This study used toluene 2,4-diisocyanate (TDI) as a flexible linker to attach functional ligands onto silica through stable urethane bonds. Four chelating sorbents were prepared by coupling 8-hydroxyquinoline (HQ), 2,3-dihydroxypyridine (DHP), 1-nitroso-2-naphthol (NN) and alizarin (AZ) to activated silica via TDI. Its two reactive isocyanate groups allowed stepwise bonding with silica hydroxyls and ligands, improving structural stability and functionality. The resulting materials were characterized by Fourier transform infrared (FT-IR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), N₂ adsorption-desorption and elemental analysis. All sorbents showed strong Zn(II) adsorption, with capacities of 8.42–8.96 mg g−1 at pH 8 and retained mesoporous structures with surface areas of 319–381 m2 g−1. The prepared sorbents were successfully applied to preconcentrate Zn(II) ions from groundwater samples, achieving recoveries of 88.66%–100.46% and detection limits of 0.0073–0.0105 μg L−1 using inductively coupled plasma mass spectrometry (ICP-MS). High reproducibility (RSD < 3%) and excellent linearity (R 2 > 0.995) confirmed their suitability for trace zinc analysis in environmental water.
This study investigated the mixed convective peristaltic transport of an incompressible viscoplastic fluid in a two-dimensional symmetric channel embedded with a novel porous structure of homogeneous permeability distributed uniformly along the channel walls. The fluid rheology is modelled using the Bingham Plastic Model, while the effects of an externally applied transverse magnetic field and buoyancy-driven heat transfer are incorporated through magnetohydrodynamic considerations and the Boussinesq approximation, respectively. The governing nonlinear equations are transformed into the wave frame using a Galilean transformation and simplified under the assumptions of long wavelength and low Reynolds number. Approximate analytical solutions are obtained using a regular perturbation method for small values of the Grashof and Bingham numbers. The analytical results are validated against numerical solutions computed using MATLAB bvp4c solver, showing excellent agreement. A detailed parametric analysis is conducted to examine the influence of key physical parameters on the velocity and temperature fields. The results indicate that increasing the Bingham number significantly suppresses the peak velocity by approximately 54% and enlarges the plug-flow region due to enhanced viscoplastic resistance. Simultaneously, the temperature distribution becomes flatter, with a reduction of nearly 72% in peak thermal intensity, indicating improved thermal regulation. The findings offer potential applications in targeted drug delivery, industrial slurry transport, bio-reactor stability and heat-assisted transport processes in porous media.
We propose a Durrmeyer-type extension of the [Formula: see text]-Szász-Mirakyan operators. By employing a unified approach based on the Lipschitz kind maximal function, weighted function spaces, and the Ditzian–Totik modulus of smoothness, we obtain several direct approximation results. Moreover, quantitative forms of Voronovskaya-type as well as Grüss–Voronovskaya-type theorems are obtained. The convergence properties of the introduced operators are also visualized and compared for certain functions with the aid of Maple software.
The novel aspect of this study is the development of a hybrid numerical–intelligent framework to evaluate entropy generation in magnetohydrodynamic Maxwell dusty nanofluid flow over an inclined stretching sheet, with particular emphasis on nanoscale geometric parameters, including the nanoparticle size range and interparticle spacing. The nonlinear transformed similarity equations are solved using bvp4c, and the resulting high-fidelity dataset is then used to train an Artificial Neural Network optimized with the Levenberg‒Marquardt backpropagation algorithm to estimate the velocity fields, temperature distribution and entropy. The hybrid model enhances better prediction stability and also saves a lot of computational cost in relation to repeated numerical simulations. These findings indicate that the geometry at the nanoscale has a quantifiable effect on thermodynamic irreversibility. Increasing the magnetic parameter to M = 0.6 reduces the near-wall fluid and particle velocities by about 18%–25% and the far-field velocities by less than 5% because of enhanced Lorentz-force-induced resistive drag. Increasing values of [Formula: see text] increase the momentum coupled between the fluid and particulate phases, leading to a faster decay of the flow velocity, where the fluid velocity is reduced by an average of 15%–22% and the particle-phase velocity is reduced by an average of 25%–35% towards the stretching surface. Quantitatively, increasing [Formula: see text] from 0.5 to 1.5 reduces the fluid temperature near the wall by approximately 205%–30%. Increasing [Formula: see text] from 0.6 to 2.5 escalations entropy generation near the surface by approximately 40%–55%, while increasing M from 0.2 to 0.6 results in a 30%–45% rise in [Formula: see text].
