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A comparison is made between three simple approximate formulas for the configurational adiabat (i.e., constant excess entropy, sex) lines in a Lennard-Jones (LJ) fluid, one of which is an analytic formula based on a harmonic approximation, which was derived by Heyes et al. [J. Chem. Phys. 159, 224504 (2023)] (analytic isomorph line, AIL). Another is where the density is normalized by the freezing density at that temperature (freezing isomorph line, FIL). It is found that the AIL formula and the average of the freezing density and the melting density ("FMIL") are configurational adiabats at all densities essentially down to the liquid–vapor binodal. The FIL approximation departs from a configurational adiabat in the vicinity of the liquid–vapor binodal close to the freezing line. The self-diffusion coefficient, D, shear viscosity, ηs, and thermal conductivity, λ, in macroscopic reduced units are essentially constant along the AIL and FMIL at all fluid densities and temperatures, but departures from this trend are found along the FIL at high liquid state densities near the liquid–vapor binodal. This supports growing evidence that for simple model systems with no or few internal degrees of freedom, isodynes are lines of constant excess entropy. It is shown that for the LJ fluid, ηs and D can be predicted accurately by an essentially analytic procedure from the high temperature limiting inverse power fluid values (apart from at very low densities), and this is demonstrated quite well also for the experimental argon viscosity. [ABSTRACT FROM AUTHOR]

The pyrite phase of sodium superoxide, NaO 2 , is studied using equilibrium molecular dynamics simulations and lattice dynamics calculations to understand the impacts of static disorder and dynamic disorder on its thermal conductivity. Three structural regimes are observed based on the rotational dynamics and orientations of O 2 − ions. At low temperatures, where the O 2 − ions librate and the system is fully ordered, thermal conductivity exhibits a crystal-like temperature dependence, decreasing with increasing temperature. As temperature increases, the static disorder regime emerges, where the O 2 − ions transition between different orientations on a time scale larger than the librational period. In this regime, the thermal conductivity continues to decrease and then becomes temperature independent. At higher temperatures, where the O 2 − ions freely rotate, the system is dynamically disordered and the thermal conductivity is temperature independent, as in an amorphous solid. Using instantaneous normal mode analysis and Allen–Feldman theory, 80% of the thermal conductivity in the dynamic disorder regime is attributed to diffusons, vibrational modes that are non-propagating and non-localized. When increasing the lattice constant at a constant temperature, transitions from librations to static disorder to dynamic disorder are also observed, with the thermal conductivity decreasing monotonically. The presented methodology can be applied to other crystals with rotational degrees of freedom, offering strategies for the design of thermal conductivity switches that are responsive to external stimuli. [ABSTRACT FROM AUTHOR]

The pyrite phase of sodium superoxide, NaO 2 , is studied using equilibrium molecular dynamics simulations and lattice dynamics calculations to understand the impacts of static disorder and dynamic disorder on its thermal conductivity. Three structural regimes are observed based on the rotational dynamics and orientations of O 2 − ions. At low temperatures, where the O 2 − ions librate and the system is fully ordered, thermal conductivity exhibits a crystal-like temperature dependence, decreasing with increasing temperature. As temperature increases, the static disorder regime emerges, where the O 2 − ions transition between different orientations on a time scale larger than the librational period. In this regime, the thermal conductivity continues to decrease and then becomes temperature independent. At higher temperatures, where the O 2 − ions freely rotate, the system is dynamically disordered and the thermal conductivity is temperature independent, as in an amorphous solid. Using instantaneous normal mode analysis and Allen–Feldman theory, 80% of the thermal conductivity in the dynamic disorder regime is attributed to diffusons, vibrational modes that are non-propagating and non-localized. When increasing the lattice constant at a constant temperature, transitions from librations to static disorder to dynamic disorder are also observed, with the thermal conductivity decreasing monotonically. The presented methodology can be applied to other crystals with rotational degrees of freedom, offering strategies for the design of thermal conductivity switches that are responsive to external stimuli. [ABSTRACT FROM AUTHOR]

Flexible electronic devices with skin-like properties are hailed as revolutionary for the development of next-generation electronic devices, such as electric-skin and humanoid robotics. Graphene is intrinsically flexible due to its structural thinness in nature and are considered next-generation materials for wearable electronics. These devices usually experience a large mechanical deformation in use so as to achieve intimate conformal contact with human skin and to coordinate complex human motions, while heat dissipation has been a major limitation when the device is under a large mechanical strain. Unlike the small deformation (<1%) induced by intrinsic material factors such as lattice mismatch between material components in devices, a large mechanical deformation (>1%) by an external loading condition could lead to apparent changes to global geometric shapes and significantly impact thermal transport. In this study, we investigated the thermal conductivities of graphene under several large mechanical strains: 2.9%, 4.3%, and 6.1%. We used a refined opto-thermal Raman technique to characterize the thermal transport properties and discovered the thermal conductivities to be 2092 ± 502, 972 ± 87, 348 ± 52, and 97 ± 13 W/(m K) for the relaxed state, 2.9%, 4.3%, and 6.1% tensile strain, respectively. Our results showed a significant decreasing trend in thermal conductivities with an increasing mechanical strain. The findings in this study reveal new thermal transport mechanisms in 2D materials and shed light on building novel flexible nanoelectronic devices with enhanced thermal management. [ABSTRACT FROM AUTHOR]

The degradation of β-Ga2O3-based devices' performance may occur when they are bombarded by charged particles in aerospace, astronomy, and nuclear-related applications. It is significant to explore the influence of irradiation on the microstructure of β-Ga2O3 and to reveal the internal relationship between the damage mechanisms and physical characteristics. Thus, we explored the low-energy recoil events of β-Ga2O3 based on the first-principles calculations in the present study. The threshold displacement energies (Eds) significantly depended on the recoil directions and the primary knock-on atoms. Eds of Ga atoms are generally larger than those of O atoms, indicating that the displacements of O atoms dominate under electron irradiation. In the neutral state, the formation energy of VO(I) is lower than that of VO(II) and VO(III), while in the +2 charge state, the case is a reversal. The formation energy of Oint(II) defect is high, and thus its equilibrium concentration is low, indicating that the Oint(II) defect is unlikely to be relevant for the thermal-mechanical properties of β-Ga2O3. The charged VO and Oint defects deteriorate the ability to resist external compression more profoundly, while defective β-Ga2O3 with lower Young's modulus is expected to possess higher elastic compliance than pristine β-Ga2O3. The lattice thermal conductivity of β-Ga2O3 decreases with increasing temperature and the charged point defects generally result in the decreasing lattice thermal conductivity more profoundly than neutral point defects. The presented results provide underlying mechanisms for defect generation in β-Ga2O3 and advance the fundamental understanding of the radiation resistances of semiconductor materials. [ABSTRACT FROM AUTHOR]

