Notes

Picky divergent revolutionary cyclization of merely one,6-dienes together with alkyl nitriles.
The reported results agree well with the existing literature for most cases and also provide new insights into gels with small elasticity.Nuclear electric field gradient (EFG) tensor parameters depend strongly on electronic structures, making their calculation from first principles an excellent metric for the prediction, refinement, and optimization of crystal structures. Here, we use plane-wave density functional theory (DFT) calculations of EFG tensors in organic solids to optimize the Grimme (D2) and Tkatchenko-Scheffler (TS) atomic-pairwise force field dispersion corrections. Refinements using these new force field correction methods result in better representations of true crystal structures, as gauged by calculations of 177 14N, 17O, and 35Cl EFG tensors from 95 materials. The most striking result is the degree by which calculations of 35Cl EFG tensors of chloride ions match with experiment, due to the ability of these new methods to properly locate the positions of hydrogen atoms participating in H···Cl- hydrogen bonds. These refined structures also feature atomic coordinates that are more similar to those of neutron diffraction structures than those obtained from calculations that do not employ the optimized force fields. Additionally, we assess the quality of these new energy-minimization protocols for the prediction of 15N magnetic shielding tensors and unit cell volumes, which complement the larger analysis using EFG tensors, since these quantities have different physical origins. It is hoped that these results will be useful in future nuclear magnetic resonance (NMR) crystallographic studies and will be of great interest to a wide variety of researchers, in fields including NMR spectroscopy, computational chemistry, crystallography, pharmaceutical sciences, and crystal engineering.Diradical character and excited-state aromaticity serve as guidelines to identify molecules that show nonlinear optical properties. Cumulenes are known to have small singlet-triplet gaps resulting in significant diradical character. Herein, we report a computational investigation on the electronic structure and excited-state properties of cumulenes of different lengths and with various terminal carbene groups. Intriguingly, cumulenes with an even number of cumulative double bonds, which barely have been studied experimentally, are predicted to be thermodynamically more stable than their odd counterparts. We propose that this is due to the stabilizing effect of electron delocalization in the helical Möbius-type frontier orbitals. Accordingly, we delineate how to control the energies of the excited states by the choice of carbene and length of the cumulene. We find that π-acceptor carbenes decrease the diradical character, whereas donors as well as captodative substitution or potentially a biscationic charge leads to an open-shell ground state. We also predict that bent allenes are better in stabilizing organic radicals than carbenes. Eventually, we identify suitable candidates for experimental endeavors toward new singlet fission molecules.Bubble nucleation is ubiquitous in gas evolving reactions that are instrumental for a variety of electrochemical systems. Fundamental understanding of the nucleation process, which is critical to system optimization, remains limited as prior works generally focused on the thermodynamics and have not considered the coupling between surface geometries and different forms of transport in the electrolytes. Here, we establish a comprehensive transport-based model framework to identify the underlying mechanism for bubble nucleation on gas evolving electrodes. We account for the complex effects on the electrical field, ion migration, ion diffusion, and gas diffusion arising from surface heterogeneities and gas pockets initiated from surface crevices. As a result, we show that neglecting these effects leads to significant underprediction of the energy needed for nucleation. Our model provides a non-monotonic relationship between the surface cavity size and the overpotential required for nucleation, which is physically more consistent than the monotonic relationship suggested by a traditional thermodynamics-based model. We also identify the significance of the gas diffuse layer thickness, a parameter controlled by external flow fields and overall electrode geometries, which has been largely overlooked in previous models. Our model framework offers guidelines for practical electrochemical systems whereby, without changing the surface chemistry, nucleation on electrodes can be tuned by engineering the cavity size and the gas diffuse layer thickness.Dialkyldiazirines have emerged as reagents of choice for biological photoaffinity labeling studies. The mechanism of crosslinking has dramatic consequences for biological applications where instantaneous labeling is desirable, as carbene insertions display different chemoselectivity and are much faster than competing mechanisms involving diazo or ylide intermediates. Here, deuterium labeling and diazo compound trapping experiments are employed to demonstrate that both carbene and diazo mechanisms operate in the reactions of a dialkyldiazirine motif that is commonly utilized for biological applications. For the fraction of intermolecular labeling that does involve a carbene mechanism, direct insertion is not necessarily involved, as products derived from a carbonyl ylide are also observed. see more We demonstrate that a strained cycloalkyne can intercept diazo compound intermediates and serve as a bioorthogonal probe for studying the contribution of the diazonium mechanism of photoaffinity labeling on a model protein under aqueous conditions.The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human ether-à-go-go-related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here, we perform a comprehensive comparison of hERG effect prediction models based on classical approaches (random forests and gradient boosting) and modern AI methods [deep neural networks (DNNs) and recurrent neural networks (RNNs)]. The training set (∼9000 compounds) was compiled by integrating the hERG bioactivity data from the ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay.
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