Bilayers of mycolic acids (MAs) form the outer membrane of Mycobacterium tuberculosis that has high strength and extremely low permeability for external molecules (including antibiotics). For the first time, we were able to study them using the all-atom long-term molecular dynamic simulations (from 300 ns up to 1.2 μs) in order to investigate the conformational changes and most favorable structures of the mycobacterial membranes. The structure and properties of the membranes are crucially dependent on the initial packing of the α-mycolic acid (AMA) molecules, as well as on the presence of the secondary membrane components, keto- and methoxy mycolic acids (KMAs and MMAs). In the case of AMA-based membranes, the most labile conformation is W while other types of conformations (sU as well as sZ, eU, and eZ) are much more stable. In the multicomponent membranes, the presence of the KMA and MMA components (in the W conformation) additionally stabilizes both the W and eU conformations of AMA. The membrane in which AMA prevails in the eU conformation is much thicker and, at the same time, much denser. Such a packing of the MA molecules promotes the formation of a significantly stronger outer mycobacterial membrane that should be much more resistant to the threatening external factors.

The process is modelled of charge transport in materials based on organic semiconductors and bioorganic molecules: dicyanvinyl oligothiophenes, porphine and quinone derivatives, which are considered as the base for creating molecular artificial neural networks (ANNs). The energy and electronic characteristics of these molecules are estimated by quantum chemical methods, and their parameters in the OPLS-AA force field are determined. The charge dynamics was modeled using the kinetic Monte Carlo method in systems of 1200 molecules balanced by the molecular dynamics method with an estimate of the tunneling constants according to the Marcus theory. Based on a comparison of the charge mobility in the simulated systems, possible ways of modifying the studied molecules to create ANNs are discussed.

Organic semiconductors are the focus of numerous studies; they are used in electronic devices. Modern research involves the production of neuromorphic organic materials, including those based on liquid crystal materials. The purpose of this work involves the theoretical modeling of molecules (the “core with branches” type) to construct a discotic mesophase capable of performing the functions of a neuromorphic material. For this purpose, the conductivity of crystal porphine, which can act as the nucleus of a molecule of the “core with branches” type, was investigated. The Marcus theory charge mobility values for the hole and electron were 0.148 and 0.088 cm²/V·s, respectively (the MOO method for calculating transfer integrals), and 0.561 and 0.160 cm²/V·s (DIPRO method). Based on TD-HF (HF-3c level of theory) calculations, possible structures of molecules for the formation of a discotic mesophase are proposed.

Electrochemical processes are of particular interest in modern chemical technologies as they have numerous advantages over classical approaches. While computational support for investigating thermochemical reaction mechanisms is well established, there is still no consistent methodology for modeling electrochemical processes beyond the computational hydrogen electrode. This work addresses this gap through the study of the Shono‐type oxidation of N‐formylpyrrolidine. Combining density functional theory calculations, the concept of computational Fc⁺/Fc electrode, Marcus–Hush approach, and Butler–Volmer model, the reaction mechanism is elucidated, including the identification of the role and position of proton‐coupled electron transfer process. Additionally, simulated cyclic voltammograms are in excellent agreement with experimental studies performed in parallel.

How Zn enhances the performance of Cu electrocatalysts for RR in nonaqueous electrolytes remains an open question. Herein, we address this question by combining electrochemical measurements, kinetic modeling, and DFT calculations to elucidate Zn's role in boosting the electrocatalytic activity of Cu. Cyclic and linear sweep voltammetry reveal enhanced reduction activity with increasing Zn content, up to threefold higher current density at 25 wt.% Zn. Kinetic modeling attributes this to improved activation, while DFT confirms that Zn sites energetically stabilize Cu‐adsorbed . This work uncovers the mechanism behind CuZn alloys’ superior performance, guiding design of efficient RR catalysts for sustainable energy conversion.