In this study, we explored the phase stability of quaternary Mo 1 − x M x AlB (M = Cr, Fe, Mn, Nb, Sc, Ta, Ti, V, and W) solid solutions by employing a cluster expansion method to generate structures with different concentrations of M atoms. Using the first-principles calculations based on density functional theory, we predicted that these compounds exhibit a preference for either fully random structures or phase-segregated (M-rich regions) phases against the competing phases. To evaluate the Gibbs free energy of Mo 1 − x M x AlB alloys, we investigated the impact of various entropy contributions, including configurational, electronic, and vibrational entropy. Our study revealed that configurational entropy plays an important role in stabilizing the random phases observed in Mo 1 − x M x AlB compounds, highlighting its importance in understanding the thermodynamic behavior of these alloys. However, the vibrational and electronic entropy changes with respect to competing phases can stabilize or destabilize Mo 1 − x M x AlB depending on their sign. Our results indicate that, while W is soluble across the entire range of mixing ratios, Sc and Ti are completely insoluble in any ratio. On the other hand, Cr, Ta, Nb, and V can be successfully incorporated into the MoAlB lattice at varying fractions at elevated temperatures. The size, valence electron concentration, and electronegativity differences between Mo and M can be utilized as descriptors to identify stable Mo 1 − x M x AlB compounds. We extensively examined the structural, dynamical stability, thermal conductivity, and mechanical properties of Mo 1 − x M x AlB compounds. We analyze their dependence on the choice of the M element incorporated in the alloy. Our findings can guide the design and engineering of these materials to tailor their properties to specific applications based on the choice of the M element. [ABSTRACT FROM AUTHOR]

The degradation of β-Ga2O3-based devices' performance may occur when they are bombarded by charged particles in aerospace, astronomy, and nuclear-related applications. It is significant to explore the influence of irradiation on the microstructure of β-Ga2O3 and to reveal the internal relationship between the damage mechanisms and physical characteristics. Thus, we explored the low-energy recoil events of β-Ga2O3 based on the first-principles calculations in the present study. The threshold displacement energies (Eds) significantly depended on the recoil directions and the primary knock-on atoms. Eds of Ga atoms are generally larger than those of O atoms, indicating that the displacements of O atoms dominate under electron irradiation. In the neutral state, the formation energy of VO(I) is lower than that of VO(II) and VO(III), while in the +2 charge state, the case is a reversal. The formation energy of Oint(II) defect is high, and thus its equilibrium concentration is low, indicating that the Oint(II) defect is unlikely to be relevant for the thermal-mechanical properties of β-Ga2O3. The charged VO and Oint defects deteriorate the ability to resist external compression more profoundly, while defective β-Ga2O3 with lower Young's modulus is expected to possess higher elastic compliance than pristine β-Ga2O3. The lattice thermal conductivity of β-Ga2O3 decreases with increasing temperature and the charged point defects generally result in the decreasing lattice thermal conductivity more profoundly than neutral point defects. The presented results provide underlying mechanisms for defect generation in β-Ga2O3 and advance the fundamental understanding of the radiation resistances of semiconductor materials. [ABSTRACT FROM AUTHOR]

The integration of a composite of porous medium with phase change material (PCM) offers significant advantages in thermal management systems, enhancing heat transfer efficiency and addressing various thermal regulation challenges. This approach utilizes the PCM's latent heat absorption and the enhanced thermal conductivity provided by the porous medium, resulting in optimized system performance. Its applicability spans across electronics cooling and building insulation systems. However, predicting the thermal behavior of this composite material is challenging, necessitating computational tools to anticipate its response under different conditions and evaluate its influence on cooling strategies. The objective of this study is to create a computational tool specifically tailored to evaluate constitutive parameters of this composite material, thereby providing a comprehensive description of its thermal behavior. To achieve this goal, the multiscale homogenization principle is employed to assess the composite's effective thermophysical material properties using the representative volume element approach. The repeating unit cell of the aluminum lattice is incorporated into the PCM to define a representative volume element. The finite element method (FEM) is utilized to solve the three-dimensional homogenization problem, yielding an orthotropic effective thermal conductivity due to the inherent symmetry of the repeating material cell. Moreover, the study leverages the apparent heat capacity method to effectively manage the phase transitions within the PCM domain, utilizing smooth and temperature-dependent functions to accurately describe the thermophysical properties of the PCM. Integrating the composite into battery pack thermal management, this study thoroughly examines thermal dynamics by comparing outcomes with and without PCM integration. The transient thermal problem is accurately tackled using the FEM, employing the evaluated effective constitutive parameters of the homogenized composite to minimize computational effort. The results indicate a notable decline in the highest temperatures of the battery pack, leading to a reduction of about 14 °C at the specific moment when the phase change material fully transitions into its liquid form. The obtained results emphasize the effectiveness and practical feasibility of the proposed thermal management strategy. The modeling approach presented provides a robust tool with significant efficiency in reducing computational time for analyzing the thermal behavior of large models, as the utilization of the homogenization technique notably decreases the computational time. [ABSTRACT FROM AUTHOR]

The outstanding radiation damage stability of an NiCoCrFePd high entropy alloy (HEA) as compared to conventional alloys poses the question for the mechanism of an ion–matter interaction. The positron annihilation lifetime spectroscopic and TEM (transmission electron microscopic) measurements are implemented to trace different kinds of defects produced by 120 MeV Au+9 ion irradiation and their evolution as a function of ion fluence. The variation of lifetimes and corresponding intensities with the ion fluence indicates the formation of dislocation-type defects at a lower ion fluence and vacancy clusters at a higher ion fluence caused by coalescence or agglomeration of dislocation defects. Formation of different types of defects in turn modulates the strain development inside the crystal. Additionally, the HR-TEM investigation of NiCoCrFePd HEA also exhibits the formation of dislocation and vacancy clusters with the average size of vacancy clusters increases from ∼2.9 ± 0.1 to ∼3.8 ± 0.1 nm with the increases in the ion fluence. Surprisingly, the average defect cluster size in NiCoCrFePd HEA is suppressed compared to NiCoCrFe MEA, thereby showing the enhanced radiation stability on Pd incorporation due to the high defect recombination caused by reduced thermal conductivity and high lattice distortion. Nano-indentation measurement shows that the radiation hardening behavior of the NiCoCrFePd HEA responded slowly owing to its damage suppression property as compared to the NiCoCrFe MEA. Additionally, softening behavior also appeared at an early fluence in NiCoCrFe MEA compared to the NiCoCrFePd HEA signifying its excellent resistance to defect accumulation. [ABSTRACT FROM AUTHOR]

Bond line thickness (BLT) is an essential parameter of thermal conductive composite gels as the thermal interface material (TIM). Extensive research has been performed on designing next-generation TIMs with high thermal conductivity; however, it remains elusive how to link laboratory measurements and the theoretically predicted BLT. Here, we propose a new model to estimate BLT based on a rheological property, in which the TIM is assumed to be a simple power law fluid. To avoid the unrealistic situation of the BLT tending to zero, we introduce a decaying exponential function to describe the influence from fillers during the lid attach process and rebuild the force balance equation. Compared with previously reported models, the theoretical prediction BLT based on the proposed model is in good agreement with the experimental data. Our model has a guiding significance in predicting the BLT, which may help to optimize the thermal performance of TIMs. [ABSTRACT FROM AUTHOR]

Machine learned potentials (MLPs) have been widely employed in molecular dynamics simulations to study thermal transport. However, the literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, gallium arsenide, graphene, and lead telluride as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors. [ABSTRACT FROM AUTHOR]

