arXiv subjects

Ronggui Yang

Publications and source records attributed to Ronggui Yang.

At least 19 recordsLinked to original sources

arXiv2026

Thermal Characterization of Buried Interfaces in Multilayer Heterostructures via TDTR with Periodic Waveform Analysis

Accurate evaluation of buried thermal interfaces is vital for understanding and optimizing heat dissipation in wide- and ultra-wide-bandgap (WBG/UWBG) semiconductor devices. Conventional time-domain thermoreflectance (TDTR) typically probes only near-surface transport due to its restricted modulation frequency range. Here, we employ a frequency-tunable periodic waveform analysis TDTR (PWA-TDTR) technique to perform depth-resolved thermal measurements on three representative systems: epitaxial {\epsilon}-Ga2O3/SiC, GaN/Si, and mechanically bonded GaN/diamond. By combining broadband multi-frequency probing with sensitivity-guided joint fitting, we quantitively determine interfacial thermal conductance, layer-specific thermal conductivity, and volumetric heat capacity, without requiring destructive sample preparation. The results reveal that the buried Ga2O3/SiC interface exhibits weak phonon transmission due to acoustic mismatch; the transition layers in GaN/Si act as phonon-impedance gradients that redistribute heat flux; and the GaN/diamond boundary remains the dominant thermal bottleneck despite diamond's ultrahigh bulk conductivity. These findings demonstrate that the modulation frequency in PWA-TDTR functions as a tunable probe of depth-dependent phonon transport, directly linking frequency-domain thermal response to interfacial energy transmission. Overall, this work positions PWA-TDTR as a versatile platform for investigating buried nonmetal-nonmetal interfaces in next-generation high-power and optoelectronic materials.

physics.app-ph↗

arXiv2025

Thermal Property Microscopy with Compressive Sensing Frequency-Domain Thermoreflectance

Spatial mapping of thermal properties is critical for unveiling the structure-property relation of materials, heterogeneous interfaces, and devices. These property images can also serve as datasets for training artificial intelligence models for material discoveries and optimization. Here we introduce a high-throughput thermal property imaging method called compressive sensing frequency domain thermoreflectance (CS-FDTR), which can robustly profile thermal property distributions with micrometer resolutions while requiring only a random subset of pixels being experimentally measured. The high-resolution thermal property image is reconstructed from the raw down-sampled data through L_1-regularized minimization. The high-throughput imaging capability of CS-FDTR is validated using the following cases: (a) the thermal conductance of a patterned heterogeneous interface, (b) thermal conductivity variations of an annealed pyrolytic graphite sample, and (c) the sharp change in thermal conductivity across a vertical aluminum/graphite interface. With less than half of the pixels being experimentally sampled, the thermal property images measured using CS-FDTR show nice agreements with the ground truth (point-by-point scanning), with a relative deviation below 15%. This work opens the possibility of high-throughput thermal property imaging without sacrificing the data quality, which is critical for materials discovery and screening.

physics.app-ph↗

arXiv2025

Extending the Low-Frequency Limit of Time-Domain Thermoreflectance via Periodic Waveform Analysis

Time-domain thermoreflectance (TDTR) is a powerful technique for characterizing the thermal properties of layered materials. However, its effectiveness at modulation frequencies below 0.1 MHz is hindered by pulse accumulation effects, limiting its ability to accurately measure in-plane thermal conductivities below 6 W/(m K). Here, we present a periodic waveform analysis-based TDTR (PWA-TDTR) method that extends the measurable frequency range down to 50 Hz with minimal modifications to the conventional setup. This advancement greatly enhances measurement sensitivity, enabling accurate measurements of in-plane thermal conductivities as low as 0.2 W/(m K). We validate the technique by measuring polymethyl methacrylate (PMMA) and fused silica, using PWA-TDTR to obtain in-plane thermal diffusivity and conventional TDTR to measure cross-plane thermal effusivity. Together, these allow the extraction of both thermal conductivity and volumetric heat capacity, with results in excellent agreement with literature values. We further demonstrate the versatility of PWA-TDTR through (1) thermal conductivity and heat capacity measurements of thin liquid films and (2) depth-resolved thermal conductivity profiling in lithium niobate crystals, revealing point defect-induced inhomogeneities at depths up to 100 um. By overcoming frequency and sensitivity constraints, PWA-TDTR significantly expands the applicability of TDTR, enabling detailed investigations of thermal transport in materials and conditions that were previously challenging to study.

