In this study, two carbazole–triazine derivatives with cyanuric acid and melamine cores, namely 2,4,6-tris(4-(9H-carbazole-9-yl)butoxy)-1,3,5-triazine (CB) and N2,N4,N⁶-tris(4-(9H-carbazole-9-yl)butyl)-1,3,5-triazine-2,4,6-triamine (MB), were synthesized and investigated as electrocatalysts for the electrooxidation of hydrazine, an environmentally benign fuel with high energy density. These organic electrocatalysts were prepared through nucleophilic substitution reactions and structurally characterized by FT-IR, NMR, XRD, SEM, and TEM analyses. Their electrochemical performance was evaluated by cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). The MB compound exhibited a current density of 16.97 mA/cm2, which is 7.68 mA/cm2 higher than that of CB. CA and EIS analyses further revealed that MB possesses higher stability and lower charge-transfer resistance than CB. These findings demonstrate that nitrogen-rich carbazole-triazine derivatives are promising metal-free anode electrocatalysts for efficient hydrazine oxidation.
The individual and combined acute toxic effects of D-limonene, menthol, aromatic compounds, and essential oils from Salvia rosmarinus Spenn., Mentha spicata L., and Salvia sclarea L. were evaluated against Planococcus citri (Risso) (Hemiptera: Pseudococcidae). Based on a comparison of lethal concentrations, menthol (LC50: 0.39 g/L) and M. spicata L. (LC50: 1.45 g/L) showed the highest efficacy, whereas D-limonene (LC50: 14.67 g/L) demonstrated the lowest. The volatile oil ingredients were identified using GC–MS/FID (Gas Chromatograph-Mass Spectrometer/Flame Ionization Detector). To predict acute and synergistic effects, the performance of several supervised machine learning algorithms was assessed using K-fold cross-validation. Among the models tested, Random Forest Regression yielded the best predictive performance. Feature selection algorithms indicated that D-limonene, menthol, alpha-pinene, carvone, 1,8-cineole, and sabinene were the primary contributors to acute toxicity. Analysis of the combination index (CI), t-distributed stochastic neighbor embedding (t-SNE), and heat maps revealed that mixtures of essential oils and aromatic compounds exhibited both synergistic and antagonistic interactions. Notably, structurally similar molecules such as limonene and its cis- and trans-isomers, α-pinene and camphene, camphor and 1,8-cineole, and borneol and isoborneol were predicted to exert predominantly synergistic effects. Machine learning and traditional methods reveal that the synergistic effects of complex essential oil mixtures on P. citri remains insufficiently understood. Aromatic compounds and essential oils show diverse biological activities against P. citri and may serve as viable alternatives to synthetic pesticides in pest management strategies. Furthermore, machine learning and deep learning may offer more rapid predictions and insights into toxicity profiles.
This study developed a novel chocolate spread incorporating yellow and blue poppy seeds as functional ingredients. Physicochemical, bioactive, textural, rheological, microstructural, FT–IR spectroscopic, and sensory properties of spreads containing 5
The genotype-level compositional stability of essential oil (EO) chemotype expression in basil (Ocimum basilicum L.) is critical for industrial quality assurance and may also be relevant for preliminary food safety screening, yet remains poorly characterised across the environmental variation encountered in production. This study characterises the environmental modulation of EO yield and chemotype composition in a pre-selected panel of 12 basil genotypes representing four chemotypes (linalool, estragole, citral, and methyl cinnamate) using a three-layer experimental design: (i) ecological variation across three contrasting Turkish locations (Bursa, Eskişehir, Tokat) over two years, (ii) ontogenetic variation across three developmental stages at Bursa, and (iii) seasonal variation between summer and autumn harvests at Tokat (144 unique EO profiles). EO yield was highest at Bursa (0.98 mL·100 g⁻1 dry weight). At Bursa, EO yield increased from pre-flowering to onset of flowering, whereas at Tokat, autumn harvests produced higher yields than summer harvests. Genotype R-10A, representing the estragole chemotype, showed very high genotype-specific compositional stability under the tested ecological conditions (Layer 1 CV = 2.8
The link between lower extremity dexterity and fall risk among people with Parkinson’s disease (PwPD) needs to be explore. To examine the relationships between lower-extremity dexterity and fall risk, fear of falling (FoF), and walking skills in PwPD. Thirty PwPD were included in the study. Fall risk was determined by the number of falls in the last six months. The Lower-Extremity Dexterity Task (LEDT) for lower-extremity dexterity; the Activities-Specific Balance Confidence Scale (ABC) for FoF; 10-Meter Walk Test (10MWT), the Timed Up and Go Test (TUG), the Figure-of-Eight Walk Test (FEWT), and the Three-Meter Backward Walk Test (TMBWT) for walking skills were used. Fallers were significantly worse than non-fallers in terms of disease duration, H Y, UPDRS-3, LEDT Most affected side (MAS), LEDT Least affected side (LAS), ABC, 10MWT, TUG, FEWT and TMBWT (p < 0.05). The LEDT-MAS and LEDT-LAS side were highly correlated with ABC, TUG, and FEWT and moderately correlated with H Y, UPDRS-3, 10MWT, TMBWT (p < 0.05). The LEDT was a predictor of fall risk with an odds ratio of 1.204 (1.042–1.391) for MAS and odds ratio 1.245 (1.055–1.469) for LAS. The cut-off time of LEDT for best discriminating fallers from non-fallers was 26.23 s for MAS and 23.11 s for LAS. Lower-extremity dexterity is an independent predictor of fall risk in PwPD. Moreover, lower-extremity dexterity is related to FoF and walking skills in PwPD.
