EN-MILD approach for Ti3C2Tx MXene synthesis with an ultrahigh electrical conductivity of about 2.4 × 104 S cm−1.

As a new class of two-dimensional materials, the MXene family has triggered attention because of its unique electrical and mechanical properties. MXene’s excellent electrical conductivity and hydrophilicity make it an ideal option for polymer nanocomposite fabrication. For the first time, polymer nanocomposites of polyvinyl alcohol (PVA)/Ti3C2T x (MXene) were used for charge storage applications in the X-band frequency range (8.2–12.4 GHz). By implementing solution casting and vacuum-assisted filtration (VAF), flexible thin films with exceptional dielectric properties (solution casting @ 10.0 wt % MXene: ε′ = 370.5 and tan δ = 0.11 and VAF @ 10.0 wt % MXene: ε′ = 3166 and tan δ = 0.09) were fabricated. The reported dielectric constants in this study are among the highest values obtained in X-band frequency with low dielectric losses. This outstanding performance originates from the high electrical conductivity of synthesized Ti3C2T x MXene (σ ≈ 1.4 ± 0.077 × 106 S/m; the highest reported value for Ti3C2T x MXene to date in the literature), great dispersion state, and the nacre-like structure of the polymer nanocomposites. Combining the exceptional properties of MXene with the effective nacre-like structure, PVA/MXene nanocomposites can be used as a novel charge storage material, fulfilling the requirements of flexible electronics and energy storage devices.

In this study attention was focused on the development of a predictive model for the viscosity of ionic liquids. A large data set of 435 experimental viscosity data points for 293 ionic liquids incorporating 146 cations and 36 anions was applied for the model derivation. A quantitative structure−property relationship (QSPR) approach was employed to develop a linear model. In this study the effects of both anions and cations were considered in the derivation of the model. Genetic function approximation is applied for the model's parameter selection (molecular descriptors) and developing a linear QSPR model. Consequently, a simple linear predictive model was obtained with satisfactory results quantified by the following statistical parameters: absolute average deviations (AAD) of the predicted properties from existing experimental values by the GFA linear equation, 8.77%; squared correlation coefficient, 0.8096; and root mean square, 0.232 cP.

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