Yellow oleander oil extraction was optimized using Response Surface Methodology (RSM) and Central Composite Design (CCD) to maximize yield. RSM refined the process and developed predictive models critical for achieving maximum oil recovery and maintaining production quality standards. The extracted oils were analyzed using FTIR and GC-MS to assess their chemical composition, purity, and fatty acid profile. The optimization process showed excellent agreement between the predicted and experimental oil yields. In particular, the RSM-CCD optimization achieved a high coefficient of determination (R² = 0.7810, Adj-R² = 0.5770). Under the optimized conditions of 125 minutes extraction time, 50°C temperature, solid/solvent ratio of 5.5, and particle size, the yield of yellow oleander oil reached 67.3%, which was largely in line with the model prediction of 67.5%. The quality of the oil met ASTM standards: saponification number 197.33 mg KOH/g, acid number 0.224 mg KOH/g, peroxide number 1.88 ± 0.04 meq O₂/kg, and iodine value 80.77 ± 0.76 g I₂/100 g. FTIR analysis revealed the presence of aliphatic, carbonyl, ester, unsaturated alkene, glyceryl, and carboxyl groups in the oil. In addition, the oil contained 45.3% unsaturated fatty acids, with oleic acid (17.3%) and arachidonic acid (16.8%) as the predominant components. These results confirm the potential of yellow oleander oil as a viable biodiesel feedstock, validated by experimental analysis.

This study reports on the determination of the concentrations of lead and cadmium in five samples of wine by stripping voltammetry using glassy carbon as working electrode, Ag/AgCl as reference and platinum as auxiliary electrode. The study was carried out in 0.16 M nitric acid containing 0.05 M potassium nitrate as supporting electrolyte. The accumulation of the metals in the wine samples was carried out at -900 mV for a period of 120 s under a hydrodynamic system and after a quiet time of 30 s, the stripping of the metals was carried out by scanning the potential from -900 mV to 200 mV. Lead was detected in a sample of St. Eves wine at a concentration of 3.9 µg/100 mL while cadmium was detected in Eva Fresh, Romex, and St. Lauren wine samples at concentrations of 9.3 µg/100 mL, 5.0 µg/100 mL, and 3.9 µg/100 mL respectively. The concentration of lead in the St. Eves wine sample (3.9 µg/100 mL) was below the maximum permissible limits of 10 µg/100 mL, while the concentrations of cadmium in Eva Fresh, Romex, and St. Lauren wine samples were above the maximum permissible limits of 1.0 µg/100 mL. The results have shown that caution should be exercised in the consumption of these locally made wines

Abstract The removal of congo-red dye from waste water is crucial due to its toxicity and environmental impact. Adsorption of commercial alumina using Congo red dye was studied at various concentrations and pH. This study investigates the use of aluminum beads as a low-cost and eco-friendly adsorbent for Congo red adsorption. The result of the study shows that aluminum beads exhibit high adsorption capacity at 2.51 mg/g at 6.5 pH with 94% removal. The study revealed that the adsorbed dye was effectively removed using aluminum beads; therefore, this research demonstrates the potentiality of aluminum beads as a sustainable adsorbent for the removal of congo-red in waste water, as alumina gets protonated from acidic solutions, inhibiting the process. Adsorption decreased with increasing initial dye concentrations. Adsorbent dosage also affected adsorption, with increased dosage of aluminum beats which inhibited active sites and decreasing total adsorption due to the decreased surface area available for Congo red dye.Keywords: Congo-red dye, aluminum beads, waste water, adsorption capacity.

The objective of this study was to investigate the effectiveness of Rumex nepalensis leaf (RNL) and root (RNR) powders as adsorbents for the removal of methylene blue (MB). The functional structure of the adsorbents was characterized using Fourier transform infrared (FTIR) spectroscopy before and after the adsorption process. The optimization of operational parameters such as pH, contact time, initial dye concentration, adsorbent dose, and temperature was conducted using a batch adsorption experiment. The adsorption isotherm was modeled using Langmuir, Freundlich, and Temkin isotherms. The highest dye removal efficiencies for RNL and RNR and the adsorption capacity of MB were observed at pH 3 and 5, respectively. The equilibrium contact time for both parts of the plant was determined to be 30 minutes at 35 °C. The Langmuir isotherm model showed the best fit with high correlation coefficients (R2 > 0.99) and a low nonlinear error function, as indicated by the nonlinear chi-square test (χ2) for both adsorbents. The maximum adsorption capacities of MB as estimated from the Langmuir isotherm model were 119 mg/g for RNL and 333.3 mg/g for RNR at 35 °C. The kinetic model revealed that the adsorption of MB on RNL and RNR followed pseudo-second-order kinetics. Thermodynamic analysis showed that the adsorption of MB on RNL was endothermic and spontaneous, whereas it was exothermic for adsorption of MB onto RNR and effective in the temperature range of 25–35 °C. These results indicated that the leaf and roots of Rumex nepalensis are promising and novel adsorbents for the removal of methylene blue from aqueous solutions.

In this work, a general theoretical description of the electrochemical determination of Sudan dyes over copper sulfide nanoparticles has been made. The analysis of the correspondent mathematical models has shown that in the system is efficient from the electroanalytical point of view, although the electrooxidation mechanism is branched. The possibility for the oscillatory and monotonic instability has also been verified.

