Thermoelectric materials interconvert thermal gradients and electric fields for power generation or for refrigeration. Thermoelectrics currently find only niche applications because of their limited efficiency, which is measured by the dimensionless parameter ZT-a function of the Seebeck coefficient or thermoelectric power, and of the electrical and thermal conductivities. Maximizing ZT is challenging because optimizing one physical parameter often adversely affects another. Several groups have achieved significant improvements in ZT through multi-component nanostructured thermoelectrics, such as Bi(2)Te(3)/Sb(2)Te(3) thin-film superlattices, or embedded PbSeTe quantum dot superlattices. Here we report efficient thermoelectric performance from the single-component system of silicon nanowires for cross-sectional areas of 10 nm x 20 nm and 20 nm x 20 nm. By varying the nanowire size and impurity doping levels, ZT values representing an approximately 100-fold improvement over bulk Si are achieved over a broad temperature range, including ZT approximately 1 at 200 K. Independent measurements of the Seebeck coefficient, the electrical conductivity and the thermal conductivity, combined with theory, indicate that the improved efficiency originates from phonon effects. These results are expected to apply to other classes of semiconductor nanomaterials.
An important property with any new material is the band gap. Standard density functional theory methods grossly underestimate band gaps. This is known as the band gap problem. Here, we show that the hybrid B3PW91 density functional returns band gaps with a mean absolute deviation (MAD) from experiment of 0.22 eV over 64 insulators with gaps spanning a factor of 500 from 0.014 to 7 eV. The MAD is 0.28 eV over 70 compounds with gaps up to 14.2 eV, with a mean error of -0.03 eV. To benchmark the quality of the hybrid method, we compared the hybrid method to the rigorous GW many-body perturbation theory method. Surprisingly, the MAD for B3PW91 is about 1.5 times smaller than the MAD for GW. Furthermore, B3PW91 is 3-4 orders of magnitude faster computationally. Hence, B3PW91 is a practical tool for predicting band gaps of materials before they are synthesized and represents a solution to the band gap prediction problem.
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