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.
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. 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.
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