Crystalline silicon is an attractive photovoltaic material because of its natural abundance, accumulated materials and process knowledge, and its appropriate band gap. To reduce cost, thin films of crystalline silicon can be used. This reduces the amount of material needed and allows material with shorter carrier diffusion lengths to be used. However, the indirect band gap of silicon requires that a light trapping approach be used to maximize optical absorption. Here, a photonic crystal (PC) based approach is used to maximize solar light harvesting in a 400 nm-thick silicon layer by tuning the coupling strength of incident radiation to quasiguided modes over a broad spectral range. The structure consists of a double layer PC with the upper layer having holes which have a smaller radius compared to the holes in the lower layer. We show that the spectrally averaged fraction of photons absorbed is increased 8-fold compared to a planar cell with equivalent volume of active material. This results in an enhancement of maximum achievable photocurrent density from 7.1 mA/cm(2) for an unstructured film to 21.8 mA/cm(2) for a film structured as the double layer photonic crystal. This photocurrent density value approaches the limit of 26.5 mA/cm(2), obtained using the Yablonovitch light trapping limit for the same volume of active material.

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The Bruch's membrane is located beneath the retina in vertebrate eyes. We have used atomic force microscopy to examine the morphological and adhesion properties of collagen fibers located in different portions of the membrane. The D-periodicity of the fibers was 62.54 +/- 4.25 nm and 63.78 +/- 4.14 nm for regions away from the optic nerve and close to it, respectively. The adhesion properties of the collagen fibers were evaluated using force volume imaging on a number of different eye samples. The adhesion force we recorded in regions away from the optic nerve was different compared to regions close to the optic nerve. The reported results allow us to understand the nanoscopic properties of connective tissues in the eye and are important for the design of new and improved biomaterials.

We have repurposed Google tensor processing units (TPUs), application-specific chips developed for machine learning, into large-scale dense linear algebra supercomputers. The TPUs’ fast intercore interconnects (ICIs), physically two-dimensional network topology, and high-bandwidth memory (HBM) permit distributed matrix multiplication algorithms to rapidly become computationally bound. In this regime, the matrix-multiply units (MXUs) dominate the runtime, yielding impressive scaling, performance, and raw size: Operating in float32 precision, a full 2,048-core pod of third-generation TPUs can multiply two matrices with linear size N = 2 20 = 1 , 048 , 576 in about 2 min. Via curated algorithms emphasizing large, single-core matrix multiplications, other tasks in dense linear algebra can similarly scale. As examples, we present 1) QR decomposition; 2) resolution of linear systems; and 3) the computation of matrix functions by polynomial iteration, demonstrated by the matrix polar factorization.