Copper‐Mediated C−H Chalcogenation of Heterocycles: Application to the Synthesis of Chalcogenoxanthones

Abstract

A copper‐mediated C(sp2)‒H chalcogenation of heterocycles with readily available dichalcogenide, benzeneselenol and thiol is described. This protocol demonstrates scalability, good chemo‐ and regio‐selectivity, as well as broad functional group tolerance, which overall provides an accessible method to valuable aryl chalcogenides. The chalcogenated picolinamide products could be further transformed into selenoxanthones and thioxanthone scaffolds through intramolecular cyclization via a semi‐one pot process.

Copper‐Mediated Cross‐Dehydrogenative Coupling Reaction of Pyrrole and Indole Derivatives with Thiols

“…[12] More recently, our group reported a copper-mediated CÀ H chalcogenation of heterocycles with dichalcogenides and thiols as coupling partners assisted by a simple amide coordinating directing group, which provided easy access to biologically important selenoxanthone and thioxanthone derivatives. [13] Despite these indisputable advances, the reactions are mainly restricted to the use of bidentate directing groups, commercially unavailable dichalcogenide coupling partners. In contrast, readily available and easy-to-handle thiol substrate, which could be applied in the reaction of direct CÀ H thiolation, is still rare.…”

Pixel‐Level Grayscale Manipulation to Improve Accuracy in Digital Light Processing 3D Printing

“…[2] In some cases, the printing method itself is modified to improve accuracy. Techniques such as high-intensity flashing, [41] reservoir dipping, [42] multi-step exposure, [43] focal length adjustment, [44] and sub-pixel shifting [45] have all been implemented to improve print resolution. Cui et al [46] designed a feedbackbased method to improve print accuracy in a more automatic manner.…”

“…One of the most promising methods for improving print quality is to leverage the grayscale capability of each pixel to compensate for distortions. [38,45] You et al [52] implemented an effective machine learning method that used a neural network to determine the pixel intensities of the projection images to achieve optimal feature clarity. This machine learning method proved to be very effective, but it provided only a solution without physical insights so that it may not be conveniently extrapolated to other problems.…”

“…One of the most promising methods for improving print quality is to leverage the grayscale capability of each pixel to compensate for distortions. [ 38,45 ] You et al. [ 52 ] implemented an effective machine learning method that used a neural network to determine the pixel intensities of the projection images to achieve optimal feature clarity.…”

Rh(III)-Catalyzed Atroposelective C–H Selenylation of 1-Aryl Isoquinolines

“…Although traditional approaches have been well-developed to prepare organoselenides, it remains a great challenge to directly construct a C–Se bond through transition-metal-catalyzed direct C–H bond functionalization, probably because of the strong coordination between organoselenide and metal . Nevertheless, transition-metal-catalyzed C–H selenylations have been realized with the assistance of various directing groups over the past decade. − In 2014, Nishihara and co-workers pioneered Pd­(II)-catalyzed C–H selenylation of benzamides, benzylamines, and 2-arylpyridines with diselenides . In 2015, Li and co-workers reported the first example of Rh­(III)-catalyzed C–H selenylation of arenes bearing oxime, azo, pyridyl, or N -oxide chelating group with selenenyl chlorides or diselenides .…”