The present paperreportsthe synthesis of a newphenanthroline-based ligand (phenIm_1)and its Zn(II) coordination compound (phenIm_Zn). Their chemical structure was determined by a combination of FT-IR and NMR spectroscopy, while their purity was confirmed byelemental analysis. Their photophysical properties were investigated in freshly prepared dichloromethane solutions.

Preparing future teachers to teach science plays an essential role in developing scientific literacy and promoting educational approaches based on investigation and active learning. This study aims to analyze students methodological preferences in the Primary and Preschool Education Pedagogy specialization and their perceptions of the usefulness of different categories of teaching resources used in science education activities. The research was conducted with a sample of 44 students, using a questionnaire structured on a 5-point Likert scale. The frequency of intention to use didactic experiments, systematic observation, didactic games, the project method, heuristic conversation, and discovery learning was investigated, as well as the perceived usefulness of digital resources, multimedia, worksheets, recyclable materials used in experiments, and manipulative materials. Statistical analysis was performed using XLSTAT, including descriptive statistics, Cronbach s alpha, the Spearman correlation coefficient, and the nonparametric Friedmantest. The results revealed excellent internal consistency for the teaching methods scale (Cronbach s a = 0.912) and good consistency for the usefulness of teaching resources scale(Cronbach s a = 0.773). At the same time, moderate and strong positive correlations were identified between active-participatory methods and educational resources, suggesting a pedagogical profile oriented toward interactive and investigative approaches. The conclusions support the need to strengthen initial teacher training by integrating strategies and resources specific to STEM education and inquiry-based learning.

Atmospheric particulate matter (PM) represents one of the most important air quality indicators due to its significant impact on human health and the environment. FTIR-ATR spectroscopy was employed to investigate the chemical composition of PM10 and PM2.5 aerosols collected at three air quality monitoring stations in Arad County, representative of urban traffic, industrial activities,urban background, and suburban environments, during both normal atmospheric conditions and Saharan dust intrusion events. Variations in filter characteristics and FTIR-ATR spectral features reflected the combined effects of seasonal conditions, local emission sources, and Saharan dust transport. The identified absorption bands corresponding to O–H, C–H, C=O, NO3-, CO32-, SO42-, and Si–O functional groups indicatedthe presence of both natural and anthropogenic aerosol components. The natural fraction was dominated by silicates, aluminosilicates, quartz, hydrated clay minerals, and carbonates, whereas the anthropogenic fraction included carbonaceous compounds, hydrocarbons, and secondary aerosols such as nitrates and sulfates. Enhanced mineral signatures during Saharan dust episodes confirmed the contribution of long-range transported mineral aerosols rich in illite, smectite, palygorskite, calcite, and dolomite to PM composition.The FTIR-ATR findings demonstrate the combined influence of local emissions, seasonal variability, and Saharan dust transport on particulate matter composition in western Romania.

The increasing interestand demandforlow-alcohol and alcohol-free beverageshas encouraged wine producers to invest in the development of processes able to reduce ethanol content while preserving the main characteristics of wine. This study evaluated the effect of dealcoholisation by rotary evaporation under reduced pressure on the antioxidant, phenolic and chromatic properties of two experimentalred wineblends, designatedas Rubra Nova and Armony. Antioxidant activity was determined by the DPPH assay, total phenolic content by the Folin–Ciocâlteumethod, while colour characteristics and visible absorption profiles were evaluated by UV–Vis spectrophotometry. Dealcoholisation caused only a slight decrease in DPPH radical inhibition in both wines, while total phenolic content showed a small increase.More evident changes were observed in the chromatic parameters, particularly in colour intensity and hue. However, the overall shape of the UV–Vis spectra remained similar before and after treatment. These results indicate that rotary evaporation under reduced pressure largely preserved the antioxidant and phenolic characteristics of the analysed wines, although some minor changes in their colour properties were observed.

The pressing need for new antimicrobial drugs has been highlighted by the growing problem of antimicrobial resistance (AMR). The quick creation of efficient medicines is hampered by the time-consuming and expensive nature of traditional drug discovery techniques. Artificial intelligence (AI) has recently surfaced as a potentially useful technology to speed up the discovery of novel antimicrobials. From anticipating chemical interactions to improving drug candidates, artificial intelligence (AI) technologiesincluding machine learning, deep learning, and natural language processing have been used at different phases of antimicrobial development. AI can find previously undiscovered chemicals and forecast their effectiveness against resistant infections by examining enormous datasets of chemical structures, biological activity, and genomic data.This study examines the use of AI in antimicrobial drug discovery, emphasizing how it can expedite the drug development process, increase target identification precision, and encourage the creation of new compounds with improved potency and selectivity. Wealso go over the drawbacks and difficulties of incorporating AI into antimicrobial research, such as the requirement for interdisciplinary cooperation, data quality, and model interpretability. Finally, AI-driven strategies present a promising opportunityto tackle the worldwide AMR epidemic and quicken the creation of critically required antibiotic treatments.As an alternative to traditional antibiotics, antimicrobial peptides (AMPs) show promise as agents against antimicrobial resistance. AMP discovery was transformed by artificial intelligence (AI) using both generation and discrimination techniques.The vast field of drug discovery is always looking for new and creative ways to find and create peptide-based medicines. The development of novel peptide medications has undergone a radical change since the introduction of artificial intelligence (AI). A variety of computer tools and algorithms provided by AI allow researchers to expedite the development of therapeutic peptides.