This study investigates the structural effects on the cross-plane thermal conductivity of Li4Ti5O12-based anode active material. Three structures are investigated: a basic structure consisting of LiBr/LiCl/Li4Ti5O12, polyvinylidene difluoride, and Super P (sample #1); a structure without Li4Ti5O12 (sample #2); and a structure without LiBr/LiCl (sample #3). Despite its high porosity level (77%), sample #1 exhibits higher thermal conductivity than sample #3 (64% porosity) in both air and vacuum conditions, potentially due to the extra structural bonding provided by LiBr/LiCl. The observed difference in cross-plane thermal conductivity between air and vacuum conditions provides insights into the configuration of the anode's active material in the heat transfer direction. The lower limit corresponds to the parallel thermal circuit configuration of active material and air, which is the product of the sample's porosity and thermal conductivity of air. Our analysis suggests that in sample #2, the anode's active material and air inside the pores demonstrate a more serial configuration, while in sample #3, they exhibit a more parallel configuration in the heat transfer direction. However, the thermal conductivity difference observed for sample #1 falls below the theoretical lower bound indicating significant thermal radiation within the pores. Furthermore, the in-plane thermal conductivity is predominantly controlled by the copper foil. Sample #2 exhibits the lowest in-plane thermal conductivity. This is attributed to the severe oxidization of the copper foil by LiBr/LiCl, which is confirmed by structure characterization. [ABSTRACT FROM AUTHOR]

Hexagonal boron nitride (h-BN) has excellent thermal conductivity and dielectric properties, which shows great potential for low-dimensional devices. However, mechanical properties of h-BN have not been comprehensively investigated through experiments. In this work, we conduct in situ direct tensile tests on freestanding single-crystal few-layer h-BN nanosheets with various layer numbers from 3 to 8, with an elaborate sample transfer and characterization protocol. Young's modulus of 573.8 ± 101.4 GPa and a tensile fracture strain up to 3.2% are revealed, which are comparable to its monolayer counterpart. Moreover, we find a tough-to-brittle transition in few-layer h-BN with the increase in layer number, which is attributed the interplay between the van der Waals interaction and in-plane covalent bonding. These findings could open up new possibilities in mechanical research of van der Waals materials and provide guidance for the design of h-BN-based devices and composites. [ABSTRACT FROM AUTHOR]

In this study, nanostructures were introduced into bulk Si, and the P content in Si was increased. Arc melting, ball milling, and spark plasma sintering were used to prepare excess P-doped Si samples (Si1−xPx, x = 0.01, 0.03, 0.06, 0.09, and 0.12). In Si0.94P0.06, thermal conductivity decreased to 18 W m−1 K−1 at room temperature, which is about 10% of that of bulk Si. Transmission electron microscopy observation of Si0.94P0.06 revealed a linearly localized alignment, in addition to the nanostructures observed in the previous studies of P-doped Si samples. Linear alignments were composed of Si-P nanoprecipitates and defects linearly localized around dislocations. These sub-microstructures can reduce thermal conductivity; as a result, ZT at 973 K increased to 0.43, which is approximately three times higher than that of single-crystal Si. [ABSTRACT FROM AUTHOR]

The two-dimensional laser-based Ångstrom method measures the in-plane thermal properties for anisotropic film-like materials. It involves periodic laser heating at the center of a suspended film sample and records its transient thermal response by infrared imaging. These spatiotemporal temperature data must be analyzed to extract the unknown thermal conductivity values in the orthotropic directions, an inverse parameter fitting problem. Previous demonstration of the metrology technique used a least-squares fitting method that relies on numerical differentiation to evaluate the second-order partial derivatives in the differential equation describing transient conduction in the physical system. This fitting approach is susceptible to measurement noise, introducing high uncertainty in the extracted properties when working with noisy data. For example, when noise of a signal-to-noise ratio of 10 is added to simulated amplitude and phase data, the error in the extracted thermal conductivity can exceed 80%. In this work, we introduce a new alternative inverse parameter fitting approach using physics-informed neural networks (PINNs) to increase the robustness of the measurement technique for noisy temperature data. We demonstrate the effectiveness of this approach even for scenarios with extreme levels of noise in the data. Specifically, the PINN-approach accurately extracts the properties to within 5% of the true values even for high noise levels (a signal-to-noise ratio of 1). This offers a promising avenue for improving the robustness and accuracy of advanced thermal metrology tools that rely on inverse parameter fitting of temperature data to extract thermal properties. [ABSTRACT FROM AUTHOR]

Non-contact photothermal pump-probe methodologies such as Frequency-Domain Thermo-Reflectance (FDTR) systems facilitate the examination of thermal characteristics spanning semiconductor materials and their associated interfaces. We underscore the significance of meticulous measurements and precise error estimation attained through the analysis of both amplitude and phase data in Thermo-Reflectance (TR). The precision of the analytical estimation hinges greatly on the assumptions made before implementing the method and notably showcases a decrease in errors when both the amplitude and phase are incorporated as input parameters. We demonstrate that frequency-domain calculations can attain high precision in measurements, with error estimations in thermal conductivity (k), thermal boundary resistance (Rth), and thermal diffusivity (α) as low as approximately 2.4%, 2.5%, and 3.0%, respectively. At the outset, we evaluate the uncertainty arising from the existence of local minima when analyzing data acquired via FDTR, wherein both the phase and amplitude are concurrently utilized for the assessment of cross-plane thermal transport properties. Expanding upon data analysis techniques, particularly through advanced deep learning approaches, can significantly enhance the accuracy and precision of predictions when analyzing TR data across a spectrum of modulation frequencies. Deep learning models enhance the quality of fitting and improve the accuracy and precision of uncertainty estimation compared to traditional Monte Carlo simulations. This is achieved by providing suitable initial guesses for data fitting, thereby enhancing the overall performance of the analysis process. [ABSTRACT FROM AUTHOR]

The application of first-principles calculations for predicting lattice thermal conductivity (LTC) in crystalline materials, in conjunction with the linearized phonon Boltzmann equation, has gained increasing popularity. In this calculation, the determination of force constants through first-principles calculations is critical for accurate LTC predictions. For material exploration, performing first-principles LTC calculations in a high-throughput manner is now expected, although it requires significant computational resources. To reduce computational demands, we integrated polynomial machine learning potentials on-the-fly during the first-principles LTC calculations. This paper presents a systematic approach to first-principles LTC calculations. We designed and optimized an efficient workflow that integrates multiple modular software packages. We applied this approach to calculate LTCs for 103 compounds of wurtzite, zinc blende, and rocksalt types to evaluate the performance of the polynomial machine learning potentials in LTC calculations. We demonstrate a significant reduction in the computational resources required for the LTC predictions. [ABSTRACT FROM AUTHOR]

Utilizing thermochemical reactions for thermal energy storage and solar fuel production has been an emerging research topic. Thermal transport properties of the materials are an important parameter that can determine the kinetics and efficiency of thermochemical reactions. With the increasing number of new thermochemical materials (TCMs); however, there is a lack of reliable techniques to monitor the thermal transport property of the materials and their changes as a function of reactions in real time. In this work, we report the in situ monitoring of thermochemical reactions using modulated photothermal radiometry (MPR). The thermal conductivities of two TCMs, namely, calcium hydroxide (Ca(OH)2) and Ba0.15Sr0.85FeO3−δ (BSF1585), were measured as a function of temperature and time using the MPR technique. The measured thermal conductivities were correlated to the reaction. The work has two significant contributions to the research communities. First, it provides a non-invasive diagnostic tool for monitoring the thermal transport properties of TCMs that can potentially be a high-throughput measurement technique conducive to optimizing TCMs, reactors, and related thermal systems. Second, for TCMs that show observable changes in thermal transport properties, a correlation between the measured thermal conductivity and the conversion fraction of the reaction can be established for monitoring the reaction kinetics based on thermal characterization. [ABSTRACT FROM AUTHOR]

