A Monte Carlo Study on the Effect of Energy Barriers on the Thermoelectric Properties of Si

ENERGYO ◽  
2018 ◽  
Author(s):  
Xanthippi Zianni ◽  
Patrice Chantrenne ◽  
Dario Narducci
2016 ◽  
Vol 3 (4) ◽  
Author(s):  
Xanthippi Zianni ◽  
Patrice Chantrenne ◽  
Dario Narducci

AbstractEnergy filtering by energy barriers has been proposed to interpret observations on large thermoelectric power factor (TPF) enhancement in highly doped nanocrystalline Si (nc-Si). Previous Boltzmann transport equation (BTE) modeling indicated that high TPFs could be explained as the result of the presence of energy barriers at the grain boundaries, the high Fermi energy due to the high doping level, and the formation of a low thermal conductivity second phase. To test the assumptions of the BTE modeling and provide more realistic simulations, we have performed Monte Carlo (MC) simulations on the transport properties of composite nc-Si structures. Here, we report on (i) the effect of an energy barrier, and (ii) the effect of multiple barriers on the conductivity and the Seebeck coefficient. In short structures, a TPF enhancement was found and it has been attributed to energy filtering by the energy barrier. The MC indicated that the TE performance can be improved by multiple barriers in close separation. It has been shown that TPF enhancement is possible even when the condition for thermal conductivity non-uniformity across the composite structure is not-fulfilled.


Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.


2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll

1993 ◽  
Vol 3 (9) ◽  
pp. 1719-1728
Author(s):  
P. Dollfus ◽  
P. Hesto ◽  
S. Galdin ◽  
C. Brisset

1987 ◽  
Vol 48 (C5) ◽  
pp. C5-199-C5-202
Author(s):  
T. MIYASAKI ◽  
K. AIZAWA ◽  
H. AOKI ◽  
C. ITOH ◽  
M. OKAZAKI

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