SearcharxivSearch

arXiv subjects

Nuo Yang

Publications and source records attributed to Nuo Yang.

At least 19 recordsLinked to original sources

Knudsen-Controlled Switching of Thermal Conductivity Response by Targeted Phonon Excitation

Targeted phonon excitation offers a route to dynamically control heat conduction, yet no general principle predicts whether a spectrally selective nonequilibrium phonon population will enhance or suppress thermal transport. A Knudsen-controlled competition between the increased contribution of long-mean-free-path phonons and excitation-enhanced intrinsic scattering governs the sign of the thermal-conductivity response. First-principles three-phonon scattering rates combined with phonon-tracking Monte Carlo simulations are used to examine Ge, Si, and 3C--SiC from bulk crystals to confined nanofilms. In bulk systems, excitation-enhanced scattering dominates and thermal conductivity is predominantly suppressed. In nanofilms, by contrast, low-frequency excitation can increase the contribution of quasi-ballistic heat-carrying channels and enhance thermal conductivity, whereas higher-frequency excitation is predominantly suppressive. At fixed background temperature and excitation strength, these opposite responses are organized in a frequency--Knudsen map based on the normalized target frequency, $\omega_{\mathrm t}/\omega_{\mathrm D}$, and the Knudsen number, $\mathrm{Kn}$. The resulting framework provides a general physical basis for controlling nonequilibrium heat transport beyond static phonon engineering.

cond-mat.mtrl-sci

The substitutional atomic distance model for predicting lattice thermal conductivity in alloys

Understanding phonon transport in alloys is crucial for the design of high-performance electronic and thermoelectric devices. However, conventional theoretical models fail to provide a clear physical picture of phonon scattering caused by atomic disorder in alloys, and their prediction accuracy is limited. In this work, a new substitutional atomic distance model for alloys is proposed, providing an intuitive physical picture. SiGe and InGaAs alloys are taken as representative systems, and their thermal conductivities are calculated, showing good agreement with previous experimental measurements. The results indicate that alloy scattering plays a dominant role in reducing thermal conductivity. This study provides new insights into phonon transport in alloys and offers guidance for tailoring thermal properties through compositional engineering.

cond-mat.mtrl-sci

Non-uniform Thermal Conductivity in Nanoscale Multiple Hotspot Systems

Understanding nanoscale hotspot thermal transport is crucial in electronic devices. Contrary to common perception, recent experiments show that closely spaced nanoscale multiple hotspots can enhance heat dissipation. Here, the thermal transport in nanoscale multiple hotspot systems is investigated by solving the phonon Boltzmann transport equation. The local thermal conductivity is proposed to describe the non-uniform spatial distribution of heat transport capability in nanoscale multiple hotspot systems. The maximum value exceeds the uniform heating case by up to 27%, which is attributed to the spatially varying fraction of unscattered phonons emitted from hotspots. Moreover, the effects and mechanisms of hotspot spacing on thermal transport are investigated, showing that reducing the hotspot spacing can enhance the heat flux by up to 40%. This work challenges the conventional view that thermal transport capability is spatially uniform throughout the system and provides fundamental insights for thermal management in high-power-density integrated circuits.

cond-mat.mes-hall

Effect of non-Fourier heat transport on temperature distribution in High Bandwidth Memory

High Bandwidth Memory (HBM), as a key development trend in future memory chip technology, significantly enhances computer performance. At the same time, the thermal challenges arising from its stacked architecture have drawn considerable attention. Most existing studies on HBM thermal management are based on Fourier's law, neglecting the non-Fourier effects introduced by the micro/nanoscale structures within HBM. In this study, the Monte Carlo method (MC) is employed to solve the phonon Boltzmann transport equation (BTE) and investigate the impact of non-Fourier heat transport on the thermal behavior of HBM structures. The results reveal that non-Fourier heat transport leads to a junction temperature that is 59.8 K higher than that predicted by Fourier's law. Furthermore, it is found that the phonon transmittance at the chip interlayers has a severe impact on heat dissipation, with the temperature variation reaching up to 56.6 K. These findings provide more accurate thermal insights, which are critical for the optimized design of HBM systems.