In the present study examined the effects of local thermal non-equilibrium on the bioconvection flow of a hybrid nanofluid containing oxytactic microbes via three different geometries using an artificial neural network. Oxytocic bacteria are used in cancer treatment to activate the immune system and prevent the growth of cancer. As a targeted medication delivery vehicle for infected cells, oxygen-repellent bacteria appear to be a viable weapon in the battle against tumours. Using modified Hamilton–Crosser models, the outcomes of the Soret and Dufour impacts on hybrid nanofluid is examined. By precisely replicating the intricate heat and mass transport within biological tissues, the model can improve the administration of Oxytocic bacteria as targeted medication carriers or direct therapeutic agents. The local thermal non-equilibrium feature is critical because it compensates for the temperature difference between the injected hybrid nanofluid (including magnetic nanoparticles and bacteria) and the surrounding tumour tissue, ensuring an accurate thermal dose. The Soret and Dufour impact model includes mass and heat diffusion, which was crucial in forecasting the dispersion of nanoparticles and bacteria in the tumor microenvironment's temperature gradient. The Levenberg‒Marquardt technique is applied to the generated synthetic data in order to minimize error and obtain approximation results for different hybrid nanofluid system scenarios. The microorganism profile reduces as the Peclet number increases.
Environmental pollution caused by the improper disposal of waste materials, particularly organic dyes from various industries, into water bodies is enormous. It is crucial to comprehensively study photocatalytic materials to effectively degrade dangerous contaminants from water. For this purpose, porous carbon-based materials have drawn great attention because of their unique properties. In this work, a cadmium sulphide-doped zinc oxide integrated carbon foam (CdS@ZnO/CF) nanocomposite has been synthesized by the hydrothermal method. The nanocomposite was characterized by FTIR, XRD, TGA, SEM and EDX. The prepared nanocomposite demonstrated high efficiency in degrading methylene blue (MB) under sunlight irradiation as compared to the individual cadmium sulphide nanoparticles (CdS NPs) and zinc oxide nanoparticle-modified carbon foam (ZnO/CF). CdS@ZnO/CF nanocomposite decreased charge recombination and increased charge separation, which enhanced the photocatalytic degradation activity (up to 93%). Different factors, such as catalyst amount, light exposure duration and concentration of MB, were optimized. After comprehensive experiments, 0.04 g of catalyst, 100 mW/cm2for 80 min of light exposure and 30 ppm concentration of MB was selected as the optimum values for further analysis. The nanocomposite recovered after several cycles and demonstrated a degradation rate of around 85%. In addition, the CdS@ZnO/CF nanocomposite successfully inhibited a famous type of pathogen.
The global challenge of neurodegenerative diseases and cancer necessitates new multifunctional therapeutics. We designed and synthesised novel chromene-hydrazone derivatives as multitarget ligands. Structures were confirmed spectroscopically and by X-ray crystallography, with data aligning with our DFT-optimised geometry, validating our computational model. The fluorinated lead compound, 10, demonstrated potent dual inhibition of acetylcholinesterase (AChE, IC₅₀ = 27.82 ± 0.97 µg/mL) and butyrylcholinesterase (BChE, IC₅₀ = 6.46 ± 0.62 µg/mL), alongside significant antitumour activity against HCT-116 and A-549 cell lines. Another analogue, 9, demonstrated the strongest α-glucosidase inhibition in the series (IC₅₀ = 65.34 ± 2.32 µg/mL; 222.8 µM), alongside moderate α-amylase inhibitory activity. Molecular docking, DFT, and %V_bur analysis revealed key structure-activity relationships, confirming that strategic fluorine substitution enhances activity. Compound 10 is a promising multifunctional candidate, warranting further optimisation and in vivo studies for Alzheimer's disease and cancer.
