An investigation on wear behavior of UHMWPE/carbide composites at elevated temperatures

Ultra-high molecular weight polyethylene (UHMWPE) is extensively used in frictional applications due to its advanced wear resistance. This advanced polymer is reinforced with hard particulate fillers for further developments against wear conditions. Since elevated temperatures prevail in the service conditions, wear behavior of UHMWPE composites is an important issue for the engineering applications. In the present work, UHMWPE-based composites including silicon carbide (SiC) fillers were fabricated in a compression molding chamber. In the specimen preparation stage, molding pressure, filler amount, and filler particle size were varied to investigate the influence of these variables. Upon deciding the optimum parameters from the wear tests conducted at room temperature, the wear experiments were repeated for the optimum specimen at elevated temperatures, such as 40 and 60°C. According to the results, the wear behavior of the SiC/UHMWPE composites is heavily changed by the effect of elevated temperature. Adhesive effect is pronounced at elevated temperatures while the wear characteristics possess the abrasive effect in the sliding path. In addition, the composites exhibit an accelerated material loss as temperature increases during the frictional system.     

Improvements of the structural,thermal, and mechanical properties of structural adhesive with functionalized boron nitride nanoparticles

Investigations on the production and development of nanoparticle-reinforced polymer materials have been attracted attention by researchers. Various nanoparticles have been used to improve the mechanical, chemical, thermal, and physical properties of polymer matrix composites. Boron compounds come to the fore to improve the mechanical and thermal properties of polymers. In this study, mechanical, thermal, and structural properties of structural adhesive have been examined by adding nano hexagonal boron nitride (h-BN) to epoxy matrix at different percentages (0.5, 1, 2, 3, 4, and 5%). For this purpose, nano h-BN particles were functionalized with 3-aminopropyltriethoxysilane (APTES) to disperse the h-BN nanoparticles homogeneously in epoxy matrix and to form a strong bond at the matrix interface. Two-component structural epoxy adhesive was modified by using functionalized h-BN nanoparticles. The structural and thermal properties of the modified adhesives were investigated by scanning electron microscopy and energy dispersion X-ray spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis techniques. Tensile test and dynamic mechanical analysis were performed to determine the mechanical properties of the adhesives. When the results obtained from analysis were examined, it was seen that the nano h-BN particles functionalized with APTES were homogeneously dispersed in the epoxy matrix and formed a strong bond. In addition that, it was concluded from the experimental results that the thermal and mechanical properties of adhesives were improved by adding functionalized nano h-BN particles into epoxy at different ratios.     

Structural,thermal, and mechanical properties of silanized boron carbide doped epoxy nanocomposites

In the presented study, the structural, thermal, and mechanical properties of the nanocomposites were investigated by doping silanized hexagonal boron carbide (h-B₄C) nanoparticles in varying proportions (0.5%, 1%, 2%, 3%, 4%, and 5%) into the epoxy resin by weight. For this purpose, the surfaces of h-B₄C nanoparticles were silanized by using 3-(glycidyloxypropyl) trimethoxysilane (GPS) to improve adhesion between h-B₄C nanoparticles and epoxy matrix. Then, the silanized nanoparticles were added to the resin by ultrasonication and mechanical stirring techniques to produce nanocomposites. The bond structure differences of silanized B₄C nanoparticles (s-B₄C) and nanoparticle doped composites were investigated by using Fourier transform infrared spectroscopy. Scanning electron microscopy and energy dispersion X-ray spectroscopy (SEM-EDS) technique was used to examine the distribution of nanoparticles in the modified nanocomposites. Differential scanning calorimetry and thermogravimetric analysis techniques were used to determine the thermal properties of the neat and s-B₄C doped nanocomposites. The tensile test and dynamic mechanical analysis were performed to determine the mechanical properties. When the experimental results were examined, changes in the bonding structure of the s-B₄C nanoparticles doped nanocomposites and significant improvements in the mechanical and thermal properties were observed. The optimum doping ratio was determined as 2% by weight. At this doping ratio, the T_g, tensile strength and storage modulus increased approximately 18%, 35%, and 44% compared to the neat composite, respectively.     

Production of NiO,NiO/Ag,NiO/Au,and NiO/Pt hollow spheres by using block copolymer stabilized microspheres as a template

Hollow spheres of nickel oxide (NiO) and silver, gold, and platinum nanoparticle loaded NiO composites were successfully produced by using polystyrene (PS) latexes as hard template. Due to the presence of tertiary amine based diblock copolymer stabilizer on the surface of PS, the tertiary amine functional groups provided homogene deposition of nickel hydroxide, and then the precursor NiO salt production on the surface of PS latexes with a controlled precipitation technique. Then, NiO and NiO/metal NP hollow spheres were produced by calcination at 600 °C. Thermogravimetric analysis indicated that the amounts of NiO and NiO-composite after calcination were in the range of 21.1–29.7 wt%. The diameters of metal oxide spheres were in the range of 2.0–2.7 μm and the shell thickness were in the range of 250–350 nm. These structures had very low densities due to their porous and hollow structures and had outer layers with highly rough surfaces due to formation of nanosheets, which may offer important advantages for catalysis studies.     

An adaptive support vector regressor controller for nonlinear systems

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.     

Robust‐Adaptive Control Strategies for a Time Delay Bioelectrochemical Process Using Interval Observers

The paper addresses the design and the analysis of adaptive and robust‐adaptive control strategies for a complex recycled wastewater treatment bioprocess. The design procedures are developed under the realistic assumptions that the bacterial growth rates are unknown and the influent flow rates are time‐varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately (asymptotic or interval based) state observer and with a parameter estimator used for on‐line estimation of unknown kinetics. These approaches are applied to a complex time delay bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor. Numerical simulations are performed in order to validate the proposed algorithms.     

Image blur and energy broadening effects in XPEEM

We report image blurring and energy broadening effects in energy-filtered XPEEM when illuminating the specimen with soft X-rays at high flux densities. With a flux of 2×10¹³ photons/s, the lateral resolution in XPEEM imaging with either core level or secondary electrons is degraded to more than 50 nm. Fermi level broadening up to several hundred meV and spectral shift to higher kinetic energies are also systematically observed. Simple considerations suggest that these artifacts result from Boersch and Loeffler effects, and that the electron-electron interactions are strongest in the initial part of the microscope optical path. Implications for aberration corrected instruments are discussed.     

Subquadratic Algorithms for 3SUM

We obtain subquadratic algorithms for 3SUM on integers and rationals in several models. On a standard word RAM with w-bit words, we obtain a running time of . In the circuit RAM with one nonstandard AC ⁰ operation, we obtain . In external memory, we achieve O(n ²/(MB)), even under the standard assumption of data indivisibility. Cache-obliviously, we obtain a running time of . In all cases, our speedup is almost quadratic in the “parallelism” the model can afford, which may be the best possible. Our algorithms are Las Vegas randomized; time bounds hold in expectation, and in most cases, with high probability.     

Interpretation of MR images using self-organizing maps and knowledge-based expert systems

A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities, first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions.