Scientific and Technical Bulletin, Series: Chemistry, Food Science and Engineering

CHARACTERIZATION BY FTIR-ATR SPECTROSCOPY OF PM10 AND PM2.5 ATMOSPHERIC PARTICLES IN ARAD (ROMANIA) IN NORMAL PERIODS AND IN EPISODES OF SAHARAN DUST

Dorina Rodica CHAMBRE — 2026-09-03

SYNTHESIS AND CHARACTERISATION OF A NOVEL PHENANTHROLINE-BASED LIGAND FUNCTIONALIZED WITH AMPHIPlLIC GROUPS AND ITS LUMINESCENT Zn(II) COMPLEX

Adelina-Antonia ANDELESCU — 2026-09-03

The present paper reports the synthesis of a new phenanthroline-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 by elemental analysis. Their photophysical properties were investigated in freshly prepared dichloromethane solutions.

AN EXPLORATORY ANALYSIS OF METHODOLOGICAL PREFERENCES IN SCIENCE EDUCATION AMONG FUTURE TEACHERS

Simona GAVRILAS — 2026-09-03

CHANGES IN ANTIOXIDANT ACTIVITY, PHENOLIC CONTENT AND COLOUR CHARACTERISTICS OF RED WINES FOLLOWING DEALCOHOLISATION

Cristian MOISA — 2026-09-03

The increasing interest and demand for low-alcohol and alcohol-free beverages has 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 experimental red wine blends, designated as Rubra Nova
and Armony. Antioxidant activity was determined by the DPPH assay, total phenolic content by the Folin–Ciocâlteu
method, 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 USE OF AI IN FINDING NEW ANTIMICROBIALS

Dana-Maria COPOLOVICI — 2026-09-03

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 timeconsuming
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) technologies including 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. We also 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 opportunity to 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.