Corrosion behavior and residual stress of microarc oxidation coated AZ31 magnesium alloy for biomedical applications

Thesis (Ph.D.) University of Alaska Fairbanks, 2012Mg alloys are potentially new biomaterials for bone repair or replacement. Appropriate coating is, however, needed to make the Mg alloy more resistant to corrosion. In this research, protective microarc oxidation (MAO) coatings were produced on AZ31 Mg alloys in sodium phosphate electrolyte. The coatings were produced under varying pulse frequency, applied voltage, oxidation time and electrolyte concentrations. This research analyzed the effects of the above four MAO process control parameters on the residual stresses and the corrosion behavior. Optimization of the MAO control parameters would allow production of AZ31 Mg alloy with high corrosion resistance. It is well accepted that residual stress and corrosion behavior are two significant factors in the development of AZ31 Mg alloys. The residual stresses in the MAO coatings were evaluated by the X-ray diffraction (XRD)-sin²ψ method. A predictive model of the residual stresses is proposed and a principal components analysis (PCA) was conducted to determine the contribution of the MAO control parameter on the residual stresses. Long-term corrosion behavior of MAO-coated Mg alloys was evaluated by the potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) tests. The porosity of the samples after various immersion durations was evaluated by the potentiodynamic polarization method. The pre- and post- corrosion microstructures and the phase composition of MAO-coated samples were studied. Post-corrosion phase identification showed that hydroxyapatite (HA) was formed on the surface of the samples. The ratio of Ca/P in HA was determined by the X-Ray Fluorescence (XRF) technique. The degradation of the MAO-coated AZ31 alloys is reduced due to the MAO coating and the formation of a corrosion product layer. A predictive model of the corrosion current density is proposed and a PCA was conducted to determine the contributions of the individual MAO control parameter on the corrosion rate. The corrosion process and mechanism of MAO-coated AZ31 alloys in SBF were modeled based on the electrochemical corrosion results and the pre- and post-corrosion surface analysis. It is believed that under optimized control parameters, the MAO-coated AZ31 Mg alloy is superior implant material for biomedical applications

Advanced Silicate-based Lubricant Additive Induced Diamond-like Carbon Structured Restoration Layer

An advanced silicate based lubricant additive has been employed in long-term pin-on-disk tribological experiments. The worn steel/steel surfaces were characterized using nano-indentation, SEM, XPS, and Raman spectroscopy for their physical, mechanical, and chemical properties. The average nano-hardness of the repaired layers on the disk and the pin is 10.2 GPa and 16.7 GPa respectively, which is substantially higher than that of the disk (HV 221, or 0.71 GPa) and the pin (HRC55, or 1.8 GPa) before tribological tests, forming super hard surfaces on the contact pair surfaces. Combined Raman spectroscopy and XPS studies suggest the formation of diamond-like carbon based restoration layers. A new formation mechanism of the restoration DLC layer contributing to hard and smooth contact surfaces is proposed

Planning of Regional Urban Bus Charging Facility:A Case Study of Fengxian, Shanghai

The electrification of public transport is of great significance to alleviating environmental pollution and energy problems. The construction of charging stations for electric buses (EBs) is the key step for the electrification of public transport and receives more and more attention. This paper proposes a new urban electric bus charging station planning algorithm which consists of two parts, park-maintaining (PM) charging station planning and midway supply (MS) charging station planning. Firstly, bus routes are classified based on charging demands. Accordingly, the PM charging station planning model is divided into full slow charging (FSC) model, Bus Rapid Transit (BRT) model and Hybrid model. Secondly, the improved grid AP algorithm is applied to plan MS charging stations to enhance the EB operation reliability. Then by multi-terminal charging pile optimization model, the economics of charging facilities construction is enhanced. Finally, via an ordered control charging algorithm, the economic profits of overall planning schemes are enhanced. The bus system in Fengxian, Shanghai is taken as an example to demonstrate the proposed method. Results prove that the proposed method can effectively meet the charging demands of EBs and improve the operating reliability of the EB system. </p

Tracking the nematicity in cuprate superconductors: a resistivity study under uniaxial pressure

Overshadowing the superconducting dome in hole-doped cuprates, the pseudogap state is still one of the mysteries that no consensus can be achieved. It has been suggested that the rotational symmetry is broken in this state and may result in a nematic phase transition, whose temperature seems to coincide with the onset temperature of the pseudogap state $T^*$ around optimal doping level, raising the question whether the pseudogap results from the establishment of the nematic order. Here we report results of resistivity measurements under uniaxial pressure on several hole-doped cuprates, where the normalized slope of the elastoresistivity $\zeta$ can be obtained as illustrated in iron-based superconductors. The temperature dependence of $\zeta$ along particular lattice axis exhibits kink feature at $T_{k}$ and shows Curie-Weiss-like behavior above it, which may suggest a spontaneous nematic transition. While $T_{k}$ seems to be the same as $T^*$ around the optimal doping and in the overdoped region, they become very different in underdoped La$_{2-x}$Sr$_{x}$CuO$_4$. Our results suggest that the nematic order, if indeed existing, is an electronic phase within the pseudogap state.Comment: 6 pages, 4 figure

Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespread applications. Previous methods on dynamic graph learning are usually sensitive to noisy graph information such as missing or spurious connections, which can yield degenerated performance and generalization. To overcome this challenge, we propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. We also propose a temporal-union graph structure and a target-context node sampling strategy for efficient and scalable training. Extensive experiments on real-world datasets illustrate that DGT presents superior performance compared with several state-of-the-art baselines