physics.app-ph↗

arXiv2025

Proper Characterization of Heat-to-Electric Conversion Efficiency of Liquid Thermogalvanic Cells

Liquid thermogalvanic cells (LTCs) have emerged as a promising technology for harvesting low-grade heat due to their low cost, compact design, and high thermopower. However, discrepancies exist in quantifying their output power and efficiency. The commonly used figure of merit, ZT = S^2\sigma T/k, is based on electrolyte properties but fails to account for electrochemical reaction kinetics at the electrode interface that significantly impact performance and losses. This work establishes an experimental protocol for accurately characterizing LTC efficiency. We propose a device-level figure of merit, ZT = S^2T/RK , where R and K represent total internal resistance and thermal conductance. This formulation, derived by linearizing the Butler-Volmer relation, incorporates irreversible losses such as mass transfer and activation overpotential. Different methods for assessing LTC output power are examined, including linear sweeping voltammetry (LSV), constant resistance discharging, and constant current step discharging. LSV tends to overestimate power due to transient effects, while the latter two methods provide more accurate steady-state measurements. Additionally, heat conduction across LTCs is carefully analyzed, highlighting the significant impact of natural convection within electrolytes. Through rigorous experimental characterization, we demonstrate that the modified figure of merit is a proper efficiency indicator at the steady-state.

physics.app-ph↗

arXiv2025

Multi-Carrier Thermal Transport in Electronic and Energy Conversion Devices

Nonequilibrium multi-carrier thermal transport is essential for both scientific research and technological applications in electronic, spintronic, and energy conversion devices. This article reviews the fundamentals of phonon, electron, spin, and ion transport driven by temperature gradients in solid-state and soft condensed matters, and the microscopic interactions between energy/charge carriers that can be leveraged for manipulating electrical and thermal transport in energy conversion devices, such as electron-phonon coupling, spin-phonon interaction, and ion-solvent interactions, etc. In coupled electron-phonon transport, we discuss the basics of electron-phonon interactions and their effects on phonon dynamics, thermalization, and nonequilibrium thermal transport. For the phonon-spin interaction, nonequilibrium transport formulation is introduced first, followed by the physics of spin thermoelectric effect and strategies to manipulate them. Contributions to thermal conductivity from magnons as heat carriers are also reviewed. For coupled transport of heat and ions/molecules, we highlight the importance of local molecular configurations that determine the magnitude of the electrochemical gradient, which is the key to improving the efficiency of low-grade heat energy conversion.

cond-mat.mtrl-sci↗

arXiv2024

API Phonons: Python Interfaces for Phonon Transport Modeling

API Phonons is a Python software package to predict the transport dynamics of heat-carrying phonons. Using the powerful syntax of Python, this package provides modules and functions interfacing between different packages for atomistic simulations, lattice dynamics, and phonon-phonon interaction calculations including LAMMPS, Quippy, Phonopy, and ShengBTE. API Phonons enabled complex phonon calculations, including (1) extracting harmonic and anharmonic force constants from arbitrary interatomic potentials, which can be used as inputs for solving Boltzmann transport equations; (2) predicting thermal conductivity using Kubo's linear response theory, which captures both quasiparticle transport and inter-band coherent transport; and (3) modeling of ultrafast pump-probe thermal responses using a Green's function approach based on mode-resolved phonon properties for studying ballistic, hydrodynamic, and diffusive transport dynamics. The package provides a flexible, easy-to-use, and extensive platform for modeling phonon transport physics through Python programming.

cond-mat.mtrl-sci↗

arXiv2024

Analytical Green's Function of Multidimensional Boltzmann Transport Equation for Modeling Hydrodynamic Second Sound