This investigation examines the mathematical modeling and explanation of the Casson tangent-hyperbolic rheological model, which captures several physical effects. The flow analysis is conducted under the influence of nonlinear buoyancy forces and magneto-hydrodynamic effects in a stratified, convectively heated medium. The investigation considers thermal radiation, viscous dissipation, chemical reaction, convective conditions, and Soret-Dufour effects to model flow, energy, and concentration expressions. Here, the Soret and Dufour effects determine the solutal and thermal performance. The model rheological expressions are simplified using the laws of fluid mechanics. A suitable transformation technique converts the nonlinear governing partial systems into differential systems. The computational construction of the Morlet wavelet neural network (MWNN) based on the Hybrid Cuckoo Search algorithm is applied to solve the model system of equations. Additionally, the error function for the nonlinear model and its associated boundary conditions are optimized using the Hybrid Cuckoo Search algorithm. The performance of MWNN-HCS in solving the nonlinear rheological model expressions is compared with state-of-the-art numerical methods to validate its precision. Furthermore, numerical assessments across 55 independent trials with 10 neuron-based networks confirm the efficacy, reliability, and consistent convergence of the proposed MWNN-HCS model.
A recurring rigidity principle in ring theory and noncommutative analysis is the weak locality assumptions that can force genuine algebraic identities when tested against a sufficiently rich separating family of functionals. Let [Formula: see text] be a locally finite poset and let [Formula: see text] be a commutative unital [Formula: see text]-algebra. We introduce coefficient-normalized anchored weak [Formula: see text]-local Hasse–Schmidt derivations on [Formula: see text] with respect to the natural coefficient functionals. The anchoring condition requires local Hasse–Schmidt witnesses to control the relevant coefficients of two elements and their convolution product, which allows the Hasse–Schmidt identities to be recovered coefficient by coefficient. The first main result proves that every coefficient-normalized anchored weak [Formula: see text]-local Hasse–Schmidt derivation on [Formula: see text] is a genuine Hasse–Schmidt derivation. More precisely, such a map has the explicit form [Formula: see text] where [Formula: see text] is the coefficientwise lift of a Hasse–Schmidt derivation of [Formula: see text], and [Formula: see text] is a transitive-weight homomorphism determined by a multiplicative cocycle on comparable pairs of [Formula: see text]. Explicit examples show that posets with nonzero first cohomology admit genuinely outer coefficient-normalized higher flows.
Frequent load shedding in rural farming areas, continued dependence on diesel pumps, and inefficient manual irrigation methods contribute to increased water wastage and carbon emissions. These challenges highlight the need for a smart irrigation system that can reduce grid and fossil-fuel dependence while improving water-use efficiency in small and medium scale farms. This paper presents a solar-powered Internet of Things (IoT)-based smart irrigation system designed to enhance water and energy efficiency in such agricultural settings. The proposed system integrates an ESP32 microcontroller with soil moisture, water level, and temperature sensors to enable automated irrigation using threshold-based control logic. Secure communication is implemented via the Message Queuing Telemetry Transport (MQTT) protocol with Transport Layer Security (TLS), supporting reliable data transmission, real-time monitoring, and remote operation through a web-based dashboard. The system supports both AUTO and MANUAL modes, including a manual override, to enhance operational flexibility. The photovoltaic (PV) subsystem was validated using PVsyst simulations, demonstrating an annual energy yield exceeding 5 MWh and a performance ratio of 83%. Field testing confirmed that the controller maintained soil moisture within the optimal range of 30–60% while preventing pump dry-run through tank-level interlock mechanisms. Long-term reliability of the solar subsystem was further supported by seasonal energy distribution analysis and P50–P90 probability assessments. Additionally, the system incorporates a drainage and water-recycling mechanism to minimize water waste. Overall, the results indicate that the proposed solution is a cost-effective, scalable, and off-grid-capable irrigation system suitable for sustainable agriculture in resource-constrained regions.
