Mechanics of Nanostructured Porous Silica Aerogel Resulting from Molecular Dynamics Simulations

Evolution Process of Fault Silica Aerogel under High Temperatures: A Molecular Dynamics Approach

Building fire will seriously threaten human safety. Silica aerogel with low thermal conductivity and thermal stability as fire-retardant material has been widely used in building fireproof structures. However, the natural fragility of silica aerogel will limit its application. In this work, the effects of faults on the thermal stability of silica aerogel are studied by molecular dynamics simulation with large simulation time (20 ns). Additionally, the atomic model of silica aerogel with random faults is built by a straining structure (tensile strains are 10%, 20%, 30%, and 40%). It is found that when the tensile strain is less than 20%, the silica backbone can remain stable. The effects of faults on the thermal stability can be neglected. The silica backbone thermally vibrates during the heating process. However, when the tensile strain is over 30%, it is observed that the faults will enhance the silica backbone merging. Silica aerogel can be stable under 800 K. It is believed that the results of this study will pave the way for the development of fireproof materials.

Energy Formulation for Infinite Structures: Order Parameter for Percolation, Critical Bonds, and Power-Law Scaling of Contact-Based Transport

Investigating heterogeneous materials' microstructure, often simulated using periodic images, is crucial for understanding their physical traits. We propose a generic spring-based representation for periodic two-component structures. The equilibrium energy in this framework serves as an order parameter, offering an analytical expression for wrapping and introducing the concept of critical bonds. We show that these minimum bonds for depercolation can be efficiently detected. The number of critical bonds scales with system size, accurately capturing contact-based transport's scaling. This approach holds potential to analyze functional robustness of networks.

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.

Deep reinforcement learning for microstructural optimisation of silica aerogels

Silica aerogels are being extensively studied for aerospace and transportation applications due to their diverse multifunctional properties. While their microstructural features dictate their thermal, mechanical, and acoustic properties, their accurate characterisation remains challenging due to their nanoporous morphology and the stochastic nature of gelation. In this work, a deep reinforcement learning (DRL) framework is presented to optimise silica aerogel microstructures modelled with the diffusion-limited cluster-cluster aggregation (DLCA) algorithm. For faster computations, two environments consisting of DLCA surrogate models are tested with the DRL framework for inverse microstructure design. The DRL framework is shown to effectively optimise the microstructure morphology, wherein the error of the material properties achieved is dependent upon the complexity of the environment. However, in all cases, with adequate training of the DRL agent, material microstructures with desired properties can be achieved by the framework. Thus, the methodology provides a resource-efficient means to design aerogels, offering computational advantages over experimental iterations or direct numerical solutions.

Using small angle x-ray scattering to examine the aggregation mechanism in silica nanoparticle-based ambigels for improved optical clarity

Silica-based aerogels are a promising low-cost solution for improving the insulation efficiency of single-pane windows and reducing the energy consumption required for space heating and cooling. Two key material properties required are high porosity and small pore sizes, which lead to low thermal conductivity and high optical transparency, respectively. However, porosity and pore size are generally directly linked, where high porosity materials also have large pore sizes. This is unfavorable as large pores scatter light, resulting in reduced transmittance in the visible regime. In this work, we utilized preformed silica colloids to explore methods for reducing pore size while maintaining high porosity. The use of preformed colloids allows us to isolate the effect of solution conditions on porous gel network formation by eliminating simultaneous nanoparticle growth and aggregation found when using typical sol-gel molecular-based silica precursors. Specifically, we used in situ synchrotron-based small-angle x-ray scattering during gel formation to better understand how pH, concentration, and colloid size affect particle aggregation and pore structure. Ex situ characterization of dried gels demonstrates that peak pore widths can be reduced from 15 to 13 nm, accompanied by a narrowing of the overall pore size distribution, while maintaining porosities of 70%-80%. Optical transparency is found to increase with decreasing pore sizes while low thermal conductivities ranging from 95 +/- 13 mW/m K are maintained. Mechanical performance was found to depend primarily on effective density and did not show a significant dependence on solution conditions. Overall, our results provide insights into methods to preserve high porosity in nanoparticle-based aerogels while improving optical transparency.

