The creation of anti-reflective surfaces is reliant on the engineering of the surface textures and patterns to enable efficient trapping or transmission of light. Here we demonstrate anti-reflective layers composed of hierarchical nano/microscale features that are prepared on Si using a combination of wet and dry etching processes, and which are both scalable and affordable. The performance of the structured surfaces was tested through optical measurements of the reflectance, transmittance, and scattering spectra from the visible to mid-infrared wavelength regions, and the results were verified using numerical simulations to identify the performance of the textured anti-reflective layers. The anti-reflective properties of the layers were shown to be dramatically improved by the composite nanostructured surfaces over a broad spectral range, which thus provides a basis for the design rules that are essential for the progress towards effective anti-reflector fabrication. At normal incidence, the hierarchical surfaces achieve reflectances that are 10–80 times lower than that of conventional single-etch nano-microstructures. Portions of the absorbed, transmitted, scattered, and reflected light in the visible-IR spectrum are presented to illustrate the results.
Nonredundant and exhaustive generation of stereoisomers of a chemical compound with a specified constitution is one of the important tools for molecular structure elucidation and molecular design. In this paper, we deal with chemical compounds composed of carbon, hydrogen, oxygen and nitrogen atoms whose graphical structures are tree-like graphs because these compounds are most fundamental, and consider stereoisomers that can be generated by asymmetric carbon atoms and double bonds between two adjacent carbon atoms. Based on dynamic programming, we propose an algorithm of generating all stereoisomers without duplication. We treat a given tree-like graph as a tree rooted at its structural center. Our algorithm first computes recursively the numbers of stereoisomers of the subgraphs induced by the descendants of each vertex, and then constructs each stereoisomer by backtracking the process of computing the numbers of stereoisomers. Our algorithm correctly counts the number of stereoisomers in O(n) time and space, and correctly enumerates all the stereoisomers in O(n) space and in O(n) time per stereoisomer, where n is the number of atoms in a given structure. The source code of the program implementing the proposed algorithm is freely available for academic use upon request.
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