The reactivity theories and characterization studies for metal-containing zeolites are often focused on probing the metal sites. We present a detailed computational study of the reactivity of Zn-modified BEA zeolite towards C-H bond activation of the methane molecule as a model system that highlights the importance of representing the active site as the whole reactive ensemble integrating the extra-framework ZnEF2+ cations, framework oxygens (OF2−), and the confined space of the zeolite pores. We demonstrate that for our model system the relationship between the Lewis acidity, defined by the probe molecule adsorption energy, and the activation energy for methane C-H bond cleavage performs with a determination coefficient R2 = 0.55. This suggests that the acid properties of the localized extra-framework cations can be used only for a rough assessment of the reactivity of the cations in the metal-containing zeolites. In turn, studying the relationship between the activation energy and pyrrole adsorption energy revealed a correlation, with R2 = 0.80. This observation was accounted for by the similarity between the local geometries of the pyrrole adsorption complexes and the transition states for methane C-H bond cleavage. The inclusion of a simple descriptor for zeolite local confinement allows transferability of the obtained property-activity relations to other zeolite topologies. Our results demonstrate that the representation of the metal cationic species as a synergistically cooperating active site ensembles allows reliable detection of the relationship between the acid properties and reactivity of the metal cation in zeolite materials.@en

Computational chemistry pipelines typically commence with geometry generation, well-established for organic compounds but presenting a considerable challenge for transition metal complexes. This paper introduces MACE, an automated computational workflow for converting chemist SMILES/MOL representations of the ligands and the metal center to 3D coordinates for all feasible stereochemical configurations for mononuclear octahedral and square planar complexes directly suitable for quantum chemical computations and implementation in high-throughput computational chemistry workflows. The workflow is validated through a structural screening of a data set of transition metal complexes extracted from the Cambridge Structural Database. To further illustrate the power and capabilities of MACE, we present the results of a model DFT study on the hemilability of pincer ligands in Ru, Fe, and Mn complexes, which highlights the utility of the workflow for both focused mechanistic studies and larger-scale high-throughput pipelines.@en

Deep eutectic solvents (DESs) represent an environmentally friendly alternative to conventional organic solvents. Their liquid range determines the areas of application, and therefore, the prediction of solid-liquid equilibrium (SLE) diagrams is essential for developing new DESs. Such predictions are not yet possible by using the current state-of-the-art computational models. Herein, we present an alternative model based on support vector regression integrating experimental data, a conductor-like screening model for real solvents simulations, and cheminformatic descriptors for predicting melting temperatures of binary metal-free DESs or ionic liquids, allowing the researcher to estimate the eutectic formation and SLE for specific combinations of components. The model was developed based on the manually collected database of 1648 mixture melting temperatures for 237 experimentally described DESs, and its accuracy was demonstrated by 5-fold cross-validation (R2 ∼ 0.8). The presented machine learning methodology empowers researchers to predefine the liquid range of the mixture and holds promise for efficient molecular combination screening, facilitating the discovery of tailored DESs for desired applications from catalysis and extraction to energy storage. By enabling a deeper understanding of DES behavior and the targeted design of these solvents, the proposed approach contributes to advancing green chemistry practices and to promoting sustainable solvent usage.@en

Any catalyst should be efficient and stable to be implemented in practice. This requirement is particularly valid for manganese hydrogenation catalysts. While representing a more sustainable alternative to conventional noble metal-based systems, manganese hydrogenation catalysts are prone to degrade under catalytic conditions once operation temperatures are high. Herein, we report a highly efficient Mn(I)-CNP pre-catalyst which gives rise to the excellent productivity (TOF° up to 41 000 h−1) and stability (TON up to 200 000) in hydrogenation catalysis. This system enables near-quantitative hydrogenation of ketones, imines, aldehydes and formate esters at the catalyst loadings as low as 5–200 p.p.m. Our analysis points to the crucial role of the catalyst activation step for the catalytic performance and stability of the system. While conventional activation employing alkoxide bases can ultimately provide catalytically competent species under hydrogen atmosphere, activation of Mn(I) pre-catalyst with hydride donor promoters, e.g. KHBEt3, dramatically improves catalytic performance of the system and eliminates induction times associated with slow catalyst activation.@en

