Enhanced Dielectric Performance of Polymer Nanocomposites Based on CNT/MnO₂ Nanowire Hybrid Nanostructure

We report a new highly efficient polymer nanocomposite for charge storage applications based on carbon nanotube (CNT) and MnO₂ nanowire (MnO₂NW). Our study suggested that combination of conductive filler (CNT) and ferroelectric filler (MnO₂NW) is an effective method to fabricate nanocomposite with outstanding dielectric permittivity and low dielectric loss if two fillers share similar length and geometry. This strategy leads us to fabricate a hybrid nanocomposite (CNT:MnO₂NW (3.0:4.5 wt %)) with a high dielectric permittivity (50.6) and low dielectric loss (0.7), which are among the best-reported values in the literature in the X-band frequency range (8.2–12.4 GHz). We postulated that superior dielectric properties of the new hybrid nanocomposites were attributed to (i) better dispersion state of CNT in the presence of MnO₂NW, which increases the effective surface area of CNTs, as nanoelectrodes, (ii) dimensionality match between the nanofillers, which increases their synergy, and (iii) barrier role of MnO₂NWs, cutting off the contact spots of CNTs and leading to lower dielectric loss. Comparison of the dielectric properties of the developed hybrid nanocomposites with the literature highlights their great potential for flexible capacitors

Prediction of Standard Enthalpy of Combustion of Pure Compounds Using a Very Accurate Group-Contribution-Based Method

The artificial neural network–group contribution (ANN–GC) method is applied to estimate the standard enthalpy of combustion of pure chemical compounds. A total of 4590 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient (<i>R</i>²) of 0.999 99, root mean square error of 12.57 kJ/mol, and average absolute deviation lower than 0.16% for the estimated properties from existing experimental values

Computation of Upper Flash Point of Chemical Compounds Using a Chemical Structure-Based Model

In this communication, a quantitative structure–property relationship (QSPR) is presented for an estimation of the upper flash point of pure compounds. The model is a multilinear equation that has eight parameters. All the parameters are solely computed based on chemical structure. To develop this model, more than 3000 parameters were evaluated using the Genetic Algorithm Multivariate Linear Regression (GA-MLR) method to select the most statistically effective ones. The maximum average absolute relative deviation (mARD), ARD, squared correlation coefficient, and root mean squares of error of the model from database (DIPPR 801) values for 1294 pure compounds are 25.76%, 3.56%, 0.95, and 17.42 K, respectively

QSPR Molecular Approach for Estimating Henry’s Law Constants of Pure Compounds in Water at Ambient Conditions

In this article, we present a comprehensive quantitative structure–property relationship (QSPR) to estimate the Henry’s law constant (<i>H</i>) of pure compounds in water at ambient conditions. This relationship is a multilinear equation containing eight chemical-structure-based parameters. The parameters were selected by the genetic algorithm multivariate linear regression (GA-MLR) method using more than 3000 molecular descriptors. The squared correlation coefficient of the model (<i>R</i>²) over 1954 pure compounds is equal to 0.983 (logarithmic-based data). Therefore, the model is comprehensive and accurate enough to be used to predict the Henry’s law constants of various compounds in water