Ruthenium-Catalyzed Direct Reductive Amination of Carbonyl Compounds for the Synthesis of Amines: An Overview

Reductive amination is a valuable method for amine synthesis that has been the topic of a century‘s worth of in-depth study in both academia and industry. Amines and their derivatives serve as incredibly adaptable building blocks for a broad array of organic substrates and are significant precursors for a myriad of advanced chemicals, physiologically active compounds, agrochemicals, biomolecules, pharmaceuticals, and polymers. The creation of innovative catalytic processes for the long-term and selective synthesis of amines from readily accessible and environmentally benign reagents remains a top priority in chemical research. Both heterogeneous and homogeneous catalysts have been designed with success to enable these reactions to explore new amines. Ruthenium catalysts are employed in reductive amination owing to their stability, selectivity, versatility, low toxicity, and high efficiency. This review comprehensively overviews the Ru-catalyzed reductive amination processes and includes the literature from 2009 to 2022.     

The Mechanical and Biological Performance of Photopolymerized Gelatin-Based Hydrogels as a Function of the Reaction Media

From the first experiments with biomaterials to mimic tissue properties, the mechanical and biochemical characterization has evolved extensively. Several properties can be described, however, what should be essential is to conduct a proper and physiologically relevant characterization. Herein, the influence of the reaction media (RM) and swelling media (SM)–phosphate buffered saline (PBS) and Dulbecco's modified Eagle's medium (DMEM) with two different glucose concentrations–is described in gelatin methacrylamide (GelMA) hydrogel mechanics and in the biological behavior of two tumoral cell lines (Caco-2 and HCT-116). All scaffolds are UV-photocrosslinked under identical conditions and evaluated for mass swelling ratio and stiffness. The results indicate that stiffness is highly susceptible to the RM, but not to the SM. Additionally, PBS-prepared hydrogels exhibited a higher photopolymerization degree according to high resolution magic-angle spinning (HR-MAS) NMR. These findings correlate with the biological response of Caco-2 and HCT-116 cells seeded on the substrates, which demonstrated flatter morphologies on stiffer hydrogels. Overall, cell viability and proliferation are excellent for both cell lines, and Caco-2 cells displayed a characteristic apical-basal polarization based on F-actin/Nuclei fluorescence images. These characterization experiments highlight the importance of conducting mechanical testing of biomaterials in the same medium as cell culture.     

ChemInform Abstract: Explaining Stability of Transition Metal Carbides — and Why TcC Does Not Exist.

The formation of transition metal (M) carbides M_xC_y and trends of their stability are systematically investigated using the USPEX code within the DFT.     

On the Statistical Calibration of Physical Models

We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data.