Evaluation of the mechanical properties of hydroxyapatite-silica aerogel/epoxy nanocomposites: Optimizing by response surface approach

This study attempted to investigate the effects of adding hydroxyapatite and silica aerogel nanoparticles on the density, tensile, Izod impact, and morphological properties of epoxy using the Response Surface Methodology (RSM). RSM relied on Box-Behnken Design (BBD) was used to design the mechanical tests. The concurrent effects of two parameters including hydroxyapatite content and silica aerogels content on the mechanical properties have been evaluated. Finally, by using the equations obtained from regression for each of the responses, their optimal states were obtained using both the desirability approach and the Multi-Objective Particle Swarm Optimization (MOPSO) method. The results from tensile, and Izod impact tests indicated the combination of hydroxyapatite and silica aerogel nanoparticles led to an improvement in the tensile properties and energy absorption of epoxy matrix. The findings related to density test demonstrated that with addition of silica aerogel to the hydroxyapatite/epoxy nanocomposites, density of these samples was decreased. The maximum tensile strength of 86.9 MPa was obtained with hydroxyapatite content of 2.38 wt% and silica aerogels content of 4 wt%. Also, the maximum impact strength of 18.14 kJ/m2 was obtained with hydroxyapatite content of 1.11 wt% and silica aerogels content of 3.51 wt%. The field emission scanning electron microscope images showed the homogeneous distribution of combined hydroxyapatite and silica aerogel nanoparticles in epoxy matrix, except in 5 wt% of hydroxyapatite nanoparticles.

Effects of Strain Rate and Temperature on the Mechanical Properties of Simulated Silica Ionogels

Ionogels are hybrid materials formed by impregnating the pore space of a solid matrix with a conducting ionic liquid. By combining the properties of both component materials, ionogels can act as self-supporting electrolytes in Li batteries. In this study, molecular dynamics simulations are used to investigate the dependence of mechanical properties of silica ionogels on solid fraction, temperature, and pore width. Comparisons are made with corresponding aerogels. We find that the solid matrix fraction increases the moduli and strength of the ionogel. This varies nonlinearly with temperature and strain rate, according to the contribution of the viscous ionic liquid to resisting deformation. Owing to the temperature and strain sensitivity of the ionic liquid viscosity, the mechanical properties approach a linear mixing law at high temperature and low strain rates. The median pore width of the solid matrix plays a complex role, with its influence varying qualitatively with deformation mode. Narrower pores increase the relevant elastic modulus under shear and uniaxial compression but reduce the modulus obtained under uniaxial tension. Conversely, shear and tensile strength are increased by narrowing the pore width. All of these pore size effects become more pronounced as the silica fraction increases. Pore size effects, similar to the effects of temperature and strain rate, are linked to the ease of fluid redistribution within the pore space during deformation-induced changes in the geometry of the pores.

3D Atomic-Scale Dynamics of Laser-Light-Induced Restructuring of Nanoparticles Unraveled by Electron Tomography

Understanding light-matter interactions in nanomaterials is crucial for optoelectronic, photonic, and plasmonic applications. Specifically, metal nanoparticles (NPs) strongly interact with light and can undergo shape transformations, fragmentation and ablation upon (pulsed) laser excitation. Despite being vital for technological applications, experimental insight into the underlying atomistic processes is still lacking due to the complexity of such measurements. Herein, atomic resolution electron tomography is performed on the same mesoporous-silica-coated gold nanorod, before and after femtosecond laser irradiation, to assess the missing information. Combined with molecular dynamics (MD) simulations based on the experimentally determined 3D atomic-scale morphology, the complex atomistic rearrangements, causing shape deformations and defect generation, are unraveled. These rearrangements are simultaneously driven by surface diffusion, facet restructuring, and strain formation, and are influenced by subtleties in the atomic distribution at the surface.

