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The proposed study investigates the characteristics of Stefan blowing and activation energy on MHD Casson Diamond- based trihybrid nanofluid over a sheet with LTNECs (local thermal non-equilibrium conditions) and permeable medium. The significance of Marangoni convection as well as heat generation are considered. In order to examine the properties of heat transmission in the absence of local thermal equilibrium conditions, this paper makes use of a simple mathematical model. Local thermal non-equilibrium situations typically result in two discrete and crucial temperature gradients in both the liquid and solid phases. In systems where material qualities and heat transfer efficiency are crucial, the utilization of Xue model and Yamada-Ota model and to assess the thermal conductivity of the nanofluid adds a comparison dimension and enables optimized design. The controlling partial differential equations are reduced to non-linear ordinary differential equations using an appropriate similarity transformation. The Bvp4c technique is used to resolve the resulting equations numerically. Applications in modern thermal management systems, especially those requiring precise heat transfer control (e.g., electronic cooling, medicinal devices, energy systems), will benefit greatly from this work. The model is especially applicable to processes where chemical reactions and internal heat sources are important, like in catalytic reactors and combustion systems, because it takes into account activation energy and heat generating effects. The findings indicate that when the value of the interphase heat transmission factor increases, the solid phase’s temperature profile and liquid phase heat transfer rate drop.

Optimizing oil production in wells employing gas lift systems is a critical challenge due to the complex interplay of operational and reservoir parameters. This study aimed to develop robust predictive models for estimating oil production rates using a comprehensive dataset from oil fields in south-eastern Iraq, leveraging advanced machine learning techniques. The dataset, comprised of 169 rigorously validated samples, includes key features such as basic sediment and water content, choke size, pressures, gas injection characteristics, gas lift valve depth, oil density, and temperature. Input and output variables were normalized and split into training and test sets to ensure fairness and reliability. Multiple machine learning models (Decision Tree, AdaBoost, Random Forest, Ensemble Learning, CNN, SVR, MLP-ANN, and Lasso Regression) were trained and evaluated using 5-fold cross-validation and key statistical metrics (R², MSE, AARE%). The Random Forest model demonstrated superior performance, achieving a test R² of 0.867 and the lowest prediction errors (MSE: 18502 and AARE: 8.76%) for the testing phase, while other models were prone to overfitting or underfitting. Sensitivity analysis and SHAP interpretability methods revealed that basic sediment and water content, choke size, and upstream pressure had the greatest influence on oil output. These findings underscore the importance of both statistical rigor and model interpretability in oil production forecasting and provide actionable insights for optimizing gas lift operations in oil wells.

Drilling operations increasingly face challenges related to poor rheology control, excessive fluid loss, and inefficient cuttings transport, especially in HPHT conditions. This study aimed to address these issues by developing a sustainable nanoparticle based additive synthesized through a green route. A biogenic extract from Pinus nigra pollen was used to produce selenium doped silver zinc oxide nanocomposites (Se@Ag/AgO–ZnO) via a single step co precipitation method. The resulting heterostructured material exhibited nanoscale crystallite size and a textured morphology confirmed by SEM. The nanocomposite was incorporated into water based drilling fluids at concentrations of 0 to 5000 ppm. Rheological behavior was evaluated using Bingham Plastic modeling, filtration performance was measured under standard and HPHT conditions, and cuttings transport was assessed through rolling oven tests. Results showed that the optimal concentration of 1000 ppm increased yield point to 12.12 Pa and plastic viscosity to 48.7 cP while maintaining high model accuracy with R2 values of at least 0.991. Filtrate volumes decreased by up to 68.5 percent in standard tests and 69.2 percent in HPHT tests due to formation of a compact and low permeability filter cake. Quartz and shale cuttings recovery reached 88 percent and 79 percent respectively at 1000 ppm. At 5000 ppm, mild performance decline was linked to particle agglomeration. These findings demonstrate that pollen derived nanocomposites can enhance drilling fluid behavior and offer a sustainable approach for improving rheology, filtration control, and hole cleaning efficiency.