The precise measurement of temperature is crucial in various fields such as biology, medicine, industrial automation, energy management, and daily life applications. While in most scenarios, sensors with a fixed thermal conductivity inevitably mismatch the analogous parameter of the medium being measured, thus causing the distortion and inaccurate detection of original temperature fields. Despite recent efforts on addressing the parameter-mismatch issue, all current solutions are constrained to a fixed working medium, whereas a more universal sensor should function in a variety of scenes. Here, we report a dynamic and reconfigurable thermal sensor capable of highly accurate measurements in diverse working environments. Remarkably, thanks to the highly tunable thermal conductivity of the expanded-plane structure, this sensor works effectively on background mediums with a wide range of conductivity. Such a development greatly enhances the robustness and adaptability of thermal sensors, setting a solid foundation for applications in multi-physical sensing scenarios. [ABSTRACT FROM AUTHOR]

The in-plane thermal conductivity of hexagonal boron nitride (h-BN) with varying thicknesses is a key property that affects the performance of various applications from electronics to optoelectronics. However, the transition of the thermal conductivity from two-dimensional (2D) to three-dimensional (3D) h-BN remains elusive. To answer this question, we have developed a machine learning interatomic potential within the neuroevolution potential (NEP) framework for h-BN, achieving a high accuracy akin to ab initio calculations in predicting its thermal conductivity and phonon transport from monolayer to multilayers and bulk. Utilizing molecular dynamics simulations based on the NEP, we predict the thermal conductivity of h-BN with a thickness up to ∼100 nm, demonstrating that its thermal conductivity quickly decreases from the monolayer and saturates to the bulk value above four layers. The saturation of its thermal conductivity is attributed to the little change in phonon group velocity and lifetime as the thickness increases beyond four layers. In particular, the weak thickness dependence of phonon lifetime in h-BN with a nanoscale thickness results from its extremely high phonon focusing along the in-plane direction. This research bridges the knowledge gap of phonon transport between 2D and 3D h-BN and will benefit the thermal design and performance optimization of relevant applications. [ABSTRACT FROM AUTHOR]

Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational cost reductions while maintaining the accuracy of first-principles calculations. Moment tensor potentials (MTPs) are among the most efficient and accurate models in this regard. In this study, we demonstrate the application of MTP to the calculation of the lattice thermal conductivity of α and β - Ga 2 O 3. Although MTP is commonly employed for lattice thermal conductivity calculations, the advantages of applying the active learning methodology for potential generation are often overlooked. Here, we emphasize its importance and illustrate how it enables the generation of a robust and accurate interatomic potential while maintaining a moderate-sized training dataset. [ABSTRACT FROM AUTHOR]

Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational cost reductions while maintaining the accuracy of first-principles calculations. Moment tensor potentials (MTPs) are among the most efficient and accurate models in this regard. In this study, we demonstrate the application of MTP to the calculation of the lattice thermal conductivity of α and β - Ga 2 O 3. Although MTP is commonly employed for lattice thermal conductivity calculations, the advantages of applying the active learning methodology for potential generation are often overlooked. Here, we emphasize its importance and illustrate how it enables the generation of a robust and accurate interatomic potential while maintaining a moderate-sized training dataset. [ABSTRACT FROM AUTHOR]

The time-periodic modulation of a temperature gradient can alter the heat transport properties of a physical system. Oscillating thermal gradients give rise to behaviors such as modified thermal conductivity and controllable time-delayed energy storage that are not present in a system with static temperatures. Here, we examine how the heat transport properties of a molecular lattice model are affected by an oscillating temperature gradient. We use analytical analysis and molecular dynamics simulations to investigate the vibrational heat flow in a molecular lattice system consisting of a chain of particles connected to two heat baths at different temperatures, where the temperature difference between baths is oscillating in time. We derive expressions for heat currents in this system using a stochastic energetics framework and a nonequilibrium Green's function approach that is modified to treat the nonstationary average energy fluxes. We find that emergent energy storage, energy release, and thermal conductance mechanisms induced by the temperature oscillations can be controlled by varying the frequency, waveform, and amplitude of the oscillating gradient. The developed theoretical approach provides a general framework to describe how vibrational heat transmission through a molecular lattice is affected by temperature gradient oscillations. [ABSTRACT FROM AUTHOR]

A power generator comprising a thermoelectric device (TED) and a phase change material (PCM) allows energy harvesting from ambient temperature variations, which exist ubiquitously; thus, such a device has received considerable attention as an energy harvester that can operate at any location. We developed a model for estimating the characteristics of a power generator in ambient air, whose temperature is forcibly changed between two temperature values, such as when an air conditioner is turned on and off. We calculated the influence of latent heat and thermal conductivity of the PCM on the characteristics of power generators with various thermal resistances between the TED/PCM interface and ambient air. Latent heat and thermal conductivity of the PCM affect the amount of heat energy (Q) transfer between the ambient air and PCM and the energy conversion efficiency (ηE), respectively, where the amount of electric energy is given by Q × ηE. The increase in Q caused by an increase in the latent heat of the PCM was almost independent of the thermal resistance between the TED/PCM interface and air. However, the increase in ηE caused by an increase in the thermal conductivity of the PCM decreased as the thermal resistance between the TED/PCM interface and air increased. These results indicate that the techniques to improve the power generation characteristics by increasing the thermal conductivity of PCM, which have been frequently investigated in recent years, are effective only when the thermal resistance between the TED/PCM interface and ambient air is small. [ABSTRACT FROM AUTHOR]

Dimensions and molecular structures play pivotal roles in the principle of heat conduction. The dimensional characteristics of a solution within nanoscale systems depend on the degrees of confinement. However, the influence of such variations on heat transfer remains inadequately understood. Here, we perform quasi-one-dimensional non-equilibrium molecular dynamics simulations to calculate the thermal conductivity of water molecules confined in carbon nanotubes. The structure of water molecules is determined depending on the nanotube radius, forming a single-file, a single-layer, and a double-layer structure, corresponding to an increasing radius order. We reveal that the thermal conductivity of liquid water has a sublinear dependency on nanotube length exclusively when water molecules form a single file. A stronger confinement leads to behavioral and structural characteristics closely resembling a one-dimensional nature. Moreover, single-layer-structured water molecules exhibit enhanced thermal conductivity. We elucidate that this is due to the increase in the local water density and the absence of transitions to another layer, which typically occurs in systems with double-layer water structures within relatively large radius nanotubes. [ABSTRACT FROM AUTHOR]

Gallium nitride (GaN) and aluminum nitride (AlN), as examples of third-generation semiconductors, have attracted significant interest due to their remarkable physical attributes, including a wide bandgap, high breakdown voltage, exceptional chemical stability, and high thermal conductivity. These characteristics render GaN and AlN highly promising for use in power and (opto)-electronic devices. Consequently, there is a growing demand for high-quality GaN and AlN crystals on the centimeter scale. As the dislocation density in these materials decreases, the need for a reliable method of dislocation characterization becomes more pressing. Synchrotron radiation x-ray topography (SR-XRT) has emerged as a superior, nondestructive technique for the precise characterization of crystal defects. This review briefly introduced the principle of XRT, and its application in the analysis of dislocations in GaN and AlN crystals is summarized. By examining the relationship between the SR-XRT image contrast and the Burgers vectors of dislocations, it is possible to categorize wafer dislocations and determine the magnitude and direction of Burgers vectors. Additionally, SR-XRT facilitates the analysis of interactions between dislocations in GaN and AlN crystals. These analyses are instrumental in advancing the development of superior crystals. This review concludes with a discussion of the current challenges faced by SR-XRT and a projection of its future applications in characterizing third-generation semiconductor crystal materials. This review offers significant guidance for the characterization of nitride crystal defects using SR-XRT. [ABSTRACT FROM AUTHOR]