Hydrodynamic second sound can be generated by heat pulses when the phonon-phonon interaction is dominantly momentum conserving, and the propagation of the temperature field becomes wavelike rather than diffusive. While the Boltzmann transport equation (BTE) has been widely applied to study phonon dynamics and thermal transport at the nanoscale, modeling the hydrodynamic transport regime remains challenging. The widely used relaxation time approximation (RTA) treats all phonon interactions as resistive without considering momentum conservation, resulting in the absence of phonon hydrodynamics. Rigorously solving BTE by inverting the full scattering matrix, however, is extremely computationally demanding and has been only applied to model one-dimensional temperature variations. Here, we present an analytical Green's function formalism for solving multidimensional Boltzmann transport equation (BTE) using phonon properties from first-principles calculations. This formalism involves Callaway's scattering approximation with separate relaxation times for momentum-conserving and momentum-destroying scattering events. The Green's function captures phonon dynamics in a wide range of temperature, spatial, and temporal scales, and successfully reproduces the transition from ballistic, hydrodynamic, to diffusive transport regimes. Our method avoids the computationally demanding inversion of large scattering matrices and shows good accuracies in predicting the temperature oscillation in ultrafast pump-probe characterizations with different geometries of thermal excitation.

cond-mat.mtrl-sci↗

arXiv2024

Thermodynamics of Ionic Thermoelectrics for Low-Grade Heat Harvesting

More than half of the waste heat rejected into the environment has temperatures lower than 100 $^\circ C$, which accounts for nearly 85 PWh/year worldwide. Efficiently harvesting low-grade heat could be a promising step toward carbon neutrality. Recent developments of ionic thermoelectrics (i-TE) with giant thermopower have provoked intensive interest in using ions as energy and charge carriers for efficient thermal energy harvesting. However, current literature primarily focuses on improving thermopower only, while the ion transport and thermodynamics affecting the efficiencies have been largely neglected. This review article clarifies the fundamentals of electrochemistry and thermodynamics for developing highly efficient i-TE devices. Two major types of i-TE devices, thermo-ionic capacitors (TIC) and thermogalvanic cells (TGC), are discussed in detail. The article analyzes the methods of enhancing ionic thermopower in the literature by taking an entropic point of view. We also derived modified thermoelectric factor Z for both TICs and TGCs that fully incorporate the dynamics of ion transport and electrochemical reactions. Recent developments of hybrid devices showing improved efficiencies, power density, and multifunctionality are reviewed. Finally, we comment on the remaining challenges and provide an outlook on future directions.

physics.chem-ph↗

arXiv2023

Glass-like Cross-plane Thermal Conductivity of Kagome Metals RbV3Sb5 and CsV3Sb5

This work reports the thermal conductivity of RbV3Sb5 and CsV3Sb5 with three-dimensional charge density wave phase transitions from 80 K to 400 K measured by pump-probe thermoreflectance techniques. The in-plane (basal plane) thermal conductivities are found moderate, i.e., 12 W/mK of RbV3Sb5 and 8.8 W/mK of CsV3Sb5 at 300 K. Low cross-plane (stacking direction) thermal conductivities are observed, with 0.72 W/mK of RbV3Sb5 and 0.49 W/mK of CsV3Sb5 at 300 K. A unique glass-like temperature dependence in the cross-plane thermal conductivity is observed, which decreases monotonically even lower than the Cahill-Pohl limit as the temperature decreases below the phase transition point TCDW. This temperature dependence is found to obey the hopping transport picture. In addition, a peak in cross-plane thermal conductivity is observed at TCDW as a fingerprint of the modulated structural distortion along the stacking direction.

