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Catalytic amounts of 1,3-di(methyl)imidazole-2-ylidene, one of the simplest and most prototypical N-heterocyclic carbenes, can up-convert aldehydes into powerful stoichiometric sources of electrons (Super Electron Donors) for reductive transformations of iodoaryls ( E red < -2 V). In particular, the hydroarylation of 1,1'-diarylethylenes, which may require high temperatures and inherently generate stoichiometric amounts of oxidized waste, was performed at room temperature, with the concomitant formation of esters as oxidized co-products., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

We report the isolation and study of dimers stemming from popular thiazol-2-ylidene organocatalysts. The model featuring 2,6-di(isopropyl)phenyl (Dipp) N-substituents was found to be a stronger reducing agent ( E ox = -0.8 V vs SCE) than bis(thiazol-2-ylidenes) previously studied in the literature. In addition, a remarkable potential gap between the first and second oxidation of the dimer also allows for the isolation of the corresponding air-persistent radical cation. The latter is an unexpected efficient promoter of the radical transformation of α-bromoamides into oxindoles., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.