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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]

We engineered the thermal conductivity of nano-grained Bi0.5Sb1.5Te3 (BST) by embedding SiO2 and diamond nanoparticles (NPs) with concentration ranging from 0.5 to 5 vol. %. The embedded NPs work as additional scattering centers for long mean free path phonons that are not effectively scattered by the grain boundaries. We found that both the SiO2 and diamond NPs materially reduced the lattice thermal conductivity (κ lat ) within the temperature range of 50–300 K, with stronger reduction occurring at a lower temperature. Furthermore, the diamond NPs were found to cause large reduction in κ lat compared to the SiO2 NPs at the same concentrations. Further theoretical analysis showed that the diamond NPs possess about tenfold higher interfacial thermal resistance with the BST matrix compared to that of SiO2 NPs, due to the larger acoustic mismatch between diamond and BST as compared to SiO2 and BST. As a result of the large reduction of κ lat , the thermoelectric figure of merit (ZT) was enhanced by 15% at room temperature with 0.5 vol. % diamond NP relative to the pristine nano-grained samples without the NPs. [ABSTRACT FROM AUTHOR]

Particle beds are widely used in various systems and processes, such as particle heat exchangers, granular flow reactors, and additive manufacturing. Accurate modeling of the thermal conductivity of particle beds and understanding of their heat transfer mechanisms are important. However, previous models were based on simple cubic packing of particles, which could not accurately represent the actual heat transfer processes under certain conditions. Here, we examine the effect of the packing structure on the thermal conductivity of particle beds. We use monodispersed silica microspheres with average particle sizes ranging from 23 to 330 μm as a model material. We employ a transient hot-wire technique to measure the thermal conductivity of the particle beds with packing density of 43%–57% within a temperature range of room temperature to 500 °C and under N2 gaseous pressures of 20–760 Torr. We then use a discrete element method (DEM) to obtain the realistic packing structure of the particles, which is then fed into a finite-element model (FEM) to calculate the thermal conductivity, with the consideration of solid conduction, gas conduction, and radiation heat transfer. Our results show that the thermal conductivity model based on the more realistic random packing structure derived from the DEM shows better agreement with the experimental data compared to that based on the simple cubic-packing structure. The combined DEM and FEM methodology can serve as a useful tool to predict the effective thermal conductivity of particle beds and to quantify different heat transfer mechanisms under various conditions. [ABSTRACT FROM AUTHOR]