Giant thermoelectric effect in graphene-based topological insulators with nanopores

Designing thermoelectric materials with high figure of merit $ZT=S^2 G T/\kappa$ requires fulfilling three often irreconcilable conditions, i.e., the high electrical conductance $G$, small thermal conductance $\kappa$ and high Seebeck coefficient $S$. Nanostructuring is one of the promising ways to achieve this goal as it can substantially suppress lattice contribution to $\kappa$. However, it may also unfavorably influence the electronic transport in an uncontrollable way. Here we theoretically demonstrate that this issue can be ideally solved by fabricating graphene nanoribbons with heavy adatoms and nanopores. These systems, acting as a two-dimensional topological insulator with robust helical edge states carrying electrical current, yield a highly optimized power factor $S^2G$ per helical conducting channel. Concurrently, their array of nanopores impedes the lattice thermal conduction through the bulk. Using quantum transport simulations coupled with first-principles electronic and phononic band structure calculations, the thermoelectric figure of merit is found to reach its maximum $ZT \simeq 3$ at $T \simeq 40$ K. This paves a way to design high-$ZT$ materials by exploiting the nontrivial topology of electronic states through nanostructuring.Comment: 7 pages, 4 figures; PDFLaTe

Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections

In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accurately in an inexpensive manner. Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method.Comment: Accepted by IGARSS 201