Thermal Energy Harvesting Devices Research Articles

Optoelectronic and thermoelectric properties of Cs2PtX6 (X=Cl, Br, I) for energy conversion applications: DFT calculations

This research employs density functional theory within the WIEN2k framework to simulate Cs2PtX6 (X = Cl, Br, I) perovskites. The study focuses on assessing the structural stability and thermodynamic properties through the computation of tolerance factor and formation energies, affirming the viability of the simulated materials. Notably, this investigation reveals a direct bandgap semiconducting character of these compounds, with specific values of the bandgap of 2.9, 2.15, and 1.08 eV for Cl, Br, and I-based compositions, respectively. The analysis of optical behavior demonstrates a redshift in absorption edge and an increase in optical conductivity, correlating with the enlarging halogen size within the series. The investigation into the thermoelectric characteristics of Cs2PtX6 perovskites, which include σ and κe as well as S, PF, and ZT, underscores the potential of Cs2PtX6 perovskites for deployment in thermal energy harvesting devices. Furthermore, the mechanical behavior assessment indicates the ductile nature of studied compounds, contributing to their overall mechanical stability. This study gives a comprehensive recognition of structural, electronic, mechanical, optical, and transport characteristics of Cs2PtX6 compounds, suggesting their promise for applications in advanced materials for optoelectronic and thermoelectric devices.

Machine learning-based optimization of segmented thermoelectric power generators using temperature-dependent performance properties

Segmented thermoelectric generators (STEGs) provide an excellent platform for thermal energy harvesting devices because they improve power generation performance across a broad range of operating temperatures. Despite the benefit of direct thermal energy-to-electricity conversion, conventional STEG optimization approaches are unable to provide a systematic method for selecting the optimal multiple stacks of p- and-n-type thermoelectric materials (TEs) legs from a set of numerous TE materials. In this study, we propose a systematic optimization method based on machine learning to find the optimal STEG for performance maximization. A deep neural network (DNN) is trained using an initial dataset generated via the Finite Element Method (FEM), with inputs including the temperature-dependent properties of p- and n-type TE materials, the lengths of each segment, and external loads, as well as the corresponding performance outputs. The trained DNN captures the inherent nonlinear relationship between these inputs and outputs. The combination of a trained DNN and a genetic algorithm (GA) efficiently navigates a vast design space using 88 p-type and 70 n-type TE materials along with device factors. It formulates four stacked segment pairs in p- and n-leg TEGs, targeting new superior designs with enhanced power, efficiency, or both. The DNN is iteratively refined via active learning (AL) by incorporating new superior STEG designs to enhance prediction accuracy. The optimized STEGs exhibit power and efficiency that are 1.91 and 1.5 times higher, respectively, than the top designs from the initial training dataset composed of 157,916 STEGs. Furthermore, compared to conventional TEG designs without segmentation, our method discovered numerous high-performing STEG designs.

Polymer Gel with Tunable Conductive Properties: A Material for Thermal Energy Harvesting.

The spontaneous gelation of poly(4-vinyl pyridine)/pyridine solution produces materials with conductive properties that are suitable for various energy conversion technologies. The gel is a thermoelectric material with a conductivity of 2.2-5.0 × 10-6 S m-1 and dielectric constant ε = 11.3. On the molecular scale, the gel contains various types of hydrogen bonding, which are formed via self-protonation of the pyridine side chains. Our measurements and calculations revealed that the gelation process produces bias-dependent polymer complexes: quasi-symmetric, strongly hydrogen-bonded species, and weakly bound protonated structures. Under an applied DC bias, the gelled complexes differ in their capacitance/conductive characteristics. In this work, we exploited the bias-responsive characteristics of poly(4-vinyl pyridine) gelled complexes to develop a prototype of a thermal energy harvesting device. The measured device efficiency is S = ΔV/ΔT = 0.18 mV/K within the temperature range of 296-360 K. Investigation of the mechanism underlying the conversion of thermal energy into electric charge showed that the heat-controlled proton diffusion (the Soret effect) produces thermogalvanic redox reactions of hydrogen ions on the anode. The charge can be stored in an external capacitor for heat energy harvesting. These results advance our understanding of the molecular mechanisms underlying thermal energy conversion in the poly(4-vinyl pyridine)/pyridine gel. A device prototype, enabling thermal energy harvesting, successfully demonstrates a simple path toward the development of inexpensive, low-energy thermoelectric generators.

Modelling the behaviour of thermal energy harvesting devices with phase-change materials

This paper presents a new general theoretical model of thermal energy harvesting devices (TEHDs), which utilise phase-change materials (PCMs) for energy storage. The model's major goal is to identify a set of parameters under which these devices perform optimally, that is, attain the largest thermal buffering capacity and exchange heat with the surrounding phase as quickly as possible. For the first time, an expression for the characteristic harvesting time is developed from the constructal theory viewpoint under the optimal performance assumption, and a dimensionless criterion that characterizes PCM performance is provided. Furthermore, a new non-field solution of the energy equation governing the process of heat transfer within TEHDs with PCMs has also been derived. An expression for the effective thermal effusivity is then obtained. Finally, under a given set of boundary conditions and geometrical constraints, a novel simple technique for the optimal choice of PCMs in TEHDs has been established.

Metamagnetic Shape Memory Alloy Thin Plates Consolidated by Compression Shearing Method at Room Temperature for Thermal Energy Harvesting Device

Metamagnetic Shape Memory Alloy Thin Plates Consolidated by Compression Shearing Method at Room Temperature for Thermal Energy Harvesting Device

Hierarchical cellular ferroelectric metamaterials: A design motif to enhance multifunctional figures of merit

The engineering of microstructures has been adopted as an effective approach to tune the overall performance of advanced ferroelectric materials. In this article, inspired by hierarchical porous biomaterials, the ferroelectric properties of hierarchical cellular ferroelectric metamaterials constructed by six commonly used primitive cubic unit cells are investigated. Both multiscale asymptotic homogenization method and scaling relationship are proposed to predict the effective ferroelectric properties of hierarchical cellular metamaterials. Analysis on the influence of design parameters, e.g. hierarchical order, cell topologies, and relative density on their effective ferroelectric figures of merit is conducted. The 2nd-order hierarchical cellular ferroelectric metamaterial exhibits remarkable improvement compared to the corresponding 2nd order unit cell. For example, the normalized FOM33 of hybrid 2nd-order T3-2/L3-1 (ρ1 = 0.2, ρ2 = 0.25) is 117.49, while 19.95 for 1st-order L3-1 (ρ = 0.05). Increasing structural hierarchical order can further improve the effective ferroelectric properties of hierarchical cellular ferroelectric metamaterials. This work highlights the potential of ultralight hierarchical ferroelectric metamaterials as the next generation of hydrophone, IR detector, flexible self-powered sensors, and thermal energy harvesting devices.

A thin film efficient pn-junction thermoelectric device fabricated by self-align shadow mask

Large area highly crystalline MoS2 and WS2 thin films were successfully grown on different substrates using radio-frequency magnetron sputtering technique. Structural, morphological and thermoelectric transport properties of MoS2, and WS2 thin films have been investigated systematically to fabricate high-efficient thermal energy harvesting devices. X-ray diffraction data revealed that crystallites of MoS2 and WS2 films are highly oriented in 002 plane with uniform grain size distribution confirmed through atomic force microscopy study. Surface roughness increases with substrate temperature and it plays a big role in electron and phonon scattering. Interestingly, MoS2 films also display low thermal conductivity at room temperature and strongly favors achievement of higher thermoelectric figure of merit value of up to 1.98. Raman spectroscopy data shows two distinct MoS2 vibrational modes at 380 cm−1 for E12g and 410 cm−1 for A1g. Thermoelectric transport studies further demonstrated that MoS2 films show p-type thermoelectric characteristics, while WS2 is an n-type material. We demonstrated high efficient pn-junction thermoelectric generator device for waste heat recovery and cooling applications.

Detailed Transient Multiphysics Model for Fast and Accurate Design, Simulation and Optimization of a Thermoelectric Generator (TEG) or Thermal Energy Harvesting Device

Described herein is a detailed and comprehensive multiphysics model of a thermoelectric generator (TEG). The one-dimensional model uses electrical–thermal analogies solved for transient response using SPICE. There are many advantages and applications of thermoelectric generators. Wider use and application advancements are generally limited by the tools available for engineering and scientific studies. Currently, available modeling tools are limited by some combination of speed, platform capabilities, or missing physics that are not used or assumed to be negligible. The TEG module model herein is made up of two sub-models, the thermoelement model and the non-thermoelement model. Rather than a lumped thermoelement model, the model herein makes use of distributed physics that include the following: Thomson effect, temperature dependence, mass, Joule heat, thermal resistance, Seebeck effect, and electrical resistance. The non-thermoelement model takes into account temperature dependence and simulates Joule heat generation, thermal resistances, thermal and electrical interface resistances, and mass for and between the ceramic, copper, and solder. The comprehensive model herein was correlated to experimental data that simultaneously varied electrical current and hot and cold side temperatures with time. Very minimal adjustments to reported thermoelectric properties were required to almost perfectly match the experimental transient power output. The effects of the non-thermoelement model, distributed Thomson effect model and distributed temperature dependent property model were quantified. The model ran very quickly, taking 2.5 real-time seconds to run a 4000 s transient simulation.

Parameter estimation of nonlinear thermoelectric structures using particle swarm optimization

The purpose of this investigation is motivated mainly for thermal energy harvesting devices and temperature feedback controllers interacting with electronic circuits. Mostly intended to design Linear Quadratic Gaussian (LQG) type controllers, suitable for uncertain dynamical systems, in which not all state variables are measured and available for proper feedback. We present a methodology for modeling and estimation of several internal parameters for a proposed thermal characterization apparatus that employs thermoelectric modules (TEMs). Repeated random vector sampling, similar to Monte Carlo method, is combined with particle swarm optimization (PSO) algorithm for parameter estimation. For the intended applications, is mandatory to drive apparatus that have embedded TEMs, not only using direct current powering, as usually done in literature, but also with alternate current signals over a large range of relevant frequencies. For exciting the many nonlinear and linear states during the identification procedure, a single embedded TEM is injected with a proper random electrical current signal having power spectral density of a band-limited white noise. Sensitivity to both initial conditions and different sets of random excitation, brings uncertainty in the estimated 21 parameters of our mechanical apparatus with two embedded TEMs and their corresponding dynamics. Simulation are presented showing the effectiveness of the proposed estimation technique, with convergence performance and parameter statistical distribution over a set of uncorrelated random current vector excitation.

Effects of thermal energy harvesting on the human – clothing – environment microsystem

The objective of this work is to perform an in depth investigation of garment-based thermal energy harvesting. The effect of human and environmental factors on the working efficiency of a thermal energy harvesting devices, or a thermoelectric generator (TEG), placed on the body is explored.. Variables that strongly effect the response of the TEG are as follows: skin temperature, human motion or speed, body location, environmental conditions, and the textile properties surrounding the TEG. In this study, the use of textiles for managing thermal comfort of wearable technology and energy harvesting are defined. By varying the stitch length and/or knit structure, one can manipulate the thermal conductivity of the garment in a specific location. Another method of improving TEG efficiency is through the use of a heat spreader, which increases the effective collection area of heat on the TEG hot side. Here we show the effect of a TEG on the thermal properties of a garment with regard to two knit stitches, jersey and 1 × 1 rib.

Characterization of Thermal Energy Harvesting Using Pyroelectric Ceramics at Elevated Temperatures

Abstract Energy harvesting has drawn increasing attention due to the fast development of wireless sensors and devices. Most research has been focused on mechanical energy harvesting using piezoelectric ceramics; however, little is known on their experimental capabilities to harvest thermal energy at different temperature ranges and the impact that the temperature range has on the energy conversion efficiency. Majority of piezoelectric ceramics are pyroelectric in nature thus enabling them to couple energy between thermal and electrical domains. This paper demonstrates the use of Lithium Niobate (LNB) as a thermal energy harvesting device for high temperature applications. A custom testing setup was developed to test the LNB sample temperatures up to 225 °C. Pyroelectric coefficient of the material was characterized at different temperature ranges. Pyroelectric coefficient was found to increase with temperature, with a maximum value of −196 μC·m−2 °C−1. Power output of the sample was also characterized in different temperature ranges. A maximum value of over 20.5 μW was found when cycling the sample between 75 °C and 100 °C. Meanwhile, a maximum value of 14.8 μW was found in the 125 °C to 150 °C range. Finally, a peak value of 255 nW was found when cycling the sample in the 200 °C to 225 °C range.