Orientation of graphene nanosheets in suspension under an electric field: theoretical model and molecular dynamic simulations.

Orientation regulation of nanoparticles in a suspension by an electric field is a powerful tool to tune its mechanical, thermal, optical, electrical properties etc. However, how molecular modification can affect the orientation of two-dimensional nanoparticles is still unclear. In this paper, the influence of molecular modification on the orientation of graphene nanosheets (GNS) in water was investigated through theoretical analyses and molecular dynamics (MD) simulations. Firstly, a new orientation angle model was proposed, which considers hydration effects, dipole moments and resistance torque. Then, MD simulations were conducted to investigate the effects of position, direction, type, and number of functional groups on the orientation of GNS. The trend observed in MD simulations is consistent with the proposed theoretical model. The results reveal that, under the combined influence of the dipole moment and hydration effects, the modification with hydrophilic functional groups can reduce the orientation angle from 21.31° to 8.34°, while the modification with hydrophobic functional groups increases it to 26.43°. Among the hydrophilic functional groups, orientation of hydroxylated GNS is the best. With an increase in the number of hydroxyl groups, orientation angle is decreased from 12.61° to 8.34°. This work can provide valuable guidance for the design of high-performance suspensions and composites, such as thermal smart materials with adjustable thermal conductivity and intelligent devices with tailored capabilities.

Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations.

Bringing advances in machine learning to chemical science is leading to a revolutionary change in the way of accelerating materials discovery and atomic-scale simulations. Currently, most successful machine learning schemes can be largely traced to the use of localized atomic environments in the structural representation of materials and molecules. However, this may undermine the reliability of machine learning models for mapping complex systems and describing long-range physical effects because of the lack of non-local correlations between atoms. To overcome such limitations, here we report a graph attention neural network as a unified framework to map materials and molecules into a generalizable and interpretable representation that combines local and non-local information of atomic environments from multiple scales. As an exemplary study, our model is applied to predict the electronic structure properties of metal-organic frameworks (MOFs) which have notable diversity in compositions and structures. The results show that our model achieves the state-of-the-art performance. The clustering analysis further demonstrates that our model enables high-level identification of MOFs with spatial and chemical resolution, which would facilitate the rational design of promising reticular materials. Furthermore, the application of our model in predicting the heat capacity of complex nanoporous materials, a critical property in a carbon capture process, showcases its versatility and accuracy in handling diverse physical properties beyond electronic structures.

Pulsed thermoreflectance imaging for thermophysical properties measurement of GaN epitaxial heterostructures.

Multilayer heterostructures composed of a substrate and an epitaxial film are widely utilized in advanced electronic devices. However, thermal bottlenecks constrain their performance and reliability, and efficient approaches to comprehensively measure the thermophysical properties of heterostructures are urgently needed. In this work, a pulsed thermoreflectance imaging (PTI) method is proposed, which combines the transient temperature mapping of thermoreflectance thermal imaging with transient pulsed excitation. By executing merely three transient tests, six thermophysical properties, including the film thermal conductivity and specific heat capacity, the substrate thermal conductivity and specific heat capacity, the film-substrate thermal boundary resistance, and the equivalent thermal conductivity of the insulating layer, can be simultaneously measured in a heterostructure sample. The proposed method applies a pulsed current excitation to a metal heater line on the sample surface and utilizes the thermoreflectance thermal imaging system to measure the temperature of different spatial regions on the sample surface at different time windows. The temporal and spatial variation information of the temperature field is then extracted and combined with finite element method inversion calculation to obtain the thermophysical properties of heterostructures. To validate the accuracy and reliability of this method, we conducted measurements on a GaN-on-SiC heterostructure sample and obtained thermophysical properties consistent with the representative literature data that have previously been reported. The proposed PTI method, characterized by its high sensitivity, demonstrates good efficiency and reliability in conducting comprehensive thermophysical property characterization of GaN epitaxial heterostructures.

Topology optimization for near-junction thermal spreading of electronics in ballistic-diffusive regime.

Hotspots in electronic devices can cause overheating and reduce performance. Enhancing the thermal spreading ability is critical for reducing device temperature to improve the reliability. However, as devices shrink, phonon ballistic effects can increase thermal resistance, making conventional optimization methods less effective. This paper presents a topology optimization method that combines the phonon Boltzmann transport equation with solid isotropic material with penalization method to optimize high thermal conductivity (HTC) material distributions for thermal spreading problems. Results show that the contraction-expansion structure can effectively reduce thermal resistance. Optimal distributions differ from that based on Fourier's heat conduction law, and only the trunk structure appears in optimized layouts due to the size effect. Additionally, HTC material with longer mean free paths tends to be filled around the heat source with a gap in a ballistic-diffusive regime. This work deepens understanding of thermal spreading and aids in thermal optimization of microelectronic chips.

Unraveling Thermal Transport Correlated with Atomistic Structures in Amorphous Gallium Oxide via Machine Learning Combined with Experiments.

Thermal transport properties of amorphous materials are crucial for their emerging applications in energy and electronic devices. However, understanding and controlling thermal transport in disordered materials remains an outstanding challenge, owing to the intrinsic limitations of computational techniques and the lack of physically intuitive descriptors for complex atomistic structures. Here, it is shown how combining machine-learning-based models and experimental observations can help to accurately describe realistic structures, thermal transport properties, and structure-property maps for disordered materials, which is illustrated by a practical application on gallium oxide. First, the experimental evidence is reported to demonstrate that machine-learning interatomic potentials, generated in a self-guided fashion with minimum quantum-mechanical computations, enable the accurate modeling of amorphous gallium oxide and its thermal transport properties. The atomistic simulations then reveal the microscopic changes in the short-range and medium-range order with density and elucidate how these changes can reduce localization modes and enhance coherences' contribution to heat transport. Finally, a physics-inspired structural descriptor for disordered phases is proposed, with which the underlying relationship between structures and thermal conductivities is predicted in a linear form. This work may shed light on the future accelerated exploration of thermal transport properties and mechanisms in disordered functional materials.

Thermal Interface Materials with High Thermal Conductivity and Low Young's Modulus Using a Solid-Liquid Metal Codoping Strategy.

Thermal interface materials (TIMs), as typical thermal functional materials, are highly required to possess both high thermal conductivity and low Young's modulus. However, the naturally synchronized change in the thermal and mechanical properties seriously hinders the development of high-performance TIMs. To tackle such a dilemma, a strategy of codoping solid fillers and liquid metal fillers into polymer substrates is proposed in this study. This strategy includes a large amount of liquid metals that play the role of thermal paths and a small amount of uniformly dispersed solid fillers that further enhance heat conduction. Through the synergistic effect of the liquid metal and solid fillers, the thermal conductivity can be improved, and Young's modulus can be kept small simultaneously. A typical TIM with a volume of 55% gallium-based liquid metal and 15% copper particles as fillers has a thermal conductivity of 3.94 W/(m·K) and a Young's modulus of 699 kPa, which had the maximum thermomechanical performance coefficient compared with liquid metal TIMs and solid filler-doped TIMs. In addition, the thermal conductivity of the solid-liquid metal codoped TIM increased sharply with an increase of liquid metal content, and Young's modulus increased rapidly with an increase of the volume ratio of copper and polymer. The high-low-temperature cycling test and large-size light-emitting diode (LED) application demonstrated that this TIM had stable physical performance. The synergistic effect of the solid fillers and liquid metal fillers provides a broad space to solve the classic tradeoff issue of the mechanical and thermal properties of composites.

Biopolymer Filament Entanglement Softens Then Hardens with Shear.

It is unsatisfactory that regarding the problem of entangled macromolecules driven out of equilibrium, experimentally based understanding is usually inferred from the ensemble average of polydisperse samples. Here, confronting with single-molecule imaging this common but poorly understood situation, over a wide range of shear rate we use single-molecule fluorescence imaging to track alignment and stretching of entangled aqueous filamentous actin filaments in a homebuilt rheo-microscope. With increasing shear rate, tube "softening" is followed by "hardening." Physically, this means that dynamical localization first weakens from molecular alignment, then strengthens from filament stretching, even for semiflexible biopolymers shorter than their persistence length.

Electron Heat Source Driven Heat Transport in GaN at Nanoscale: Electron-Phonon Monte Carlo Simulations and a Two Temperature Model.