physics.app-ph

Higher-Order-Phonon Scattering Governs Targeted Control of Heat Conduction in Bulk Boron Arsenide

Conventional approaches for modulating thermal conductivity usually rely on structural modifications and therefore cannot achieve reversible in situ regulation. Targeted phonon excitation has recently emerged as a promising strategy for dynamically tuning thermal transport, but its applicability has so far been demonstrated mainly in two-dimensional systems. Here, we extend this strategy to a three-dimensional bulk material by taking boron arsenide (BAs) as a representative example. Based on first-principles calculations and phonon Boltzmann transport analysis, we show that targeted phonon excitation modulates the thermal conductivity of bulk BAs in a strongly frequency-dependent manner. Within the three-phonon-only framework, the modulation at 300 K is weak but clearly bidirectional. However, once four-phonon scattering is included, the modulation changes qualitatively to a predominantly suppressive behavior. In the combined three-phonon plus four-phonon (3ph+4ph) framework, the strongest suppression occurs at 20.5 THz, where the relative thermal conductivity decreases to 0.828 and 0.415 for excitation intensities of 5 and 25, respectively. By comparing the 3ph-only and 3ph+4ph results, we show that four-phonon scattering plays a decisive role in determining the net modulation effect by raising the intrinsic scattering background and promoting a more systematic excitation-induced increase in the scattering of low-frequency heat-carrying phonons.

cond-mat.mtrl-sci

Machine learning potential for predicting thermal conductivity of {\theta}-phase and amorphous Tantalum Nitride

Tantalum nitride (TaN) has attracted considerable attention due to its unique electronic and thermal properties, high thermal conductivity, and applications in electronic components. However, for the {\theta}-phase of TaN, significant discrepancies exist between previous experimental measurements and theoretical predictions. In this study, deep potential models for TaN in both the {\theta}-phase and amorphous phase were developed and employed in molecular dynamics simulations to investigate the thermal conductivities of bulk and nanofilms. The simulation results were compared with reported experimental and theoretical results, and the mechanism for differences were discussed. This study provides insights into the thermal transport mechanisms of TaN, offering guidance for its application in advanced electronic and thermal management devices.

cond-mat.mtrl-sci

Unveiling the thermal transport mechanism in compressed plastic crystals assisted by deep potential

The unique properties of plastic crystals highlight their potential for use in solid-state refrigeration. However, their practical applications are limited by thermal hysteresis due to low thermal conductivity. In this study, the effect of compressive strain on the thermal transport properties of plastic crystal [(CH3)4N][FeCl4] was investigated using molecular dynamic simulation with a deep neural network potential. It is found that a 9% strain along [001] direction enhances thermal conductivity sixfold. The underlying mechanisms are analyzed through vibrational density of states, spectral energy densities, and mean square displacements. The enhancement in thermal conductivity is primarily due to increased group velocity and reduced phonon scattering, driven by volume compression within the 0-1 THz. These findings offer theoretical insights for the practical application of plastic crystals in thermal management systems.

cond-mat.mtrl-sci

Achieving ultra-high anisotropy in thermal conductivity of plastic crystal through megapascal pressure of hot pressing

Plastic crystals, owing to their exceptional properties, are gradually finding applications in solid-state refrigeration and ferroelectric fields. However, their inherently low thermal conductivity restricts their utilization in electronic devices. This study demonstrates that applying megapascal pressure of hot pressing can enhance the thermal conductivity of plastic crystal films. Most importantly, it induces significant anisotropy in thermal conductivity. Such anisotropy in thermal conductivity is beneficial for specialized thermal management applications, such as directing heat flow paths in electronic devices. In this study, [(CH3)4N][FeCl4] PCs films were prepared by hot pressing. At a pressure of 16 MPa, the ratio of in-plane to cross-plane thermal conductivity in the film reaches a remarkable 5.5. This is attributed to the preferential orientation along the (002) crystal plane induced by uniaxial pressure, leading to the formation of a layered structure and the creation of a flat and dense film. Furthermore, according to molecular dynamics simulations, the thermal conductivity along the [100] and [010] directions (parallel to the (002) crystal plane) is higher than in other directions. Therefore, significant modulation of anisotropy in thermal conductivity is achieved in [(CH3)4N][FeCl4] films by applying uniaxial hot pressing pressure. This phenomenon has the potential to greatly broaden the application of plastic crystals in the field of flexible electronic devices.

