University of Sialkot

Cobalt-doped zinc ferrite is a contemporary material with significant structural and magnetic characteristics. Our study explores the magnetic properties of cobalt-substituted zinc ferrite (ZnxCo1-xFe2O4), synthesized via a simple sol-gel method. By varying the cobalt ratio from 0 to 0.5, we found that zinc substitution impacts both the magnetization and lattice parameters. FTIR analysis suggested the presence of functional groups, particularly depicting an M-O stretching band, within octahedral and tetrahedral clusters. X-ray diffraction analysis confirmed the phase purity and cubic structure. The synthesized materials exhibited an average particle size of 24-75 nm. Scanning electron microscopy revealed the morphological properties, confirming the formation of truncated octahedral particles. In order to determine the stability, mass loss (%), and thermal behavior, a thermal analysis (thermogravimetric analysis (TGA)/differential thermal analysis (DTA)) was performed. The magnetic properties of the synthesized ferrites were confirmed via a vibrating sample magnetometer (VSM). Finally, the highest saturated magnetization and lowest coercivity values were observed with higher concentrations of the cobalt dopant substituting zinc. The synthesized nanomaterials have good stability as compared to other such materials and can be used for magnetization in the near future.

Abstract Purpose In-stream advertising has grown in popularity as a mobile social media advertising option. However, consumers often skip these advertisements, highlighting the need to understand the underlying psychological mechanisms driving such avoidance. Therefore, the purpose of this study is to examine the stimulus–organism–response (SOR) mechanism underlying consumer annoyance and avoidance of in-stream advertising on mobile social media, with anger and perceived threat to freedom serving as key organismic states. Research design/methodology A survey was administered to Facebook and YouTube users through mobiles from Gen-Z (N = 995) at universities, colleges, shopping malls and parks in major cities of Pakistan. Partial least Squares Structural Equation Modeling (PLS-SEM) technique was employed to test the research model. Findings Gen-Z users displayed ad annoyance and ad avoidance behaviors (i.e., zipping, muting, and zapping) when exposed to in-stream advertising. Additionally, results indicated that ad intrusiveness and ad irritation (stimuli) directly influenced anger and perceived threats to freedom (organism), which then resulted in annoyance and avoidance of in-stream ads (response). Originality/value This study enhances the literature on in-stream advertisement annoyance and avoidance by empirically validating the stimulus-organism-response model to elucidate consumers’ reactions to in-stream ads on mobile social media, offering significant managerial insights for advertising professionals and marketers, and social media operators.

This study presents an extensive graph-theoretic analysis of the thermodynamic properties of indium telluride (InTe). We analyze the significant relationship between the molecular structure of InTe and its macroscopic behavior through the application of graph entropy, a fundamental metric in information theory and statistical thermodynamics. In this framework, the chemical structure is depicted as a graph, with atoms as vertices and chemical bonds as edges. We calculate a set of topological indices for InTe, a material that is very useful in thermoelectrics, phase-change memory, and infrared optics. These indices encode the material’s structural connectivity in numbers. These indices are used to figure out a range of graph entropies, which measure how complex and information-rich the molecular lattice is. The main part of this work shows strong quantitative links between these graph entropies and important thermodynamic properties, such as heat capacity and heat of formation. We use suitable quadratic surface-fitting models that show exactly how these thermodynamic responses depend on pairs of graph entropies in a nonlinear way. All computational modeling and analysis are conducted to enhance statistical fit, guaranteeing that the chosen models deliver the most dependable predictive capability for comprehending and anticipating the material’s stability and thermal characteristics.

This study develops a NiFe-LDH@ZIF-67 composite for efficient co-adsorption of methyl green (MG, 239.05 mg/g) and oxytetracycline (OTC, 147.39 mg/g) in various water matrices. Comprehensive characterization (FTIR, SEM-EDS, XRD, XPS, TGA) confirmed the material's structural integrity. Adsorption followed pseudo-second-order kinetics (R-2 > 0.98) and Langmuir isotherms, with MG showing higher affinity due to stronger electrostatic and pi-pi interactions. Real-water tests demonstrated superior performance in tap water (231.34 mg/g) versus lake water (209.61 mg/g), with faster kinetics (alpha = 899.501 vs 485.279). The composite exhibited excellent reusability (>95 % efficiency after 5 cycles) and stability post-regeneration. Computational studies revealed binding energies of -1.54 eV (MG) and -1.21 eV (OTC), with hydrogen bonding (N/OHO) driving preferential adsorption of MG. An optimized ANN model predicted optimal conditions (pH 6, 40 degrees C) with < 5 % error, facilitating industrial scale-up. The work combines experimental validation with multiscale simulations (DFT/Monte Carlo) to establish structure-property relationships, while machine learning enables process optimization. This integrated approach advances the design of hybrid adsorbents for practical water treatment applications, addressing key challenges of selectivity and stability in complex wastewater systems. The study provides both fundamental insights into adsorption mechanisms and a practical framework for developing efficient, reusable materials for multi-pollutant removal.

This study examines the thermal and hydrodynamic behavior of Williamson non-Newtonian mucus in a two-dimensional divergent wavy microchannel, simulating ciliated surfaces with an eighth-order waveform. The flow is modeled as laminar, incompressible, and governed by low Reynolds number hydrodynamics, with equations transformed into a cilia-attached moving frame. Employing a long-wavelength approximation, inertia effects are neglected, and a perturbation method analyzes deviations from rheological and geometric nonlinearities. The interplay between shear-thinning effects, channel divergence, and undulation-driven periodicity is explored, with temperature distribution incorporating viscous dissipation and heat transfer. Graphical results illustrate velocity profiles, pressure gradients, streamline patterns, and thermal performance, highlighting how cilia motion enhances mixing and heat transfer in microfluidic systems. Crucially, divergence angle and Williamson rheology are shown to significantly alter flow structure, as evidenced by velocity vector fields. These findings provide critical insights for the design of bio-inspired microfluidic devices, particularly those requiring optimized thermal management and mixing.