Constitutive Modeling of the Densification Behavior in Open-Porous Cellular Solids

The macroscopic mechanical behavior of open-porous cellular materials is dictated by the geometric and material properties of their microscopic cell walls. The overall compressive response of such materials is divided into three regimes, namely, the linear elastic, plateau and densification. In this paper, a constitutive model is presented, which captures not only the linear elastic regime and the subsequent pore-collapse, but is also shown to be capable of capturing the hardening upon the densification of the network. Here, the network is considered to be made up of idealized square-shaped cells, whose cell walls undergo bending and buckling under compression. Depending on the choice of damage criterion, viz. elastic buckling or irreversible bending, the cell walls collapse. These collapsed cells are then assumed to behave as nonlinear springs, acting as a foundation to the elastic network of active open cells. To this end, the network is decomposed into an active network and a collapsed one. The compressive strain at the onset of densification is then shown to be quantified by the point of intersection of the two network stress-strain curves. A parameter sensitivity analysis is presented to demonstrate the range of different material characteristics that the model is capable of capturing. The proposed constitutive model is further validated against two different types of nanoporous materials and shows good agreement.

Cellular Automata Modeling of Silica Aerogel Condensation Kinetics

The formation of silica aerogels and the kinetics of condensation were investigated numerically. The influence of the reaction-limited to the diffusion-limited aggregation (RLA to DLA) transition on the reaction kinetics curves and the evolution of the aggregate size distribution during condensation were examined. The 2D cellular automaton was developed and applied to reflect the process of secondary particle aggregation. Several tendencies were observed due to the adjustment of the model parameters: the probability of condensation reaction and the particles' concentration. The final wet-gel structures' visualizations proves that the structure becomes more dense and compact due to entering the RLA regime. The simulation time (associated with the gelation time) decreased along with the increase in both model parameters. The lower the collision probability, the slower reaction becomes, and particles are more likely to penetrate the structure deeper until they finally join the aggregate. The developed model reflects the condensation process's nature and its mechanisms properly and indicates a significant potential for further aerogel synthesis investigations and for the prediction of wet-gel properties according to condensation parameters.

Modeling and Simulation of the Aggregation and the Structural and Mechanical Properties of Silica Aerogels

Mechanical properties of aerogels are controlled by the connectivity of their network. In this paper, in order to study these properties, computational models of silica aerogels with different morphological entities have been generated by means of the diffusion-limited cluster-cluster aggregation (DLCA) algorithm. New insights into the influence of the model parameters on the generated aerogel structures and on the finite deformation under mechanical loads are provided. First, the structural and fractal properties of the modeled aerogels are investigated. The dependence of morphological properties such as the particle radius and density on these properties is studied. The results are correlated with experimental small-angle X-ray scattering (SAXS) data of a silica aerogel. The DLCA models of silica aerogels are analyzed for their mechanical properties with finite element simulations. There, the aerogel particles are modeled as nodes and the interparticle bonds as beam elements to account for bond stretching, bending, and torsion. The scaling relation between the elastic moduli E and relative density ρ, E ∝ ρ^m, is investigated and the exponent m = 3.61 is determined. Backbone paths evidently appear in the 3-d network structure under deformation, while the majority of the bonds in the network do not bear loads. The sensitivity of particle neck-sizes on the mechanical properties is also studied. All the results are shown to be qualitatively as well as quantitatively in agreement with the experimental data or with the available literature.

Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber

Molecular dynamics (MD) simulation is used to analyze the mechanical properties of polymerized and nanoscale filled rubber. Unfortunately, the computation time for a simulation can require several months’ computing power, because the interactions of thousands of filler particles must be calculated. To alleviate this problem, we introduce a surrogate convolutional neural network model to achieve faster and more accurate predictions. The major difficulty when employing machine-learning-based surrogate models is the shortage of training data, contributing to the huge simulation costs. To derive a highly accurate surrogate model using only a small amount of training data, we increase the number of training instances by dividing the large-scale simulation results into 3D images of middle-scale filler morphologies and corresponding regional stresses. The images include fringe regions to reflect the influence of the filler constituents outside the core regions. The resultant surrogate model provides higher prediction accuracy than that trained only by images of the entire region. Afterwards, we extract the fillers that dominate the mechanical properties using the surrogate model and we confirm their validity using MD.

