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1. Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels

   https://doi.org/10.1088/2399-1984/ac3c8f  

   Zheng, Bowen; Zheng, Zeyu; Gu, Grace X (December 2021, Nano Futures)

   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. 

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2. Thermoresponsive Conductivity of Graphene‐Based Fibers

   https://doi.org/10.1002/smll.202204981  

   Salim, Muhammad G.; Vasudevan, Vaibhav; Schulman, Nicholas; Zamani, Somayeh; Kersey, Kyle D.; Joshi, Yash; AlAmer, Mohammed; Choi, Ji Il; Jang, Seung Soon; Joo, Yong Lak (February 2023, Small)

   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. 

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3. Engineer Energy Dissipation in 3D Graphene Nanolattice Via Reversible Snap-Through Instability

   https://doi.org/10.1115/1.4045544  

   Ni, Bo; Gao, Huajian (March 2020, Journal of Applied Mechanics)

   Abstract Carbon micro/nanolattice materials, defined as three-dimensional (3D) architected metamaterials made of micro/nanoscale carbon constituents, have demonstrated exceptional mechanical properties, including ultrahigh specific strength, stiffness, and extensive deformability through experiments and simulations. The ductility of these carbon micro/nanolattices is also important for robust performance. In this work, we present a novel design of using reversible snap-through instability to engineer energy dissipation in 3D graphene nanolattices. Inspired by the shell structure of flexible straws, we construct a type of graphene counterpart via topological design and demonstrate its associated snap-through instability through molecular dynamics (MD) simulations. One-dimensional (1D) straw-like carbon nanotube (SCNT) and 3D graphene nanolattices are constructed from a unit cell. These graphene nanolattices possess multiple stable states and are elastically reconfigurable. A theoretical model of the 1D bi-stable element chain is adopted to understand the collective deformation behavior of the nanolattice. Reversible pseudoplastic behavior with a finite hysteresis loop is predicted and further validated via MD. Enhanced by these novel energy dissipation mechanisms, the 3D graphene nanolattice shows good tolerance of crack-like flaws and is predicted to approach a specific energy dissipation of 233 kJ/kg in a loading cycle with no permanent damage (one order higher than the energy absorbed by carbon steel at failure, 16 kJ/kg). This study provides a novel mechanism for 3D carbon nanolattice to dissipate energy with no accumulative damage and improve resistance to fracture, broadening the promising application of 3D carbon in energy absorption and programmable materials. 

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4. Computational Investigation of the Effect of Network Architecture on Mechanical Properties of Dynamically Cross‐Linked Polymer Materials

   https://doi.org/10.1002/mats.201900008  

   Zanjani, Mehdi B.; Zhang, Borui; Ahammed, Ballal; Chamberlin, Joseph P.; Chakma, Progyateg; Konkolewicz, Dominik; Ye, Zhijiang (April 2019, Macromolecular Theory and Simulations)

   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. 

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5. Exploring Thermal Transport in Electrochemical Energy Storage Systems Utilizing Two-Dimensional Materials: Prospects and Hurdles

   https://doi.org/10.1615/AnnualRevHeatTransfer.2023049365  

   DATTA, DIBAKAR; Lee, Eon Soo (January 2023, Annual Review of Heat Transfer)

   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. 

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