Graphene/polyethylene terephthalate nanocomposites with enhanced mechanical and thermal properties

Nanocomposites made of poly(ethylene terephthalate) (PET) and graphene nanoplatelets (GNPs) were fabricated through micro-compounding and micro-injection molding. With an objective of improving the interactions between GNP sheets and PET chains, PET pellets were ground into a fine powder. PET pellets and powders were mixed with GNPs at 2%, 5%, 7.5%, and 10% (wt.%), molded to fabricate the nanocomposites, and then tested using several analytical characterization tools. Mechanical testing showed greater improvement through powder mixing, resulting in a 58% increase in the elastic modulus of the nanocomposites at 10% weight fraction. Thermal behavior of the nanocomposites was evaluated through differential scanning calorimetry (DSC), and it was observed that addition of GNPs into PET powders at 10% increased the crystallinity of the PET 50%. Confocal microscopy confirmed that mixing GNP with PET powders results in a more uniform distribution of the GNPs in the matrix compared to the mixture with PET pellets. X-ray diffraction (XRD) analysis confirmed the presence of GNPs with preferred orientation within the PET matrix.

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Graphical abstract GNP distribution analysis in melt-compounded PET nanocomposites

     

Mesoporous silica nanoparticles prepared by different methods for biomedical applications: Comparative study

In the current investigation, mesoporous silica nanoparticles were obtained by various techniques, namely sol–gel (S1), micro‐emulsion (S2) and hydrothermal synthesis (S3). The effect of those methods on the final features of the obtained mesoporous silica nanoparticles was studied. The obtained nanoparticles were investigated by TEM, BET surface area, Zetasizer, XRD and FTIR. The preparation method effect was evaluated on the drug release behaviour using doxycycline hyclate as a model drug. In addition, the degree of their compatibility against Saos‐2 cell line was also determined. The morphology and microstructure of silica nanoparticles were found to be dependent on the utilised method. Those techniques produced particles with particle sizes of 50, 30–20 and 15 nm and also surface areas of 111.04, 164 and 538.72 m²/g, respectively, for S1, S2 and S3. However, different preparation methods showed no remarkable changes for the physical and chemical integrities. The drug release test showed faster release from S2 compared with S1 and S3, which make them more applicable in cases require large doses for short periods. However, the release behaviour of S3 was satisfied for treatments which require long period with relatively highest release rate. The preparation method influenced the cell viability as S1 and S2 showed acceptable cell cytotoxicity compared with S3.     

New Jersey ethics opinion will alter how neutrals operate at mediation centers,highlighting the nature and structure not only of their relationships,but also the parties' obligations

A New Jersey State Supreme Court committee opinion puts new obligations on consumers of ADR provider services, and neutrals. Hanan M. Isaacs, of Princeton, N.J., and Jennifer L. B. Katz, of Buffalo, N.Y., discuss what to look for in your local rules     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response to audiovisual stimuli for the mentioned emotions. Using a bandpass filter with a bandwidth of 1–100 Hz, recorded raw EEG signals are preprocessed. The preprocessed signals then further analyzed and twelve selected features in different domains are extracted. The Random forest (RF) and multilayer perceptron (MLP) algorithms are then used for the classification of the emotions through extracted features. The proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80% and 88% using MLP and RF classifiers respectively on hybrid features for experimental signals of different subjects. The proposed model outperforms in terms of cost and accuracy.     

Second-order hyperpolarizability and nonlinear optical properties of novel organic compound-doped poly (O-methoxyaniline) polymer film

Journal of Materials Science: Materials in Electronics - The structural characterization of a new organic compound (3, 5-bis (4-chlorophenyl)-4,5-dihydro-1H-Pyrazole) was analyzed experimentally by...     

Effectiveness of oil shale ash in reducing alkali-silica reaction expansions

The ash obtained from central Jordanian oil shales was subjected to standard tests to evaluate its effectiveness as an admixture in reducing mortar or concrete expansion due to alkali-silica reactions. According to the results obtained with Pyrex glass aggregate, the ash only partially complied with the specified requirements but still performed better than two locally available pozzolanic admixtures. The ash was also helpful in reducing the expansion of mortars containing a local chert aggregate. Considering the existence of high-alkali cements, potentially reactive aggregates and large oil shale reserves in Jordan, the findings of this study would further encourage the utilization of the oil shale ash as a pozzolanic admixture in mortar or concrete.

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Resume On a soumis les cendres obtenues à partir des schistes bitumineux de Jordanie centrale à des essais courants afin d’évaluer leur aptitude en tant qu’adjuvant à réduire le gonflement du mortier ou du béton causé par les réactions silico-alcalines. D’après les résultats obtenus avec du granulat de Pyrex, les cendres n’ont satisfait que partiellement aux spécifications, mais néanmoins davantage que deux adjuvants pouzzolaniques disponibles localement. Les cendres ont aussi contribuè à réduire la dilatation de mortiers renfermant un granulat de silice local. Etant donné la présence de ciments fortement alcalins, de granulats potentiellement réactifs et de vastes réserves de schistes bitumineux en Jordanie, les résultats de cette étude encourageraient l’utilisation des cendres de schiste bitumineux comme adjuvant pouzzolanique dans le mortier et le béton.

     

Adaptive energy aware cluster-based routing protocol for wireless sensor networks

Wireless sensor networks (WSNs) have grown excessively due to their various applications and low installation cost. In WSN, the main concern is to reduce energy consumption among nodes while maintaining timely and reliable data forwarding. However, most of the existing energy aware routing protocols incur unbalanced energy consumption, which results in inefficient load balancing and compromised network lifetime. Therefore, the main target of this research paper is to present adaptive energy aware cluster-based routing (AECR) protocol for improving energy conservation and data delivery performance. Our proposed AECR protocol differs from other energy efficient routing schemes in some aspects. Firstly, it generates balance sized clusters based on nodes distribution and avoids random clusters formation. Secondly, it optimizes both intra-cluster and inter-cluster routing paths for improving data delivery performance while balancing data traffic on constructed forwarding routes and at the end, in order to reduce the excessive energy consumption and improving load distribution, the role of Cluster Head (CH) is shifted dynamically among nodes by exploit of network conditions. Simulation results demonstrate that AECR protocol outperforms state of the art in terms of various performance metrics.