Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Recent Trends in Polymeric Foams and Porous Structures for Electromagnetic Interference Shielding Applications

Polymer-based (nano)composite foams containing conductive (nano)fillers limit electromagnetic interference (EMI) pollution, and have been shown to act as good shielding materials in electronic devices. However, due to their high (micro)structural complexity, there is still a great deal to learn about the shielding mechanisms in these materials; understanding this is necessary to study the relationship between the properties of the microstructure and the porous structure, especially their EMI shielding efficiency (EMI SE). Targeting and controlling the electrical conductivity through a controlled distribution of conductive nanofillers are two of the main objectives when combining foaming with the addition of nanofillers; to achieve this, both single or combined nanofillers (nanohybrids) are used (as there is a direct relationship between electrical conductivity and EMI SE), as are the main shielding mechanisms working on the foams (which are expected to be absorption-dominated). The present review considers the most significant developments over the last three years concerning polymer-based foams containing conductive nanofillers, especially carbon-based nanofillers, as well as other porous structures created using new technologies such as 3D printing for EMI shielding applications. It starts by detailing the microcellular foaming strategy, which develops polymer foams with enhanced EMI shielding, and it particularly focuses on technologies using supercritical CO2 (sCO2). It also notes the use of polymer foams as templates to prepare carbon foams with high EMI shielding performances for high temperature applications, as well as a recent strategy which combines different functional (nano)fillers to create nanohybrids. This review also explains the control and selective distribution of the nanofillers, which favor an effective conductive network formation, which thus promotes the enhancement of the EMI SE. The recent use of computational approaches to tailor the EMI shielding properties are given, as are new possibilities for creating components with varied porous structures using the abovementioned materials and 3D printing. Finally, future perspectives are discussed.

Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) Bionanocomposites with Crystalline Nanocellulose and Graphene Oxide: Experimental Results and Support Vector Machine Modeling

Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) is a biodegradable and biocompatible bacterial copolymer used in the biomedical and food industries. However, it displays low stiffness and strength for certain applications. This issue can be solved via reinforcement with nanofillers. In this work, PHBHHx-based bionanocomposites reinforced with different loadings of crystalline nanocellulose (CNC) and graphene oxide (GO) were developed by a green and straightforward solution casting technique. Their crystalline nature and surface topography were explored via X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM), respectively, their composition was corroborated via Fourier-transformed infrared spectroscopy (FTIR), and their crystallization and melting behavior were determined via differential scanning calorimetry (DSC). The nanofillers had a nucleating role, raising the crystallization temperature of the polymer, whilst hardly any changes were found in the melting temperature. Further, significant enhancements in the stiffness, strength, and thermal stability of the PHBHHx matrix were observed with the incorporation of both nanofillers, which was attributed to a synergic effect. The mechanical properties for various concentrations of CNC and GO were accurately predicted using a machine learning (ML) model in the form of a support vector machine (SVM). The model performance was evaluated in terms of the mean absolute error (MAE), the mean square error (MSE), and the correlation coefficient (R2). These bio-based nanocomposites are a valuable alternative to conventional petroleum-based synthetic polymeric materials used nowadays for biomedicine and food packaging applications.

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Predicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV)-based bionanocomposites reinforced with graphene oxide (GO) and montmorillonite nanoclay were prepared herein via an environmentally friendly electrochemical process followed by solution casting. The aim was to evaluate the effectiveness of different Machine Learning (ML) models, namely Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM), in predicting their mechanical properties. The algorithms' input data were the Young's modulus, tensile strength, and elongation at break for various concentrations of the nanofillers (GO and nanoclay). The correlation coefficient (R2), mean absolute error (MAE), and mean square error (MSE) were used as statistical indicators to assess the performance of the models. The results demonstrated that ANN and SVM are useful for estimating the Young's modulus and elongation at break, with MSE values in the range of 0.64-1.0% and 0.14-0.28%, respectively. On the other hand, DT was more suitable for predicting the tensile strength, with the indicated error in the range of 0.02-9.11%. This study paves the way for the application of ML models as confident tools for predicting the mechanical properties of polymeric nanocomposites reinforced with different types of nanofiller, with a view to using them in practical applications such as biomedicine.

The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

Prediction and Control of Thermal Transport at Defective State Gr/h-BN Heterojunction Interfaces

The control of interfacial thermal conductivity is the key to two-dimensional heterojunction in semiconductor devices. In this paper, by using non-equilibrium molecular dynamics (NEMD) simulations, we analyze the regulation of interfacial thermal energy transport in graphene (Gr)/hexagonal boron nitride (h-BN) heterojunctions and reveal the variation mechanism of interfacial thermal energy transport. The calculated results show that 2.16% atomic doping can effectively improve interfacial heat transport by more than 15.6%, which is attributed to the enhanced phonon coupling in the mid-frequency region (15-25 THz). The single vacancy in both N and B atoms can significantly reduce the interfacial thermal conductivity (ITC), and the ITC decreases linearly with the increase in vacancy defect concentration, mainly due to the single vacancy defects leading to an increased phonon participation rate (PPR) below 0.4 in the low-frequency region (0-13 THz), which shows the phonon the localization feature, which hinders the interfacial heat transport. Finally, a BP neural network algorithm is constructed using machine learning to achieve fast prediction of the ITC of Gr/h-BN two-dimensional heterogeneous structures, and the results show that the prediction error of the model is less than 2%, and the method will provide guidance and reference for the design and optimization of the ITC of more complex defect-state heterogeneous structures.

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.