In this paper, a vertical-aligned silicon nanowires (Si NWs) array has been synthesized and implemented to the Si NW-array-textured solar cells for photovoltaic application. The optical properties of a Si NWs array on both the plane and pyramid-array-textured substrates were examined in terms of optical reflection property. Less than 2% reflection ratio at 800 nm wavelength was achieved. Using leftover monocrystalline Si (c-Si) wafer (125×125 mm2), a 16.5% energy conversion efficiency, with 35.4% enhancement compared to the pyramid-array-textured c-Si solar cells, was made by the Si NW-array-textured solar cells due to their enhanced optical absorption characteristics. However, without SiNx passivation, the short circuit current reduced due to the increased surface recombination when using Si NWs array as surface texturing, indicating that an optimum surface passivation was prerequisite in high-efficiency Si NW-array-textured solar cells.
Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has attracted extensive attention in recent years. The essence of sparse signal reconstruction is how to recover the original signal accurately and effectively from an underdetermined linear system equation (ULSE). For this problem, we propose a new algorithm called regularization reweighted smoothed L0 norm minimization algorithm, which is simply called RRSL0 algorithm. Three innovations are made under the framework of this method: (1) a new smoothed function called compound inverse proportional function (CIPF) is proposed; (2) a new reweighted function is proposed; and (3) a mixed conjugate gradient (MCG) method is proposed. In this algorithm, the reweighted function and the new smoothed function are combined as the sparsity promoting objective, and the constraint condition y-Φx22 is taken as a deviation term. Both of them constitute an unconstrained optimization problem under the Tikhonov regularization criterion and the MCG method constructed is used to optimize the problem and realize high-precision reconstruction of sparse signals under noise conditions. Sparse signal recovery experiments on both the simulated and real data show the proposed RRSL0 algorithm performs better than other popular approaches and achieves state-of-the-art performances in signal and image processing.
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