Teerthanker Mahaveer University

ABSTRACT Schiff bases are the chemically diverse group of organic compounds containing carbon–nitrogen double bonds (─C═N─), derived from the condensation of aldehydes or ketones with primary amines in solvents such as methanol, ethanol, etc. These compounds emerged as promising candidates due to their diverse chemical structures and biological activities. Antimicrobial drug resistance is a major challenge to deal. In a recent WHO report, the annual death rate directly caused by antimicrobial resistance is predicted to rise to 10 million by 2050. The mechanisms underlying the antimicrobial activity of Schiff bases involve disruption of bacterial cell membranes, inhibition of essential enzymes, and interference with bacterial nucleic acid synthesis. These compounds demonstrate efficacy against a wide range of Gram‐positive and Gram‐negative bacteria, including drug‐resistant strains such as methicillin‐resistant Staphylococcus aureus (MRSA). Combination strategies enhanced antibacterial effects, reduced the development of resistance, and improved therapeutic outcomes. Heterocyclic rings are proven to be the crucial structural motifs to interact with the disease targets as they contain heteroatoms and found to have diverse therapeutic activities. Heterocyclic rings containing Schiff bases are shown to be effective in various strains with good MIC values even in resistant strains. This paper focuses on five‐membered thiazole and pyrazole ring containing Schiff bases and their antibacterial and antifungal activities in various strains and the binding properties of some of the compounds with DNA gyrase as an important target for antimicrobial action.

ABSTRACT Machine learning (ML) is becoming a valuable tool for materials science, driving both basic and applied research. It promises to be especially helpful to model complicated surface dynamics and material property prediction—essential for semiconductor manufacturing, thin‐film deposition, and nanotechnology. Although existing ML applications in materials science tend to be straightforward fitting procedures or small‐scale data, this research investigates its ability in 1+1 and 2+1 dimensional growth models and displays precise predictions for material stability and crystal structures. The paper includes ML basics (algorithms, descriptors, databases) and uses ML for surface dynamics, emphasizing ML's contribution to material property control.

ABSTRACT The development of photonic and optoelectronic technologies, such as laser systems, high‐speed optical communication, quantum information science, and medical imaging, relies significantly on nonlinear optical (NLO) materials. Understanding and predicting their nonlinear optical responses is paramount for material innovation. Density Functional Theory (DFT) is important computational method for predicting electronic structure and calculating optical material properties. The present review gives an overview of application of DFT to study NLO materials that can be classified into organic molecules, inorganic crystals, metal‐organic frameworks (MOFs), hybrid perovskites, and 2D materials. We have also discussed the challenges, recent advancements, and emerging research areas in this field.

ABSTRACT The contamination of water resources with phenolic compounds has emerged as a significant environmental concern due to their toxicity, carcinogenicity, and persistence in the ecosystem. Various industrial effluents, including those from petrochemical, pharmaceutical, and pesticide manufacturing, contain high concentrations of phenol, necessitating effective treatment strategies. Activated carbon has been widely recognized as a promising adsorbent for phenol removal owing to its high surface area, porous structure, and chemical stability. However, the adsorption capacity of pristine activated carbon can be limited by factors such as surface chemistry, pore size distribution, and functional groups. Surface modification of activated carbon has been explored as a viable approach to enhance its adsorption efficiency and selectivity toward phenol. Various modification techniques, including chemical treatment, physical activation, and biological modification, have been investigated to tailor the surface properties of activated carbon. These modifications can introduce specific functional groups, alter the surface charge, and enhance the π–π interactions between the adsorbent and phenol molecules. This review provides a comprehensive overview of the effects of surface modification on the adsorption capacity of activated carbon for phenol removal from wastewater. The discussion encompasses the mechanism of adsorption–desorption analysis, the FTIR analysis, and the impact of modification methods on adsorption isotherms. Furthermore, the review highlights the future prospects of using surface‐modified activated carbon for phenol removal, including the need for scalable and cost‐effective modification techniques.

Calcium channel blockers (CCBs) regulate calcium ion transport across cell membranes and are central to the management of hypertension, angina, arrhythmias, and cerebrovascular disorders, with emerging roles in neurological and oncological diseases. Most CCBs undergo significant first-pass metabolism mediated by cytochrome P450, which affects their interindividual variability, dosage, and bioavailability. Recent research highlights the potential of T-type CCBs in cancer treatment and the use of nanocarriers to overcome their low bioavailability and rapid metabolism. Since the introduction of verapamil in the 1960s, newer drugs with improved selectivity and safety, such as cilnidipine and azelnidipine, have been developed. Despite their widespread use, challenges remain in maximizing long-term efficacy, minimizing side effects, and individualizing treatment. This review provides an updated overview of the pharmacokinetics, pharmacodynamics, and therapeutic applications of dihydropyridine and non-dihydropyridine CCBs, while identifying research gaps and future directions to enhance their clinical utility. By integrating established pharmacological knowledge with recent advances, this study underscores the continued relevance of CCBs in modern medicine.

ABSTRACT This work focuses on synthesizing amorphous carbon nanotube (CNT) and thereafter developing nanofluid by dispersing CNTs in polymer liquid crystal in different volume fractions (φ). Experimental results showed improved thermal conductivity of the nanofluid with increasing CNT volume fraction φ. This result was explained theoretically by Landau‐de Gennes and Doi type free energy model where the enhancement of thermal transport was explained as a result of increased nematic order parameter with ‘φ’ inside the nanofluid, during first order isotropic‐nematic phase transition. Theoretically calculated specific heat and thermal conductivity of the nanocomposite also showed enhancement with increasing ‘φ’ which validates the experimental findings.