Formation of Tetrahydrofurano-, Aryltetralin, and Butyrolactone Norlignans through the Epoxidation of 9-Norlignans

Molybdenum, Vanadium, and Tungsten-Based Catalysts for Sustainable (ep)Oxidation

This article gives an overview of the research activity of the LAC2 team at LCC developed at Castres in the field of sustainable chemistry with an emphasis on the collaboration with a research team from the University of Zagreb, Faculty of Science, Croatia. The work is situated within the context of sustainable chemistry for the development of catalytic processes. Those processes imply molecular complexes containing oxido-molybdenum, -vanadium, -tungsten or simple polyoxometalates (POMs) as catalysts for organic solvent-free epoxidation. The studies considered first the influence of the nature of complexes (and related ligands) on the reactivity (assessing mechanisms through DFT calculations) with model substrates. From those model processes, the work has been enlarged to the valorization of biomass resources. A part concerns the activity on vanadium chemistry and the final part concerns the use of POMs as catalysts, from molecular to grafted catalysts, (ep)oxidizing substrates from fossil and biomass resources.

RanDepict: Random chemical structure depiction generator

The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.

Molybdenum-, Vanadium-, and Tungsten-Containing Materials for Catalytic Applications

As chemists, we are still fascinated by the magic of nature [...]