Homogeneously catalyzed reactions often make use of additives and promotors that affect reactivity patterns and improve catalytic performance. While the role of reaction promotors is often discussed in view of their chemical reactivity, we demonstrate that they can be involved in catalysis indirectly. In particular, we demonstrate that promotors can adjust the thermodynamics of key transformations in homogeneous hydrogenation catalysis and enable reactions that would be unfavorable otherwise. We identified this phenomenon in a set of well-established and new Mn pincer catalysts that suffer from persistent product inhibition in ester hydrogenation. Although alkoxide base additives do not directly participate in inhibitory transformations, they can affect the equilibrium constants of these processes. Experimentally, we confirm that by varying the base promotor concentration one can control catalyst speciation and inflict substantial changes to the standard free energies of the key steps in the catalytic cycle. Despite the fact that the latter are universally assumed to be constant, we demonstrate that reaction thermodynamics and catalyst state are subject to external control. These results suggest that reaction promotors can be viewed as an integral component of the reaction medium, on its own capable of improving the catalytic performance and reshaping the seemingly rigid thermodynamic landscape of the catalytic transformation. @en

Magnetite (Fe3O4) nanoparticles have found numerous applications due to the ease of fabrication, favourable combination of physical and chemical properties, as well as environmental and biological safety. At the same time, their functional applications in memristive devices remain underexplored, especially with regard to nanocrystalline phases that can be obtained by various means of solution chemistry. In this study, we examine the physical properties, morphology, and biocompatibility of magnetite nanoparticles obtained by hydrolytic and non-hydrolytic synthesis routes. For this purpose, we have revisited two solution chemistry methods for obtaining magnetite nanoparticles in water and lower alcohols. Notably, magnetite nanoparticles obtainedviathe hydrolytic route have demonstrated an appreciably high value of the resistive switching ratioROFF/RON∼ 103which is comparable to the highest values ever reported for iron oxide memristors. The advantageous suitability of the hydrolytically synthesized magnetite nanoparticles for resistive switching applications is rationalized by their higher purity and crystallinity, as well as by the plausible activity of residual water molecules and hydroxy groups on facilitating the topotactic redox reaction associated with the resistive switching phenomenon.@en

The influence of the model and method choice on the DFT predicted 13C NMR chemical shifts of zeolite surface methoxide species has been systematically analyzed. Twelve 13C NMR chemical shift calculation protocols on full periodic and hybrid periodic-cluster DFT calculations with varied structural relaxation procedures are examined. The primary assessment of the accuracy of the computational protocols has been carried out for the Si-O(CH3)-Al surface methoxide species in ZSM-5 zeolite with well-defined experimental NMR parameters (chemical shift, δ(13C) value) as a reference. Different configurations of these surface intermediates and their location inside the ZSM-5 pores are considered explicitly. The predicted δ value deviates by up to ±0.8 ppm from the experimental value of 59 ppm due to the varied confinement of the methoxide species at different zeolite sites (model accuracy). The choice of the exchange-correlation functional (method accuracy) introduces ±1.5 ppm uncertainty in the computed chemical shifts. The accuracy of the predicted 13C NMR chemical shifts for the computational assignment of spectral characteristics of zeolite intermediates has been further analyzed by considering the potential intermediate species formed upon methane activation by Cu/ZSM-5 zeolite. The presence of Cu species in the vicinity of surface methoxide increases the prediction uncertainty to ±2.5 ppm. The full geometry relaxation of the local environment of an active site at an appropriate level of theory is critical to ensure a good agreement between the experimental and computed NMR data. Chemical shifts (δ) calculated via full geometry relaxation of a cluster model of a relevant portion of the zeolite lattice site are in the best agreement with the experimental values. Our analysis indicates that the full geometry optimization of a cluster model at the PBE0-D3/6-311G(d,p) level of theory followed by GIAO/PBE0-D3/aug-cc-pVDZ calculations is the most suitable approach for the calculation of 13C chemical shifts of zeolite surface intermediates.@en