•Hydrodynamic and thermal slip is influential microscopic mechanisms in convection.•Microscopic mechanisms are correlated, but can also be controlled separately.•Influence and strength of microscopic ...mechanisms depend on atomistic behaviors.•Ratio of thermal slip to system size is the key factor in high-heat-flux convection.•Correlations between convection and relative thermal slip are suggested.
Convection heat transfer is assessed for laminarly flowing liquid water through graphene nanochannels via molecular dynamics (MD) simulations. The use of MD simulations allows for direct assessment of the minute details and mechanisms influencing overall heat transfer behaviors within our study; despite the presence of unrealistic axial conduction from temperature resetting and periodic boundary conditions within MD, hydrodynamically and thermally fully-developed water flow conditions are observed. It is indicated that the physics of convective heat transfer deviate from traditional macroscale theory as the no-slip boundary condition is violated with dimensional sizes descending towards the nanoscale; investigation into hydrodynamic slip and thermal slip, termed microscopic mechanisms, is performed for their influence on nanoscale convective outcomes. The parameters of graphene-water interaction strength, channel height, water velocity, and wall temperature are manipulated to evaluate resultant convection behaviors while comparing the effects of differing magnitudes of microscopic mechanisms imposed under various test conditions. This study finds microscopic interfacial mechanisms to significantly augment momentum and thermal behaviors and thus the conduct of convective heat transfer. Hydrodynamic and thermal slip are strongly correlated in all test case scenarios with the exception of velocity manipulation; the influence of thermal slip is found to dominate over that of hydrodynamic slip as surface advection is insignificant in high heat flux environments. Convective performance correlation is suggested as the ratio of thermal slip length to system size.
•Graphene in-plane phonon transport is largely suppressed by Si dopant scattering.•Graphene/SiO2 interfacial transport is enhanced by Si doping (or lower resistance).•Weakened bonding by Si and their ...atomic mass increase the interfacial transport.•Effects of dopant's mass and interaction mismatch on phonon transport were found.•Doping effects on phonon kinetics were quantified using molecular dynamics.
The effects of silicon (Si) doping on the in-plane and cross-plane thermal transport of suspended and silicon dioxide (SiO2) supported graphene were investigated via molecular dynamics simulations. Due to the large mismatch in atomic mass and interaction with neighboring carbon atoms, Si can act as an effective phonon scatterer, thus suppressing the thermal transport. In this study, we evaluated the contributions of mass and interaction mismatches of Si dopants to the reduction in the in-plane thermal conductivity and the cross-plane thermal resistance through systematic control of the dopant's properties. 2% Si doping reduces the in-plane transport of suspended graphene by ~94% due to the increased scattering, while the SiO2-supported graphene is less affected. The phonon scattering by Si linearly increases with the Si content, and the interaction mismatch has a greater influence on the phonon kinetics during in-plane transport than the mass mismatch. In contrast, the cross-plane transport is enhanced by Si doping, decreasing the interfacial thermal resistance by ~30%, because of the stronger interfacial interactions by weaker in-plane bonding and the smaller atomic mass mismatch with the substrate material. The enhanced understanding of doping effects on thermal transport from this research is expected to provide insights for effective thermal transport control in various graphene structures.
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Thermal rectification in defect-engineered graphene with asymmetric hole arrangements is assessed via molecular dynamics simulations. Asymmetry in two different configurations (triangular and ...rectangular hole arrangements) is controlled by manipulating geometrical parameters, such as hole size; effects of geometry on the resultant rectification are investigated. Filtering of phonon propagation directions by geometrical confinement, and asymmetric relaxation distance induce a difference in heat transfer depending on transport direction, or thermal rectification. Increase in porosity, which results in additional confinement and larger difference in relaxation, produces more significant thermal rectification. While a rectangular arrangement of holes results in 70% of the maximum thermal rectification, up to 78% of rectification was achieved using a triangular arrangement within 47.5 nm of graphene, which can be attributed to more effective phonon-hole boundary scattering with a triangular arrangement. This study suggests a feasible approach to create thermal rectification and enables its fine control, contributing to the development of phononic devices and enhancement of thermal system design for electronics.
