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Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid-solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.
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Aprendizaje Automático , Modelos Teóricos , Medios de Cultivo , PorosidadRESUMEN
This article explores the deep learning approach towards approximating the effective electrical and thermal conductivities of copper (Cu)-carbon nanotube (CNT) composites with CNTs aligned to the field direction. Convolutional neural networks (CNN) are trained to map the two-dimensional images of stochastic Cu-CNT networks to corresponding conductivities. The CNN model learns to estimate the Cu-CNT composite conductivities for various CNT volume fractions, interfacial electrical resistances, Rc = 20 Ω-20 kΩ, and interfacial thermal resistances, Râ³t,c = 10-10-10-7 m2K/W. For training the CNNs, the hyperparameters such as learning rate, minibatch size, and hidden layer neurons are optimized. Without iteratively solving the physical governing equations, the trained CNN model approximates the electrical and thermal conductivities within a second with the coefficient of determination (R2) greater than 98%, which may take longer than 100 min for a convectional numerical simulation. This work demonstrates the potential of the deep learning surrogate model for the complex transport processes in composite materials.
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Nanotubos de Carbono , Simulación por Computador , Conductividad Eléctrica , Redes Neurales de la Computación , Conductividad TérmicaRESUMEN
Thermoelectric power generation offers a promising way to recover waste heat. The geometrical design of thermoelectric legs in modules is important to ensure sustainable power generation but cannot be easily achieved by traditional fabrication processes. Herein, we propose the design of cellular thermoelectric architectures for efficient and durable power generation, realized by the extrusion-based 3D printing process of Cu2Se thermoelectric materials. We design the optimum aspect ratio of a cuboid thermoelectric leg to maximize the power output and extend this design to the mechanically stiff cellular architectures of hollow hexagonal column- and honeycomb-based thermoelectric legs. Moreover, we develop organic binder-free Cu2Se-based 3D-printing inks with desirable viscoelasticity, tailored with an additive of inorganic Se82- polyanion, fabricating the designed topologies. The computational simulation and experimental measurement demonstrate the superior power output and mechanical stiffness of the proposed cellular thermoelectric architectures to other designs, unveiling the importance of topological designs of thermoelectric legs toward higher power and longer durability.
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Although the Harman method evaluates the thermoelectric figure-of-merit in a rapid and simple fashion, the accuracy of this method is affected by several electrical and thermal extrinsic factors that have not been thoroughly investigated. Here, we study the relevant extrinsic effects and a correction scheme for them. A finite element model simulates the electrical potential and temperature fields of a sample, and enables the detailed analysis of electrical and thermal transport. The model predicts that the measurement strongly depends on the materials, sample geometries, and contact resistance of the electrodes. To verify the model, we measure the thermoelectric properties of Bi2-Te3 based alloys with systematically varied sample geometries and either with a point or a surface current source. By comparing the model and experimental data, we understand how the measurement conditions determine the extrinsic effects, and, furthermore, able to extract the intrinsic thermoelectric properties. A correction scheme is proposed to eliminate the associated extrinsic effects for an accurate evaluation. This work will help the Harman method be more consistent and accurate and contribute to the development of thermoelectric materials.
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Accuracy of the Harman measurement largely depends on the heat transfer between the sample and its surroundings, so-called parasitic thermal effects (PTEs). Similar to the material evaluations, measuring thermoelectric modules (TEMs) is also affected by the PTEs especially when measuring under atmospheric condition. Here, we study the correction methods for the Harman measurements with systematically varied samples (both bulk materials and TEMs) at various conditions. Among several PTEs, the heat transfer via electric wires is critical. Thus, we estimate the thermal conductance of the electric wires, and correct the measured properties for a certain sample shape and measuring temperature. The PTEs are responsible for the underestimation of the TEM properties especially under atmospheric conditions (10-35%). This study will be useful to accurately characterize the thermoelectric properties of materials and modules.
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Output power of thermoelectric generators depends on device engineering minimizing heat loss as well as inherent material properties. However, the device engineering has been largely neglected due to the limited flat or angular shape of devices. Considering that the surface of most heat sources where these planar devices are attached is curved, a considerable amount of heat loss is inevitable. To address this issue, here, we present the shape-engineerable thermoelectric painting, geometrically compatible to surfaces of any shape. We prepared Bi2Te3-based inorganic paints using the molecular Sb2Te3 chalcogenidometalate as a sintering aid for thermoelectric particles, with ZT values of 0.67 for n-type and 1.21 for p-type painted materials that compete the bulk values. Devices directly brush-painted onto curved surfaces produced the high output power of 4.0 mW cm-2. This approach paves the way to designing materials and devices that can be easily transferred to other applications.