cond-mat.mtrl-sci

Deep potential for interaction between hydrated Cs+ and graphene

The influence of hydrated cation-{\pi} interaction forces on the adsorption and filtration capabilities of graphene-based membrane materials is significant. However, the lack of interaction potential between hydrated Cs+ and graphene limits the scope of adsorption studies. Here, it is developed that a deep neural network potential function model to predict the interaction force between hydrated Cs+ and graphene. The deep potential has DFT-level accuracy, enabling accurate property prediction. This deep potential is employed to investigate the properties of the graphene surface solution, including the density distribution, mean square displacement, and vibrational power spectrum of water. Furthermore, calculations of the molecular orbital electron distributions indicate the presence of electron migration in the molecular orbitals of graphene and hydrated Cs+, resulting in a strong electrostatic interaction force. The method provides a powerful tool to study the adsorption behavior of hydrated cations on graphene surfaces and offers a new solution for handling radionuclides.

physics.comp-ph

Using Targeted Phonon Excitation to Modulate Thermal Conductivity of Boron Nitride

Recent advancements in thermal conductivity modulating strategies have shown promising enhancements to the thermal management capabilities of two-dimensional materials. In this article, both iterative Boltzmann transport equation solution and two-temperature model were employed to investigate the efficacy of targeted phonon excitation applied to hexagonal boron nitride. The results indicate significant modifications to hBN's thermal conductivity, achieving increases of up to 30.1% as well as decreases of up to 59.8%. These findings validate the reliability of the strategy, expand its scope of applicability, and establish it as a powerful tool for tailoring thermal properties across a wider range of fields.

cond-mat.mtrl-sci

The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

physics.app-ph

Enhancing interfacial thermal conductance of Si/PVDF by strengthening atomic couplings

The thermal transport across inorganic/organic interfaces attracts interest for both academic and industry due to its widely applications in flexible electronics etc. Here, the interfacial thermal conductance of inorganic/organic interfaces consisting of silicon and polyvinylidene fluoride is systematically investigated by molecular dynamics simulations. Interestingly, it is demonstrated that a modified silicon surface with hydroxyl groups can drastically enhance the conductance by 698%. These results are elucidated based on interfacial couplings and lattice dynamics insights. This study not only provides feasible strategies to effectively modulate the interfacial thermal conductance of inorganic/organic interfaces but also deepens the understanding of the fundamental physics underlying phonon transport across interfaces.

cond-mat.mtrl-sci

Deformation insensitive thermal conductance of the designed Si metamaterial

The thermal management have been widely focused due to broad applications. Generally, the deformation can largely tune the thermal transport. The main challenge of flexible electronics/ materials is to maintain thermal conductance under large deformation. This work investigates the thermal conductance of a nano-designed Si metamaterial constructed with curved nanobeams by molecular dynamics simulation. Interestingly, it shows that the thermal conductance of the nano-designed Si metamaterial is insensitive under a large deformation (strain~-41%). The new feature comes from the designed curved nanobeams which makes a quasi-zero stiffness. Further calculations show that, when under a large deformation, the average stress in nanobeam is ultra-small (<151 MPa) and its phonon density of states are little changed. This work provides valuable insights on multifunction, such as both stable thermal and mechanical properties, of nano-designed metamaterials.

physics.app-ph

Modulating Thermal Conductivity via Targeted Phonon Excitation

Thermal conductivity is a critical material property in numerous applications, such as those related to thermoelectric devices and heat dissipation. Effectively modulating thermal conductivity has become a great concern in the field of heat conduction. In this study, a quantum strategy is proposed to modulate thermal conductivity by exciting targeted phonons. The results show that the thermal conductivity of graphene can be tailored in the range of 1559 W/m-K (49%) to 4093 W/m-K (128%), compared with the intrinsic value of 3189 W/m-K. A similar trend is also observed for graphene nanoribbons. The results are obtained through both ab initio calculations and molecular dynamics simulations. This brand-new quantum strategy to modulate thermal conductivity paves a way for quantum heat conduction.

