Islamic Azad University Quchan

This study evaluated the antioxidant and antibacterial properties of zinc oxide nanoparticles containing Bunium persicum (Boiss.) B. Fedtsch. essential oil synthesized using a green method. The analyses were conducted with three replications using a completely randomized methodology. The means were compared using Duncan’s multiple range test at a significance level of 5% using SPSS version 22 statistical software. In the microbial test, the antimicrobial property of zinc oxide nanoparticles with cumin essential oil was significantly higher than that of B. persicum (Boiss.) B. Fedtsch. essential oil in both disc diffusion and microbroth dilution methods (p < 0.05). In addition, zinc oxide nanoparticles with B. persicum (Boiss.) B. Fedtsch. essential oil had the most significant effect on Candida Albicans yeast, then on the gram-positive Staphylococcus aureus bacteria, and finally on the gram-negative Escherichia coli bacteria. Zinc oxide nanoparticles combined with B. persicum (Boiss.) B. Fedtsch. essential oil showed a better result than B. persicum (Boiss.) B. Fedtsch. essential oil in measuring the amount of total phenolic compounds (p < 0.05). In the test of particle size and dispersion index, there was a significant difference in the samples (p < 0.05). The lowest particle size and particle size dispersion index were related to the sample of zinc oxide nanoparticles with B. persicum (Boiss.) B. Fedtsch. essential oil.

An interface can be delicately designed using interactions between nanoparticles and surfactants by controlling surface properties such as activity and charge equilibrium. This study seeks to provide insights into how surfactant concentration impacts the stability and dynamics of nanoparticle-surfactant interfaces, with potential applications in material science and interface engineering. This study investigates the interactions between Graphene Function (Gr, Graphene function in this text refers to functionalizing the graphene sheets with -COOH groups via acidic reactions.), Polystyrene sulfonate (PSS), and the surfactant Cetyl trimethylammonium bromide (CTAB) at the air/water interface. We examined various ratios of CTAB to Gr-PSS to determine the effects of surfactant concentration, focusing on conditions up to the critical micelle concentration (CMC). Specifically, we utilized different concentrations of CTAB ranging from 0 to 1 CMC (0.82mM), while the concentration of Gr-PSS varied between 0 and 1wt% and 20-50ppm. To analyze the dynamic interfacial properties, including dynamic surface tension and dilational viscoelasticity, we employed drop profile analysis tensiometry (PAT) to measure area perturbation frequency at the air/water interface. The study aimed to elucidate the behavior of the CTAB/Gr-PSS complex at this interface. We discussed the adsorption of the CTAB/Gr-PSS complex on the droplet surface and its varying roles by examining surface pressure across different area change domains and conducting elasticity measurements. The results indicate that the attachment of CTAB molecules to Gr particles and PSS leads to the formation of surface-active complexes. As the surfactant concentration increases, excess CTAB monomers compete with the CTAB/Gr-PSS complexes for access to the interfaces, causing the larger complexes to migrate into the liquid bulk, as confirmed by elasticity assessments.

In this study, the focus is on ethanol nano biosensors based on alcohol oxidase (AOX) enzymatic reactions and the feasibility of generating electric current for bio batteries. The aim is to convert the latent energy in ethanol into electrical energy through the enzymatic oxidation process in the presence of AOX enzyme. The release of electrons and the creation of a potential difference make the use of ethanol as a bio fuel cell (BFC)/self-power biosensor in biologically sensitive systems feasible. To achieve this, glassy carbon electrodes were modified with gold nanoparticles to enhance conductivity, and the AOX enzyme was immobilized on the working electrode. The current generated through the enzymatic process was measured in various pH and analyte concentration conditions. Afterwards, machine learning models, including MLP, DNN, DT, and RF, were employed to assess the impact of parameters on electric current generation, evaluate the error rate, and compare the results. The results indicated that the MLP model was the most suitable method for predicting the electric current produced under different pH, temperature, and ethanol concentration values. These findings can be utilized to identify optimal conditions and increase the current output for use as a reliable energy source in self-powered biosensors. In conclusion, this study suggests a promising way to generate electricity by oxidizing ethanol with the AOX enzyme. The use of machine learning to analyze experimental data has provided insight into optimal conditions for maximizing electric current output for developing sustainable energy sources in biologically sensitive systems and bio battery technology.

