Abstract Graphene aerogels (GAs), a special class of 3D graphene assemblies, are well known for their exceptional combination of high strength, lightweightness, and high porosity. However, due to microstructural randomness, the mechanical properties of GAs are also highly stochastic, an issue that has been observed but insufficiently addressed. In this work, we develop Gaussian process metamodels to not only predict important mechanical properties of GAs but also quantify their uncertainties. Using the molecular dynamics simulation technique, GAs are assembled from randomly distributed graphene flakes and spherical inclusions, and are subsequently subject to a quasi-static uniaxial tensile load to deduce mechanical properties. Results show that given the same density, mechanical properties such as the Young’s modulus and the ultimate tensile strength can vary substantially. Treating density, Young’s modulus, and ultimate tensile strength as functions of the inclusion size, and using the simulated GA results as training data, we build Gaussian process metamodels that can efficiently predict the properties of unseen GAs. In addition, statistically valid confidence intervals centered around the predictions are established. This metamodel approach is particularly beneficial when the data acquisition requires expensive experiments or computation, which is the case for GA simulations. The present research quantifies the uncertain mechanical properties of GAs, which may shed light on the statistical analysis of novel nanomaterials of a broad variety.
more »
« less
This content will become publicly available on August 23, 2024
Investigation of mechanical properties and structural integrity of graphene aerogels via molecular dynamics simulations
Graphene aerogel (GA), a 3D carbon-based nanostructure built on 2D graphene sheets, is well known for being the lightest solid material ever synthesized. It also possesses many other exceptional properties, such as high specific surface area and large liquid absorption capacity, thanks to its ultra-high porosity. Computationally, the mechanical properties of GA have been studied by molecular dynamics (MD) simulations, which uncover nanoscale mechanisms beyond experimental observations. However, studies on how GA structures and properties evolve in response to simulation parameter changes, which provide valuable insights to experimentalists, have been lacking. In addition, the differences between the calculated properties via simulations and experimental measurements have rarely been discussed. To address the shortcomings mentioned above, in this study, we systematically study various mechanical properties and the structural integrity of GA as a function of a wide range of simulation parameters. Results show that during the in silico GA preparation, smaller and less spherical inclusions (mimicking the effect of water clusters in experiments) are conducive to strength and stiffness but may lead to brittleness. Additionally, it is revealed that a structurally valid GA in the MD simulation requires the number of bonds per atom to be at least 1.40, otherwise the GA building blocks are not fully interconnected. Finally, our calculation results are compared with experiments to showcase both the power and the limitations of the simulation technique. This work may shed light on the improvement of computational approaches for GA as well as other novel nanomaterials.
more »
« less
- Award ID(s):
- 2119276
- NSF-PAR ID:
- 10451493
- Date Published:
- Journal Name:
- Physical Chemistry Chemical Physics
- Volume:
- 25
- Issue:
- 33
- ISSN:
- 1463-9076
- Page Range / eLocation ID:
- 21897 to 21907
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Chu, Wilson (Ed.)Two-dimensional materials (e.g., graphene and transition metal dichalcogenides) and their heterostructures have enormous applications in electrochemical energy storage systems such as batteries. A comprehensive and solid understanding of these materials’ thermal transport and mechanism is essential for practical device design. Several advanced experimental techniques have been developed to measure the intrinsic thermal conductivity of materials. However, experiments have challenges in providing improved control and characterization of complex structures, especially for low-dimensional materials. Theoretical and simulation tools, such as first-principles calculations, Boltzmann transport equations, molecular dynamics simulations, lattice dynamics simulation, and nonequilibrium Green’s function, provide reliable predictions of thermal conductivity and physical insights to understand the underlying thermal transport mechanism in materials. However, doing these calculations requires high computational resources. The development of new materials synthesis technology and fast-growing demand for rapid and accurate prediction of physical properties requires novel computational approaches. The machine learning method provides a promising solution to address such needs. This review details the recent development in atomistic/molecular studies and machine learning of thermal transport in two-dimensional materials. The paper also addresses the latest significant experimental advances. However, designing the best two-dimensional materials-based heterostructures is like a multivariate optimization problem. For example, a particular heterostructure may be suitable for thermal transport but can have lower mechanical strength/stability. For bilayer and multilayer structures, the interlayer distance may influence the thermal transport properties and interlayer strength. Therefore, the last part of this review addresses the future research direction in two-dimensional materials-based heterostructure design for thermal transport in energy storage systems.more » « less
-
more » « less
Abstract Dynamically cross‐linked polymer networks have attracted significant interest in recent years due to their unique and improved properties including increased toughness, malleability, shape memory, and self‐healing. Here, a computational study on the mechanical behavior of dynamically cross‐linked polymer networks is presented. Coarse grained models for different polymer network configurations are established and their mechanical properties using molecular dynamics (MD) simulations are predicted. Consistent with the experimental measurements, the simulation results show that interpenetrating networks (IPN) withstand considerably higher stress compared to the single networks (SN). Additionally, the MD results demonstrate that the origin of this variation in mechanical behavior of IPN and SN configurations goes back to the difference in spatial degrees of freedom of the noncovalent cross‐linkers, represented by nonbonded interactions within the two system types. The results of this work provide new insight for future studies on the design of systems with dual dynamic cross‐linkers where one linkage exchanges rapidly and provides autonomic dynamic character, while the other is a stimulus responsive dynamic covalent linkage that provides stability with dynamic exchange on‐demand.
-
more » « less
Summary The mechanical and hydraulic properties of unsaturated clay under nonisothermal conditions have practical implications in geotechnical engineering applications such as geothermal energy harvest, landfill cover design, and nuclear waste disposal facilities. The water menisci among clay particles impact the mechanical and hydraulic properties of unsaturated clay. Molecular dynamics (MD) modeling has been proven to be an effective method in investigating clay structures and their hydromechanical behavior at the atomic scale. In this study, we examine the impact of temperature increase on the capillary force and capillary pressure of the partially saturated clay‐water system through high‐performance computing. The water meniscus formed between two parallel clay particles is studied via a full‐scale MD modeling at different elevated temperatures. The numerical results have shown that the temperature increase impacts the capillary force, capillary pressure, and contact angle at the atomic scale. The capillary force on the clay particle obtained from MD simulations is also compared with the results from the macroscopic theory. The full‐scale MD simulation of the partially saturated clay‐water system can not only provide a fundamental understanding of the impact of temperature on the interface physics of such system at the atomic scale, but also has practical implication in formulating physics‐based multiscale models for unsaturated soils by providing interface physical properties of such materials directly through high‐performance computing.
-
more » « less
Abstract Smart materials are versatile material systems which exhibit a measurable response to external stimuli. Recently, smart material systems have been developed which incorporate graphene in order to share on its various advantageous properties, such as mechanical strength, electrical conductivity, and thermal conductivity as well as to achieve unique stimuli‐dependent responses. Here, a graphene fiber‐based smart material that exhibits reversible electrical conductivity switching at a relatively low temperature (60 °C), is reported. Using molecular dynamics (MD) simulation and density functional theory‐based non‐equilibrium Green's function (DFT‐NEGF) approach, it is revealed that this thermo‐response behavior is due to the change in configuration of amphiphilic triblock dispersant molecules occurring in the graphene fiber during heating or cooling. These conformational changes alter the total number of graphene‐graphene contacts within the composite material system, and thus the electrical conductivity as well. Additionally, this graphene fiber fabrication approach uses a scalable, facile, water‐based method, that makes it easy to modify material composition ratios. In all, this work represents an important step forward to enable complete functional tuning of graphene‐based smart materials at the nanoscale while increasing commercialization viability.
