Enhanced phosphonic functional group (PFG)-based mesoporous silicas (MSs) were synthesized by hydrothermal method for uranium [U(VI)] selective adsorption from aqueous solutions. Considering that PFGs are directly related to U(VI) adsorption, the main idea of this research was to synthesize enhanced PFG-MSs and consequently enhance U(VI) adsorption. We synthesized two kinds of MSs based on acetic and phosphoric acids at weakly acidic pH, which allows high-loading phosphonic functionality. The main sodium and phosphonic functionality sources were sodium metasilicate and diethylphosphatoethyltriethoxysilane (DPTS). Adsorption experiment results exhibit enhanced U(VI) adsorption capacity from 55.75 mg/g to 207.6 mg/g for acetic and phosphoric acids, respectively. This finding was due to the enhancement of PFGs by phosphoric acids. The highest adsorption selectivity was 79.82% for U(VI) among the six different elements, including Pb, As, Cu, Mo, Ni, and K. Structural characterization of the samples was performed by Fourier transform infrared, X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Brunauer–Emmett–Teller analysis methods. Element concentrations were measured by inductively coupled plasma optical emission spectrometry. Several parameters affecting adsorption capacity, including pH, contact time, initial U(VI) concentration and solution volume, and adsorbent concentration, were also investigated.

A novel mesoporous silica with enhanced phosphonic functional groups (PFGs) and without any silane agents was provided by Sol-Gel technique for selective adsorption of U(VI) from aqueous solutions (AqS). The absorbent was synthesized based on the achieving the four best performance characteristics including availability, economically, adsorption efficiency, and selectivity which are undoubtedly defined the usefulness of the adsorbents. The sorption results exhibited the highest uranium adsorption capacity, qe, of 820.7 mg/g at pH ≈ 8 which indicated the adsorbent is the best alternative for uranium adsorption from the nearly neutral solutions such as seawater. The recovery percentages by the adsorbent in the aqueous environments involving other elements such as Mg, Cd, Hg, As, Ca, Na, Ni and the salty environment with high concentration of Cl⁻ ions are indicated that the adsorbent presents the acceptable selectivity for uranium adsorption from the AqS such as industrial wastewater. Several activities and factors including removing the silane agents, using sodium metasilicate as an available and low-cost source of silica, and using the Sol-gel method as an unexpansive synthesis technique caused to reduce the synthesis costs from 222.787 EUR/kg for the template method to 60.078 EUR/kg for Sol-gel method which confirm the synthesis of a cost-effective adsorbent.

We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold displacement energies and sputtering yields. We find that clustering and recrystallization of radiation-induced defects, propagation pattern of cascades, and coordination defects in the heat spike phase show striking differences to the widely used analytical potentials. The results reveal that small defect clusters are predominant and show new defect structures such as a vacancy surrounded by three interstitials. GRAPHICAL ABSTRACT Impact statement Quantum-mechanical level of accuracy in simulation of primary damage was achieved by a silicon machine learning potential. The results show quantitative and qualitative differences from the damage predicted by any previous models.

This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based, or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa for the prediction of elastic constants and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations. This breakthrough promises to expedite materials design processes and facilitate scale-bridging in materials science, offering a new perspective on addressing computational demands in atomistic simulations.