Higher-order scalar field models in two dimensions, including the ϕ8 model, have been researched. It has been shown that for some special cases of the minima positions of the potential, the explicit kink solutions can be found. However, in physical applications, it is very important to know all the explicit solutions of a model for any minima position. In the present study, with the help of some deformation functions, we have shown that higher-order scalar field theories can be obtained with explicit kinks. In particular, we introduced two deformation functions that, when applied to the well known ϕ4 and ϕ6 models, produce modified ϕ8 and ϕ10 models, respectively, with all their explicit kink-like solutions which depend on a single parameter. Since this parameter controls the position of the minima of the potential, we have found interesting new solutions in many distinct cases. We have also studied the kink mass, the behavior of the excitation spectra and several kink-antikink collisions for these two new modified models. The collision outcome is determined by the initial configuration, specifically the sequence in which the kink-antikink and antikink-kink pairings emerge. Another interesting finding is the suppression of resonance windows, which may be explained by the presence of a set of internal modes in the model.

Introduction: Recent research has confirmed the crucial role of social security in promoting social justice.However, despite its significance, these services are sometimes deemed unnecessary by both employers and workers.This can lead to insurance evasion.Method: The study was conducted using a qualitative research method based on grounded theory, during which 14 experts of the Social Security Organization were selected using a purposive sampling technique.These people responded to the main question of the research (identification of the factors affecting insurance evasion) during structured and issue-oriented interviews.Findings: After analyzing the data, the core category was identified as "insurance evasion, short-term profitmaking of stakeholders, and long-term instability in the production of collective good social welfare."This central phenomenon is influenced by several causal conditions, including "lack of awareness among actors about the benefits of insurance," "successive waves of economic crises," "complexity and ambiguity in laws and regulations," and "the lack of an effective monitoring and evaluation system."Additionally, the findings revealed that social actors encountering this issue have implemented strategies such as "prioritizing income for the labor force", "associating labor based on bribery/threats", and "neglect of insurance benefits".These strategies have resulted in consequences such as "the challenge of financing the uninsured workforce" and "the gradual and increasing collapse of income/cost of insurance companies".Discussion: The insurance evasion occurs under the influence of economic and structural factors.The inflation economic crises, and deficiencies in the social security laws regarding the determination of insurance payment and insurance styles affect the performance of capital and labor owners more than any other factor.

In this study, the solubility of oxycodone hydrochloride (OXH) in supercritical carbon dioxide (SC–CO2) was investigated at various conditions, temperature (308–338 K) and pressure (120–270 bar), for the first time. The solubility ranged from 0.007 to 0.109 g/L, corresponding to mole fractions ranging from 0.051 × 10−5 to 0.699 × 10−5. Three different model groups were used to analyze the experimental data. The first group comprised seven semi-empirical models, with 3–6 adjustable parameters. These models include Sparks, Sodeifian 1 and 2, Bian, Jouyban, Gordillo and Jafari-Nejad. The second group employed two state equations, namely the Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) with van der Waals mixing rule. The average absolute relative deviation percentage (AARD%) was 9.73 and 10.63 for PR and SRK, respectively. The third group utilized four machine learning algorithms including DNN, RF, MLP and DTs with the respective R2 values 0.992, 0.980, 0.964 and 0.961, respectively. All of the models exhibited satisfactory agreement with the experimental data. Finally, the enthalpies of vaporization (79.71 kJ/mol) and solvation (−19.25 kJ/mol) were calculated for the first time.