Obtaining accurate lattice thermal conductivity data of LiF under extreme conditions not only provides important reference for performance evaluation, prediction, and control of materials, but also helps to alleviate the significant challenges of precise experimental measurements. The high-temperature phonon properties and lattice thermal conductivity (LTC) of solid LiF were calculated by combining on-the-fly machine learning force fields (MLFFs) with the Green–Kubo method. The introduction of MLFF successfully combines the accuracy of ab initio molecular dynamics with the scalability advantage of classical molecular dynamics. At high temperatures, there is a significant enhancement in the vibrational coupling between the acoustic and optical branches of LiF, as well as resonant effects between Li and F atoms, resulting in strong anharmonicity. Additionally, the main peak of the phonon density of states shows a noticeable redshift compared to the harmonic case. The enhanced coupling of TO and TA modes at high temperature leads to a significant increase in phonon scattering rate. By considering higher-order phonon anharmonicity, the predicted LTC is significantly reduced compared to the results obtained from considering only three-phonon interactions. Along the Hugoniot curve up to 100 GPa (2150 K), the predicted LTC agrees well with the experimental values. These findings demonstrate the crucial role of phonon anharmonicity in promoting phonon scattering. [ABSTRACT FROM AUTHOR]

Designing polymers with high intrinsic thermal conductivity (TC) is critically important for the thermal management of organic electronics and photonics. However, this is a challenging task owing to the diversity of the chemical space and the barriers to advanced synthetic experiments/characterization techniques for polymers. In this Tutorial, the fundamentals and implementation of combining classical molecular dynamics simulation and machine learning (ML) for the development of polymers with high TC are comprehensively introduced. We begin by describing the core components of a universal ML framework, involving polymer data sets, property calculators, feature engineering, and informatics algorithms. Then, the process of constructing interpretable regression algorithms for TC prediction is introduced, aiming to extract the underlying relationships between microstructures and TCs for polymers. We also explore the design of sequence-ordered polymers with high TC using lightweight and mainstream active learning algorithms. Lastly, we conclude by addressing the current limitations and suggesting potential avenues for future research on this topic. [ABSTRACT FROM AUTHOR]

Lead zirconate titanate (PZT) thin films offer advantages in microelectromechanical systems (MEMSs) including large motion, lower drive voltage, and high energy densities. Depending on the application, different substrates are sometimes required. Self-heating occurs in the PZT MEMS due to the energy loss from domain wall motion, which can degrade the device performance and reliability. In this work, the self-heating of PZT thin films on Si and glass and a film released from a substrate were investigated to understand the effect of substrates on the device temperature rise. Nano-particle assisted Raman thermometry was employed to quantify the operational temperature rise of these PZT actuators. The results were validated using a finite element thermal model, where the volumetric heat generation was experimentally determined from the hysteresis loss. While the volumetric heat generation of the PZT films on different substrates was similar, the PZT films on the Si substrate showed a minimal temperature rise due to the effective heat dissipation through the high thermal conductivity substrate. The temperature rise on the released structure is 6.8× higher than that on the glass substrates due to the absence of vertical heat dissipation. The experimental and modeling results show that the thin layer of residual Si remaining after etching plays a crucial role in mitigating the effect of device self-heating. The outcomes of this study suggest that high thermal conductivity passive elastic layers can be used as an effective thermal management solution for PZT-based MEMS actuators. [ABSTRACT FROM AUTHOR]

Packaging insulation materials with high thermal conductivity and excellent dielectric properties are favorable to meet the high demand and rapid development of third generation power semiconductors. In this study, we propose to improve the thermal conductivity of epoxy resin (EP) by incorporating a three-dimensional boron nitride thermally conductive network. Detailedly, polyurethane foam (PU) was used as a supporter, and boron nitride nanosheets (BNNSs) were loaded onto the PU supporter through chemical bonding (BNNS@PU). After immersing BNNS@PU into the EP resin, EP-based thermally conductive composites were prepared by vacuum-assisted impregnation. Fourier transform infrared spectrometer and scanning electron microscope were used to characterize the chemical bonding and morphological structure of BNNS@PU, respectively. The content of BNNS in BNNS@PU/EP composites was quantitatively analyzed by TGA. The results show that the thermal conductivity of the BNNS@PU/EP composites reaches 0.521 W/m K with an enhancement rate η of 30.89 at an ultra-low BNNS filler content (5.93 wt. %). Additionally, the BNNS@PU/EP composites have excellent dielectric properties with the frequency range from 101 to 106 Hz. This paper provides an interesting idea for developing high thermal conductivity insulating materials used for power semiconductor packaging. [ABSTRACT FROM AUTHOR]

Using non-equilibrium molecular dynamics simulations, we investigate thermal transport in organic/inorganic Au/molecular nanolayer (MNL) nanolaminate. We examine the tunability of thermal conductivity via interfacial bonding by (i) homogeneous change of bonding strength and heterogeneous change of (ii) bond density and (iii) molecular coverage at the interface. By comparing the thermal conductivity of the nanolaminates with the interfacial thermal conductance of corresponding individual interfaces, we conclude that phenomenologically the thermal conductivity can be predicted from independent interfacial resistors connected in a series model, particularly at higher temperatures. However, interfacial thermal conductance shows a moderate increase with temperature, whereas the thermal conductivity of Au/MNL nanolaminates shows the opposite effect. We elucidate this apparent contradiction via phonon wave packet simulations at individual and multiple interface structures. [ABSTRACT FROM AUTHOR]

The binary high-pressure phase diagram of the Ta–Sb system was constructed for the first time in this study, utilizing the evolutionary algorithm USPEX and density functional theory (DFT). Ten pressurized dynamically and mechanically stable or metastable novel phases of Ta–Sb were discovered, including I4/mmm-TaSb2, P4/nmm-TaSb, P-3-Ta2Sb7, I4/mmm-Ta2Sb3, P-4m2-Ta7Sb, Pm-3-Ta7Sb, Pmm2-Ta15Sb, P4/nmm-TaSb3, I4/mmm-Ta3Sb4, and I4/mmm-Ta2Sb5. The compounds P-4m2-Ta7Sb and Pmm2-Ta15Sb exhibit promising characteristics as non-centrosymmetric superconductors (NCSs), with their superconducting critical temperature (TC) being 3.831 and 3.221 K, respectively. The application of pressure tuning is predicted to transform the topological characteristics of P4/nmm-TaSb, causing it to transition from a topological insulator state to a Dirac semimetal state and ultimately reverting back to a topological insulator state. Therefore, the P4/nmm-TaSb compound is considered a promising candidate to investigate topological and superconducting excitations. Moreover, the mechanical and thermal properties of Ta–Sb binary phases were also investigated. The thermal conductivity of I4/mmm-TaSb2, P4/nmm-TaSb, and P4/nmm-TaSb3 all surpasses 20 W m−1 K−1 at 1000 K, showcasing their excellent thermal conductivity properties. The present study addresses the research gap concerning high-pressure structures in the Ta–Sb binary system, thereby offering valuable insights for the design and development of intermetallic compounds within this binary system. [ABSTRACT FROM AUTHOR]