cond-mat.mtrl-sci↗

arXiv2023

Effect of solvation shell structure on thermopower of liquid redox pairs

Recent advancements in thermogalvanic batteries offer a promising route to efficient harvesting of low-grade heat with temperatures below 100 {\deg}C. The thermogalvanic temperature coefficient {\alpha}, usually referred to as effective thermopower, is the key parameter determining the power density and efficiency of thermogalvanic batteries. However, the current understanding of improving {\alpha} of redox pairs remains at the phenomenological level without microscopic insights, and the development of electrolytes with high {\alpha} largely relies on experimental trial and error. This work applies the free energy perturbation method based on molecular dynamics simulations to predict the {\alpha} of the {Fe^{3+}/Fe^{2+}} redox pair in aqueous and acetone solutions. We showed that {\alpha} of the {Fe^{3+}/Fe^{2+}} redox pair can be increased from 1.5{\pm}0.3 mV/K to 4.1{\pm}0.4 mV/K with the increased acetone to water fraction. The predicted {\alpha} of {Fe^{3+}/Fe^{2+}} both in pure water and acetone show excellent agreement with experimental values. By monitoring the fluctuation of dipole orientations in the first solvation shell, we discovered a significant change in the variance of solvent dipole orientation between Fe^{3+} and Fe^{2+}, which can be a microscopic indicator for large magnitudes of {\alpha}. The effect of acetone weight fraction in the mixed acetone-water solvent on the {\alpha} of {Fe^{3+}/Fe^{2+}} is also studied. Acetone molecules are found to intercalate into the first solvation shell of the {Fe^{2+}} ion at high acetone fractions, while this phenomenon is not observed in the solvation shell of the Fe^{3+} ion. Such solvation shell structure change of {Fe^{2+}} ions contributes to the enhanced {\alpha} at high acetone fractions. Our discovery provides atomistic insights into how solvation shell order can be leveraged to develop electrolytes with high thermopower.

physics.chem-ph↗

arXiv2023

Thermo-optic phase shifter based on hydrogen-doped indium oxide microheater

Thermo-optic (TO) phase shifters are very fundamental units in large-scale active silicon photonic integrated circuits (PICs). However, due to the limitation of microheater materials with a trade-off between heating efficiency and absorption loss, designs reported so far typically suffer from slow response time, high power consumption, low yields, and so on. Here, we demonstrate an energy-efficient, fast-response, and low-loss TO phase shifter by introducing hydrogen-doped indium oxide (IHO) films as microheater, and the optimized electron concentration with enhanced mobility endows the IHO high conductivity as well as high near-infrared (NIR) transparency, which allow it to directly contact the silicon waveguide without any insulating layer for efficient tuning and fast response. The TO phase shifter achieves a sub-microsecond response time (970 ns/980 ns) with a {\pi} phase shift power consumption of 9.6 mW. And the insertion loss introduced by the IHO microheater is ~ 0.5 dB. The proposed IHO-based microheaters with compatible processing technology illustrate the great potential of such material in the application of large-scale silicon PICs.

physics.optics↗

arXiv2022

Machine learning reconstruction of depth-dependent thermal conductivity profile from pump-probe thermoreflectance signals

Characterizing materials with spatially varying thermal conductivities is significant to unveil the structure-property relation for a wide range of functional materials, such as chemical-vapor-deposited diamonds, ion-irradiated materials, nuclear materials under radiation, and battery electrode materials. Although the development of thermal conductivity microscopy based on time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning of thermal conductivity profile, measuring depth-dependent thermal conductivity remains challenging. This work proposed a machine-learning-based reconstruction method for extracting depth-dependent thermal conductivity K(z) directly from frequency-domain phase signals. We demonstrated that the simple supervised-learning algorithm kernel ridge regression (KRR) can reconstruct K(z) without requiring pre-knowledge about the functional form of the profile. The reconstruction method can not only accurately reproduce typical K(z) distributions such as the pre-assumed exponential profile of chemical-vapor-deposited (CVD) diamonds and Gaussian profile of ion-irradiated materials, but also complex profiles artificially constructed by superimposing Gaussian, exponential, polynomial, and logarithmic functions. In addition to FDTR, the method also shows excellent performances of reconstructing K(z) of ion-irradiated semiconductors from TDTR signals. This work demonstrates that combining machine learning with pump-probe thermoreflectance is an effective way for depth-dependent thermal property mapping.

