1,720,976 research outputs found
Development of thin-film chalcogenide materials deposited by LPCVD for thermoelectric energy generation
No full textThermoelectric generators have long been seen as a possible renewable energy source for both small scale and large scale applications. These devices use no direct fuel and therefore fossil fuels to produce power and are solid state so require little maintenance. However, efficiencies of these devices are currently insufficient to be seriously considered as primary power sources and are currently only considered for small scale applications, or where this is the only option such as in radioisotope thermoelectric generators for deep space probes. To improve these devices, two main approaches can be considered, one is to improve the thermal and electrical performance of devices by carefully optimised design, and the other is to improve the materials electrical conductivity, thermal conductivity and Seebeck coefficient. A new corrugated thin film thermoelectric generator design is considered and an analytical model for this is verified using finite element method simulations showing a maximum discrepancy of 15% over a wide range of parameters. The result of simulation and modelling shows that increasing the interconnect electrical conductivity and reducing the pitch of the device increases the power density. The power density is also increased by increasing the fill factor, and this thin film design can achieve higher fill factors compared to that of a conventional device at a specific minimum feature size. To evaluate thin film thermoelectric materials, methods for the measurement of thermoelectric properties are developed. For the measurement of the Seebeck coefficient and electrical conductivity, a Joule Yacht MRS-3L is used allowing for measurements from 100 - 600 K. The capabilities of this tool have been extended to allow for the more precise measurement of highly resistive films. A ω − 3ω system is developed for the measurement of thermal conductivity of films. This system is verified by the measurement of bulk silicon and thin films of bismuth telluride and show good agreement with literature values for both materials. Thin films of low pressure chemical vapour deposited (LPCVD) Bi2Te3 are optimised by the alloying of Bi2Te3 and Bi2Se3 to deposit ternary Bi2Te3-xSex. The composition of the ternary films are tuned to optimise the combination of carrier concentration and mobility to give a three-fold enhancement of the thermoelectric power factor at 300 K, and six-fold enhancement at 500 K, with respect to Bi2Te3 . This improvement from the substitution of Te with Se is believed to be due to donor effects, as well as point defects caused by substitution. Pre-patterned substrates with open SiO2 holes on TiN were used for selective deposition of Bi2Te3-xSex on to the conductive TiN. This selective deposition behaviour allows for a reduction in fabrication steps for a thermoelectric micro-generator, and a reduction in wasted material. Deposition of Sb2Te3 by LPCVD is optimised by varying deposition temperature. The carrier concentration and mobility of the films can be optimised by reducing the deposition temperature to 364 ◦C, resulting in a power factor of 16.5 µW cm−1 K −2 at 350 K. The Sb2Te3 films also shows selective behaviour on the conductive TiN surface, which enabled the fabrication of a single-type thermoelectric micro-generator. The fabricated generator had a pitch of 400 µm, a fill factor of 25%, and 72 Sb2Te3 thermoelements. This prototype device was measured using a custom system and a maximum temperature difference of 0.11 K was achieved across the 500 nm thick thermoelements, leading to a voltage of 0.4 mV and current of 0.7 µA giving a power output of 280 pW. It is then shown by simulation that this power output is significantly limited by the interconnect resistance, and that by reducing the pitch down to 10 µm the power output could reach 500 nW. The thermoelectric properties of tin chalcogenides are investigated by comparing SnS, SnSe, and SnTe. It is found that the SnS and SnSe films deposited by LPCVD are much more resistive than the SnTe. The high resistivity is caused by low carrier concentrations, which also lead to high Seebeck coefficients of 650 and 790 µV K−1 at 300 K for SnS and SnSe, respectively. Comparatively, the SnTe films show a resistivity of 4 orders of magnitude lower, due to a high carrier concentration and comparable mobility. Overall the SnTe films show the highest power factor of 8.3 µW cm−1 K−2 at 615 K. The SnTe films also show selective behaviour, but the SnS and SnSe do not
Comprehensive analysis of radiative cooling enabled thermoelectric energy harvesting
No full textThe market for Internet-of-Things (IoT) with integrated wireless sensor networks (WSN) is expanding at a rate never seen before. The thriving of IoT also brings an unprecedented demand for sustainable micro-Watt-scale power supplies. Radiative cooling (RC) can provide a continuous temperature difference which can be converted by a thermoelectric generator (TEG) into electrical power. This novel combination of radiative cooling with TEG expands the category of sustainable energy sources for energy harvesting. However, the further application of RC-TEG requires a holistic investigation of its RC-TEG performance which is dependent on many different parameters. Using 3D finite element method simulation, this works provides a comprehensive analysis of the concept of RC-TEG by investigating the impact of radiative cooler properties, TEG parameters, and environmental conditions, to provide a full picture of the performance of RC-TEG devices. The capability of RC-TEG to provide continuous power supply is tested using real-time environmental data from both Singapore and London on two different days of the year, demonstrating continuous power supply sufficient for a wide range of physical devices
Dataset supporting the journal article 'Comprehensive analysis of radiative cooling enabled thermoelectric energy harvesting'
No full textData supporting the article "Comprehensive analysis of radiative cooling enabled thermoelectric energy harvesting" Yuxiao Zhu, Daniel W. Newbrook, C. H. de Groot and Ruomeng Huang. Journal of Physics: Photonics, Volume 5, Number 2, 025002, DOI 10.1088/2515-7647/accac1
The Data is an excel file containing raw data for the paper:
Fig. 1 Schematic and temperature-dependent material properties of the N-type and P-type thermoelectric materials used in our RC-TEG model. (
Fig. 2 Temperature profile of the RC-TEG under typical working conditions. (a) exposed radiative surface (b) shielded counter surface.
Fig. 3 FEM simulation performance of RC-TEG under various top surface convection (ConvT) and bottom surface convection (ConvB) conditions.
