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Adsorption is widely applied to remove arsenic from water. This paper reviewed and compared the recent progresses on the arsenic removal by adsorption using two-dimensional and three-dimensional graphene-based materials as adsorbents. Functional graphene sheet achieved the largest As(III) adsorption capacity of 138.79mg/g, while Mg-Al LDH/GO2 showed the largest As(V) adsorption capacity of 183.11mg/g. Parameters including pH, temperature, co-existing ions and loaded metal or metal oxide affected the adsorption process. The adsorption mechanisms of graphene-based materials for As(III) and As(V) could be explained by surface complexation and the electrostatic attraction, respectively. Future works are suggested to focus on regenerating of two-dimensional graphene-based adsorbents and developing the three-dimensional with large specific surface area and better adsorption performance.

Ultrafine nanoparticles owing to their increased surface to volume ratio, coupled with the ability to tune their surface properties through molecular modification have made them ideal for their detection and remediation of broad range of environmental contaminants. Arsenic contamination has become a worldwide epidemic and remediation of this problem needs the development of technology with improved materials and systems with high efficiency. In the present study, we have demonstrated a simple and efficient method using surface functionalized ultrafine iron oxide nanoparticles for absolute removal of arsenic from arsenic treated water with low contact time period and low adsorbent dose. The efficiency of arsenic removal has been drastically improved by considering nanoparticles of size 10 nm and subsequent surface engineering of the nanoparticles resulting more adsorption sites being exposed to arsenic. The mechanism for adsorption was identified through electron microscopic and spectroscopic studies. The adsorption equilibrium data were well fitted to Freundlich isotherm.

An analysis of the equations used for modeling thermal arsenic diffusion in silicon has been carried out. It was shown that for arsenic diffusion governed by the vacancy-impurity pairs and the pairs formed due to interaction of impurity atoms with silicon self-interstitials in a neutral charge state, the doping process can be described by the Fick’s second law equation with a single effective diffusion coefficient which takes into account two impurity flows arising due to interaction of arsenic atoms with vacancies and silicon self-interstitials, respectively. Arsenic concentration profiles calculated with the use of the effective diffusivity agree well with experimental data if the maximal impurity concentration is near the intrinsic carrier concentration. On the other hand, for higher impurity concentrations a certain deviation in the local regions of arsenic distribution is observed. The difference from the experiment can occur due to the incorrect use of effective diffusivity for the description of two different impurity flows or due to the formation of nonuniform distributions of neutral vacancies and neutral self-interstitials in heavily doped silicon layers. We also suppose that the migration of nonequilibrium arsenic interstitial atoms makes a significant contribution to the formation of a low concentration region on thermal arsenic diffusion.

Man has always used his environment to heal himself. Until 1869 all medicines came mainly from plants (e.g. opium for pain relief, Figure 8.46, Section 8.8.1) or animals (e.g. badger skin and meat to relieve snake or scorpion bites). In 2010, there were 1000 active ingredients in drugs sold in pharmacies, of which 10% were unmodified natural products, 29% were derivatives of natural products (hemisynthesis) and 61% were synthetic products. Using bio-informatics and artificial intelligence methods, an estimated 166 billion different molecules can be prepared by combining 17 atoms comprising C, N, O, S, F, Cl, Br and I, and by applying known synthesis methods and standard stability criteria. By applying medicinal chemistry criteria (structure/biological activity relationships) to this molecular space called GBD17, it is estimated that 10 million of these molecules could become drugs…