In this research, we utilized density functional theory (DFT) computations to perform ab initio molecular dynamics simulations and static calculations on graphene, hexagonal boron nitride, and their heterostructures, subjecting them to strains, perturbations, twist angles, and defects. The gathered energy, force, and virial information informed the creation of a training set comprising 1253 structures. Employing the Neural Evolutionary Potential framework integrated into Graphics Processing Units Molecular Dynamics, we fitted a machine learning potential (MLP) that closely mirrored the DFT potential energy surface. Rigorous validation of lattice constants and phonon dispersion relations confirmed the precision and dependability of the MLP, establishing a solid foundation for subsequent thermal transport investigations. A further analysis of the impact of twist angles uncovered a significant reduction in thermal conductivity, particularly notable in heterostructures with a decline exceeding 35%. The reduction in thermal conductivity primarily stems from the twist angle-induced softening of phonon modes and the accompanying increase in phonon scattering rates, which intensifies anharmonic interactions among phonons. Our study underscores the efficacy of the MLP in delineating the thermal transport attributes of two-dimensional materials and their heterostructures, while also elucidating the micro-mechanisms behind the influence of the twist angle on thermal conductivity, offering fresh perspectives for the design of advanced thermal management materials. [ABSTRACT FROM AUTHOR]

The lattice thermal conductivity stands as a pivotal thermos-physical parameter of high-entropy alloys; nonetheless, achieving precise predictions of the lattice thermal conductivity for high-entropy alloys poses a formidable challenge due to their complex composition and structure. In this study, machine learning models were built to predict the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy based on molecular dynamic simulations. Our model shows high accuracy with R2, mean absolute percentage error, and root mean square error of the test set is 0.91, 0.031, and 1.128 W m−1 k−1, respectively. In addition, a high-entropy alloy with low a lattice thermal conductivity of 2.06 W m−1 k−1 (Al8Cr30Co19Ni20Fe23) and with a high lattice thermal conductivity of 5.29 W m−1 k−1 (Al0.5Cr28.5Co25Ni25.5Fe20.5) was successfully predicted, which shows good agreement with the results from molecular dynamics simulations. The mechanisms of the thermal conductivity divergence are further explained through their phonon density of states and elastic modulus. The established model provides a powerful tool for developing high-entropy alloys with the desired properties. [ABSTRACT FROM AUTHOR]

The glassy state of zeolitic imidazolate frameworks (ZIFs) has shown great potential for energy-related applications, including solid electrolytes. However, their thermal conductivity (κ), an essential parameter influencing thermal dissipation, remains largely unexplored. In this work, using a combination of experiments, atomistic simulations, and lattice dynamics calculations, we investigate κ and the underlying heat conduction mechanism in ZIF glasses with varying ratios of imidazolate (Im) to benzimidazolate (bIm) linkers. The substitution of bIm for Im tunes the node–linker couplings but exhibits only a minor impact on the average diffusivity of low-frequency lattice modes. On the other hand, the linker substitution induces significant volume expansion, which, in turn, suppresses the contributions from lattice vibrations to κ, leading to decreased total heat conduction. Furthermore, spatial localization of internal high-frequency linker vibrations is promoted upon substitution, reducing their mode diffusivities. This is ascribed to structural deformations of the bIm units in the glasses. Our work unveils the detailed influences of linker substitution on the dual heat conduction characteristics of ZIF glasses and guides the κ regulation of related hybrid materials in practical applications. [ABSTRACT FROM AUTHOR]

MXenes exhibit significant potential in thermoelectric materials owing to their exceptional electrical conductivity; however, their limited number of semiconductors restricts their application. Thus, it is highly desirable to expand the MXene family beyond carbides and nitrides to broaden their applications in thermoelectricity. In this work, we systematically investigate the thermoelectric transport of Ti2OX2 (X = F, Cl) MOene through comprehensively evaluating the electron–phonon coupling (EPC) from first principles. Our findings first emphasize the limitations of the deformation potential theory method and stress the importance of considering EPC. Ti2OF2 (Ti2OCl2) monolayer exhibits exceptional electronic transport, with Seebeck coefficients reaching 1483.87 (1206.22) μV/K and electrical conductivity reaching 9.5 × 105 (7.6 × 105) Ω−1 m−1 at room temperature for its N-type counterpart. Additionally, the presence of degenerate multiple valleys and peaks significantly enhances their electronic transport. For phonon transport, EPC results in a significant reduction in lattice thermal conductivity (kL) [e.g., at 300 K with 1.44 × 1015 (1.68 × 1015) cm−2 of hole, the reduction is 86.3% (73.3%) for Ti2OF2 (Ti2OCl2)]. Additionally, their kL demonstrates a strong correlation with the density of states at corresponding Fermi levels. Moreover, the kL and total thermal conductivity of P-type Ti2OF2 show T-independence, making it suitable for applications in aviation and thermal insulation materials. Finally, N-type Ti2OF2 and Ti2OCl2 demonstrate superior zT values of 0.63 and 0.9 at 900 K, respectively. This study provides in-depth insights into the superior thermoelectric properties of Ti2OX2 (X = F, Cl) MOene with considering EPC, providing a novel platform for the next-generation thermoelectric field. [ABSTRACT FROM AUTHOR]

A novel interfacial resistance-free (IRF) bifunctional camouflage (transparent and invisible) device is proposed in this paper. The thermal and electric conductivities of the shell and background are the same to eliminate the interfacial resistance. The IRF bifunctional camouflage device can operate in thermal–electric fields based on the neutral inclusion method. The distribution of isotherm and equipotential lines are studied quantitatively by the simulations. It is confirmed that the IRF bifunctional camouflage device with arbitrary natural materials can effectively achieve not only the invisible function but also the transparent function in thermal–electric fields. This method provides a window to the realization of bifunctions and the development of multi-physics fields. [ABSTRACT FROM AUTHOR]

Owing to the unique asymmetric geometry, Janus monolayer compounds exhibit various exotic thermal properties and have promising applications in thermal management. In this study, we combine machine learning potentials and the phonon Boltzmann transport equation to perform a comparative study of the thermal transport properties in Janus XBAlY (X = Se, S, Te; Y = S, Se, O; X ≠ Y) monolayers. Our findings unveil a thermal conductivity (κ p) ranking as SeBAlS > TeBAlO > SBAlSe, contradicting the conventional expectation that a higher κ p is typically observed when the average atomic mass is smaller. At room temperature, the κ p of SeBAlS is 174 Wm−1 K−1, which is 4.8 times that of SBAlSe when considering three-phonon scattering processes. Moreover, the consideration of four-phonon scatterings does not alter such ranking. The anomalous κ p phenomenon was explained through a detailed analysis of the phonon–phonon scattering mechanism, phonon bandgap, phonon anharmonicity, and chemical bond strength. This study highlights the intricate relationship between atomic mass, bonding characteristics, and thermal properties, offering insights for designing Janus materials with tailored thermal conductivity. [ABSTRACT FROM AUTHOR]