Advanced Opacified Fiber-Reinforced Silica-Based Aerogel Composites for Superinsulation of Exhaust Tubing Systems in Semi-Stationary Motors

Within this study, monolithic three-dimensional silica aerogel (SA) composite parts with super insulating properties are presented. A generic part based on fiber-reinforced (FR) silica aerogel for thermal insulation of the exhaust tubing system—to keep the exhaust gases as hot as possible to improve the efficiency of the catalyst system—was produced via a sol-gel-based molding process in combination with a supercritical drying using scCO₂. A thermal conductivity of 16 mW m⁻¹ K⁻¹ was measured via a heat flow meter technique. In this manuscript, we present a full cycle of the material compound design, starting with fundamental material evaluation including aerogel optimization, opacifier influence, and casting process. The obtained generic part in shape of a half-shell for pipe insulation is characterized under real conditions.

A Review on Brittle Fracture Nanomechanics by All-Atom Simulations

Despite a wide range of current and potential applications, one primary concern of brittle materials is their sudden and swift collapse. This failure phenomenon exhibits an inability of the materials to sustain tension stresses in a predictable and reliable manner. However, advances in the field of fracture mechanics, especially at the nanoscale, have contributed to the understanding of the material response and failure nature to predict most of the potential dangers. In the following contribution, a comprehensive review is carried out on molecular dynamics (MD) simulations of brittle fracture, wherein the method provides new data and exciting insights into fracture mechanism that cannot be obtained easily from theories or experiments on other scales. In the present review, an abstract introduction to MD simulations, advantages, current limitations and their applications to a range of brittle fracture problems are presented. Additionally, a brief discussion highlights the theoretical background of the macroscopic techniques, such as Griffith's criterion, crack tip opening displacement, J-integral and other criteria that can be linked to the fracture mechanical properties at the nanoscale. The main focus of the review is on the recent advances in fracture analysis of highly brittle materials, such as carbon nanotubes, graphene, silicon carbide, amorphous silica, calcium carbonate and silica aerogel at the nanoscale. These materials are presented here due to their extraordinary mechanical properties and a wide scope of applications. The underlying review grants a more extensive unravelling of the fracture behaviour and mechanical properties at the nanoscale of brittle materials.

Modelling of Mechanical Behavior of Biopolymer Alginate Aerogels Using the Bonded-Particle Model

A novel mesoscale modelling approach for the investigation of mechanical properties of alginate aerogels is proposed. This method is based on the discrete element method and bonded-particle model. The nanostructure of aerogel is not directly considered, instead the highly porous structure of aerogels is represented on the mesoscale as a set of solid particles connected by solid bonds. To describe the rheological material behavior, a new elastic-plastic functional model for the solids bonds has been developed. This model has been derived based on the self-similarity principle for the material behavior on the macro and mesoscales. To analyze the effectiveness of the proposed method, the behavior of alginate aerogels with different crosslinking degrees (calcium content) was analyzed. The comparison between experimental and numerical results has shown that the proposed approach can be effectively used to predict the mechanical behavior of aerogels on the macroscale.

Nanoindentation of Graphene-Reinforced Silica Aerogel: A Molecular Dynamics Study

In the present work, we performed nanoindentation tests using molecular dynamics (MD) simulations on graphene, native silica aerogels, and single- and multi-layered graphene-reinforced silica aerogel nanocomposites. This work mainly focused on the two aspects of nanoindentation simulations: first, the resultant indentation force-depth curves, and second, the associated mechanical deformation behavior. We found that in the single-layer graphene-reinforced silica aerogel nanocomposite, the indentation resistance was four-fold that of native silica aerogels. Moreover, the combined system proved to be higher in stiffness compared to the individual material. Furthermore, the indentation resistance was increased significantly as we proceeded from single- to two-layered graphene-reinforced silica aerogel nanocomposites. The results of the study provide a detailed understanding of the mechanical behavior during the indentation tests of nanocomposites, which helps to design advanced nanoscale multi-layered materials.