The direct preparation of grafting polymer brushes from commercial poly (vinylidene fluoride) (PVDF) films with surface‐initiated atom transfer radical polymerization (ATRP) is demonstrated. The direct initiation of the secondary fluorinated site of PVDF facilitated grafting of the hydrophilic monomers from the PVDF surface. Homopolymer brushes of 2‐(N,N‐dimethylamino)ethyl methacrylate (DMAEMA) and poly (ethylene glycol) monomethacrylate (PEGMA) were prepared by ATRP from the PVDF surface. The chemical composition and surface topography of the graft‐functionalized PVDF surfaces were characterized by X‐ray photoelectron spectroscopy, attenuated total reflectance/Fourier transform infrared spectroscopy, and atomic force microscopy. A kinetic study revealed a linear increase in the graft concentration of poly[2‐(N,N‐dimethylamino)ethyl methacrylate] (PDMAEMA) and poly[poly(ethylene glycol) monomethacrylate] (PPEGMA) with the reaction time, indicating that the chain growth from the surface was consistent with a controlled or living process. The living chain ends were used as macroinitiators for the synthesis of diblock copolymer brushes. The water contact angles on PVDF films were reduced by the surface grafting of DMAEMA and PEGMA. Protein adsorption experiments revealed a substantial antifouling property of PPEGMA‐grafted PVDF films and PDMAEMA‐grafted PVDF films in comparison with the pristine PVDF surface. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 3434–3443, 2006

Increasing absorption efficiency and decreasing total thickness of the acoustic absorber is favorable to promote its practical application. Four compressed porous metals with compression ratios of 0%, 30%, 60%, and 90% were prepared to assemble the four-layer gradient compressed porous metals, which aimed to develop the acoustic absorber with high-efficiency and thin thickness. Through deriving structural parameters of thickness, porosity, and static flow resistivity for the compressed porous metals, theoretical models of sound absorption coefficients of the gradient compressed porous metals were constructed through transfer matrix method according to the Johnson–Champoux–Allard model. Sound absorption coefficients of four-layer gradient compressed porous metals with the different permutations were theoretically analyzed and experimentally measured, and the optimal average sound absorption coefficient of 60.33% in 100–6000 Hz was obtained with the total thickness of 11 mm. Sound absorption coefficients of the optimal gradient compressed porous metal were further compared with those of the simple superposed compressed porous metal, which proved that the former could obtain higher absorption efficiency with thinner thickness and fewer materials. These phenomena were explored by morphology characterizations. The developed high-efficiency and thin-thickness acoustic absorber of gradient compressed porous metal can be applied in acoustic environmental detection and industrial noise reduction.

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Bridge crack detection is essential to ensure bridge safety. The introduction of deep learning technology has made it possible to detect bridge cracks automatically and accurately. In this study, the Inception-Resnet-v2 algorithm was systematically improved and applied to the real-time detection of bridge cracks. We propose an end-to-end bridge crack detection model based on a convolutional neural network. This model combines the advantages of Inception convolution and residual networks, broadening the network width and alleviating the training problem of the deep network. The calculation speed is improved while still ensuring accuracy. Multi-scale feature fusion enables the network to extract contextual information of different scales, which improves the accuracy of crack recognition. The GKA (K-means clustering method based on a genetic algorithm) realizes the accurate segmentation of the target area, greatly enhances the clustering effect, and effectively improves the detection speed. In this model, large fracture datasets are used for training and testing without pre-training. The experimental results show that the performance of this method was improved in all aspects: accuracy, 99.24%; recall, 99.03%; F-measure, 98.79%; and FPS(Frames Per Second), 196. INDEX TERMS bridge crack detection, Inception-Resnet-v2, multiscale feature fusion, GKAThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.