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Herein, utilizing acetonitrile as the probe molecule, the acidity and host-guest interactions of H-mordenite (H-MOR) zeolites are investigated comprehensively by solid-state NMR spectroscopy and theoretical calculation. The locations and local configurations of Brønsted acid sites (BASs) in H-MOR are revealed by multinuclear and multidimensional NMR experiments with adsorption/coadsorption of acetonitrile (CD3CN) and trimethylphosphine (TMP). Moreover, the confinement effect of dual pores in MOR has been characterized via the quantitative determination of host-guest interactions between CH3CN and BASs. The 1H-15N dipolar measurement results and DFT calculations demonstrate that there are two kinds of acetonitrile molecules adsorbed in 12-membered ring (12MR) main channels with distinct mobility, where acetonitrile undergoes either partially restricted or highly flexible motion in the time scale of nanoseconds to microseconds. These two types of acetonitrile can exchange with temperature rising. In contrast, the mobility of acetonitrile in 8-membered ring (8MR) channels is very restricted due to the confinement of the framework.Ion migration in perovskite layers can significantly reduce the long-term stability of the devices. While perovskite composition engineering has proven an interesting tool to mitigate ion migration, many optoelectronic devices require a specific bandgap and thus require a specific perovskite composition. Here, we look at the effect of grain size to mitigate ion migration. We find that in MAPbBr3 solar cells prepared with grain sizes varying from 2 to 11 μm the activation energy for bromide ion migration increases from 0.17 to 0.28 eV. Moreover, we observe the appearance of a second bromide ion migration pathway for the devices with largest grain size, which we attribute to ion migration mediated by the bulk of the perovskite, as opposed to ion migration mediated by the grain boundaries. Together, these results suggest the beneficial nature of grain engineering for reduction of ion migration in perovskite solar cells.Nanoparticle motion and self-assembly have been regarded as a promising pathway for forming ordered nanostructures. However, the detailed dynamics processes induced by ligand involvement remained poorly understood. Here, we used in situ liquid-cell electron microscopy technology to image the formation of face-to-face Pt cube ordered structures pairs, linear chains, and squares. The van der Waals interaction between the two neighboring cubes was quantified in real time. Interestingly, the two different formation processes of the square phase were achieved via a rotational and translational method. It is found that the space between two neighboring cubes was the same as the ex-TEM results. The density functional theory calculation demonstrated that it was attributed to the DMF ligand interactions of the cubes that promoted their face-to-face attachment.Morphology transition of block copolymer assemblies in response to external stimuli has attracted considerable attention. However, our knowledge about the mechanism of such a transition is still limited, especially for rod-coil block copolymers. Here, we report a programmable morphology evolution of assemblies induced by variation of chain ordering for rod-coil-rod triblock copolymers. A sequence of morphology transition from ellipsoids to disks, bowls, and vesicles is observed by increasing the solution temperature. At high temperatures, the mobility of the rod chain increases and the rigidity of the rod chain decreases. selleck chemicals This gives rise to an ellipsoid-to-vesicle morphology transition. Dissipative particle dynamics theoretical simulations were performed to reveal the mechanism of this morphology transition process. It was found that the increase of rod chain mobility and the decrease of rod chain rigidity induce a decrease of chain ordering of rod blocks as temperature increases, which results in an ellipsoid-to-vesicle morphology transition. The gained information can guide the construction of nanoassemblies based on the rod-coil block copolymers.Drug design with patient centricity for ease of administration and pill burden requires robust understanding of the impact of chemical modifications on relevant physicochemical properties early in lead optimization. To this end, we have developed a physics-based ensemble approach to predict aqueous thermodynamic crystalline solubility, with a 2D chemical structure as the input. Predictions for the bromodomain and extraterminal domain (BET) inhibitor series show very close match (0.5 log unit) with measured thermodynamic solubility for cases with low crystal anisotropy and good match (1 log unit) for high anisotropy structures. The importance of thermodynamic solubility is clearly demonstrated by up to a 4 log unit drop in solubility compared to kinetic (amorphous) solubility in some cases and implications thereof, for instance on human dose. We have also demonstrated that incorporating predicted crystal structures in thermodynamic solubility prediction is necessary to differentiate (up to 4 log unit) between solubility of molecules within the series. Finally, our physics-based ensemble approach provides valuable structural insights into the origins of 3-D conformational landscapes, crystal polymorphism, and anisotropy that can be leveraged for both drug design and development.The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.
Website: https://www.selleckchem.com/
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