This study fabricated nanocomposite films based on a ternary polymer blend of carboxymethyl cellulose (CMC), polyvinyl alcohol (PVA) and polyvinyl pyrrolidone (PVP), reinforced with a constant concentration of titanium-doped manganese tungstate (MnW0.9T0.1O4) and varying amounts of carbon nanoparticles (CNPs: 0, 0.2, 0.4, 0.6, 0.8 wt%) via solution casting method. At 1 kHz, the sample containing 0.2 wt% CNPs exhibits a dielectric constant epsilon ' = 20.44 and a dielectric loss = 0.72, representing the optimal balance between energy storage and dissipation. X-ray diffraction confirmed successful filler integration into the polymer matrix, and scanning electron microscopy revealed sub-micron filler dispersion. The direct optical bandgap increased from 4.71 eV (pristine blend) to 4.99 eV with MnW0.9T0.1O4 addition, then decreased to 4.87-4.93 eV upon CNPs incorporation, demonstrating tunable bandgap engineering. Calculated nonlinear optical parameters (Chi((1)), Chi((3)), n(2)), obtained from empirical bandgap-refractive index relations, exhibited composition-dependent trends fluorescence exhibited excitation-dependent colour tunability from deep blue to orange-yellow, as validated by CIE coordinates. Although higher CNP loadings (0.4-0.8 wt%) gave larger dielectric constants (28.5-29.0), their loss and conductivity were 2-2.7 times higher, which would reduce breakdown strength and efficiency. Thus, the 0.2 wt% CNPs composite shows promising dielectric properties (moderate epsilon ', low dielectric loss, low sigma(ac)) that warrant further investigation for optoelectronic devices and dielectric energy storage applications.
This research examines PMMA/PEO/MoS2 ternary nanocomposites functionalized with tetrabutylammonium (TBAI), tetrahexylammonium (THAI), and tetramethylammonium (TMAI) iodides. XRD reveals reduced PEO crystallinity, with TMAI showing the strongest polymer-filler interactions. Cation size dictates distinct mechanisms: TBAI's bulky butyl groups enable MoS2 interlayer intercalation, THAI provides optimal steric balance for uniform dispersion, while TMAI's small methyl groups favor surface adsorption, limiting polarization. Dielectric properties follow Maxwell-Wagner-Sillars polarization; TBAI achieves the highest permittivity (epsilon 'approximate to 51.2) and loss (epsilon ''approximate to 32.3) at 353 K, whereas TMAI exhibits minimal loss akin to the pure blend. Energy density shows no net gain due to the permittivity-loss trade-off. AC conductivity confirms order-of-magnitude enhancement for TBAI (sigma ac approximate to 1.79 & times;10-6 S/cm), with impedance and modulus analyses validating enhanced ionic mobility for THAI and TBAI. THAI provides the optimal dielectric-transport balance. However, breakdown strength, cycling stability, thermal stability, and mechanical flexibility remain unevaluated, rendering practical application claims speculative. These findings offer fundamental guidance for future energy-storage material design.
This research examines solitary waves in a dual-mode nonlinear Landau-Ginzburg-Higgs setup utilizing novel treatments within a theoretical and numerical context. Following Korsunsky's approach, transformations are made to the nonlinear Landau-Ginzburg-Higgs equation of the dual-mode type. This formulation presents a framework in which wave propagation is modelled as dual wavefronts moving in opposite directions simultaneously. While dual-mode approaches have been implemented in the literature in various contexts, the combination of these methodologies along with the fractional order Landau-Ginzburg-Higgs formulations remains to be uncovered. For this work, analytical solutions are drawn using the generalized logistic equation approach. The solutions are shown to exhibit static and moving behaviour, tanh and anti-tanh wavefronts, as well as the coth-based wavefronts. The numerical and analytical solutions are shown to have high convergence and close agreement with each other. Various types of graphs, both 2D and 3D, are employed to depict multicriteria comparisons of the numerous solution characteristics.
