Materials science has made progress in maximizing or minimizing the thermal conductivity of materials; however, the thermal effusivity—related to the product of conductivity and capacity—has received limited attention, despite its importance in the coupling of thermal energy to the environment. Herein, we design materials that maximize the thermal effusivity by impregnating copper and nickel foams with conformal, chemical-vapor-deposited graphene and octadecane as a phase change material. These materials are ideal for ambient energy harvesting in the form of what we call thermal resonators to generate persistent electrical power from thermal fluctuations over large ranges of frequencies. Theory and experiment demonstrate that the harvestable power for these devices is proportional to the thermal effusivity of the dominant thermal mass. To illustrate, we measure persistent energy harvesting from diurnal frequencies, extracting as high as 350 mV and 1.3 mW from approximately 10 °C diurnal temperature differences.

Thermal diodes, or devices that transport thermal energy asymmetrically, analogous to electrical diodes, hold promise for thermal energy harvesting and conservation, as well as for phononics or information processing. The junction of a phase change material and phase invariant material can form a thermal diode; however, there are limited constituent materials available for a given target temperature, particularly near ambient. In this work, we demonstrate that a micro and nanoporous polystyrene foam can house a paraffin-based phase change material, fused to PMMA, to produce mechanically robust, solid-state thermal diodes capable of ambient operation with Young's moduli larger than 11.5 MPa and 55.2 MPa above and below the melting transition point, respectively. Moreover, the composites show significant changes in thermal conductivity above and below the melting point of the constituent paraffin and rectification that is well-described by our previous theory and the Maxwell-Eucken model. Maximum thermal rectifications range from 1.18 to 1.34. We show that such devices perform reliably enough to operate in thermal diode bridges, dynamic thermal circuits capable of transforming oscillating temperature inputs into single polarity temperature differences - analogous to an electrical diode bridge with widespread implications for transient thermal energy harvesting and conservation. Overall, our approach yields mechanically robust, solid-state thermal diodes capable of engineering design from a mathematical model of phase change and thermal transport, with implications for energy harvesting.

Metal–insulator transition (MIT) compounds are materials that may exhibit metallic or insulating behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. An important subset of MIT materials are those with a transition driven by temperature. The number of thermally driven MIT materials, however, is scarce, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Most research that addresses thermal MITs is limited by the domain knowledge of the scientists to a subset of MIT materials and is often focused on a limited subset of possible features. Here, using a combination of domain knowledge and natural language processing (NLP) searches, we have built a material database comprising thermally driven MITs as well as metals and insulators with similar chemical composition and stoichiometries to the MIT compounds. We featurized this data set using a wide variety of compositional, structural, and energetic descriptors, including two MIT relevant energy scales, the estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then performed supervised classification on this data set, constructing three electronic-state classifiers: metal vs nonmetal (M), insulator vs noninsulator (I), and MIT vs non-MIT (T). This classification allows us to identify new features separating MIT materials from non-MIT materials. These include the 2D feature space consisting of the average deviation of the covalent radius, the range of the Mendeleev number and Ewald energy. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. We then elaborate on other features (GII and Ewald energy) and examine how they affect the classification of binary vanadium and titanium oxides. Last, we implement an online and publicly accessible version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file. The broad accessibility of our database, newly identified features, and user-friendly classifier models will aid in accelerating the discovery of MIT materials.

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