We report the first successful synthesis of a novel magnetically recoverable nanocatalyst, Fe3O4@Al2O3/Co-Cs, incorporating cesium (Cs) as a co-catalytic promoter for efficient and sustainable hydrogen generation via NaBH4 hydrolysis. In this system, magnetite (Fe3O4) serves as a magnetic core, enabling facile separation and recyclability, whereas the Al2O3 shell enhances thermal stability, structural integrity, and active-site dispersion. Cobalt (Co) acts as the primary active metal, whereas Cs is introduced here for the first time in NaBH4 hydrolysis as an electronic promoter, significantly enhancing electron transfer and accelerating the hydrogen generation rate (HGR). Comprehensive characterization using FTIR, XRD, FE-SEM/EDX, and BET analyses confirmed the successful formation of a hierarchical core-shell structure. FE-SEM images revealed a flower-like morphology composed of aggregated nanosheets and nanoparticles that promoted efficient mass transfer and gas diffusion. Catalytic performance tests conducted at 30 degrees C demonstrated a high hydrogen generation rate of 17.24 L g- 1 min- 1. The apparent activation energy was determined to be 27.18 kJ mol-1, indicating favorable reaction kinetics under mild conditions. Thermodynamic analysis revealed a low enthalpy of adsorption (Delta Hads = 35.66 +/- 0.01 kJ mol-1) and a small entropy change (Delta S degrees = 0.09022 +/- 0.01 kJ mol- 1 K-1), suggesting favorable interactions between the reactants and catalyst surface. Density functional theory (DFT) calculations further confirmed that the Fe3O4@Al2O3/Co-Cs system exhibited enhanced adsorption strength and reduced activation barriers, facilitating NaBH4 hydrolysis through efficient charge transfer, intermediate stabilization, and synergistic catalytic effects. Overall, this study highlights the novel role of cesium as an alkali metal promoter in NaBH4 hydrolysis, opening new opportunities for the design of advanced non-noble metal catalysts for hydrogen generation.
Thermoelectric cooler (TEC) design couples Peltier pumping, Fourier back-conduction and Joule heating, making Pareto solutions sensitive to calibration, bounds and omitted transport. We combine a manufacturer-calibrated lumped model, an algebraic physics-informed neural network (PINN) surrogate audit that does not solve a partial differential equation, direct multi-objective optimization and requirement-based selection. Thermal scaling fitted at 5.2 A yielded withheld cooling-capacity RMSE values of 0.601 W at 1.7 A and 1.410 W for nonzero-lift 8.7 A points. With 40,000 samples, surrogate holdout RMSE values were 0.655 W for cooling capacity and 1.174 W for input power. A matched ablation confirmed that the algebraic residual adds no independent information at labelled points. Collocation reduced mean sparse-label errors in all five paired runs, but the exact tests gave p=0.0625 two-sided and p=0.03125 for the directional alternative, limiting inference. At N=127, MOPSO achieved the best mean HV and IGD+ in a matched four-algorithm benchmark. The source-bounded balanced design gave COP =0.854 and PD =55.21 kW m−2. Relaxing bounds raised PD to 76.34 kW m−2 without increasing COP. Contact resistance changed COP ranking and selected geometry; a one-dimensional Thomson audit preserved Pareto membership but changed exact selections. The workflow supports conditional TEC design selection within the stated bounds.
Land consolidation (LC) is a widely adopted land management instrument for improving agricultural efficiency through parcel restructuring and reducing land fragmentation. However, how LC reshapes the spatial distribution of land value remains insufficiently understood. This study investigates the spatial determinants of postconsolidation land value potential (LVP) using parcel-level data from an agricultural LC project in T & uuml;rkiye. The analysis examines the effects of changes in parcel compactness (Delta CI), fragmentation, and pre-consolidation LVP within a spatial econometric framework. The average LVP increased from 0.491 before LC to 0.737 after LC, representing an improvement of approximately 50%. OLS residuals exhibited significant spatial autocorrelation (Moran's I = 0.198, p < 0.001), confirming that conventional regression was inadequate. Model comparison identified the Spatial Error Model (SEM) as the most appropriate specification (AIC=-510.51), indicating that post-consolidation LVP is primarily influenced by spatially structured contextual factors rather than direct parcel-to-parcel interactions. The findings demonstrate that LC reshapes rather than eliminates inherited spatial value structures and highlight the importance of accounting for spatial dependence when evaluating rural land management interventions. Methodologically, the study provides a reproducible spatial econometric framework that can support more robust and equitable evaluations of LC programmes.
This study introduces a numerical solution of the inhomogeneous partial integro-differential equations of wave type equipped with a double integral on 2D half-line, employing the matrix-collocation method based on the orthoexponential and Taylor polynomials. The method readily converts the terms into the matrix forms consisting of the matrix relations at the collocation points. To do this, the convergence is studied in association with a collaborative matrix relation and also the existence of a singular double integral is analyzed via a novel representation of the solution form. The matrix form of the integral part is, thus, formally settled into a matrix equation and validated for the computations. A benchmark example with multiple cases is solved for successive computation limits and different domains. In fact, these cases aim to force the limitation of the proposed method in the sense of several type of kernel functions and exact solutions. Thereby, the graphical and numerical results approve the validity, efficiency and accuracy of the proposed method, including lemma and theorems.
