Impact of synthesis temperature on morphology, rheology and electromagnetic interference shielding of CVD-grown carbon nanotube/polyvinylidene fluoride nanocomposites

Employing chemical vapor deposition technique, multi-walled carbon nanotubes (CNTs) were synthesized over Fe catalyst at a broad range of temperatures, i.e. 550° C to 950° C (at 100° C intervals). CNTs were melt-mixed into a polyvinylidene fluoride (PVDF) matrix at various loadings, and then compression molded. Surprisingly, despite the ascending trend of CNT powder conductivity with the synthesis temperature, the nanocomposites made with CNT synthesized at 650° C had significantly lower percolation threshold (around 0.4 wt%) and higher electromagnetic interference shielding effectiveness (EMI SE) (20.3 dB over the X-band for 3.5 wt% CNT and 1.1 mm thickness) than the other temperatures. Exhaustive characterization studies were conducted on both CNTs and composites to unveil their morphological and electrical characteristics. Superior EMI shielding of CNT650° C was attributed to a combination of high carbon purity, aspect ratio, crystallinity, and moderate powder conductivity along with decent state of dispersion within the PVDF matrix

Computation of upper flash point of chemical compounds using a chemical structure-based model

International audienceIn 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

Impact of synthesis temperature on structure of carbon nanotubes and morphological and electrical characterization of their polymeric nanocomposites

Carbon nanotubes (CNTs) were synthesized by chemical vapor deposition technique at a broad range of temperatures, i.e. 550°C to 950°C (at 100°C intervals). CNTs were synthesized by flowing source and carrier gases (ethane, argon, and hydrogen) over Fe catalyst in a quartz tubular reactor. CNTs were melt mixed with a polyvinylidene fluoride (PVDF) matrix in a miniature mixer. The resulting nanocomposites were then compression molded, and electrically and morphologically characterized. Moreover, a wide range of characterization techniques were employed to obtain detailed information about the physical and morphological characteristics of CNTs. It was surprisingly observed that, despite the ascending trend of powder conductivity with the synthesis temperature, the nanocomposites made with (CNT)650°C had significantly lower percolation threshold (around 0.4wt.%) and higher electromagnetic interference shielding (20.3dB over the X-band for 1.1mm thickness) compared to the other temperatures. The characterization of nanofillers showed that the synthesis yield and quality of (CNT)650°C were highly superior to the other types of CNTs. At 850°C and 950°C, most of the synthesized carbonaceous materials formed graphitic structures around the sintered catalyst particles. It was also observed that the dispersion state of (CNT)650°C within the PVDF matrix was much better than that of CNTs synthesized at the other temperatures. Superior electrical properties of (CNT)650°C nanocomposites can be attributed to a combination of high synthesis yield, low diameter and decent quality of CNTs coupled with good state of dispersion within the PVDF matrix

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

QSPR molecular approach for estimating Henry's law constants of pure compounds in water at ambient conditions

International audienceIn this article, we present a comprehensive quantitative structure-property relationship (QSPR) to estimate the Henry's law constant (H) 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 (R 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

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