A simple technique for determining the Seebeck coefficient of thermoelectric materials
ABSTRACT It is shown that the Seebeck coefficient of a semiconductor against copper can be determined in terms of that for a copper-constantan thermocouple by means of a rapid potentiometric measurement. Values obtained for typical samples of bismuth telluride alloy, using the apparatus that is described, are compared with those given by a conventional method and indicate that the technique is perfectly satisfactory.
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ABSTRACT: The on-going interest in thermoelectric (TE) materials, in the form of bulk and films, motivates investigation of materials that exhibit low thermal conductivity and good electrical conductivity. Such materials are phonon-glass electron-crystals (PGEC), and the multi-component type-I clathrate Ba8Ga16Ge30 is in this category. This work reports the first investigation of Ba8Ga16Ge30 films grown by pulsed laser deposition (PLD). This dissertation details the in-situ growth of polycrystalline type-I clathrate Ba8Ga16Ge30 thin-films by pulsed laser ablation. Films deposited using conventional laser ablation produced films that contained a high density of particulates and exhibited weak crystallinity. In order to produce high quality, polycrystalline, particulate-free films, a dual-laser ablation process was used that combines the pulses of (UV) KrF excimer and (IR) CO2 lasers that are temporally synchronized and spatially overlapped on the target surface. The effect of the laser energy on stoichiometric removal of material and morphology of the target has been investigated. In addition, in-situ time-gated emission spectroscopy and imaging techniques were used to monitor expansion of components in the ablated plumes. Through these investigations, the growth parameters were optimized not only to significantly reduce the particulate density but also to produce large area stoichiometric films. Structure and electrical transport properties of the resultant films were also evaluated. This work provides new insight toward the in-situ growth of complex multi-component structures in thin-film form for potential TE applications.
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ABSTRACT: B. Ciylan. Determination of Output Parameters of a Thermoelectric Module using Artificial Neural Networks // Electronics and Electrical Engineering. - Kaunas: Technologija, 2011. - No. 10(116). - P. 63-66. Determination of instant dynamic output parameters of thermoelectric module which is worked in any system is very important. Despite of the new methods this process takes a lot of times. In this study, two artificial neural network (ANN) models are designed for the estimation of dynamic output parameters at any desired moment of the thermoelectric modules. MATLAB-ANN tools and an ANN simulator program are used for creating the models. Experimental dynamic output parameters data which obtained from eight different thermal load conditions were used for training the ANN Models. On the designed ANN models which were created to estimate instant dynamic output parameters of the thermoelectric module, the Levenberg-Marquardt (LM) learning algorithm has been used. The results obtained with these ANN models, compared with the experimental data and it was shown in graphs. III. 6, bibl. 20, tabl. 1 (in English; abstracts in English and Lithuanian).12/2011; 116(10). DOI:10.5755/j01.eee.116.10.884
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ABSTRACT: In the present study, a new method for searching all exit parameters of the thermoelectric module has been realized. The new method is based on measurement of thermo-emf of a working module and comparing the results obtained with those of classical methods. The new method is used for investigation of the values of parameters of a standard thermoelectric module of Melcor with production code of CP1.0-127-05L, used in medical coolers for renal hypothermia. The theoretical results calculated by the means of new and classical methods for the parameters of an actual thermoelectric module have been compared with the experimental results and the advantage of the new method in terms of approximation accuracy has been proven.Instrumentation Science & Technology 01/2009; 37(1):102-123. DOI:10.1080/10739140802584772 · 0.80 Impact Factor