The synthesis of the MCM-41 materials with novel hollow tubular morphology in high alkaline condition was studied in detail. The tubular structure can be obtained in a narrow range of water/C 16 TMAB molar ratio, in which lamellar membranes consisting of the hexagonal arrangement of rod micelles exist. Tubular structures result from a membraneto-tubules transformation. Adding a proper amount of salts into the synthesis gel of higher water/C 16 TMAB molar ratio can help forming tubular structure. Temperature of the reaction system has to be controlled near 30 °C in order to obtain the tubular MCM-41. Incorporation of aluminum into the framework affects the morphology and crystallinity of resultant MCM-41 products. But the stirring rate does not have an appreciable effect on the formation of tubular morphology. The sample synthesized with the C 16 TMACl-silicate system with a suitable water content leads to about 70% in tubular morphology, and the hexagonal structure of MCM-41 channels was formed from a lamellar intermediate. Surfactants with shorter carbon chain length (n < 16) would inhibit the formation of tubular morphology. The product of the C 12 TMAB-silicate system was in particulate form and that of C 14 TMAB-silicate in broken tubular morphology.

Addition of cations such as tetraalkylammonium or sodium ions to the synthesis gel results in considerable improvement of the hydrothermal stability of mesoporous molecular sieve MCM-41.

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Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm. 1 R. Fan, J. Jiao and M. Liu are with the Robotics and Multi-Perception Laboratory in Robotics Institute at

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