The thermal energy transport in semiconductors is mostly determined by phonon transport. However in polar semiconductors like GaN electronic contribution to the thermal transport is non-negligible. In this paper, we use an electron-phonon Monte Carlo (MC) method to study temperature distribution and thermal properties in a two-dimensional GaN computational domain with a localized, steady and continuous electron heat source at one end. Overall, the domain mimics the two-dimensional electron gas (2DEG) channel of a typical GaN high electron mobility transistor (HEMT). High energy electrons entering the domain from the source interact with the phonons, and drift under the influence of an external electric field. Cases of the electric field being uniform and non-uniform are investigated separately. A two step/temperature analytical model is proposed to describe the electron as well as phonon temperature profiles and solved using the finite difference method (FDM). The FDM results are compared with the MC results and found to be in good agreement.

Thermomass Theory in the Framework of GENERIC.

Thermomass theory was developed to deal with the non-Fourier heat conduction phenomena involving the influence of heat inertia. However, its structure, derived from an analogy to fluid mechanics, requires further mathematical verification. In this paper, General Equation for Non-Equilibrium Reversible-Irreversible Coupling (GENERIC) framework, which is a geometrical and mathematical structure in nonequilibrium thermodynamics, was employed to verify the thermomass theory. At first, the thermomass theory was introduced briefly; then, the GENERIC framework was applied in the thermomass gas system with state variables, thermomass gas density ρh and thermomass momentum mh, and the time evolution equations obtained from GENERIC framework were compared with those in thermomass theory. It was demonstrated that the equations generated by GENERIC theory were the same as the continuity and momentum equations in thermomass theory with proper potentials and eta-function. Thermomass theory gives a physical interpretation to the GENERIC theory in non-Fourier heat conduction phenomena. By combining these two theories, it was found that the Hamiltonian energy in reversible process and the dissipation potential in irreversible process could be unified into one formulation, i.e., the thermomass energy. Furthermore, via the framework of GENERIC, thermomass theory could be extended to involve more state variables, such as internal source term and distortion matrix term. Numerical simulations investigated the influences of the convective term and distortion matrix term in the equations. It was found that the convective term changed the shape of thermal energy distribution and enhanced the spreading behaviors of thermal energy. The distortion matrix implies the elasticity and viscosity of the thermomass gas.

Machine learning interatomic potential developed for molecular simulations on thermal properties of ß-Ga2O3.

The thermal properties of ß-Ga2O3 can significantly affect the performance and reliability of high-power electronic devices. To date, due to the absence of a reliable interatomic potential, first-principles calculations based on density functional theory (DFT) have been routinely used to probe the thermal properties of ß-Ga2O3. DFT calculations can only tackle small-scale systems due to the huge computational cost, while the thermal transport processes are usually associated with large time and length scales. In this work, we develop a machine learning based Gaussian approximation potential (GAP) for accurately describing the lattice dynamics of perfect crystalline ß-Ga2O3 and accelerating atomic-scale simulations. The GAP model shows excellent convergence, which can faithfully reproduce the DFT potential energy surface at a training data size of 32 000 local atomic environments. The GAP model is then used to predict ground-state lattice parameters, coefficients of thermal expansion, heat capacity, phonon dispersions at 0 K, and anisotropic thermal conductivity of ß-Ga2O3, which are all in excellent agreement with either the DFT results or experiments. The accurate predictions of phonon dispersions and thermal conductivities demonstrate that the GAP model can well describe the harmonic and anharmonic interactions of phonons. Additionally, the successful application of our GAP model to the phonon density of states of a 2500-atom ß-Ga2O3 structure at elevated temperature indicates the strength of machine learning potentials to tackle large-scale atomic systems in long molecular simulations, which would be almost impossible to generate with DFT-based molecular simulations at present.

Tuning the thermal conductivity of nanoparticle suspensions by electric field.

Active thermal management is essential for the operation of modern technologies like electronic circuits and spacecraft systems to deal with the complex control and conversion of thermal energy. One basic requirement for the materials is its tunable and reversible thermal properties. Here, we try to provide a systematic investigation of the thermal smart materials composed of low-dimensional solid particles suspended in liquid media, whose structures and properties can be tuned by external field. A two-step theoretical model, which takes into account the effects from particle aggregation and orientational variation, was proposed and obtained reasonable agreement with both literature and our own experimental results. Graphene nanosheets/Mg-Al layered double hydroxides (GNS/LDH) were fabricated and their silicone oil suspension shows reversible thermal conductivity switching under DC electric field due to the formation/break-up of chain-like structures with a maximum switching ratio around 1.35×. This study reveals the underlying mechanism of thermal conductivity enhancement in nanoparticle suspensions, and provides a preliminary example to design and fabricate responsive thermal materials for the next generation technologies.