Hafnia (HfO2) is a potential candidate for the high-k gate dielectrics in next-generation high-power electronics. Its thermal transport properties, which determine the performance of these related high-power electronics, are critical while rarely investigated. Here, the thermal transport properties of HfO2 in a wide temperature range of 300–2000 K with a phase transition between monoclinic and tetragonal phases at ∼1765 K, are systematically studied based on the temperature-dependent effective potential landscapes with both propagating and coherence thermal transport considered. It is found that the cage-like structure of monoclinic HfO2 results in the avoid crossing in the phonon band structures, which increases the three-phonon scattering largely. Some phonon modes with significant scattering matrix can have relatively larger 3ph and 4ph scattering rates in tetragonal HfO2. Consequently, the thermal conductivity of HfO2 is only 11.95–1.72 W/mK at 300–2000 K. Our results further show that propagating phonon channels dominate the thermal transport in HfO2 and contribute at least 70% to the total thermal conductivity. The rest of the thermal conductivity of HfO2 results from the coherence thermal transport channels, which is caused by the overlap of phonons. Four-phonon scatterings are found to be significant for the thermal transport in tetragonal HfO2, which can result in a thermal conductivity reduction of ∼50%. Our results here advance the understanding of the thermal transport in HfO2, which may benefit the performance optimization of HfO2-related electronics. [ABSTRACT FROM AUTHOR]

Polymers, known for their lightweight, high strength, and ease of processing, serve as a key component in engineering materials. Polymers with high thermal conductivity (TC) present enormous potential applications in thermal management for high-performance electronic devices. However, the discovery of thermally conductive polymers is still in a time-consuming and labor-intensive trial-and-error process, which undoubtedly hinders the progress in related applications. Fortunately, machine learning (ML) enables to overcome this obstacle by building precise models to predict the TC of polymers through learning from a large volume of data and it can quickly identify polymers with high TC and provide significant insights to guide further design and innovation. In this mini review, we briefly describe the general process of using ML to predict polymers with high TC and then give guidance for the selection and utilization of three important components: database, descriptor, and algorithm. Furthermore, we summarize the predicted thermally conductive single polymer chains, amorphous polymers, and metal-organic frameworks via ML and identify the key factors that lead to high TC. Finally, we touch on the challenges faced when utilizing ML to predict the TC of polymer and provide a foresight into future research endeavors. [ABSTRACT FROM AUTHOR]

Despite more than seven decades of active research and development in thermoelectricity, the accurate measurement of the thermoelectric (TE) properties of bulk materials has remained a challenge, mainly because of the strong interrelation between thermal and electrical phenomena. This work highlights practical advancements in methods and instrumentation dedicated to the simultaneous measurements of TE properties such as the Seebeck coefficient (S), the thermal (κ), and electrical (σ) conductivities and the dimensionless TE figure of merit ZT = S2σT/κ. The accuracy of a Harman based approach, as implemented by the ZT-Scanner (TEMTE Inc.), applicable to the simultaneous measurement of the above TE properties, has been made possible by a self-contained calibration procedure, which is based on the availability of two samples of the same homogeneous material having different shape factors. It is of practical importance that this approach provides a simple procedure to obtain the calibration for the figure of merit ZT and the thermal conductivity in the temperature interval from 300 to 720 K. In addition, we show that a simplified Harman setup with no thermocouples attached to the sample can also be used for self-contained calibrated ZT measurements. It is concluded that the implemented steady-state approach decreases the relative error down to 1%–2% for ZT measurements and can be recommended for most applications not involving dynamical behavior. In particular, it is proposed that self-generated calibration samples can critically increase the quality and ease of comparison of TE measurements if they are adopted by the TE community. [ABSTRACT FROM AUTHOR]

First-principles molecular dynamics combined with the approach-to-equilibrium molecular dynamics methodology is employed to calculate the thermal conductivity of non-stoichiometric amorphous SiN. This is achieved by implementing thermal transients in five distinct models of different sizes along the direction of the heat transport. Such models have identical structural features and are representative of the same material, thereby allowing for a reliable analysis of thermal conductivity trends as a function of the relevant cell dimension. In line with the known physical law of heat propagation at short scale, the thermal conductivity increases in size with the direction of heat transport. The observed behavior is rationalized accounting for previous results on crystalline and amorphous materials, thus providing a unified description holding for a large class of materials and spanning a wide range of heat propagation lengths. [ABSTRACT FROM AUTHOR]

High-pressure treatment of oxide glasses can lead to significant alteration of various material properties such as increased density, ductility, and elastic moduli. In this study, a model of melt-quenched bulk silica glass was subject to high-pressure treatments (up to 16 GPa) using molecular dynamics simulations. The thermal conductivity of such prepared glass structures was determined using the equilibrium Green–Kubo method. We observed that, up to the pressure treatments of ∼ 6 GPa, the structure exhibits moderate density increase and a much steeper increase between 6 and 16 GPa, with associated density increase of fivefold silicon atoms. We also observed a noticeable increase (up to 20%) of the thermal conductivity in samples subjected to high-pressure treatments. The observed increases are somewhat, but not significantly, larger than those predicted by the minimum thermal conductivity model, accounting for density and elastic moduli increase. [ABSTRACT FROM AUTHOR]

Thermal phonon localization, rooted in phonon wave nature, is widely observed in disordered atomic systems. Binary superlattices, with structural diversity from abundant interfaces, allow for disorder introduction by engineering interfacial structures. In this study, two different disorder entities, namely, aperiodicity (randomized layer thicknesses) and interfacial mixing, were introduced to graphene/h-BN superlattices. Molecular dynamics simulations revealed that both disordered structures can significantly reduce the thermal conductivity, with interfacial mixing more effectively impeding thermal transport. The combined effect of these disorders further decreased thermal conductivity. The underlying mechanism involves Anderson localization of phonons, demonstrated by the exponential decay of phonon transmission and suppressed phonon participation ratio. Phase-breaking interactions at higher temperatures delocalize localized modes. This study offers valuable guidance for structurally designing materials targeting low thermal conductivity through the manipulation of phonon localization. [ABSTRACT FROM AUTHOR]

The mesmerizing physical properties of two-dimensional (2D) nanomaterials have resulted in their enormous potential for high-power solar energy conversion and long-term stability devices. The present work systematically investigated the fundamental properties of monolayered 2D group-IV–V materials using a combined approach of first-principles calculations and Boltzmann transport theory, specifically the thermoelectric and optical properties, for the first time. The structural and lattice dynamics analysis disclosed the energetic, dynamical, and mechanical stabilities of 17 out of 25 considered materials. The electronic properties' calculation shows that all the stable materials exhibit a semiconducting nature. Additionally, the energy–momentum relation in a few systems reveals the quartic Mexican-hat-like dispersion in their valence band edges. Owing to the larger depth of Mexican-hat dispersion and the larger height of density step function modes, the hole carrier mobilities of SnN (761.43 m2/Vs), GeN (422.80 m2/Vs), and SiN (108.90 m2/Vs) materials were found to be significantly higher than their electron mobilities at room temperature. The achieved high Seebeck coefficient and electrical conductivity at room temperature result in excellent thermoelectric power factors for GeN (3190 mW/mK2), SiN (1473 mW/mK2), and CAs (774 mW/mK2) materials, manifesting their potential for thermoelectric devices. Further, the calculated optical and solar parameters demonstrate an exceptionally high value (27.25%) of theoretical limits of power conversion efficiency for the SnBi material, making it a suitable candidate as a light-absorbing material in solar cell devices. The present theoretical work filters out the potential 2D group-IV–V materials for solar and heat energy-harvesting devices. [ABSTRACT FROM AUTHOR]

