Heavy metal toxicity in water is a serious problem that may have harmful effects on human health and the ecosystem. Lead [Pb(II)] and cadmium [Cd(II)] are two such heavy metal ions, present in water, whose severity is well-known and well-studied. In the current research, magnetic biochar composite (MBC) is studied as an adsorbent material for the effective removal of lead and cadmium ions from water solutions. Magnetite (Fe₃O₄) nanoparticles and pine-needle-derived ultrasonicated magnetic biochar were used in different weight ratios to prepare APTES (3-aminopropyl triethoxysilane)-functionalized MBC (FMBC). An average crystalline size of ~10 nm for magnetite NPs was obtained via XRD analysis. The adsorption characteristics of both Pb(II) and Cd(II) ions were investigated in a batch experiment. The FTIR spectra of raw biochar, MBC, FMBC, and metal-loaded FMBC were obtained at different stages. The decrease in the intensity of the –NH₂ functional group in the FTIR spectra of the residue confirmed the successful adsorption of heavy metal ions. The SEM-EDX spectra of the residue showed the uniform adsorption of Pb(II) and Cd(II) heavy metal ions onto the surface of the adsorbent. Magnetic biochar composite (MBC) was found to be a very effective adsorbent at basic pH, as a maximum of 97% instantaneous heavy metal removal was observed for both ions in synthetic water solutions. The Langmuir isotherm model predicted the monolayer adsorption and good affinity between the metal ions and adsorbent. The prepared MBC is low-cost, environmentally friendly, and it has shown good adsorption performance. Therefore, our study suggests that the magnetic biochar composite under study is an effective adsorbent for lead and cadmium metal ion removal from aqueous solutions at normal room temperature. Only a few hundred milligrams of the adsorbent dose is sufficient to remove higher concentrations (~100 ppm) of lead and cadmium at basic pH conditions of aqueous solutions.

A contemporary and decisive optimization algorithm is developed for inverting gravity anomalies due to listric faults. The cross‐section of listric faults are generally concave up, and the dip of the fault plane gradually decreases with depth. Quadratic Bezier curves are utilized to represent the curvature of the fault plane. The densities of sediment deposition are assumed to be known and can take any linear or non‐linear functional form of depth. By constraining the density, a global optimization algorithm is adopted to estimate the fault structure by inverting control point parameters of Bezier curves. The presented algorithm is implemented in two different synthetic models having fixed and depth varying density contrasts. The robustness of the algorithm is authenticated by incorporating white Gaussian noise into synthetic gravity anomalies. A detailed uncertainty appraisal is also performed to justify the reliability of the algorithm. Finally, a real structure is reconstructed using observed gravity anomalies, and the estimated structure is verified with the structure obtained in previously published literature. Furthermore, a Matlab based GUI is developed such that any user can estimate real listric fault structure without any computational difficulties.

We fabricate and characterize waveguides composed of closely spaced and longitudinally oriented silicon ridges etched into silicon-on-insulator wafers. Through both guided mode and bulk measurements, we demonstrate that the patterning of silicon waveguides on such a deeply subwavelength scale is desirable for nonlinear and sensing applications alike. The proposed waveguide geometry simultaneously exhibits comparable propagation losses to similar schemes proposed in literature, an enhanced effective third-order nonlinear susceptibility, and high sensitivity to perturbations in its environment.