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Thin film evaporation is a widely-used thermal management solution for micro/nano-devices with high energy densities. Local measurements of the evaporation rate at a liquid-vapor interface, however, are limited. We present a continuous profile of the evaporation heat transfer coefficient ($$h_{\text {evap}}$$$h_{\text{evap}}$) in the submicron thin film region of a water meniscus obtained through local measurements interpreted by a machine learned surrogate of the physical system. Frequency domain thermoreflectance (FDTR), a non-contact laser-based method with micrometer lateral resolution, is used to induce and measure the meniscus evaporation. A neural network is then trained using finite element simulations to extract the$$h_{\text {evap}}$$$h_{\text{evap}}$profile from the FDTR data. For a substrate superheat of 20 K, the maximum$$h_{\text {evap}}$$$h_{\text{evap}}$is$$1.0_{-0.3}^{+0.5}$$$1.0_{-0.3}^{+0.5}$ MW/$$\text {m}^2$$${\text{m}}^2$-K at a film thickness of$$15_{-3}^{+29}$$${15}_{-3}^{+29}$ nm. This ultrahigh$$h_{\text {evap}}$$$h_{\text{evap}}$value is two orders of magnitude larger than the heat transfer coefficient for single-phase forced convection or evaporation from a bulk liquid. Under the assumption of constant wall temperature, our profiles of$$h_{\text {evap}}$$$h_{\text{evap}}$and meniscus thickness suggest that 62% of the heat transfer comes from the region lying 0.1–1 μm from the meniscus edge, whereas just 29% comes from the next 100 μm.

Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivitykin MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm⁻³), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m⁻¹ K⁻¹), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m⁻¹ K⁻¹), the structures of which we describe in additional detail.

An eXtreme Gradient Boosting (XGBoost) machine learning model is built to predict the electrocaloric (EC) temperature change of a ceramic based on its composition (encoded by Magpie elemental properties), dielectric constant, Curie temperature, and characterization conditions. A dataset of 97 EC ceramics is assembled from the experimental literature. By sampling data from clusters in the feature space, the model can achieve a coefficient of determination of 0.77 and a root mean square error of 0.38 K for the test data. Feature analysis shows that the model captures known physics for effective EC materials. The Magpie features help the model to distinguish between materials, with the elemental electronegativities and ionic charges identified as key features. The model is applied to 66 ferroelectrics whose EC performance has not been characterized. Lead-free candidates with a predicted EC temperature change above 2 K at room temperature and 100 kV/cm are identified.

Real‐time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery‐powered devices. In this study, a methodology to predict the entire constant‐current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO₂‐based batteries at a constant C‐rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO₂‐based) using open‐access datasets. The prediction error of the charge curves for the LiCoO₂‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.

Whether the presence of adsorbates increases or decreases thermal conductivity in metal-organic frameworks (MOFs) has been an open question. Here we report observations of thermal transport in the metal-organic framework HKUST-1 in the presence of various liquid adsorbates: water, methanol, and ethanol. Experimental thermoreflectance measurements were performed on single crystals and thin films, and theoretical predictions were made using molecular dynamics simulations. We find that the thermal conductivity of HKUST-1 decreases by 40 – 80% depending on the adsorbate, a result that cannot be explained by effective medium approximations. Our findings demonstrate that adsorbates introduce additional phonon scattering in HKUST-1, which particularly shortens the lifetimes of low-frequency phonon modes. As a result, the system thermal conductivity is lowered to a greater extent than the increase expected by the creation of additional heat transfer channels. Finally, we show that thermal diffusivity is even more greatly reduced than thermal conductivity by adsorption.