Cure kinetics of an epoxy/liquid aromatic diamine modified with poly(ether imide)

The cure kinetics and mechanisms of an epoxy oligomer based on diglycidyl ether of bisphenol A (DGEBA), polymerized with a liquid aromatic diamine based on diethyl toluene diamine (DETDA 80), and its blends with poly(ether imide) (PEI) at concentrations of 0–15 wt % were studied with differential scanning calorimetry under dynamic and isothermal conditions. The kinetic analyses were performed with a phenomenological approach. The reaction mechanism of the blends remained the same as that of the neat epoxy. However, the addition of PEI had a marked effect on the cure kinetics in the DGEBA/DETDA 80 system. The rate of reaction decreased with an increase in the thermoplastic content. Diffusion control was incorporated to describe the cure behavior of the blends in the latter stages. Greater diffusion control was observed as the PEI concentration increased and the cure temperature decreased. Polymer blends based on this epoxy/liquid aromatic diamine had not been previously studied from a kinetic viewpoint. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 660–672, 2005     

Preparation,characterization and in vitro drug release properties of polytrimethylene carbonate/polyadipic anhydride blend microspheres

Polyadipic anhydride (PAA), an aliphatic polyanhydride, and polytrimethylene carbonate (PTMC), an aliphatic polycarbonate, were synthesized via ring opening polymerization of oxepan‐2,7‐dione and melt‐condensation of trimethylene carbonate (1,3 dioxan‐2‐one), respectively. PTMC–PAA blend microspheres containing different ratios of buprenorphine HCl (2, 5, and 10%) were prepared by an oil‐in‐oil emulsion solvent removal method. Microspheres with different ratios of PTMC–PAA (85/15, 70/30, and 55/45) containing 5% buprenorphine HCl were prepared. Microspheres were spherical with visible cracks and pores on the surface. The average particle size of microspheres was around 200 μm for all microspheres. Drug loading efficiency of PTMC–PAA microspheres (85/15, 70/30, and 55/45) was 97.2, 95.2, and 70.2%, respectively. With the increase in the PTMC ratio, the melting point and the enthalpy of melting were both decreased. The mechanism for drug release from PTMC–PAA blend microspheres were generally a combination of drug diffusion through polymers and biodegradation of the polymers. In first three days, the release from microspheres followed zero order kinetics and was dependent on the PAA content. After three days the drug release from microspheres followed first order kinetics. In conclusion it was demonstrated that buprenorphine HCl release from microspheres could be successfully controlled by using different ratios of PTMC–PAA blends. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2377–2383, 2006     

Effect of Cation Substitution and Non-Stoichiometry on the Microwave Dielectric Properties of Sr(B'0.5Ta0.5)O3 [B'=Lanthanides] Perovskites

The Sr(B'_0.5Ta_0.5)O₃ ceramics where B'=La, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Y, Er, and Yb have been prepared by the conventional solid-state ceramic route and their microwave dielectric properties have been investigated. The structure and microstructure of the ceramics have been characterized by X-ray diffraction and scanning electron microscope techniques. The relative permittiviy (ɛ_r) varies linearly with B'-site ionic radii, except for La, and the temperature coefficient of resonant frequency (τ_f) varies linearly with the tolerance factor. The Sr(B'_0.5Ta_0.5)O₃ ceramics have ɛ_r in the range 25.9–32, Q _u× f =4500–54 300 GHz, and τ_f=−79 to −42 ppm/°C. A slight deviation from stoichiometry affects the dielectric properties of these double perovskites. Partial substitution of Ba for Sr could tune the dielectric properties. Addition of rutile (TiO₂) lowered the sintering temperature and improved the dielectric properties of Sr(B'_0.5Ta_0.5)O₃ ceramics.     

A secure and improved multi server authentication protocol using fuzzy commitment

Multimedia Tools and Applications - The advancement in communication and computation technologies has paved a way for connecting large number of heterogeneous devices to offer specified services....     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

     

Progress and challenges in sustainability,compatibility, and production of eco-composites: A state-of-art review

Owing to economic and environmental benefits, new generations of materials/commodities follow “from waste to wealth” strategy. Recently, there has been a huge upsurge in research on the development of eco-composites using recycled plastic polymers and agro-residues because the eco-composites satisfy the stringent environment regulations and are cost-effective. Herein, we present a detailed review on the potential use of several types of natural fillers as an efficient reinforcement for recycled plastic polymers. In particular, the characterization of different categories of eco-composites according to their morphological, physical, thermal, and mechanical properties is extensively reviewed and their results are analyzed, compared, and highlighted. Furthermore, a framework to produce functional eco-composites, which includes functionalization of ingredients, critical issues on microstructural parameters, processing, and fabrication methods, is outlined and supported with sufficient data from the literature. Finally, the review outlines the emerging challenges and future prospects of eco-composites to be addressed by interested researchers to bridge the gap between research and commercialization of such a class of material. Overall, the acquired knowledge will guide researchers, scientists, and manufacturers to plan, select, and develop various forms of eco-composites with enhanced properties and optimized production processes.     

Crack growth angle prediction of an internal crack under mixed mode load for unfilled elastomer using the strain energy density factor

This article investigates the prediction of the crack growth angle of an existing internal crack under mixed mode loading at the crack tip for an unfilled ethylene propylene diene terpolymer rubber (EPDM). For the realization of mixed mode loading, the cracks of the uniaxial loaded specimens were oriented with different angles to the loading direction. The energy density factor was used as a potential criterion for determining the crack growth angle. The determination of the strain energy density factor was carried out simulatively in Abaqus. The second-order Ogden model was used to describe the rubber-like material behavior. The relative local minimum of the strain energy density factor provides the possible growth angle. The experimental investigations show that the initial cracks grow orthogonally to the loading direction for the different crack orientation angles. For the crack orientation angle parallel to the load direction, the crack growth was observed because the strong stretching of the specimen caused strong necking in the crack region. The crack growth for the remaining crack orientation angles were induced due to shear loading at the crack tip. The predictive angle of different crack orientation angles shows very good accordance to the measured crack growth angles.     

Fourier transform infrared (FTIR) authentication and batch-to-batch consistency for different types of paints using benchtop and handheld FTIR spectrophotometers for oil and gas industry

Standardization of Fourier transform infrared (FTIR) fingerprint region for paints and assessment on the reproducibility using different spectrophotometers were investigated. While selective fingerprint regions may be confusing for technicians/analysts who are non-chemists, we attempt to generalize these regions (e.g., 1300–1000 cm⁻¹ for Epoxy part A and 1400–1000 cm⁻¹ for Epoxy part B) by choosing a universal region (2000–900 cm⁻¹) that works for different paints. Comparison result using a paired student t-test shows that the degree of similarity (r) values from the studied regions are not statistically different. The paint fails the screening analysis occasionally on-site when analyzed using handheld FTIR due to the higher level of noise that gives low r values (r < 0.900 ± 0.002). The same samples were analyzed using a benchtop FTIR and the r values are above 0.900 ± 0.002. While the screening may lead to a false rejection of the sample on-site, there could be occurrence of false acceptance. The on-site screening of EPZ part A with different formulations, for instance, shows that the r values over the entire IR spectrum are above 0.900 ± 0.002 when analyzed using handheld FTIR. After the samples were analyzed using the benchtop, the r values fall below 0.900 ± 0.002.