Sliding wear behavior of plasma sprayed Fe3Al–Al2O3 graded coatings

Fe3Al–Al2O3 double-layer coatings (DC), Fe3Al-Fe3Al/50%Al2O3–Al2O3 triple-layer coatings (TC) and Fe3Al-Al2O3 graded coatings (GC) were produced from a series of Fe3Al/Al2O3 composite powders with different compositions on low carbon steel substrate using PLAXAIR plasma spraying equipment. Friction behaviors and wear resistance of the three kinds of coatings have been investigated under different loads. Tests were carried out using an MRH-3 standard machine, in lineal contact sliding under dry condition against hardmetal, at a sliding velocity of about 1.57 ms−1. Wear rates under different loads were measured and the friction coefficients were recorded. SEM analysis was carried out to identify the wear mechanisms. The results show that the GC has higher wear-resistance than DC and TC. The tribological characteristics of graded coating were different along the depth of the coatings, and the surface of coatings with pure Al2O3 does not show the best wear resistance. The wear rate and friction coefficients were also different under different loads. The failure types of plasma-sprayed Fe3Al-Al2O3 graded coatings in lineal contact were: loosening of ceramic particles, crack nucleation and propagation, brittle fracture, plastic deformation, and adhesive wear.

Gamma-irradiated carbon nanotube yarn as substrate for high-performance fiber supercapacitors

As an electrical double layer capacitor, dry-spun carbon nanotube yarn possesses relatively low specific capacitance. This can be significantly increased as a result of the pseudocapacitance of functional groups on the carbon nanotubes developed by oxidation using a gamma irradiation treatment in the presence of air. When coated with high-performance polyaniline nanowires, the gamma-irradiated carbon nanotube yarn acts as a high-strength reinforcement and a high-efficiency current collector in two-ply yarn supercapacitors for transporting charges generated along the long electrodes. The resulting supercapacitors demonstrate excellent electrochemical performance, cycle stability, and resistance to folding-unfolding that are required in wearable electronic textiles.

Ionic liquid adsorption and nanotribology at the silica-oil interface: hundred-fold dilution in oil lubricates as effectively as the pure ionic liquid

The remarkable physical properties of ionic liquids (ILs) make them potentially excellent lubricants. One of the challenges for using ILs as lubricants is their high cost. In this article, atomic force microscopy (AFM) nanotribology measurements reveal that a 1 mol % solution of IL dissolved in an oil lubricates the silica surface as effectively as the pure IL. The adsorption isotherm shows that the IL surface excess need only be approximately half of the saturation value to prevent surface contact and effectively lubricate the sliding surfaces. Using ILs in this way makes them viable for large-scale applications.

Functionally graded porous scaffolds made of Ti-based agglomerates

Mono- and double-layer porous scaffolds were successfully fabricated using ball-milled agglomerates of Ti and Ti-10Nb-3Mo alloy. For selectively controlling the level of porosity and pore size, the agglomerates were sieved into two different size fractions of 100-300μm and 300-500μm. Compressive mechanical properties were measured on a series of cylindrical sintered compacts with different ratios of solid core diameter to porous layer width. The graded porous scaffolds exhibited stress-strain curves typical for metallic foams with a defined plateau region after yielding. The compressive strengths and elastic moduli ranged from 300 to 700MPa and 14 to 55GPa, respectively, depending on the core diameter and the material used. The obtained properties make these materials suitable for load-bearing implant applications.

Sol-gel derived HA/TiO2 double coatings on Ti scaffolds for orthopaedic applications

Hydroxyapatite/titania (HA/TiO₂) double layers were coated onto Ti scaffolds throughout for orthopaedic applications by sol-gel method. Differential scanning calorimetry (DSC), thermogravimetric analysis (TG) and X-ray diffractometry (XRD) were used for the characterisation of the phase transformations of the dried gels and coated surface structures. Scanning electron microscope (SEM) equipped with energy dispersive spectrometry (EDS) was used for the observation and evaluation of the morphology and phases of the surface layers and for the assessment of the in vitro tests. The in vitro assessments were performed by soaking the HA/TiO₂ double coated samples into the simulated body fluid (SBF) for various periods. The TiO₂ layer was coated by a dipping-coating method at a speed of 12 cm/min, followed by a heat treatment at 600 °C for 20 min. The HA layer was subsequently dipping-coated on the outer surface at the same speed and then heat-treated at difference temperatures. The results indicat that the HA phase begins to crystallize after a heat treatment at 560 °C. The crystallinity increases obviously at 760 °C. SEM observations find no delamination or crack at the interfaces of HA/TiO₂ and TiO₂/Ti. The HA/TiO₂ coated Ti scaffolds displays excellent bone-like apatite forming ability when it is soaked into SBF. Ti scaffolds after HA/TiO₂ double coatings can be anticipated as promising implant materials for orthopaedic applications

Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes

Cardiac autonomic neuropathy (CAN) poses an important clinical problem, which often remains undetected due difficulty of conducting the current tests and their lack of sensitivity. CAN has been associated with growth in the risk of unexpected death in cardiac patients with diabetes mellitus. Heart rate variability (HRV) attributes have been actively investigated, since they are important for diagnostics in diabetes, Parkinson's disease, cardiac and renal disease. Due to the adverse effects of CAN it is important to obtain a robust and highly accurate diagnostic tool for identification of early CAN, when treatment has the best outcome. Use of HRV attributes to enhance the effectiveness of diagnosis of CAN progression may provide such a tool. In the present paper we propose a new machine learning algorithm, the Multi-Layer Attribute Selection and Classification (MLASC), for the diagnosis of CAN progression based on HRV attributes. It incorporates our new automated attribute selection procedure, Double Wrapper Subset Evaluator with Particle Swarm Optimization (DWSE-PSO). We present the results of experiments, which compare MLASC with other simpler versions and counterpart methods. The experiments used our large and well-known diabetes complications database. The results of experiments demonstrate that MLASC has significantly outperformed other simpler techniques.