Fractional-order heat conduction models from generalized Boltzmann transport equation.

The relationship between fractional-order heat conduction models and Boltzmann transport equations (BTEs) lacks a detailed investigation. In this paper, the continuity, constitutive and governing equations of heat conduction are derived based on fractional-order phonon BTEs. The underlying microscopic regimes of the generalized Cattaneo equation are thereafter presented. The effective thermal conductivity κeff converges in the subdiffusive regime and diverges in the superdiffusive regime. A connection between the divergence and mean-square displacement 〈|Δx|2〉 ∼ tγ is established, namely, κeff ∼ tγ-1, which coincides with the linear response theory. Entropic concepts, including the entropy density, entropy flux and entropy production rate, are studied likewise. Two non-trivial behaviours are observed, including the fractional-order expression of entropy flux and initial effects on the entropy production rate. In contrast with the continuous time random walk model, the results involve the non-classical continuity equations and entropic concepts. This article is part of the theme issue 'Advanced materials modelling via fractional calculus: challenges and perspectives'.

Molecular dynamic simulation of thermal transport in monolayer C3B x N1-x alloy.

Recently, two-dimensional (2D) monolayers C3B and C3N attract growing research interest due to the excellent physical properties. In this work, the thermal conductivities (k) of the monolayer C3B x N1-x alloy and the special C3B0.5N0.5 superlattice (C3B0.5N0.5-SL) alloy are systematically evaluated by using molecular dynamic simulation. First, the k of monolayer C3B x N1-x alloy presents a U-shaped profile with the increasing random doping ratio (x), in which the lowest k exists in x = 0.5. Second, we further calculate the thermal conductivity of C3B0.5N0.5-SL. The result shows an initial decreasing and then rising trend, and the coherent length is 5.11 nm which occupies the minimum thermal conductivity. Furthermore, to uncover the phonon thermal transport mechanism, we calculate the spatiotemporal thermal transport, phonon density of states, phonon group velocity, participation ratio and the phonon wave packet simulations in monolayer alloy system. We note that on account of the random doping atoms, the enhancive phonon-impurity scattering and phonon localization reduce the thermal conductivity in monolayer C3B x N1-x alloy. In C3B0.5N0.5-SL, when the period length is smaller than the coherent length, coherent phonon modes emerge because of the phonon interference, in which the superlattice can be regarded as a 'newly generated material'. However, when the period length is larger than the coherent length, the decreasing number of the interface in superlattice lessens phonon-interface scattering and cause the increasing thermal conductivity. This work contributes the fundamental knowledge for thermal management in 2D monolayer C3B x N1-x alloy based nanoelectronics.

Diffusion Tensors of Arbitrary-Shaped Nanoparticles in Fluid by Molecular Dynamics Simulation.

The anisotropic diffusive behavior of nanoparticles with complex shapes attracts great interest due to its potential applications in many fields ranging from bionics to aeronautic industry. Although molecular dynamics (MD) simulations are used widely to investigate nanoparticle diffusion properties, universal methods to describe the diffusion process comprehensively are still lacking. Here, we address this problem by introducing diffusion tensor as it can describe translational and rotational diffusion in three dimensions both individually and their coupling. We take carbon triple sphere suspended in argon fluid as our model system. The consistency of our results and velocity autocorrelation function(VAF) method validates our simulations. The coupling between translational and rotational diffusion is observed directly from analyzing diffusion tensor, and quantified by coupling diffusion coefficient. Our simulation reveals non-trivial effect of some factors in diffusion at nanoscale, which was not considered in previous theories. In addition to introducing an effective method to calculate the diffusion tensor in MD simulations, our work also provides insights for understanding the diffusion process of arbitrary-shaped particles in nanoengineering.

Extracting optical constants of solid materials with micro-rough surfaces from ellipsometry without using effective medium approximation.

The effective medium approximation (EMA) model may cause a large deviation in the data analysis of spectroscopic ellipsometry (SE) for solid materials with randomly micro-rough surfaces since it ignores the influence of the lateral irregularities of the rough surfaces on the electromagnetic scattering. In this work, a novel inversion framework is developed to extract optical constants from the SE parameters for solid materials with randomly micro-rough surfaces. Our approach enables the integration of the Levenberg-Marquardt optimization algorithm and the first-principles calculations of electromagnetic scattering. In each iterative step, the electromagnetic interactions with rough surfaces are accurately obtained from first-principles calculations without using the EMA model for rough estimation, which significantly guarantees the precision and wide applicability of our method for actual surfaces without a perfectly Gaussian height distribution. Furthermore, a superior advantage of our approach is that its error can be feasibly evaluated from the instrumental errors of the surface morphology detectors and the SE.

The Effect of Thermal Contact Number on the Tube⁻Tube Contact Conductance of Single-Walled Carbon Nanotubes.

The contact conductance of single, double, and triple thermal contacts of single-walled carbon nanotubes (SWCNTs) was investigated using molecular dynamics simulations. Our results showed that the effect of the thermal contact number on the contact conductance was not as strong as previously reported. The percentages of contact conductance of double and triple thermal contacts were about 72% and 67%, respectively, compared to that of a single thermal contact. Moreover, we found that the contact conductance of the double and triple thermal contacts was associated with the SWCNT length and the positional relationship of the thermal contacts.

On Entropic Framework Based on Standard and Fractional Phonon Boltzmann Transport Equations.

Generalized expressions of the entropy and related concepts in non-Fourier heat conduction have attracted increasing attention in recent years. Based on standard and fractional phonon Boltzmann transport equations (BTEs), we study entropic functionals including entropy density, entropy flux and entropy production rate. Using the relaxation time approximation and power series expansion, macroscopic approximations are derived for these entropic concepts. For the standard BTE, our results can recover the entropic frameworks of classical irreversible thermodynamics (CIT) and extended irreversible thermodynamics (EIT) as if there exists a well-defined effective thermal conductivity. For the fractional BTEs corresponding to the generalized Cattaneo equation (GCE) class, the entropy flux and entropy production rate will deviate from the forms in CIT and EIT. In these cases, the entropy flux and entropy production rate will contain fractional-order operators, which reflect memory effects.

The effect of structural asymmetry on thermal rectification in nanostructures.

Three SWCNT-graphene nanostructure-based models are designed to probe the thermal rectification caused by the structural asymmetry in the boundary thermal contacts, the device, and the whole system, respectively. We find that both the asymmetry of entire system and the asymmetry of the device are not necessary condition for the existence of thermal rectification, and the asymmetry in boundary thermal contacts is more important than the asymmetry in device toward determining both the magnitude and the direction of thermal rectification. Interestingly, notable thermal rectification can exist in the systems with overall structural symmetry when the boundary thermal contacts are structurally asymmetric. Moreover, nanostructures with a structurally symmetric device and structurally asymmetric boundary thermal contacts can still display significant thermal rectification. These findings could offer insight into the future design and performance improvement of nanostructured thermal rectifiers.

Thermal rectification at the bimaterial nanocontact interface.

Thermal rectification can help develop modern thermal manipulation devices but has been rarely engineered. Here, we validated the nanoscale bimaterial interface-induced thermal rectification experimentally for the first time and investigated its underlying mechanism via molecular dynamics simulations. The thermal diode consists of polyamide (PA) and silicon (Si) nanowires in contact with each other. The thermal rectification ratio measured by a high-precision nanoscale experiment reached 4% with an uncertainty of <1%. The temperature has little influence on the ratio, while the decrease in contact length or increase in temperature differences can increase the ratio. The molecular dynamics simulations further confirmed the thermal rectification in the PA/Si nanowires. We found that the localized modes generally gather on the edge, and the higher extent of phonon localization is responsible for the lower thermal conductance in the thermal rectification. Our findings not only have guiding significance, but can also promote the development of interface-based solid-state thermal diodes.

Giant Thermal Rectification from Single-Carbon Nanotube-Graphene Junction.

We describe the influence of the geometry parameters on the thermal rectification of single-carbon nanotube-graphene junction. The two-dimensional (2D) distribution of the thermal rectification with respect to the tube length and the side length of the graphene nanosheet are calculated and visualized. The maximum thermal rectification ratios of the designed single-carbon nanotube-graphene junction can reach 1244.1% and 1681.6% at average temperatures of 300 and 200 K, respectively. These values are much higher than those reported for single-material nanostructure-based thermal rectifiers. The thermal rectification ratios of the nanotube-graphene junction are fairly sensitive to geometry size and are almost entirely dominated by the degree of overlap of the power spectra under negative thermal bias. These findings could offer useful guidelines for the design and performance improvement of thermal diodes.