Numerical methods for analyzing diffusion phenomena involving strong discontinuities and complicated interfaces are of great scientific and technical importance. In this paper, we extend the existing step-balanced random walk, which overcomes the problem of detailed balance of a random walker in three-dimensional (3D), diffusion-tensor discontinuous systems, to particle density (ρ) discontinuous systems, and introduce volumetric heat capacity C and temperature T by considering ρ as heat density, i.e., ρ = C T , to apply it to thermal problems. Two types of thermophysical simulations are demonstrated: one is steady-state heat flow due to a temperature difference at the end surfaces on a two-phase slab model unit cell with periodic boundary conditions; the other is the time variation of temperature distribution due to heat diffusion from a point heat source in a repeated two-phase slab model with no periodic boundary conditions. We see the correct behavior of heat and temperature expected in 3D discontinuous systems composed of two phases with anisotropic thermal conductivity. The applicability to problems other than pure diffusion and the limitations of the method are also discussed. [ABSTRACT FROM AUTHOR]

For multi-layered 2D materials, although its c-axis has a much lower thermal conductivity than the a-axis, its phonon mean free path has been confirmed to be very long, e.g., in the order of 100s nm at room temperature for multi-layered graphene. An anisotropic specific heat concept has been proposed in the past to explain this very long mean free path. This work carries out detailed atomistic modeling to quantify the anisotropic specific heat concept and reports the discovery of anisotropic temperatures in multi-layered 2D materials under ultrafast surface heating. Extremely fast c-phonon energy transport is discovered, and the non-Fourier effect is observed for both a-phonons and c-phonons. The energy coupling factor between these two modes of phonons is determined to be in the order of 1016 W K−1 m−3, with the specific number depending on the structure location. The anisotropic temperature concept is also quantitatively confirmed based on the lattice Boltzmann method simulation. The anisotropic temperature concept does not violate the physics that temperature is a scalar; rather, it is developed to distinguish the temperatures of phonons that travel in different directions. This concept is universally applicable to other 2D materials to describe the heat conduction in the in-plane and out-of-plane directions that feature different interatomic bonds. [ABSTRACT FROM AUTHOR]

The massive amount of wasted heat energy from industry has pushed the development of thermoelectric (TE) materials that directly convert heat into electricity to a new level of concern. Recently, multicomponent alloys such as GeTe-based and PbSe-based high-entropy (HE) chalcogenides have attracted a great deal of attention due to their potential application as TE materials. The nature of the interatomic bonding, lattice distortion (LD), and the electronic structure in this class of materials is not fully understood. Herein, we report a comprehensive computational investigation of nine GeTe-based HE alloys with eight metallic elements (Ag, Pb, Sb, Bi, Cu, Cd, Mn, and Sn) with large supercells of 1080 atoms each; seven PbSe-based HE solid solutions: Pb0.99−ySb0.012SnySe1−2xTexSx (x = 0.1, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, with y = 0) with supercells of 1000 atoms each; and five Pb0.99−ySb0.012SnySe1−2xTexSx (y = 0.05, 0.1, 0.15, 0.2, 0.25 with x = 0.25) solid solutions with supercells of 1000 atoms each. All these HE models are theoretically investigated for the first time. The electronic structure, interatomic bonding, charge transfer, and lattice distortion (LD) are investigated by first-principles calculations based on density functional theory. Multicomponent HE alloys can cause a significant LD, which affects their mechanical, thermal, and TE properties. The calculations for the GeTe-based HE chalcogenides showed that they are semiconductors with a narrow bandgap, except for m8, which has a semi-metallic characteristic, and this makes them good candidates for TE applications. For most of these models, the Fermi level shifts upward and locates deeply in the conduction bands, resulting in the enhancement of the electrical conductivity (σ). The bonding properties showed that most bonds in m5 are more dispersed, indicating highest LD and lower lattice thermal conductivity. For PbSe-based HE solid solutions, the LD calculations showed that the models Pb0.99Sb0.012Se0.5Te0.25S0.25 and Pb0.89Sb0.012Sn0.1Se0.5Te0.25S0.25 have the higher LD, and thus a lower lattice thermal conductivity. Such investigations are in high demand since it enables us to design new HE chalcogenides for TE applications. We use the novel concept of total bond order density as a single quantum mechanical metric to characterize the internal cohesion of these HE alloys and correlate with calculated properties, especially the mechanical properties. This work provides a solid database for HE chalcogenides and a road map for many potential applications. Moreover, the computational procedure we developed can be used to design new HE chalcogenides for specific TE applications. [ABSTRACT FROM AUTHOR]

We report the experimental characterization and finite element modeling of magnetic fluid hyperthermia (MFH) in tissue-like media using tetramethyl ammonium hydroxide coated superparamagnetic iron oxide magnetic nanoparticles (MNPs) of size ∼19.6 ± 1.2 nm, prepared using a co-precipitation technique. MFH properties are probed for the MNPs in ∼1 wt. % agar, resembling the tumor and surrounding normal tissues. The field-induced temperature rise (ΔT) is experimentally measured in real-time utilizing an infrared camera. A finite element model (FEM) is utilized to simulate the spatiotemporal variations in the thermal profiles, which are found to be in good agreement with the experimental data. FEM-based parametric studies reveal that the thermal conductivity of the medium is the most significant parameter influencing the thermal profiles. The spatiotemporal variations in the thermal profiles are numerically studied for seven different tissues, and the obtained results indicate the highest ΔT for the breast tissue in the tumor and the surrounding regions, which is due to the lowest volumetric specific heat and the highest thermal conductivity of the breast tissue, respectively. Numerical studies on the thermal profiles for sub-surface tumors with parametrically varying depths indicate a strong exponential correlation between the surface and tumor temperature, where the regression coefficients are found to be correlated with the thermo-physical properties of the tissues. The obtained findings are beneficial for developing a simplistic and easily deployable framework for a priori generation of the thermal profiles for various tissues during MFH, which is useful for appropriate planning and parameter selection for MFH-based therapy. [ABSTRACT FROM AUTHOR]

This paper investigates a systematic theoretical study of the lattice dynamical, electronic, and thermoelectric transport properties of PbTe, which is subjected to hydrostatic pressure and uniaxial stress along the [100], [110], and [111] directions, by using first-principles calculations. Our study demonstrates that stress is an effective tool for regulating thermoelectric properties in materials, and different types of stress affect these properties through distinct mechanisms. Specifically, under hydrostatic pressure and uniaxial stress along the [100] direction, PbTe exhibits similar electronic behavior but different lattice dynamical properties. Bandgap closure and reopening are observed under hydrostatic pressure and uniaxial stress along the [100] direction, whereas only the gap opposite trends are seen for uniaxial stress along the [110] and [111] directions. Under uniaxial stress along the [100] direction, phonon lifetimes decrease, leading to low thermal conductivity. In contrast, under hydrostatic pressure and uniaxial stress in the [110] and [111] directions, phonon lifetimes are enhanced due to a weakening of the anharmonic effect. Our findings provide a comprehensive understanding of the implication of different stress types on the thermoelectric properties of PbTe. [ABSTRACT FROM AUTHOR]