Synthesis and characterization of hydrophilic nanocomposite coating on glass substrate

Hydrophilic coatings based on 3‐glicidoxy propyl trimethoxy silane (GPTMS) and polyethylene glycol (PEG) were prepared with the incorporation of tetramethoxysilane (TMOS) and silica nanoparticle colloidal suspension by a sol–gel process. Characterization of the coatings has been performed by Fourier Transform Infrared (FTIR) and Attenuated Total Reflectance Infrared (ATR‐IR) techniques. Morphological properties were characterized by Scanning Electron Microscopy (SEM). The distribution of Si atoms in the hybrid system was obtained by Si mapping. The particle size in sol solution of the coating was measured by light scattering analyzer. Optical properties were characterized by using UV–vis spectrophotometer. The hydrophilicity of the coating was determined by contact angle measurements, and also the results have been confirmed by surface energy and water uptake investigations. The obtained results indicate that the surfactants affected the contact angles remarkably but did not change the transparency. It has been found that applying silica nano particles leads to coatings with different properties than those using TMOS, while siloxane contents were the same in these two set of coatings. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 5322–5329, 2006     

Synthesis,characterization, and cell viability of bifunctional medical-grade polyurethane nanofiber: Functionalization by bone inducing and bacteria ablating materials

There is an extensive possibility of improving characteristics of fibers used in hard tissue engineering, being hydrophobic and less osteoconductive, resulting in the dynamic growth of new tissues. The current work focuses on the fabrication of nanofibers incorporated with titanium dioxide (TiO₂) ''as osteoconductive'' and silver (Ag) ''as self-healing'' nanoparticles (NPs). The incorporation of AgNO₃ by in situ method not only helped to impart the antibacterial activity but also changed the contact angle from 81 ± 03° in the case of pristine nanofibers to 74 ± 03°, 61 ± 03°, 50 ± 08°, and 39 ± 1.1°, in the composite scaffolds containing 0.01, 0.03, 0.05, and 0.07 M of Ag salts. The incubation in simulated body fluid at 37°C to induce mineralization on nanofiber scaffolds indicated Ca and P crystals' formation. The antibacterial activity showed significantly more toxicity toward E. coli (8.3 ± 0.9 mm) than S. aureus (1.2 ± 0.1 mm). Biocompatibility studies using MTT assay on the pre-osteoblasts showed that both TiO₂ and Ag NPs present in the nanofibers are non-toxic to the bone-like cells. However, results show that a higher concentration of Ag NPs (i.e., 0.07 M) is toxic to cells growing. Finally, all the results suggest that the nanofiber scaffolds have considerable scope for future bone tissue engineering materials.     

A novel composite model approach to improve software quality prediction

Context:How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances.ObjectiveThe main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques.MethodIn this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a “composite” model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances.ResultsThe results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones.ConclusionOur approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.     

Superconductivity and Fluctuation-Induced Conductivity (FIC) Analysis of (Cu0.5Tl0.5?x M x )Ba2Ca2Cu3O10?δ

Superconductivity has been improved by partial substitution of slightly higher electronegative (M=Bi, Hg) elements at Tl sites in (Cu_0.5Tl_0.5?x M _x )Ba₂O_4??? (x=0,0.25) charge reservoir layer of (Cu_0.5Tl_0.5?x M _x )Ba₂Ca₂Cu₃O_10??? superconductor. These samples were characterized by X-ray diffractometry, dc-resistivity, ac-susceptibility, FTIR absorption spectroscopy, and fluctuation-induced conductivity (FIC) analysis. The FIC analysis has been carried out in the light of Aslamasov?CLarkin (AL) theory on the resistivity versus temperature curves of (Cu_0.5Tl_0.5?x M _x )Ba₂Ca₂Cu₃O_10??? superconductor. The microscopic parameters such as cross-over temperature (T _o ), zero-temperature coherence length ?? _c (0), inter-layer coupling (J), and critical exponents (?? _2D and ?? _3D) have been determined from FIC analysis of these samples and tried to correlate them to the superconductivity order parameters. A?direct correlation between the cross-over temperatures (T _o ) and zero resistivity critical temperature {T _c (R=0)} and carrier concentration in these samples has been observed. The improvement in T _c (R=0) and the shift of 3D AL region to higher temperature values with the doping of Bi and Hg have also been observed.     

An acidic ionic liquid-conventional alkali-catalyzed biodiesel production process

A study was undertaken to prepare biodiesel via two-step process using ionic liquid as first step catalyst due to the unsuitability of using the straight alkaline-catalyzed transesterification of high FFA presented in crude palm oil (CPO). In the first step, esterification of the FFA presented in the CPO was carried out using butylimidazolium hydrogen sulfate (BIMHSO₄), in which the acid value was reduced from 6.93 to 1.02mg KOH/g and then, KOH-catalyzed transesterification was applied. The conversion rate of FFA attained 85.3% when 4.8 wt% of BIMHSO₄ was applied to the reaction system containing methanol to CPO ratio of 12: 1 reacted at 170 °C for 150min. The final yield in 97.3% revealed that the process proposed in this study could lead to an excellent biodiesel meeting the ASTM requirements. Furthermore, this new two-step catalysis process could solve the old conventional catalysis process drawbacks.     

Synthesis and physicochemical investigation of chitosan-built hydrogel with induced glucose sensitivity

There is an urgent need to treat diabetes, and therefore, this work reports on a chitosan-built hydrogel functionalized by a glucose sensing moiety, which simulates pancreatic activity. The effect of external stimuli on various internal properties was investigated to establish the action of the hydrogel. The model drugs, fluorescein (D1) and rhodamine (D2), with a diol architecture, were investigated spectroscopically with 75.94% loading and 65.63% release. Consequently, a ligand to glucose ratio of 2:1 in comparison with a ligand to model drug ratio of 1:1 was addressed. The system was expected to lead to findings on applications for the self-controlled release of insulin in response to blood glucose levels.     

Thin-film iron-oxide nanobeads as bifunctional electrocatalyst for high activity overall water splitting

Developing only Fe derived bifunctional overall water splitting electrocatalyst both for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) while performing at low onset overpotential and with high catalytic stability is a rare instance. We present here the first demonstration of unique iron-oxide nanobeads (FeO_x-NBs) based electrocatalyst executing both OER and HER with high activity. Thin-film electrocatalytic FeO_x-NBs assembly is surface grown via simple spray coating (SC). The unique SC/FeO_x-NBs propels OER initiating water oxidation just at 1.49 V_RHE (η = 260 mV) that is the lowest observable onset potential for OER on simple Fe-oxide based catalytic films reported so far. Catalyst also reveals decently high HER activity and competent overall water splitting performance in the FeO_x-NBs two-electrode system as well. Catalyst also presents stable kinetics, with promising high electrochemically active surface area (ECSA) of 1765 cm², notable Tafel slopes of just 54 mV dec^1? (OER) and 85 mV dec^1? (HER), high exchange current density of 1.10 mA cm^2? (OER), 0.58 mA cm^2? (HER) and TOF of 74.29s^1?@1.58V_RHE, 262s^1?@1.62V_RHE (OER) and 82.5s^1?@-0.45V_RHE, 681s^1?@-0.56V_RHE (HER).     

Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates

Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study utilizes recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach utilizes the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationally efficient prediction model for the foreign currency exchange rates. The results demonstrated a prediction accuracy of 98.872 percent (using 6 neurons only) for a single-day prediction in advance and, on average, 92% for predicting a 1000 days’ exchange rate in advance based on ten days of data history. The results prove RCGPANN to be the ultimate choice for any time series data prediction, and its capabilities can be explored in a range of other fields.