Low-temperature,chemical vapor deposition of thin-layer pyrolytic carbon coatings derived from camphor as a green precursor

Camphor, C₁₀H₁₆O, as a natural and renewable carbon precursor, can be pyrolyzed to pyrolytic carbon (PyC; pyrocarbon) with significant industrial applications from conducting electrodes to biomedical implant coatings. Here, a simple but controllable chemical vapor deposition setup, operating at low temperatures (650–800 °C) in nitrogen atmosphere at ambient pressure in the absence of catalyst, was used. According to XRD and Raman spectroscopy, nanocrystalline thin PyC films were obtained at this temperature range without a significant change in L _c and d ₀₀₂ values. When the deposition temperature increased from 700 to 800 °C, L _a and crystallinity percentage values were increased from 2.40 nm and 73.16% to 4.15 nm to 87.58%, respectively. SEM and AFM analyses showed smooth (Ra ≈ 1 nm) and shiny surface for the thin films with 10–500-nm range thickness. The films were hydrophilic on surface (water contact angle ≈ 72.45°) with surface free energy of ≈ 41 mN/m. Young’s modulus, hardness and friction coefficient of the thin PyC coatings were calculated using nanoindentation technique as ≈ 29.9, 3.5 GPa and 0.09, respectively. Resistivity of the films was 2.21 × 10^?5 Ωm, so it can be anticipated to repel the blood cells. Cytocompatibility screening in direct contact mode and in vitro biocompatibility findings supported cyto- and hemocompatible properties for the PyC specimens synthesized from camphor.     

Formulation and corneal permeation of ketorolac tromethamine-loaded chitosan nanoparticles

Abstract

The aim of this work was to formulate chitosan (CS)-based nanoparticles (NPs) loaded with ketorolac tromethamine (KT) intended for topical ocular delivery. NPs were prepared using ionic gelation method incorporating tri-polyphosphate (TPP) as cross-linker. Following the preparation, the composition of the system was optimized in terms of their particle size, zeta potential, entrapment efficiency (EE) and morphology, as well as performing structural characterization studies using Fourier transform infrared spectroscopy (FT-IR) and differential scanning calorimetry (DSC). The data suggested that the size of the NPs was affected by CS/TPP ratio where the diameter of the NPs ranged from 108.0?±?2.4?nm to 257.2?±?18.6?nm. A correlation between drug EE and the corresponding drug concentration added to the formulation was observed, where the EE of the NPs increased with increasing drug concentration, for up to 10?mg/mL. FT-IR and DSC revealed that KT was dispersed within the NPs where the phosphate groups of TPP were associated with the ammonium groups of CS. The in vitro release profile of KT from CS NPs showed significant differences (p?<?0.05) compared to KT solution. Furthermore, mucoadhesion studies revealed adhesive properties of the formulated NPs. The KT-loaded NPs were found to be stable when stored at different storage conditions for a period of 3 months. The ex vivo corneal permeation studies performed on excised porcine eye balls confirmed the ability of NPs in retaining the drug on the eye surface for a relatively longer time. These results demonstrate the potential of CS-based NPs for the ocular delivery of KT.     

An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes

In this study, a comprehensive empirical test is conducted to analyse the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover, Laplace crossover and simulated binary crossover operators for the global optimization of continuous problems. To do so, 13 well-known numerical benchmark problems in three distinctive dimensions, namely 50D, 100D and 200D, are considered and the genetic algorithm (GA) with simple version and chaos-enhanced versions of the mentioned crossover operators are utilized for optimizing these functions. Furthermore, a time complexity analysis is conducted to find out the impact of hybridizing the chaos and the evolutionary operators on the computational complexity of GA. The results of the experimental analysis provide us with fruitful information regarding the scalability, computational complexity and exploration/exploitation capability of the considered rival optimization algorithms, as well as, demonstrate the efficacy of chaos-evolutionary computing for numerical continuous optimizations.     

A linear unsupervised transfer learning by preservation of cluster-and-neighborhood data organization

Pattern Analysis and Applications - The paper has proposed a linear unsupervised transfer learning (LUTL). Therefore, a cost function has been introduced. In the cost function of the proposed LUTL,...     

