A supervised learning-assisted multi-scale study for thermal and mechanical behavior of porous Silica.

This paper presents a comprehensive investigation of mesoporous Silica utilizing a multi-scale modeling approach under periodic boundary conditions integrated with machine learning algorithms. The study begins with Molecular Dynamics (MD) simulations to extract Silica's elastic properties and thermal conductivity at the nano-scale, employing the Tersoff potential. Subsequently, the derived material characteristics are applied to a series of generated porous Representative Volume Elements (RVEs) at the microscale. This phase involves the exploration of porosity and void shape effects on Silica's thermal and mechanical properties, considering inhomogeneities' distributions along the X-axis and random dispersion of pore cells within a three-dimensional space. Furthermore, the influence of pore shape is examined by defining open and closed-cell models, encompassing spherical and ellipsoidal voids with aspect ratios of 2 and 4. To predict the properties of porous Silica, a shallow Artificial Neural Network (ANN) is deployed, utilizing geometric parameters of the RVEs and porosity. Subsequently, it is revealed that Silica's thermal and mechanical behavior is linked to pore geometry, distribution, and porosity model. Finally, to classify the behavior of porous Silica into three categories, quasi-isotropic, orthotropic, and transversely-isotropic, three methodologies of decision tree approach, K-Nearest Neighbors (KNN) algorithm, and Support Vector Machines (SVMs) are employed. Among these, SVMs employing a quadratic kernel function demonstrate robust performance in categorizing the thermal and mechanical behavior of porous Silica.

Data-driven supervised machine learning to predict the compressive response of porous PVA/Gelatin hydrogels and in-vitro assessments: Employing design of experiments.

The purpose of this study is to design and evaluate a series of porous hydrogels by considering three independent variables using the Box-Behnken method. Accordingly, concentrations of the constituent macromolecules of the hydrogels, Polyvinyl Alcohol and Gelatin, and concentration of the crosslinking agent are varied to fabricate sixteen different porous samples utilizing the lyophilization process. Subsequently, the porous hydrogels are subjected to a battery of tests, including Fourier Transform Infrared spectroscopy, morphology assessment, pore-size study, porosimetry, uniaxial compression, and swelling measurements. Additionally, in-vitro cell assessments are performed by culturing mouse fibroblast cells (L-929) on the hydrogels, where viability, proliferation, adhesion, and morphology of the L-929 cells are monitored over 24, 48, and 72 h to evaluate the biocompatibility of these biomaterials. To better understand the mechanical behavior of the hydrogels under compressive loadings, Deep Neural Networks (DNNs) are implemented to predict and capture their compressive stress-strain responses as a function of the constituent materials' concentrations and duration of the performed mechanical tests. Overall, this study emphasizes the importance of considering multiple variables in the design of porous hydrogels, provides a comprehensive evaluation of their mechanical and biological properties, and, particularly, implements DNNs in the prediction of the hydrogels' stress-strain responses.

Application of artificial neural networks to predict Young's moduli of cartilage scaffolds: An in-vitro and micromechanical study.

In this study, four-phase Gelatin-Polypyrrole-Akermanite-Magnetite scaffolds were fabricated and analyzed using in-vitro tests and numerical simulations. Such scaffolds contained various amounts of Magnetite bioceramics as much as 0, 5, 10, and 15 wt% of Gelatin-Polypyrrole-Akermanite biocomposite. X-ray diffraction analysis and Fourier transform infrared spectroscopy were conducted. Swelling and degradation of the scaffolds were studied by immersing them in phosphate-buffered saline, PBS, solution. Magnetite bioceramics decreased the swelling percent and degradation duration. By immersing scaffolds in simulated body fluid, the highest formation rate of Apatite was observed in the 15 wt% Magnetite samples. The mean pore size was in an acceptable range to provide suitable conditions for cell proliferation. MG-63 cells were cultured on extracts of the scaffolds for 24, 48, and 72 h and their surfaces for 24 h. Cell viabilities and cell morphologies were assessed. Afterward, micromechanical models with spherical and polyhedral voids and artificial neural networks were employed to predict Young's moduli of the scaffolds. Based on the results of finite element analyses, spherical-shaped void models made the best predictions of elastic behavior in the 0, 5 wt% Magnetite scaffolds compared to the experimental data. Results of the simulations and experimental tests for the ten wt% Magnetite samples were well matched in both micromechanical models. In the 15 wt% Magnetite sample, models with polyhedral voids could precisely predict Young's modulus of such scaffolds.