterozygous glucocerebrosidase mutation were compared with those of 41 idiopathic Parkinson's disease patients and 30 controls using gas chromatography. RESULTS: Parkinson's disease patients with a heterozygous glucocerebrosidase mutation had lower levels of palmitoleic (P ≤ .007), oleic (P ≤ .016), linoleic (P ≤ .005), arachidonic (P ≤ .003), eicosapentaenoic (P ≤ .003) and decosahexaenoic (P ≤ .03) acids and lower levels of total fatty acids (P < .005) compared with both idiopathic Parkinson's disease patients and control subjects. CONCLUSIONS: These results suggest that abnormalities of fatty acid metabolism are specifically involved in the pathogenesis of Parkinson's disease associated with a heterozygous glucocerebrosidase mutation.

Reduced but not oxidized cerebrospinal fluid glutathione levels are lowered in Lewy body diseases.

Reduced (GSH(R)) but not oxidized glutathione (GSSG) has been shown to be dramatically altered in the substantia nigra (SN) of Lewy body disease (LBD) patients post mortem; but up to now, there is no convincing evidence that these changes can be monitored in vivo. We investigated GSH(R) and GSSG in rapidly processed cerebrospinal fluid (CSF) and plasma samples of 80 LBD and 35 control subjects and detected reduced CSF GSH(R) levels in LBD subjects. The reduction was negatively associated with age but not with disease-associated parameters. Plasma GSH(R), CSF GSSG, and plasma GSSG levels did not significantly differ between the groups. Our findings confirm the results from neuropathologic studies, which demonstrated an alteration of the glutathione system in LBD. We hypothesize that alterations of the glutathione system occur in a very early stage of the disease or may even represent a risk marker for LBD.