Chemistry methods : new approaches to solving problems in chemistry最新文献

Catalyzed reactions of organic substrates that operate continuously by extrusion techniques are rare. In this study, we developed a mechanochemical bromination of unactivated naphthalene (1) with 1,3-dibromo-5,5-dimethylhydantoin (DBDMH), catalyzed by zeolites in a ball mill. The use of DBDMH enabled the atom economy of the reaction to be superior compared to the other brominating agents evaluated. Among the zeolites tested, a FAU-type zeolite demonstrated high catalytic activity and recyclability. The success of the bromination route on a small scale enabled the development of a continuous catalyzed bromination of 1 by twin-screw extrusion.

Pulsed Field Gradient (PFG) NMR is recognised as an analytical technique used to characterise the tortuosity of porous media by measurement of the self-diffusion coefficient of a fluid contained within the pore space of the material of interest. Such measurements are usually performed on high magnetic field NMR hardware (>300 MHz). However, many materials of interest, in particular heterogeneous catalysts, contain significant amounts of paramagnetic species, which make such measurements impossible due to their characteristic short spin-spin relaxation times. Here it is demonstrated that by performing PFG NMR measurements on a low field magnet (2 MHz), tortuosity measurements can be obtained for a range of titania (TiO₂) based carriers and catalyst precursors containing paramagnetic species up to a 20 wt.% loading. The approach is also used to compare the tortuosity of two catalyst precursors of the same metal loading prepared by different methods.

The Front Cover shows the ChemPlot-visualized reduced chemical space of molecules enhanced with two-dimensional illustrations of molecules. In addition to being easy-to-use, free and open source, a noteworthy feature of ChemPlot is the application of tailored similarity for the property-sensitive visualization of chemical spaces. ChemPlot streamlines the analysis of molecular datasets by reducing the information to human perception level, tackling the activity/property cliff problem, and facilitating the assessment of the applicability domain of machine learning models in molecular studies. More information can be found in the Research Article by Murat C. Sorkun et al.

Invited for this month's cover is the Autonomous Energy Materials Discovery [AMD] Group of Dr. Süleyman Er at DIFFER, and colleagues at CCER and Eindhoven University of Technology (Netherlands). The cover picture shows the ChemPlot-visualized reduced chemical space of molecules enhanced with two-dimensional illustrations of molecules. In addition to being easy-to-use, free and open source, a noteworthy feature of ChemPlot is the application of tailored similarity for the property-sensitive visualization of chemical spaces. ChemPlot streamlines the analysis of molecular datasets by reducing the information to human perception level, tackling the activity/property cliff problem, and facilitating the assessment of the applicability domain of machine learning models in molecular studies. Read the full text of their Research Article at 10.1002/cmtd.202200005.

The use of grazing-incidence scattering methods for the characterization of 2D patterned organic thin films is limited due to the elongated 1D footprint of the X-ray beam on the sample. However, this characteristic feature can be turned into an advantage, when combined with tomographic reconstruction. In this pilot study we show, how the use of a chosen texture reflection and a diffuse reflectivity signal can each provide 2D images of the deposits, simultaneously revealing the organic film's crystal orientation and the location of the metal electrodes in a field-effect transistor structure from a single sequence of diffraction images.

A specially designed high-throughput experimentation facility, used for the highly effective exploration of electrolyte formulations in composition space for diverse battery chemistries and targeted applications, is presented. It follows a high-throughput formulation-characterization-optimization chain based on a set of previously established electrolyte-related requirements. Here, the facility is used to acquire large dataset of ionic conductivities of non-aqueous battery electrolytes in the conducting salt-solvent/co-solvent-additive composition space. The measured temperature dependence is mapped on three generalized Arrhenius parameters, including deviations from simple activated dynamics. This reduced dataset is thereafter analyzed by a scalable data-driven workflow, based on linear and Gaussian process regression, providing detailed information about the compositional dependence of the conductivity. Complete insensitivity to the addition of electrolyte additives for otherwise constant molar composition is observed. Quantitative dependencies, for example, on the temperature-dependent conducting salt content for optimum conductivity are provided and discussed in light of physical properties such as viscosity and ion association effects.

Visualizing chemical spaces streamlines the analysis of molecular datasets by reducing the information to human perception level, hence it forms an integral piece of molecular engineering, including chemical library design, high-throughput screening, diversity analysis, and outlier detection. We present here ChemPlot, which enables users to visualize the chemical space of molecular datasets in both static and interactive ways. ChemPlot features structural and tailored similarity methods, together with three different dimensionality reduction methods: PCA, t-SNE, and UMAP. ChemPlot is the first visualization software that tackles the activity/property cliff problem by incorporating tailored similarity. With tailored similarity, the chemical space is constructed in a supervised manner considering target properties. Additionally, we propose a metric, the Distance Property Relationship score, to quantify the property difference of similar (i. e. close) molecules in the visualized chemical space. ChemPlot can be installed via Conda or PyPI (pip) and a web application is freely accessible at https://www.amdlab.nl/chemplot/.

The documentation and storage of experimental data is crucial in research data management and science in general. With regard to automated data curation and the generation of data for machine learning processes, the collection and sharing of machine-readable data, including negative results, is a key step. The electronic laboratory notebook (ELN) Chemotion provides the possibility to share synthesis data with other scientists taking the mentioned aspects into account. In these guidelines, we offer general information on how to share data in Chemotion and present our sharing policy as a best practice example on how to use Chemotion's sharing functions in a working group with several group members on various hierarchy levels.

The calculation of hydrogen atom transfer (HAT) barriers within the cytochrome P450 catalytic cycle is of central importance for the prediction of metabolites formed from medicinally relevant compounds. We report the accurate estimation of hydrogen atom transfer barriers using inexpensive descriptors computed for a panel of 21 compounds. By a simple univariate linear regression between barriers previously computed using density functional theory (DFT) and newly computed “frozen radical” bond dissociation energies using the GFN2-xTB method, a mean absolute error of 1 kcal mol⁻¹ is achieved. Other affordable levels of theory are studied to assess differences in performance and computational cost. Multiple linear regression incorporating additional descriptors using GFN2-xTB is shown to predict HAT barriers with mean absolute errors of 0.82 kcal mol⁻¹. With computing times in milliseconds on modest computing hardware, this systematic approach is accessible and extensible to large scale screening workflows.