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Lattice thermal conductivity is important for many applications, but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable. Machine learning approaches that can achieve similar accuracy have been a long-standing open question. Despite recent progress, machine learning models using structural information as descriptors fall short of experimental or first principles accuracy. This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy. The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance. Transfer learning between different orders of phonon scattering can further improve the model performance. Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.

 

Micro-Raman spectroscopy has become an important tool in probing thermophysical properties in functional materials. Localized heating by the focused Raman excitation laser beam can produce both stress and local nonequilibrium phonons in the material. Here, we investigate the effects of hot optical phonons in the Raman spectra of molybdenum disulfide and distinguish them from those caused by thermally induced compressive stress, which causes a Raman frequency blue shift. We use a thermomechanical analysis to correct for this stress effect in the equivalent lattice temperature extracted from the measured Raman peak shift. When the heating Gaussian laser beam is reduced to 0.71  μm, the corrected peak shift temperature rise is 17% and 8%, respectively, higher than those determined from the measured peak shift and linewidth without the stress correction, and 32% smaller than the optical phonon temperature rise obtained from the anti-Stokes to Stokes intensity ratio. This nonequilibrium between the hot optical phonons and the lattice vanishes as the beam width increases to 1.53 μm. Much less pronounced than those reported in prior micro-Raman measurements of suspended graphene, this observed hot phonon behavior agrees with a first-principles based multitemperature model of overpopulated zone-center optical phonons compared to other optical phonons in the Brillouin zone and acoustic phonons of this prototypical transition metal dichalcogenide. The findings provide detailed insight into the energy relaxation processes in this emerging electronic and optoelectronic material and clarify an important question in micro-Raman measurements of thermal transport in this and other two-dimensional materials. 

Thermophotovoltaic (TPV) technology converts heat into electricity using thermal radiation. Increasing operating temperature is a highly effective approach to improving the efficiency of TPV systems. However, most reported TPV selective emitters degrade rapidly via. oxidation as operating temperatures increase. To address this issue, replacing nanostructured oxide‐metal films with oxide–oxide films is a promising way to greatly limit oxidation, even under high‐temperature conditions. This study introduces new all‐oxide photonic crystal designs for high‐temperature stable TPV systems, overcoming limitations of metal phases and offering promising material choices. The designs utilize both yttria‐stabilized zirconia (YSZ)/MgO and CeO₂/MgO combinations with a multilayer structure and stable high‐quality growth. Both designsexhibit positive optical dielectric constants with tunable reflectivity, measured via optical characterization. Thermal stability testing using in situ heating X‐ray diffraction (XRD) suggests high‐temperature stability (up to 1000 °C) of both YSZ/MgO and CeO₂/MgO systems. The results demonstrate a new and promising approach to improve the high‐temperature stability of TPV systems, which can be extended to a wide range of material selection and potential designs.