Double perovskites are promising for energy applications to their unique crystal structures and electronic properties. Density Functional Theory (DFT) calculations using WIEN2K were employed to investigate the optoelectronic, thermodynamic, thermoelectric and mechanical properties of Ca2InSbO6 and Ca2FeSbO6. Structural stability is confirmed through energy-volume curves, Gibbs free energy and phonon dispersion analysis. The lattice parameters are a(0) = b(0) = c(0) = 8.16 & Aring; for Ca2InSbO6, and a(0) = 9.85 & Aring;, b(0) = 8.08 & Aring;, c(0) = 5.80 & Aring; for Ca2FeSbO6. Band gaps of 3.37 eV and 2.97 eV indicate suitability for optoelectronic applications. Optical properties show dielectric constants of 3.7 and 4.9, with refractive indices of 1.9 and 2.2, respectively. Thermoelectric evaluations, including lattice thermal conductivity, reveal maximum total ZT values of 0.90 (at 100 K) and 0.70 (at 250 K) for Ca2InSbO6 and Ca2FeSbO6 respectively, indicating that these compounds fall below the threshold required for practical solid-state power generation.
This paper highlights the Prandtl nanofluid flow past a linearly stretchable surface in the existence of nanoparticles and gyrotactic microorganisms. The Prandtl fluid is a non-Newtonian behaviour which is represented by an extra stress tensor. Similarity variables are used to transform the PDEs into a coupled system of non-linear ordinary differential equations. An artificial neural network (ANN) method is also employed to estimate and validate the solution profiles. The results show that the activation energy and chemical reaction greatly affect mass transport, while the heat source/sink strongly affects heat transfer. Moreover, the gyrotactic microorganisms affect the bioconvective response of the nanofluid and improve the stability of the system in given circumstances. The comparison of the numerical and ANN-reliant solutions shows high correspondence, which proves the reliability and efficiency of the suggested ANN framework. The findings may support the design engineering of bio-nanofluid thermal and bioconvective transport systems.
Marine sponges are a valuable source of bioactive metabolites. This study investigated the polar fraction of the hydroethanolic extract of the Peruvian marine sponge Dysidea ligneana. Two known polar metabolites, taurine (TAU) and trigonelline (TRG), were isolated and identified by 1D/2D NMR and MS. Their antiproliferative activity was evaluated against HepG-2, MCF-7, HeLa, and WI-38 cells. TRG showed the stronger antiproliferative profile and was further assessed in a diethylnitrosamine-induced hepatocellular carcinoma model, where it attenuated biochemical, oxidative stress, inflammatory, and immunohistochemical alterations in a dose-dependent manner. Neuroprotective activity was evaluated in ceramide-challenged CAD neuronal and MO3.13 oligodendroglial cells, in which TAU showed the more consistent protective effect. Docking analyses suggested plausible interactions of TRG with Keap1 and TAU with caspase-3. These findings support further evaluation of TRG as an antiproliferative candidate and TAU as a neuroprotective candidate.
In the current research, the removal efficiency of the toxic synthetic dye Brilliant Blue FCF-E133 from aqueous solutions was studied by synthesizing Mg-Al layered double hydroxide nanoparticles. These nanoparticles are synthesized by the coprecipitation method and analyzed by FT-IR, SEM, EDS and XRD results. Batch mode adsorption experiments are carried out to understand the results in terms of the effect of pH, temperature, concentration and adsorbent dosage. Finally, the removal efficiency is at an optimized pH of 9. Thermodynamic and kinetic studies are carried out to understand the adsorption mechanisms. Moreover, density functional theory simulation results are given, which are carried out by the B3LYP/DND. From the results and simulation findings, it can be concluded that the synthesized Mg-Al hydroxide particles are an efficient and economic adsorbent that can be employed in the removal of organic dye from wastewater.
This study evaluated the cytotoxic effects of quercetin (Qu) and quercetin-loaded silver nanoparticles (Qu-AgNPs) on MCF-7 breast cancer cells and compared these effects to doxorubicin (Doxo). We also performed molecular docking of Qu and a modeled Qu-AgNP complex against TNF-alpha, NF-kappa B and HSP family proteins. Both Qu and Qu-AgNPs reduced MCF-7 viability (IC50 reported) and decreased mRNA expression levels of TNF-alpha, NF-kappa B, HSP27, HSP70 and HSP90. In silico docking predicted that a Qu-AgNP construct could form additional hydrogen bonds and hydrophobic contacts versus Qu alone, yielding modestly improved predicted binding affinities. These results indicate the anti-proliferative potential of Qu and Qu-AgNPs in MCF-7 cells and provide preliminary in silico ADMET and docking data to guide future mechanistic and in vivo studies.