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Redirection of energy carrier propagation by geometric confinement is studied through the analysis of in-plane and cross-plane thermal transport within various graphene nanomesh (GNM) configurations ...using molecular dynamics (MD) simulations. As the transport channel width decreases with an increase in porosity, the effect of redirection increases; thus, the in-plane thermal conductivity of large-porosity GNM is more dependent on hole arrangement. Since higher porosities weaken the GNM structure due to a larger population of broken bonds, carbon atoms within the graphene structures are more easily influenced by interactions with the substrate silicon (Si) block. Subsequently, increase in porosity leads to the decrease of interfacial thermal resistance. At higher porosities, lower interfacial resistance and in-plane thermal conductivity cause diversions (and redirections) in heat flow from the GNM to the underlying Si substrate. Our study suggests that this method of heat flow redirection can be applied as an effective means to control and manage heat transfer within numerous applications; extension to the improved conductivity calculation accuracy can also be achieved through the inclusion of this diversion analysis.
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•Deformation and transformation of θ′-Al2 Cu precipitate in Al matrix is studied.•Directional dependence of diffusion properties is reported.•Mechanism of possible phase ...transformation from θ′-Al2 Cu to θ -Al2 Cu is unraveled.•Metallurgical technique is proposed to maintain the precipitate stability.
Metastable θ′-Al2Cu precipitates enhance mechanical properties of Al alloy, but gradually transform to θ phase due to diffusion related coarsening at higher temperatures (>200 °C). To improve understanding of the phase instability and transformation of θ′-Al2Cu, we investigated the interfacial atomic mobility of θ′-Al2Cu precipitate in Al matrix via molecular dynamics (MD). To characterize the interfacial atomic mobility, diffusion properties (activation energy, diffusivity, and jump attempt frequency) with respect to atomic species, interfacial structure, and temperature are calculated using atomic trajectories from MD. For the enhanced accuracy of this analysis, especially at low temperatures, temporal scale of molecular dynamics is significantly extended by employing the parallel replica dynamics. This study, as the first MD investigation on atomic diffusion of θ′-Al2Cu in Al matrix, suggests and discusses (i) structural deformation activated at the semi-coherent θ′-Al2Cu/Al interfaces, (ii) transformation initiated at the edge of the coherent θ′-Al2Cu/Al interface, and (iii) directional dependence of diffusion properties. These findings are expected to be employed in a larger scale phase-field modeling of precipitate growth, and to contribute to the understanding of phase transformation process and the development of alloys with θ′-phase retained to higher temperatures.
A large number of microstructural parameters and a wide range of transport physics impose challenges on thermal transport analysis of alloy. Herein, modern data science techniques are employed to ...overcome the challenges, pursuing effective calculation of thermal transport properties. This emerging approach is tested for precipitate‐hardened aluminum (Al) alloy with consideration of precipitate morphology. The finite element method (FEM) is employed to create a database of effective thermal conductivity of hypothetical Al alloys with varying precipitate morphological and thermal transport features. Using the FEM‐generated data sets, the correlation analysis is conducted to qualitatively evaluate the importance of various precipitate features. The correlation analysis identifies the surface area, average diameter, and volume fraction of precipitates as the most descriptive features for determining the thermal conductivity of alloys. Afterward machine learning (ML) models are trained to accurately predict the effective thermal conductivity. Comparing the ML predictions with effective thermal conductivity and microstructural information from experiments, precipitate thermal transport properties can be calculated, such as interfacial conductance between Al matrix and precipitate, without atomistic simulations. This research demonstrates the feasibility of data‐driven approaches for effective thermal transport calculation and the promise of the FEM‐generated data analysis for more comprehensive evaluation of metallic alloys.
Modern data analytics are applied to metallic alloy thermal transport analysis. A database of effective thermal conductivity of aluminum alloy with varying precipitate features is created, employing the finite element method. Correlation analysis identifies the most significant features in determining the effective thermal conductivity, and machine learning is used to predict the alloy thermal transport and to calculate elementary thermal transport properties.
•Quantify uncertainty in bulk thermal conductivity predictions for Si.•Sensitivity analysis of Stillinger–Weber potential parameters.•Reduced-order surrogate for uncertainty propagation.•Calibration ...of potential parameters in a Bayesian setting.
Bulk thermal conductivity estimates based on predictions from non-equilibrium molecular dynamics (NEMD) using the so-called direct method are known to be severely under-predicted since finite simulation length-scales are unable to mimic bulk transport. Moreover, subjecting the system to a temperature gradient by means of thermostatting tends to impact phonon transport adversely. Additionally, NEMD predictions are tightly coupled with the choice of the inter-atomic potential and the underlying values associated with its parameters. In the case of silicon (Si), nominal estimates of the Stillinger-Weber (SW) potential parameters are largely based on a constrained regression approach aimed at agreement with experimental data while ensuring structural stability. However, this approach has its shortcomings and it may not be ideal to use the same set of parameters to study a wide variety of Si-based systems subjected to different thermodynamic conditions. In this study, NEMD simulations are performed on a Si bar to investigate the impact of bar-length, and the applied temperature gradient on the discrepancy between predictions and the available measurement for bulk thermal conductivity at 300 K by constructing statistical response surfaces at different temperatures. The approach helps quantify the discrepancy, observed to be largely dependent on the system-size, with minimal computational effort. A computationally efficient approach based on derivative-based sensitivity measures to construct a reduced-order polynomial chaos surrogate for NEMD predictions is also presented. The surrogate is used to perform parametric sensitivity analysis, forward propagation of the uncertainty, and calibration of the important SW potential parameters in a Bayesian setting. It is found that only two (out of seven) parameters contribute significantly to the uncertainty in bulk thermal conductivity estimates for Si.
•DFT calculations predict two distinct isomers for Si defects in strained graphene.•STEM images capture transitions between distinct Si structures with the same bonding.•Anisotropic strain can be ...generated and also exposed by Si defects in graphene.
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In the last decade, the atomically-focused electron beams utilized in scanning transmission electron microscopes (STEMs) have been shown to induce a broad set of local structural transformations in materials, opening pathways for directing material synthesis and modification atom-by-atom. The mechanisms underlying these transformations remain largely unknown, due to the intractability of modeling the myriad of reaction pathways that can be accessed through high-energy electron scattering. The information on materials’ structure and dynamics that can be extracted from STEM images is similarly left underexplored. Here, we report the observation of anomalous on-site dynamics of individual silicon impurity atoms in graphene during STEM imaging. Density functional theory-based structural optimizations of anisotropically-strained molecular nanographenes reveal two distinct (but nearly degenerate) stable structures for four-fold coordinated silicon impurities, where interconversion between the two structures manifests slight changes of the silicon position within the lattice site. Implications for defect-based strain engineering in graphene are discussed.
Structure, bonding, and chemical dynamics of reactions at surfaces and interfaces, and therefore most material properties are intrinsically tied to the energetic landscape in which each atom resides. ...Here, we demonstrate that a moving atom under electron beam excitation can be used to probe the energy landscape along (confined) step edges, providing information about atomic-scale potentials. In conclusion, the techniques for experimentally exploring atomic potentials holds promise for predictive atom-by-atom fabrication using electron beams.
The broad incorporation of microscopic methods is yielding a wealth of information on the atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. ...Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here, we develop a method for the stochastic reconstruction of effective local potentials solely from observed structural data collected from molecular dynamics simulations (i.e., data analogous to those obtained via atomically resolved microscopies). Using the silicon vacancy defect in graphene as a model, we apply the statistical framework presented herein to reconstruct the free energy landscape from the calculated atomic displacements. Evidence of consistency between the reconstructed local potential and the trajectory data from which it was produced is presented, along with a quantitative assessment of the uncertainty in the inferred parameters.
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