physics.app-ph↗

arXiv2022

Ballistic Thermal Transport at Sub-10 nm Laser-Induced Hot Spots in GaN Crystal

Gallium nitride (GaN) is a typical wide-bandgap semiconductor with a critical role in a wide range of electronic applications. Ballistic thermal transport at nanoscale hotspots will greatly reduce the performance of a device when its characteristic length reaches the nanometer scale, due to heat dissipation. In this work, we developed a tip-enhanced Raman thermometry approach to study ballistic thermal transport within the range of 10 nm in GaN, simultaneously achieving laser heating and measuring the local temperature. The Raman results showed that the temperature increase from an Au-coated tip-focused hotspot was up to two times higher (40 K) than that in a bare tip-focused region (20 K). To further investigate the possible mechanisms behind this temperature difference, we performed electromagnetic simulations to generate a highly focused heating field, and observed a highly localized optical penetration, within a range of 10 nm. The phonon mean free path (MFP) of the GaN substrate could thus be determined by comparing the numerical simulation results with the experimentally measured temperature increase which was in good agreement with the average MFP weighted by the mode-specific thermal conductivity, as calculated from first-principles simulations. Our results demonstrate that the phonon MFP of a material can be rapidly predicted through a combination of experiments and simulations, which can find wide application in the thermal management of GaN-based electronics.

cond-mat.mes-hall↗

arXiv2022

A new spatial-scan thermoreflectance method to measure a broad range of anisotropic in-plane thermal conductivity

In-plane thermal conductivities of small-scale samples are hard to measure, especially for the lowly conductive ones and those lacking in-plane symmetry (i.e., transversely anisotropic materials). State-of-the-art pump-probe techniques including both the time-domain and the frequency-domain thermoreflectance (TDTR and FDTR) are advantageous in measuring the thermal conductivity of small-scale samples, and various advanced TDTR and FDTR techniques have been developed to measure transversely anisotropic materials. However, the measurable in-plane thermal conductivity (k_in) is usually limited to be >10 W/(m K). In this work, a new spatial-scan thermoreflectance (SSTR) method has been developed to measure a broad range of k_in of millimeter-scale small samples, including those lacking in-plane symmetry, extending the current limit of the measurable k_in to as low as 1 W/(m K). This SSTR method establishes a new scheme of measurements using the optimized laser spot size and modulation frequency and a new scheme of data processing, enabling measurements of in-plane thermal conductivity tensors of a broad range of k_in values with both high accuracy and ease of operation. Some details such as the requirement on the sample geometry, the effect of the transducer layer, and the effect of heat loss are also discussed. As a verification, the k_in of some transversely isotropic reference samples with a wide range of k_in values including fused silica, sapphire, silicon, and highly ordered pyrolytic graphite (HOPG) have been measured using this new SSTR method. The measured k_in agree perfectly well with the literature values with a typical uncertainty of 5%. As a demonstration of the unique capability of this method, the in-plane thermal conductivity tensor of x-cut quartz, an in-plane anisotropic material, has also been measured.

cond-mat.mtrl-sci↗

arXiv2021

Confinement Effect on Thermopower of Electrolytes

Ionic Seebeck effect of electrolytes has shown promising applications in harvesting energy from low-grade waste-heat sources with small temperature difference from the environment, which can power sensors and Internet-of-Things devices. Recent experiments have demonstrated giant thermopower (~ 10 mV/K) of electrolytes under confinement due to the overlapping of electric double layer (EDL). Nonetheless, there has been no consensus on the theory of the ionic Seebeck effect, especially whether the thermopower depends on ionic diffusivities, imposing confusion on the theoretical interpretation of experimental discovery on giant thermopower of confined electrolytes. This article presents a linear perturbative solution of Poisson-Nernst-Planck (PNP) equations to describe the ionic Seebeck effect of confined liquid electrolytes. We provide both analytical and numerical solutions to the PNP equations for closed systems and open systems connected to reservoirs of electrolytes. The analytical solution captured the confinement effect both along and perpendicular to the temperature gradient, and showed excellent agreement with numerically solved PNP equations for a wide range of EDL potentials, channel widths, and lengths. Finally, we show that for polyelectrolytes with largely mismatched diffusivities, thermopower can only be enhanced for closed system through confinement perpendicular to the temperature gradient.