cond-mat.mtrl-sci

Boosting current-induced molecular dynamics with machine-learning potential

In a current-carrying single-molecular junction (SMJ), a hierarchy of hybrid energy transport processes takes place under a highly nonequilibrium situation, including energy transfer from electrons to molecular vibrations via electron-vibration interaction, energy redistribution within different vibrational modes via anharmonic coupling, and eventual energy transport to surrounding electrodes. A comprehensive understanding of such processes is a prerequisite for their potential applications as single-molecular devices. $Ab$ $initio$ current-induced molecular dynamics (MD) is an ideal approach to address this complicated problem. But the computational cost hinders its usage in systematic study of realistic SMJs. Here, we achieve orders of magnitude improvement in the speed of MD simulation by employing machine-learning potential with accuracy comparable to density functional theory. Using this approach, we show that SMJs with graphene electrodes generate order of magnitude less heating than those with gold electrodes. Our work illustrates the superior heat transport property of graphene as electrodes for SMJs, thanks to its better phonon spectral overlap with molecular vibrations.

cond-mat.mes-hall

A unified theory of second sound in two dimensional materials

We develop a unified theory for the second sound in two dimensional materials. Previously studied drifting and driftless second sound are two limiting cases of the theory, corresponding to the drift and diffusive part of the energy flux, respectively. We find that due to the presence of quadratic flexural phonons the drifting second sound does not exist in the thermodynamic limit, while the driftless mode is less affected. This is understood as a result of infinite effective inertia of flexual phonons, due to their constant density states and divergent Bose-Einstein distribution in the long wave length limit. Consequently, the group velocity of the drifting mode is smaller than that of the driftless mode. However, upon tensile strain, the velocity of drifting mode becomes larger. Both of them increase with tensile strain due to the linearization of the flexural phonon dispersion. Our results clarify several puzzles encountered previously and pave the way for exploring wave-like heat transport beyond hydrodynamic regime.

cond-mat.mes-hall

Temperature-dependent thermal transport of single molecular junctions from semi-classical Langevin molecular dynamics

Thermal conductance of single molecular junctions at room temperature has been measured recently using picowatt-resolution scanning probes. However, fully understanding thermal transport in a much wider temperature range is needed for the exploration of energy transfer at single-molecular limit and the development of single-molecular devices. Here, employing a semiclassical Langevin molecular dynamics method, a comparative study is performed on the thermal transport of an alkane chain between Au and graphene electrodes, respectively. We illustrate the different roles of quantum statistics and anharmonic interaction in the two types of junctions. For a graphene junction, quantum statistics is essential at room temperature, while the anharmonic interaction is negligible. For a Au junction, it is the other way. Our study paves the way for theoretically understanding thermal transport of realistic single-molecular junctions in the full temperature range by including both quantum statistics and anharmonic interaction within one theoretical framework.

cond-mat.mes-hall

Improving the mass transfer rate and energy efficiency of solar still by enhancing the inner air circulation

Solar still is an eco-friendly and convenient desalination system that can provide fresh water for remote areas and emergencies. The energy efficiency and productivity of conventional solar still are unsatisfying and need improvement, which requires a deep understanding of the heat and mass transfer process in solar still. In this work, the effect of the inner air circulation on the system's heat and mass transfer performance and energy efficiency are studied theoretically and experimentally. The theoretical results reveal that a weak acceleration of the air circulation inside the SS will significantly increase its performance, due to the improved mass transfer process. By enhancing the inner air circulation, the evaporation and condensation in the solar still can reach up to the limit, and the theoretical energy efficiency reaches up to 87%, 91.5%, and 94.5%, for the input power density at 300 W/m2, 500 W/m2, and 700 W/m2, respectively. Besides, lower ambient temperature and higher ambient convective heat transfer coefficient will decrease the energy efficiency. Given the heat loss, the experimental energy efficiencies are only 3% to 6% lower than the theoretical results, which indicates that the great performance predicted by the theory can be realized in practical application. This work provides a new understanding and strategy for improving the performance of the solar still.

physics.app-ph