Fig. 4 Performance of RC-TEG obtained in COMSOL simulation as a function of atmosphere emissivity (a) Temperature difference (ΔT) and (b) Power density (PDmax).
Fig. 5 Performance of RC-TEG as a function of emissivity in the solar and thermal IR spectrum.
Fig. 6 (a) Temperature difference, (b) Power (Pmax), (c) Power density (PDmax) obtained from COMSOL simulation as a function of pitch and WCooler. (
Fig. 7 (a) Temperature difference, (b) PDmax obtained from COMSOL simulation as a function of HTE and WTE.
Fig. 8 (a) Real-time temperature and (b) solar irradiance of London on 1st July 2021, 11th January 2021, Singapore on 1st July 2021, and 1st January 2021.
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Dataset in support of the Southampton doctoral thesis 'Flexible Thermoelectric Energy Generators for E-textiles'
No full textThe dataset collected in the research thesis of "Flexible Thermoelectric Energy Generators for E-textiles". Contains SEM images, EDX elemental analysis and XRD crystal lattice characterisations. Additionally, the dataset contains hall probe and thermoelectric data and device power outputs and thermal images of devices at different thermal gradients. Data used in the dataset contributed to the publication:
“Screen-printed Bismuth telluride nanostructured composites for flexible thermoelectric
applications” A. Amin, R. Huang, D. W. Newbrook, V. Sethi, S. P. Beeby and I. S. Nandhakumar, J.
Phys. Energy, , DOI:10.1088/2515-7655/ac572e.
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Dataset of Segmented thermoelectric generator modelling and optimization using artificial neural networks by iterative training
No full textThis paper "Segmented thermoelectric generator modelling and optimization using artificial neural networks by iterative training" Published in Energy and AI.
https://doi.org/10.1016/j.egyai.2022.100225 </span
Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator
No full textThe ever-increasing demand for renewable energy and zero carbon dioxide emission have been the driving force for the development of thermoelectric generators with better power generation performance. Alongside with the effort to discover thermoelectric materials with higher figure-of-merit, the geometrical and structural optimisation of thermoelectric generators are also essential for maximized power generation and efficiency. This work demonstrates for the first time the application of artificial neural network, a deep learning technique, in forward modelling the maximum power generation and efficiency of a thermoelectric generator and its application in the generator design and optimisation. After training using a dataset containing 5000 3-D finite element method based simulations, the artificial neural networks with 5 layers and 400 neurons per layer demonstrate extremely high prediction accuracy over 98% and are able to operate under both constant temperature difference and heat flux conditions while taking into account of the contact electrical resistance, surface heat transfer and other thermoelectric effects. Coupling with genetic algorithm, the trained artificial neural networks can optimise the leg height, leg width, fill factor and interconnect height of the thermoelectric generator for different operating and contact resistance conditions. With almost identical optimised values obtained, our neural networks can realise geometrical optimisation within 40 s for each operating condition, which is averagely over 1,000 times faster than the optimisation performed by finite element method. The up-front computational time for the neural network can be recovered when more than 2 optimisations are needed. The successful application of this data-driven approach in this work clearly represents a new and cost-effective avenue for conducting system level design and optimisation of thermoelectric generators and other energy harvesting technologies
Dataset in support of the Southampton doctoral thesis 'Thin film thermoelectric materials and generators deposited by chemical vapour processes'
No full textThis dataset includes various data relating to the characterisation of various deposited thermoelectric thin films (i.e. GeTe, WS2xSe2-2x, and AZO). Thin films were characterised by SEM, EDX, XRD, XPS, Raman, AFM, Hall and Seebeck measurements. Further details of the dataset can be found in the README files attached.</span
Screen-printed bismuth telluride nanostructured composites for flexible thermoelectric applications
No full textWe herein report the results of a facile two-step surfactant assisted reflux synthesis of bismuth telluride (Bi2Te3) nanowires (NWs). The as-synthesised NWs had diameters ranging from 70 to 110 nm with a length varying between 0.4 and 3 µm and a preferential lattice orientation of (0 1 5) as determined by grazing incidence x-ray diffraction. We demonstrate for the first time that a solvent/binder paste formulation of N-methyl-2-pyrrolidone/polyvinylidene fluoride (PVDF) is suitable for screen-printing the Bi2Te3 NWs with the potential for the fabrication of flexible thermoelectric (TE) materials. The wt% of PVDF in the composite films was varied from 10% to 20% to identify the optimal composition with a view to achieving maximum film flexibility whilst retaining the best TE performance. The films were screen-printed onto Kapton substrates and subjected to a post-printing annealing process to improve TE performance. The annealed and screen printed Bi2Te3/PVDF NW composites yielded a maximum Seebeck coefficient −192 µV K−1 with a power factor of 34 µW m−1K−2 at 225 K. The flexible screen printed composite films were flexible and found to be intact even after 2000 bending cycles
Artificial neural network enabled photovoltaic-thermoelectric generator modelling and analysis
No full textMathematical model and optimization of a thin-film thermoelectric generator
No full textThe thriving of Internet of Things is set to increase the demands of low power wireless sensing devices. Thin-film thermoelectric generators are ideal as a sustainable power source for Internet of Things device, as they allow for low maintenance and energy autonomy. This work presents a model to estimate the performance of a thin-film thermoelectric generator. Verified by finite element method simulation, the results from the model show that by increasing interconnect electrical conductivity and reducing device pitch increases the power density. The power density can also be increased by increasing fill factor and reducing the thermal conductivity of insulating materials. A new corrugated thin-film thermoelectric generator design is proposed in this work that allows for higher fill factors compared to conventional square designs where a limit on the minimum feature size is imposed, as is the case with photo-lithography
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