The area of computational chemistry and molecular modelling has undergone a revolution as a result of machine learning's emergence as a potent and adaptable tool for studying hydrogen bonding interactions. An overview of machine learning's uses and developments in hydrogen bonding characterisation is given in this abstract. Machine learning algorithms have been successfully used to predict hydrogen bond properties, identify donors and acceptors, and analyze complex molecular structures with high accuracy and efficiency. These algorithms include supervised and unsupervised learning methods, and deep learning architectures. Molecular interactions are better understood by combining machine learning with other computational techniques including quantum mechanical calculations, molecular dynamics simulations, and docking investigations. This has improved hydrogen bonding analysis. Additionally, explainable AI methods have improved the interpretability of models, enabling researchers to comprehend the variables driving model predictions. Our understanding of chemical and biological processes has substantially improved because to the widespread use of machine learning in hydrogen bonding analyses, which has also given us important new knowledge about molecular behaviour, drug design, materials research, and other fields of study. Future developments in algorithms and data representation are anticipated to further enhance the model's precision, understand ability, and scalability, fostering new ideas and scientific understanding of hydrogen bonding characterisation.

The study explored the effectiveness of an ethanolic extract derived from Urena lobata (UL) as a natural inhibitor of corrosion for mild steel when exposed to 0.3 M HCl acid. Various analytical methods including gravimetric analysis, Potentiodynamic Polarization (PDP), Fourier Transform Infrared Spectroscopy (FT-IR), and Scanning Electron Microscopy (SEM) were utilized. The experiments were conducted under different conditions including varying temperatures (303 K, 313 K, and 323 K), inhibitor concentrations (0.2 g/L, 0.4 g/L, and 0.6 g/L), at a fixed acid concentration of 0.3M, and with an immersion time of 4 hours. The findings revealed that the inhibition efficiency (%IE) increased with higher inhibitor concentrations but decreased with rising temperature and immersion time. The highest inhibition efficiency observed was 69.26%, while the lowest was 53.08%. SEM images confirmed the inhibitor's ability to shield the metal surface, while FT-IR spectra identified the functional groups responsible for corrosion inhibition. The enthalpy of activation (∆H) indicated an exothermic process, and the entropy of activation (∆S) pointed to a decrease in disorder, both reflected by negative values. The values of the Free Energy of Adsorption (∆Gads) indicated the spontaneity and viability of the process, with physical adsorption being proposed as the underlying mechanism. The Freundlich adsorption isotherm was deemed the most applicable model for UL. The activation energy (Ea) increased with higher plant extract concentrations, and first-order kinetics seemed to best describe the inhibition mechanism based on reported R2 values, rate constants, and half-life. Potentiodynamic polarization (PDP) result indicates that the inhibition efficiency was quite high showing 84.77 %IE and is a single type inhibitor (anodic). Phytochemical screening analysis indicates that U.L leaves contained alkaloids, tannins, sterols, glycosides, triterpenoid and saponins. Therefore, the leaves extract of UL moderately inhibits mild steel corrosion in hydrochloric acid.

The shortage of freshwater for irrigating vegetables in Akungba Akoko, Nigeria, is a critical concern during the dry season, demanding urgent attention. Local farmers rely heavily on polluted well and stream water for irrigation, which poses significant health risks due to contamination from refuse and pollutants. Addressing this challenge requires the development of a simple, cost-effective treatment facility to remove contaminants and make the water suitable for irrigation. This research aimed to assess the effectiveness of a straightforward filtration system using various physical materials to improve water quality. Conducted at Adekunle Ajasin University, Akungba-Akoko, the study focused on evaluating granite and river sand filtration on water collected from a local stream at Ibaka, Akungba-Akoko on April 7th, 2023. The filtered and unfiltered waters, categorized as follows: T0 = Borehole water (Control), T1 = Unfiltered water, T2 = water filtered with granite, T3 = water filtered with pure river sand, and T4 = water filtered with combined physical filters were subjected to physicochemical and microbiological analyses to determine its suitability for irrigation purposes. The study revealed that using single or combined physical filtration materials led to a notable decrease in microbial levels in the water samples. Additionally, significant reductions in total solids (TS), total suspended solids (TSS), and total dissolved solids (TDS) were observed in water filtered through these materials, either alone or in combination. Granite filtration (T2) resulted in notably higher pH (5.57 Ms/cm), EC (172.00 μ.S/cm) and nitrogen (27.00 mg/L) levels, while combined filtration (granite and pure river sand) (T4) showed higher levels of phosphorus (9.35 mg/L). These findings demonstrate the efficacy of both singular and combined physical filtration materials in improving wastewater quality. Thus, employing these filtration methods, either individually or in combination, is recommended for local farmers, especially in Akungba Akoko, South West Nigeria, to enhance water quality for agricultural purposes.

The use of cobalt(III) oxyhydroxyde as an electrode modifier for zolpidem in vivo and in vitro has been evaluated by mechanistic theoretic way. The correspondent mathematical model was evaluated by means of linear stability theory and bifurcation analysis. It was shown, that, in the case of CoO(OH) use as a modifier for zolpidem electrooxidation, the steady-state stability will be maintained in the vast parameter region, which implies clear and rapid electroanalytical response. The oscillatory and monotonic instabilities´ probability has been also investigated.

The global shift towards sustainable energy resources highlights the importance of a promising path towards achieving energy security and environmental sustainability. The heavy dependence on fossil fuel has its attendant economic and environmental effects. Biomass-to-hydrogen (BTH) conversion technologies presents an attractive alternative solution to energy and environmental crises. However, its full potential is yet to be harnessed due to arising inadequacies and challenges. This review looks into recent technological advancements and challenges in BTH energy conversion processes, as well as prospects in Africa.