Chemistry

Vividly colored cholesteric liquid crystal polymer network (CLCN) patterns based on epoxy resin are used in decorative and anti-counterfeiting applications. These films are typically prepared via cationic photopolymerization and post-polymerization to achieve a high cross-linking degree. In this work, the cross-linking degree is controlled by varying the UV irradiation dosage during photopolymerization. Following this, the reflection band of the CLCN film changes after removing non-cross-linked compounds with acetone. Leveraging the low cationic polymerization rate and the chain termination capability of methanol, a structurally colored CLCN film with regionally tailored cross-linking was fabricated. With the treatment of acetone, a colorful pattern was observed. Moreover, upon immersion in methanol, the film swelled, revealing a colorful pattern. After the evaporation of methanol, the pattern disappeared. Consequently, this CLCN film holds significant potential for information encryption applications.

The search for new antibacterial agents is an important task due to the emergence of resistance to widely used drugs. Bromine-, chlorine-, and nitro-substituted phenyl ring azomethines with long alkyl chains (C₁₂, C₁₄, C₁₆, and C₁₈) were synthesized and characterized using several experimental methods (NMR and IR spectroscopy, elemental analysis, mass spectrometry). Antibacterial and antifungal activity was tested on several cultures; the synthesized compounds show activity at the level of some commercial antiseptics. Lipophilicity (an important descriptor for predicting biological properties) of the experimentally synthesized and isomeric molecules was determined by three different approaches: quantum chemistry, machine learning (GraphormerLogP model), and an atom contribution model (RDKit library). The quantum-chemical method can account for any spatial arrangements and can be considered the most accurate of the approaches used, but it requires significant computational time. The atom contribution model is the fastest of the methods used, but it gives underestimated results, and different isomers have exactly the same values, in contrast to the quantum chemistry results. Machine learning-based methods (GraphormerLogP) demonstrate acceptable accuracy, sensitivity to isomerism, and orders-of-magnitude higher throughput, making them an optimal tool for high-throughput screening.

In this study, the corrosion behavior of pure aluminum in methyl esters with different degrees of unsaturation and chain lengths, as found in biodiesel, was investigated using electrochemical techniques. The methyl esters evaluated included methyl acrylate (C₄H₆O₂) and methyl linoleate (C₁₉H₃₄O₂), which were added to methyl propionate (C₄H₈O₂) and methyl oleate (C₁₉H₃₆O₂), respectively. The electrochemical techniques employed were electrochemical impedance spectroscopy (EIS) and electrochemical noise (EN), complemented by detailed scanning electron microscopy (SEM) analyses. The results indicated that both the corrosion rate and the susceptibility to localized corrosion, such as pitting, increased with higher degrees of unsaturation and longer alkyl chain lengths. The corrosion process remained under charge transfer control and was not directly influenced by these factors. However, the charge transfer resistance decreased with increasing unsaturation and chain length, consistent with the observed increase in corrosion rate.