cond-mat.soft↗

arXiv2021

Machine Learning for Predicting Thermal Transport Properties of Solids

Quantitative descriptions of the structure-thermal property correlation have been a bottleneck in designing materials with superb thermal properties. In the past decade, the first-principles phonon calculations using density functional theory and the Boltzmann transport equation have become a common practice for predicting the thermal conductivity of new materials. However, first-principles calculations are too costly for high-throughput material screening and multi-scale structural design. First-principles calculations also face several fundamental challenges in modeling thermal transport properties, e.g., of crystalline materials with defects, of amorphous materials, and for materials at high temperatures. In the past five years, machine learning started to play a role in solving these challenges. This review provides a comprehensive summary and discussion on the state-of-the-art, future opportunities, and the remaining challenges in implementing machine learning for studying thermal conductivity. After an introduction to the working principles of machine learning and descriptors of material structures, recent research using machine learning to study thermal transport is discussed. Three major applications of machine learning for predicting thermal properties are discussed. First, machine learning is applied to solve the challenges in modeling phonon transport of crystals with defects, in amorphous materials, and at high temperatures. Machine learning is used to build high-fidelity interatomic potentials to bridge the gap between first-principles calculations and molecular dynamics simulations. Second, machine learning can be used to study the correlation between thermal conductivity and other properties for high-throughput materials screening. Finally, machine learning is a powerful tool for structural design to achieve target thermal conductance or thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2021

Passive sub-ambient cooling: radiative cooling versus evaporative cooling

Day-and-night radiative sky cooling has emerged as a potential alternative to conventional cooling technologies such as refrigeration-based air conditioning and evaporative wet cooling. Both radiative cooling and evaporative cooling can passively achieve sub-ambient cooling without consuming electricity. Although both cooling techniques are subject to impacts from various weather conditions, the extents of the impacts under the same conditions are not well understood. In this work, we experimentally and theoretically study the thermal performances of a passive radiative cooler and a passive evaporative cooler when exposed to a clear night sky. We show that evaporative cooling is better suited for high-temperature and low-humidity weather conditions, with the measured sub-ambient temperatures of the radiative and evaporative coolers being -13.5{\deg}C and -15.0{\deg}C, respectively, at a low relative humidity of 13% and a high ambient temperature of 26.0{\deg}C. On the other hand, radiative cooling is relatively more resilient than evaporative cooling under high-humidity and/or low-temperature weather conditions, with the measured sub-ambient temperatures of the coolers being -11.5{\deg}C and -10.5{\deg}C, respectively, at a slightly higher relative humidity of 32.0% and a slightly lower ambient temperature of 17.0{\deg}C. Depending on water availability and weather conditions, both evaporative cooling and radiative cooling can be adopted as mutually supplemental cooling technologies.

physics.app-ph↗

arXiv2020

Tunable Anisotropic Thermal Transport in Super-Aligned Carbon Nanotube Films

Super-aligned carbon nanotube (CNT) films have intriguing anisotropic thermal transport properties due to the anisotropic nature of individual nanotubes and the important role of nanotube alignment. However, the relationship between the alignment and the anisotropic thermal conductivities was not well understood due to the challenges in both the preparation of high-quality super-aligned CNT film samples and the thermal characterization of such highly anisotropic and porous thin films. Here, super-aligned CNT films with different alignment configurations are designed and their anisotropic thermal conductivities are measured using time-domain thermoreflectance (TDTR) with an elliptical-beam approach. The results suggest that the alignment configuration could tune the cross-plane thermal conductivity k_z from 6.4 to 1.5 W/mK and the in-plane anisotropic ratio from 1.2 to 13.5. This work confirms the important role of CNT alignment in tuning the thermal transport properties of super-aligned CNT films and provides an efficient way to design thermally anisotropic films for thermal management.

physics.app-ph↗