Magnesium and magnesium alloys as degradable metallic biomaterials

Drawbacks associated with permanent metallic implants lead to the search for degradable metallic biomaterials. Magnesium has been considered as it is essential to bodies and has a high biodegradation potential. For magnesium and its alloys to be used as biodegradable implant materials, their degradation rates should be consistent with the rate of healing of the affected tissue, and the release of the degradation products should be within the body's acceptable absorption levels. Conventional magnesium degrades rapidly, which is undesirable. In this study, biodegradation behaviours of high purity magnesium and commercial purity magnesium alloy AZ31 in both static and dynamic Hank's solution have been systematically investigated. The results show that magnesium purification and selective alloying are effective approaches to reduce the degradation rate of magnesium. In the static condition, the corrosion products accumulate on the materials surface as a protective layer, which results in a lower degradation rate than the dynamic condition. Anodised coating can significantly further reduce the degradation rate of magnesium. This study indicates that magnesium can be used as degradable implant materials as long as the degradation is controlled at a low rate. Magnesium purification, selective alloying and anodised coating are three effective approaches to reduce the rate of degradation

Orthogonal learning particle swarm optimization

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness

Topological Bose-Mott Insulators in a One-Dimensional Optical Superlattice

We study topological properties of the Bose-Hubbard model with repulsive interactions in a one-dimensional optical superlattice. We find that the Mott insulator states of the single-component (two-component) Bose-Hubbard model under fractional fillings are topological insulators characterized by a nonzero charge (or spin) Chern number with nontrivial edge states. For ultracold atomic experiments, we show that the topological Chern number can be detected through measuring the density profiles of the bosonic atoms in a harmonic trap.Comment: 5 pages, published versio

Preliminary experimental comparison and feasibility analysis of CO2/R134a mixture in Organic Rankine Cycle for waste heat recovery from diesel engines

This paper presents results of a preliminary experimental study of the Organic Rankine Cycle (ORC) using CO2/R134a mixture based on an expansion valve. The goal of the research was to examine the feasibility and effectiveness of using CO2 mixtures to improve system performance and expand the range of condensation temperature for ORC system. The mixture of CO2/R134a (0.6/0.4) on a mass basis was selected for comparison with pure CO2 in both the preheating ORC (P-ORC) and the preheating regenerative ORC (PR-ORC). Then, the feasibility and application potential of CO2/R134a (0.6/0.4) mixture for waste heat recovery from engines was tested under ambient cooling conditions. Preliminary experimental results using an expansion valve indicate that CO2/R134a (0.6/0.4) mixture exhibits better system performance than pure CO2. For PR-ORC using CO2/R134a (0.6/0.4) mixture, assuming a turbine isentropic efficiency of 0.7, the net power output estimation, thermal efficiency and exergy efficiency reached up to 5.30 kW, 10.14% and 24.34%, respectively. For the fitting value at an expansion inlet pressure of 10 MPa, the net power output estimation, thermal efficiency and exergy efficiency using CO2/R134a (0.6/0.4) mixture achieved increases of 23.3%, 16.4% and 23.7%, respectively, versus results using pure CO2 as the working fluid. Finally, experiments showed that the ORC system using CO2/R134a (0.6/0.4) mixture is capable of operating stably under ambient cooling conditions (25.2–31.5 °C), demonstrating that CO2/R134a mixture can expand the range of condensation temperature and alleviate the low-temperature condensation issue encountered with CO2. Under the ambient cooling source, it is expected that ORC using CO2/R134a (0.6/0.4) mixture will improve the thermal efficiency of a diesel engine by 1.9%