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Interfaces, such as grain boundaries in a solid material, are excellent regions to explore novel properties that emerge as the result of local symmetry-breaking. For instance, at the interface of a layered-chalcogenide material, the potential reconfiguration of the atoms at the boundaries can lead to a significant modification of the electronic properties because of their complex atomic bonding structure. Here, we report the experimental observation of an electron source at 60° twin boundaries in Bi2Te3, a representative layered-chalcogenide material. First-principles calculations reveal that the modification of the interatomic distance at the 60° twin boundary to accommodate structural misfits can alter the electronic structure of Bi2Te3. The change in the electronic structure generates occupied states within the original bandgap in a favourable condition to create carriers and enlarges the density-of-states near the conduction band minimum. The present work provides insight into the various transport behaviours of thermoelectrics and topological insulators.
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The two-dimensional electron gas (2DEG) at the interface between insulating LaAlO3 and SrTiO3 is intriguing both as a fundamental science topic and for possible applications in electronics or sensors. For example, because the electrical conductance of the 2DEG at the LaAlO3/SrTiO3 interface can be tuned by applying an electric field, new electronic devices utilizing the 2DEG at the LaAlO3/SrTiO3 interface could be possible. For the implementation of field-effect devices utilizing the 2DEG, determining the on/off switching voltage for the devices and ensuring their stability are essential. However, the factors influencing the threshold voltage have not been extensively investigated. Here, we report the voltage-induced shift of the threshold voltage of Pt/LaAlO3/SrTiO3 heterostructures. A large negative voltage induces an irreversible positive shift in the threshold voltage. In fact, after the application of such a large negative voltage, the original threshold voltage cannot be recovered even by application of a large positive electric field. This irreversibility is attributed to the generation of deep traps near the LaAlO3/SrTiO3 interface under the negative voltage. This finding could contribute to the implementation of nanoelectronic devices using the 2DEG at the LaAlO3/SrTiO3 interface.
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Manipulation of electrons in a solid through transmitting, storing, and switching is the fundamental basis for the microelectronic devices. Recently, the electroresistance effect in the ferroelectric capacitors has provided a novel way to modulate the electron transport by polarization reversal. Here, we demonstrate a giant electroresistive ferroelectric diode integrating a ferroelectric capacitor into two-dimensional electron gas (2DEG) at oxide interface. As a model system, we fabricate an epitaxial Au/Pb(Zr(0.2)Ti(0.8))O3/LaAlO3/SrTiO3 heterostructure, where 2DEG is formed at LaAlO3/SrTiO3 interface. This device functions as a two-terminal, non-volatile memory of 1 diode-1 resistor with a large I+/I- ratio (>10(8) at ± 6 V) and I(on)/I(off) ratio (>10(7)). This is attributed to not only Schottky barrier modulation at metal/ferroelectric interface by polarization reversal but also the field-effect metal-insulator transition of 2DEG. Moreover, using this heterostructure, we can demonstrate a memristive behavior for an artificial synapse memory, where the resistance can be continuously tuned by partial polarization switching, and the electrons are only unidirectionally transmitted. Beyond non-volatile memory and logic devices, our results will provide new opportunities to emerging electronic devices such as multifunctional nanoelectronics and neuromorphic electronics.
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Harman method is a rapid and simple technique to measure thermoelectric properties. However, its validity has been often questioned due to the over-simplified assumptions that this method relies on. Here, we quantitatively investigate the influence of the previously ignored parasitic thermal effects on the Harman method and develop a method to determine an intrinsic ZT. We expand the original Harman relation with three extra terms: heat losses via both the lead wires and radiation, and Joule heating within the sample. Based on the expanded Harman relation, we use differential measurement of the sample geometry to measure the intrinsic ZT. To separately evaluate the parasitic terms, the measured ZTs with systematically varied sample geometries and the lead wire types are fitted to the expanded relation. A huge discrepancy (â¼28%) of the measured ZTs depending on the measurement configuration is observed. We are able to separately evaluate those parasitic terms. This work will help to evaluate the intrinsic thermoelectric property with Harman method by eliminating ambiguities coming from extrinsic effects.
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This paper investigates the dynamic thermomechanical response of bimaterial microcantilevers to periodic heating by an infrared laser operating at a wavelenegth of 10.35 µm. A model relates incident radiation, heat transfer, temperature distribution in the cantilever, and thermal expansion mismatch to find the cantilever displacement. Experiments were conducted on two custom-fabricated bimaterial cantilevers and two commercially available bimaterial microcantilevers. The cantilever response was measured as a function of the modulation frequency of the laser over the range of 0.01-30 kHz. The model and the method of cantilever displacement calibration can be applied for bimaterial cantilever with thick coating layer. The sensitivity and signal-to-noise of bimaterial cantilevers were evaluated in terms of either total incident power or incident flux. The custom-fabricated bimaterial cantilevers showed 9X or 190X sensitivity improvement compared to commercial cantilevers. The detection limit on incident flux is as small as 0.10 pW µm(-2) Hz(-1/2).