Index of Measures

Seebeck coefficient
 
 
Results 1-4 of 4

Automated Hall facility - Determination of electric transport properties

DLR Institute of Materials Research

Fundamental semiconductor properties like concentration and mobility of charge carriers (electrons, holes) provide information for application-oriented materials optimisation. An optimal magnitude of the carrier density has to be adjusted to achieve the maximal thermoelectric figure of merit of semiconductors.

High temperature measurement of Seebeck coefficient and electrical conductivity (300 K–1000 K)

DLR Institute of Materials Research

The Seebeck coefficient is the central material parameter for the development of thermoelectric materials and applications. It is the essential parameter for the formation of an output voltage by thermoelectric generators and for the sensitivity of thermoelectric sensors. In most materials it is closely related to the concentration of charge carriers, which has to be adjusted to reach best thermoelectric materials and system properties.

Measurement of the Seebeck coefficient at intermediate temperature (80–800 K)

DLR Institute of Materials Research

Temperature dependent determination of the Seebeck coefficient on plate-shaped specimens is done between the temperature of liquid nitrogen and about 500 °C. A facility was developed at DLR for a quick and reliable Seebeck measurement on specimens of variable geometry. The set-up allows for a simple and quick exchange of specimens without a lot of time-consuming preparation.

Seebeck micro-thermoprobe

DLR Institute of Materials Research

The Seebeck coefficient S as the most relevant thermoelectric (TE) material parameter is deter¬mined by the TE material itself, by variation of the chemical composition (for example for solid solutions) as well as by microstructure, in particular the concentration of grain boundaries or electrically conductive inclusions etc.

 
Results 1-4 of 4

Similar Measures

Subsequent references to data sheets are given which describe with high probability similar measured values. The selection is made on the basis of semantic affinity: