Theoretical Prediction of Electrical Conductivity Percolation of Poly(lactic acid)-Carbon Nanotube Composites in DC and RF Regime.

Polymer composites based on polylactic acid (PLA) reinforced with 0.25-5 wt.% of carbon nanotubes (CNTs) were synthesized by melt blending. The static (DC) and microwave (RF) electrical conductivity have been investigated on the PLA-CNT composites. The electrical percolation threshold has been theoretically determined using classical models of percolation in order to predict the conductivity of the different nanocomposites. Through the fitting process, it has been found that the percolation threshold is obtained at 1 wt.% of CNTs in the DC regime and reached below 0.25 wt.% of CNTs in the microwave regime. Among the Mamunya, McLachlan, or GEM models, the McCullough model remarkably fits the experimental DC and RF electrical conductivities. The obtained results are correlated to the electrical properties of a range of CNT-based composites, corresponding to the percolation threshold required for a three-dimensional network of CNTs into the polymer matrix.

Absorption Performances of PLA-Montmorillonite Nanocomposites Thin Films in Salisbury and Rozanov Configurations: Influence of Aging and Mechanical Recycling.

The present paper aims to address the crucial concern of pollution induced by growing plastic waste and electromagnetic interference (EMI). Nanocomposites combining poly(lactic acid) (PLA) and organo-modified montmorillonite (OMMT) are synthesized and compression molded into thin films. A first set of samples, referred as virgin, was kept as is, while a second set of samples were photochemically, thermally and hydrolytically aged before mechanical recycling via extruding and second compression molding, resulting in the so-called recycled composite. The electromagnetic (EM) properties with a focus on microwave absorption performances of virgin and recycled samples are compared for various thicknesses and weight concentrations of OMMT in PLA matrix. The EM performances are gauges by Rozanov and Salisbury structures that consist in one- and two-layer stacks of composite films back-coated by a metal foil. Characterization in Rozanov configuration shows an average absorption index over the Ka band of 29.3% and 21.1% for, respectively, virgin and recycled PLA reinforced with 4 wt.% OMMT. An optimization of the film thickness is proposed; up to 61.85% and 80% of absorption with a thickness of 1.4 mm and 3.75 mm, respectively, is reached with a metal back-coated rPLA-4%OMMT film. Characterization in Salisbury configuration gives advantage to the recycled structure with an average absorption of 49.6% for a total thickness of 1.4 mm. The requirements of EMI shielding are met by PLA-OMMT composites with a certain benefit of recycling process on EM performance.

A Wearable Low-Power Sensing Platform for Environmental and Health Monitoring: The Convergence Project.

The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NOx, COx, NHx particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO2 sensor based on graphene and CO sensor based on polyaniline ink.

Predictive Optimization of Electrical Conductivity of Polycarbonate Composites at Different Concentrations of Carbon Nanotubes: A Valorization of Conductive Nanocomposite Theoretical Models.

Polycarbonate-carbon nanotube (PC-CNT) conductive composites containing CNT concentration covering 0.25-4.5 wt.% were prepared by melt blending extrusion. The alternating current (AC) conductivity of the composites has been investigated. The percolation threshold of the PC-CNT composites was theoretically determined using the classical theory of percolation followed by numerical analysis, quantifying the conductivity of PC-CNT at the critical volume CNT concentration. Different theoretical models like Bueche, McCullough and Mamunya have been applied to predict the AC conductivity of the composites using a hyperparameter optimization method. Through multiple series of the hyperparameter optimization process, it was found that McCullough and Mamunya theoretical models for electrical conductivity fit remarkably with our experimental results; the degree of chain branching and the aspect ratio are estimated to be 0.91 and 167 according to these models. The development of a new model based on a modified Sohi model is in good agreement with our data, with a coefficient of determination R2=0.922 for an optimized design model. The conductivity is correlated to the electromagnetic absorption (EM) index showing a fine fit with Steffen-Boltzmann (SB) model, indicating the ultimate CNTs volume concentration for microwave absorption at the studied frequency range.

Simulation and Optimization of Electromagnetic Absorption of Polycarbonate/CNT Composites Using Machine Learning.

Electronic devices that transmit, distribute, or utilize electrical energy create electromagnetic interference (EMI) that can lead to malfunctioning and degradation of electronic devices. EMI shielding materials block the unwanted electromagnetic waves from reaching the target material. EMI issues can be solved by using a new family of building blocks constituted of polymer and nanofillers. The electromagnetic absorption index of this material is calculated by measuring the "S-parameters". In this article, we investigated the use of artificial intelligence (AI) in the EMI shielding field by developing a new system based on a multilayer perceptron neural network designed to predict the electromagnetic absorption of polycarbonate-carbon nanotubes composites films. The proposed system included 15 different multilayer perception (MLP) networks; each network was specialized to predict the absorption value of a specific category sample. The selection of appropriate networks was done automatically, using an independent block. Optimization of the hyper-parameters using hold-out validation was required to ensure the best results. To evaluate the performance of our system, we calculated the similarity error, precision accuracy, and calculation time. The results obtained over our database showed clearly that the system provided a very good result with an average accuracy of 99.7997%, with an overall average calculation time of 0.01295 s. The composite based on polycarbonate-5 wt.% carbon nanotube was found to be the ultimate absorber over microwave range according to Rozanov formalism.