Using learning automata in brain emotional learning for speech emotion recognition

We propose an improved version of brain emotional learning (BEL) model trained via learning automata (LA) for speech emotion recognition. Inspiring from the limbic system in mammalian brain, the original BEL model is composed of two neural network components, namely amygdala and orbitofrontal cortex. In this modified BEL model, named brain emotional learning based on learning automata (BELBLA), we have employed the theory of the stochastic LA in error back-propagation to train the BEL model in decreasing the high computational complexity of the traditional gradient method. Hence, the performance of the model can be enhanced. For a speech emotion recognition task, we extract the usual features, such as energy, pitch, formants, amplitude, zero crossing rate and MFCC, from average short-term signals of the emotional Berlin dataset. The experimental results show that the BELBLA outperforms some opponents, like hidden Markov model, Gaussian mixture model, k-nearest neighbor, support vector machines and artificial neural networks, for this application.     

Effect of Tio2 Nanoparticles on the Antibacterial and Physical Properties of Low-Density Polyethylene Film

In this study, TiO₂ nanoparticles were incorporated into low-density polyethylene by melt blending. Morphological properties and dispersion behavior of TiO₂ nanocomposite were characterized through field emission scanning electron microscope, X-ray diffraction, and Fourier transform infrared spectroscopy. Thermal stability of the nanocomposites was determined by thermogravimetric analysis. Moreover, the mechanical properties of nanocomposites were determined. Antimicrobial activity of TiO₂ nanocomposites was investigated by in vitro test. Dispersion of the nanoparticles was good in nanocomposites_. According to thermogravimetric analysis, incorporation of TiO₂ nanoparticles into low-density polyethylene enhanced the thermal stability. Mechanical properties of nanocomposites were improved by TiO₂ nanoparticles. Results showed that the antibacterial effect of low-density polyethylene –TiO₂ nanocomposite was significantly enhanced by TiO₂nanoparticles (p?2 nanoparticles not only can improve the properties of low-density polyethylene but they also have the potential to be used as an active food packaging film.     

An efficient fully bio‐based reactive diluent for epoxy thermosets: 2‐[(Oxiran‐2‐ylmethoxy) methyl] furan versus a petroleum‐based counterpart

Furfuryl alcohol and bio‐based epichlorohydrin were used to prepare a fully bio‐based reactive diluent, 2‐[(oxiran‐2‐ylmethoxy) methyl] furan (FOM). After spectral characterization, FOM was blended with epoxy resin, diglycidylether bisphenol A (DGEBA), at different ratios for reducing the viscosity. For a comparison, Cardura (one of the most common commercial reactive diluents), was separately incorporated to DGEBA. Amine‐curing process of the blends was recorded by FTIR and DSC. Similar trends of curing progression for DGEBA containing the reactive diluents were observed. Thermogravimetric analysis and dynamic mechanical thermal analysis, mechanical (hardness, adhesion, and stress–strain) and morphological properties were also investigated to study characteristics of the epoxy matrices formulated with FOM or Cardura. It was concluded that FOM could be considered as an efficient reactive diluent in formulations of polymer composites, structural adhesives and surface coatings based on epoxy resins. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44957.     

CBDL: Context‐based distance learning for categorical attributes

Distance learning is an important notion and has played a critical role in success of various machine learning algorithms. Any learning algorithm that requires dissimilarity/similarity measures has to assume some forms of distance functions, either explicitly or implicitly. Hence, in recent years a considerable amount of research has been devoted to distance learning. Despite great achievements in this field, a number of important issues need to be further explored for real world datasets mainly containing categorical attributes. Based on these considerations, the current research presents a Context‐Based Distance Learning approach (CBDL) to advance the state of the art existing researches on distance metric learning for categorical datasets. CBDL is designed and developed based on the idea that distance between two values of a given categorical attribute can be estimated by using information inherently exists within subset of attributes called context. CBDL composes of two main components: context extraction component and distance learning component. Context extraction component is responsible for extracting the relevant subset of feature set for a given attribute, while distance learning component tries to learn distance between each pair of values based on the extracted context. To have a comprehensive analysis, we conduct wide range of experiments in both supervised and unsupervised environments in the presence of noise. Our experimental results reveal that CBDL is the method of choice distance learning approach by offering a comparable or better performance compared to the state of the art existing distance learning schemes according to studied evaluation measures. © 2011 Wiley Periodicals, Inc.     

Dynamically loaded bearings operating with non-Newtonian lubricant films

The behaviour of a laminar non-Newtonian film (power law) was studied for dynamically loaded bearings. The momentum equation for an infinitely long bearing is solved by assuming that the velocity profile is a superposition of couette and Poiseuille flow for power law fluids. An expression for the modified Reynolds equation is obtained for the non-Newtonian fluid film. The load- carrying capacity is obtained for various values of the viscometric exponent of the power law model. It is shown that non-Newtonian lubricants can have a pronounced effect on dynamically loaded bearings.