Enhancement of Seebeck coefficient in graphene superlattices by electron filtering technique

We show theoretically that the Seebeck coefficient and the thermoelectric figure of merit can be increased by using electron filtering technique in graphene superlattice based thermoelectric devices. The average Seebeck coefficient for graphene-based thermoelectric devices is proportional to the integral of the distribution of Seebeck coefficient versus energy of electrons. The low energy electrons in the distribution curve are found to reduce the average Seebeck coefficient as their contribution is negative. We show that, with electron energy filtering technique using multiple graphene superlattice heterostructures, the low energy electrons can be filtered out and the Seebeck coefficient can be increased. The multiple graphene superlattice heterostructures can be formed by graphene superlattices with different periodic electric potentials applied above the superlattice. The overall electronic band gap of the multiple heterostructures is dependent upon the individual band gap of the graphene superlattices and can be tuned by varying the periodic electric potentials. The overall electronic band gap of the multiple heterostructures has to be properly chosen such that, the low energy electrons which cause negative Seebeck distribution in single graphene superlattice thermoelectric devices fall within the overall band gap formed by the multiple heterostructures. Although the electrical conductance is decreased in this technique reducing the thermoelectric figure of merit, the overall figure of merit is increased due to huge increase in Seebeck coefficient and its square dependency upon the Seebeck coefficient. This is an easy technique to make graphene superlattice based thermoelectric devices more efficient and has the potential to significantly improve the technology of energy harvesting and sensors. (paper)