The impact of Stefan blowing on the stagnation point flow of HNF (hybrid nanofluid) across a sheet containing gyrotactic microorganisms under local thermal non-equilibrium conditions (LTNECs) is briefly discussed in this paper. The present work uses a simplified mathematical model to inspect the characteristics of heat transfer in the absence of LTNECs (local thermal equilibrium conditions). LTNECs, traditionally provide two distinct fundamental temperature gradients for the liquid and solid phases simultaneously. A hybrid nanofluid is a mixture of water as the base fluid and single-walled carbon nanotubes and multi-walled carbon nanotubes . Gyrotactic microorganisms are included into nanoparticles to increase their thermal efficiency in a variety of systems, including microbial fuel cells, enzyme biosensors, bacteria powered micromixers, chip-shaped microdevices like bio-microsystems, and micro-volumes like microfluidic devices. This model can also help environmental engineering by enhancing wastewater treatment procedures by allowing microorganisms to break down pollutants more effectively. It advances the development of more productive photo bioreactors, increasing the output of biofuels in the field of renewable energy. Material scientists can utilize this concept to develop controlled nanostructured materials with consistent composition and thermal properties. The considerable similarity transformation is used to build ordinary differential equations for the nonlinear dimensionless system. This problem is solved numerically by using the Bvp4c method. The results determine that when the Stefan blowing parameter increases, fluid flow increases but temperature, mass transfer rate, and heat transfer are decreased.

The impact of Stefan blowing on the stagnation point flow of HNF (hybrid nanofluid) across a sheet containing gyrotactic microorganisms under local thermal non-equilibrium conditions (LTNECs) is briefly discussed in this paper. The present work uses a simplified mathematical model to inspect the characteristics of heat transfer in the absence of LTNECs (local thermal equilibrium conditions). LTNECs, traditionally provide two distinct fundamental temperature gradients for the liquid and solid phases simultaneously. A hybrid nanofluid is a mixture of water as the base fluid and single-walled carbon nanotubes and multi-walled carbon nanotubes . Gyrotactic microorganisms are included into nanoparticles to increase their thermal efficiency in a variety of systems, including microbial fuel cells, enzyme biosensors, bacteria powered micromixers, chip-shaped microdevices like bio-microsystems, and micro-volumes like microfluidic devices. This model can also help environmental engineering by enhancing wastewater treatment procedures by allowing microorganisms to break down pollutants more effectively. It advances the development of more productive photo bioreactors, increasing the output of biofuels in the field of renewable energy. Material scientists can utilize this concept to develop controlled nanostructured materials with consistent composition and thermal properties. The considerable similarity transformation is used to build ordinary differential equations for the nonlinear dimensionless system. This problem is solved numerically by using the Bvp4c method. The results determine that when the Stefan blowing parameter increases, fluid flow increases but temperature, mass transfer rate, and heat transfer are decreased. Graphical abstract

In this work, Density Functional Theory (DFT) has been employed to examine the structural, electronic, magnetic and optical characteristics of alkaline earth metals doped (Be, Mg and Ca) doped (Hf1-xAxO2) alloys using the FP-LAPW (full-potential augmented plane wave plus local orbital) method and GGA and TB-mBJ exchange correlation methods. All studied compounds are structurally stable due to negative (formation energy) values. The computed results, including the band gap and lattice characteristics, correspond well with the existing experimental results. According to the band structure and density of states calculation, Hf1-xAxO2 has wider band gaps for spin up configuration and reduced energy gaps of 4.71 3.41, 3.35 and 3.02 eV has been found for pure c-HfO2, Hf1-xMgxO2, Hf1-xCaxO2 and Hf1-xBexO2 respectively. The PDOS results demonstrate that the formation of conduction and valance bands is due to Hf-6s orbitals and Mg/Ca/Be-s states. Additionally, we have investigated the optical characteristics that correspond to the real and imaginary portion of the dielectric function in the region of 0–12 eV, such as absorption coefficient, optical conductivity, refractive index, reflectivity and energy loss function. Moreover, Hf1-xCaxO2 has a wide range of absorption in the UV-visible region, which indicates that this material is suitable for opto-electronic and solar cell applications. The refractive index suggests that Hf1-xCaxO2 could be a potential candidate for applications to high-density optical data storage devices.

Photoelectrochemical (PEC) techniques seamlessly combine electrochemical and spectroscopic principles, offering a powerful platform for the detection of biomarkers and biological molecules in clinical and biomedical settings. This review provides a comprehensive overview of microfluidic PEC probes, emphasizing their potential for ultrasensitive detection through enhanced light absorption and charge transfer processes. Key advantages of microfluidic PEC include real-time monitoring of biological processes, non-invasive detection, and the possibility of multiplexing when integrated with various quantification modalities. However, the practical implementation of PEC faces challenges such as bulky setup, matrix interference, and stability of PEC-active materials. Also, this paper discusses the intricate mechanisms of PEC sensing, highlighting the roles of nanomaterials in enhancing microfluidic PEC systems. Additionally, the limitations inherent in PEC material selection, including stability and bandgap engineering, are critically discussed. Solutions such as doping and the development of composite materials are proposed to address these issues. Through presented examples of PEC applications in biomedical fields, this review elucidates the future potential of PEC-based methods as reliable and effective tools for diagnostic applications. Additionally, this review proposes the most effective probes for future investigations to develop commercial devices. Graphical Abstract

The analytical solution is conducted for two two-phase linear flows of Williamson fluid through a porous medium to examine the heat transfer mechanism under the suspension of solid rigid nanoparticles. To produce the two-phase flow, the mathematical model of fluid and particle phases is developed by using the continuity, momentum, and heat equations. The momentum and energy-balanced equations of both phases are solved analytically by employing the perturbation series method. The computational results revealed that the magnetic field parameter diminishes both velocity and temperature profiles while the velocity and thermal fields are increasing in function of density number and Darcy number. Moreover, the fluid phase velocity is much smaller than the particle phase velocity under both suspensions of the particles. The thermal slip parameter generates 10% and 20% more heat transfer in a system with the suspension of gold and silver nanoparticles, respectively. The results of the present analysis are beneficial to designing modern solar cell devices to stock more solar energy due to less heating release under the suspension of gold and silver nanoparticles.

In this study, a novel magnetic g-C 3 N 4 /FeTiO 3 /MnFe 2 O 4 nanocomposite were successfully prepared using a simple hydrothermal synthetic route. The physicochemical and optical characteristics of the obtained samples were investigated through various techniques. Ternary g-C 3 N 4 /FeTiO 3 /MnFe 2 O 4 nanocomposite significantly accelerated the degradation of crystal violet (CV) compared to bare and binary samples under irradiation of visible light. The decoration of FeTiO 3 and MnFe 2 O 4 on g-C 3 N 4 led to formation a heterojunction, which effectively prevent the recombination of photogenerated charge carriers. The effects of initial concentration of CV, photocatalyst amount, pH of solution on the photocatalyst activity of g-C 3 N 4 /FeTiO 3 /MnFe 2 O 4 were investigated, and the results revealed that the maximum photodegradation efficiency (99.05%) was obtained at initial CV concentration of 20 mg/L, photocatalyst amount of 0.4 g/L, and pH of 8. The kinetic studies show that, the rate constant value of CV photodegradation using g-C 3 N 4 /FeTiO 3 /MnFe 2 O 4 nanocomposite was higher than those of g-C 3 N 4 , FeTiO 3 , MnFe 2 O 4 , g-C 3 N 4 /FeTiO 3 , g-C 3 N 4 /MnFe 2 O 4 , and FeTiO 3 /MnFe 2 O 4 . The scavenging analyses were shown that the key active species in the CV photocatalytic degradation were h + and • OH, while • O 2 − was secondary species in the photodegradation process. Finally, a possible mechanism for CV photodegradation using g-C 3 N 4 /FeTiO 3 /MnFe 2 O 4 nanocomposites was proposed.

Tuberculosis (TB) remains a formidable global health challenge, exacerbated by the rise of drug-resistant strains like MDR-TB and XDR-TB. This study introduces an advanced eight-compartment deterministic model that transcends conventional frameworks by incorporating critical stages often overlooked in TB dynamics: chronic carriers, persistent cases, and advanced treatment pathways for drug-resistant TB, alongside vaccinated and recovered populations. The model’s novelty lies in its integration of time-dependent vaccination efficacy and adaptive treatment strategies, revealing that targeted vaccination campaigns outperform mere treatment expansion in reducing prevalence, a finding validated through real-world vulnerability indices. By deriving the basic reproduction number ℜ 0 , we establish threshold criteria for disease persistence and prove global stability using Lyapunov theory. Sensitivity analysis identifies transmission rate ( β ) and vaccination coverage ( ξ ) as the most influential parameters, offering policymakers actionable levers for intervention. Our optimal control analysis demonstrates that combining time-varying strategies enhanced vaccination ( u 1 ), accelerated treatment ( u 2 ), and transmission suppression ( u 3 ) can reduce TB incidence by up to 40% in high-burden settings while optimizing resource allocation. Numerical simulations underscore the efficacy of these strategies, showing delayed peaks in infectious compartments and faster recovery rates under controlled scenarios. The model’s practical utility extends to designing cost-effective TB programs in resource-limited regions, particularly where socio-economic barriers impede treatment adherence. Future work will explore spatial heterogeneity and co-infections (e.g., HIV-TB) to further refine intervention scalability.