Preparation of Hydroxyapatite/Tannic Acid Coating to Enhance the Corrosion Resistance and Cytocompatibility of AZ31 Magnesium Alloys

Hydroxyapatite/tannic acid coating (HA/TA) were prepared on AZ31 magnesium alloys (AZ31) via chemical conversion and biomimetic methods. The characterization and properties of the coating were studied by scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), corrosion testing, MC3T3-E1 cell proliferation assay, and MC3T3-E1 cell morphology observation. The results showed that tannic acid as an inducer increased the number of nucleation centers of hydroxyapatite and rendered the morphology more uniform. Compared to bare AZ31 magnesium (Mg) alloys (Ecorr = −1.462 ± 0.006 V, Icorr = (4.8978 ± 0.2455) × 10−6 A/cm2), the corrosion current density of the HA/TA-coated magnesium alloys ((5.6494 ± 0.3187) × 10−8 A/cm2) decreased two orders of magnitude, and the corrosion potential of the HA/TA-coated Mg alloys (Ecorr = −1.304 ± 0.006 V) increased by about 158 mV. This indicated that the HA/TA coating was effectively protecting the AZ31 against corrosion in simulated body fluid (SBF). Cell proliferation assays and cell morphology observations results showed that the HA/TA coating was not toxic to the MC3T3-E1 cells

Multi-Relation Attention Network for Image Patch Matching

International audienceDeep convolutional neural networks attract increasing attention in image patch matching. However, most of them rely on a single similarity learning model, such as feature distance and the correlation of concatenated features. Their performances will degenerate due to the complex relation between matching patches caused by various imagery changes. To tackle this challenge, we propose a multi-relation attention learning network (MRAN) for image patch matching. Specifically, we propose to fuse multiple feature relations (MR) for matching, which can benefit from the complementary advantages between different feature relations and achieve significant improvements on matching tasks. Furthermore, we propose a relation attention learning module to learn the fused relation adaptively. With this module, meaningful feature relations are emphasized and the others are suppressed. Extensive experiments show that our MRAN achieves best matching performances, and has good generalization on multi-modal image patch matching, multi-modal remote sensing image patch matching and image retrieval tasks