Iron Oxide Nanoparticle-Assisted Delamination of Ti3C2Tx MXenes: A New Approach to Produce Magnetic MXene-Based Composites.

Ti3C2Tx MXene is one of the most comprehensively studied 2D materials in terms of its adsorptive, transport, and catalytic properties, cytotoxic performance, etc. Still, conventional MXene synthesis approaches provide low single-flake MXene yield and frequently uncontrollable properties, demanding further post-processing. The MXene family also lacks magnetism, which is helpful for producing effective nanoadsorbents as their magnetic decantation is the cheapest and most convenient way to remove the spent adsorbent from water. Composite materials consisting of magnetic nanoparticles grown on top of MXene flakes are commonly used to provide magnetic properties to the resulting nanocomposite. In this paper, we study the possibility to delaminate multilayer Ti3C2Tx MXene sheets directly by growing iron oxide magnetic nanoparticles inside their interlayer spacing. We find out that, with a mass fraction of particles comparable or exceeding that of MXenes, their growth is accompanied by an effective enhancement of single-layer MXene yield and suitable magnetic properties of the resulting composite. The developed approach can be further used for simplifying synthesis protocols to obtain magnetic MXene-based nanoadsorbents with tunable properties.

Calculation of Lipophilicity of Organophosphate Pesticides Using Density Functional Theory.

Higher lipophilicity facilitates the passage of a substance across lipid cell membranes, the blood-brain barrier and protein binding, and may also indicate its toxicity. We proposed eight methods for predicting the lipophilicity of the 22 most commonly used organophosphate pesticides. In this work, to determine the lipophilicity and thermodynamic parameters of the solvation of pesticides, we used methods of density functional theory with various basis sets, as well as modern Grimm methods. The prediction models were evaluated and compared against eight performance statistics, as well as time and RAM used in the calculation. The results show that the PBE-SVP method provided the best of the proposed predictive capabilities. In addition, this method consumes relatively less CPU and RAM resources. These methods make it possible to reliably predict the ability of pesticide molecules to penetrate cell membranes and have a negative effect on cells and the organism as a whole.