The adsorption configurations of molecules adsorbed on substrates can significantly affect their physical and chemical properties. A standing configuration can be difficult to determine by traditional techniques, such as scanning tunneling microscopy (STM) due to the superposition of electronic states. In this paper, we report the real-space observation of the standing adsorption configuration of phenylacetylene on Cu (111) by non-contact atomic force microscopy (nc-AFM). Deposition of phenylacetylene at 25 K shows featureless bright spots in STM images. Using nc-AFM, the line features representing the C–H and C–C bonds in benzene rings are evident, which implies a standing adsorption configuration. Further density functional theory (DFT) calculations reveal multiple optimized adsorption configurations with phenylacetylene breaking its acetylenic bond and forming C–Cu bond(s) with the underlying copper atoms, and hence stand on the substrate. By comparing the nc-AFM simulations with the experimental observation, we identify the standing adsorption configuration of phenylacetylene on Cu (111). Our work demonstrates an application of combining nc-AFM measurements and DFT calculations to the study of standing molecules on substrates, which enriches our knowledge of the adsorption behaviors of small molecules on solid surfaces at low temperatures.
The application of remote sensing monitoring techniques plays a crucial role in evaluating and governing the vast amount of ecological construction projects in China. However, extracting information of ecological engineering target through high-resolution satellite image is arduous due to the unique topography and complicated spatial pattern on the Loess Plateau of China. As a result, enhancing classification accuracy is a huge challenge to high-resolution image processing techniques. Image processing techniques have a definitive effect on image properties and the selection of different parameters may change the final classification accuracy during post-classification processing. The common method of eliminating noise and smoothing image is majority filtering. However, the filter function may modify the original classified image and the final accuracy. The aim of this study is to develop an efficient and accurate post-processing technique for acquiring information of soil and water conservation engineering, on the Loess Plateau of China, using SPOT image with 2.5 m resolution. We argue that it is vital to optimize satellite image filtering parameters for special areas and purposes, which focus on monitoring ecological construction projects. We want to know how image filtering influences final classified results and which filtering kernel is optimum. The study design used a series of window sizes to filter the original classified image, and then assess the accuracy of each output map and image quality. We measured the relationship between filtering window size and classification accuracy, and optimized the post-processing techniques of SPOT5 satellite images. We conclude that (1) smoothing with the majority filter is sensitive to the information accuracy of soil and water conservation engineering, and (2) for SPOT5 2.5 m image, the 5×5 pixel majority filter is most suitable kernel for extracting information of ecological construction sites in the Loess Plateau of China.
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