Tuned interactions of silver nanoparticles with ZSM-5 zeolite by adhesion-promoting poly(acrylic acid) deposited by electrospray ionization (ESI)

The electrospray ionization (ESI) method was used for deposition of thin films of poly(acrylic acid) (PAA) on Cu/ZSM-5 (5 wt.% Cu) and Ag–Cu/ZSM-5 (1 wt.% Ag and 4 wt.% Cu) composites. For comparative purposes, the ZSM-5 zeolite was synthesized under hydrothermal conditions and loaded with PAA under the same treating conditions as the composites. This method allowed the formation of uniform polymer films of controlled thickness on conductive substrates. The structural characteristics were characterized by X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, atomic force microscopy and X-ray diffraction (XRD). The deposited PAA layer over ZSM-5 acts as a common dispersing and stabilizing agent through coordination-driven guest-templated polymer via interaction of Ag⁺ and Cu²⁺ with carboxylic acid groups, thus increasing and controlling the adhesion and the release of metallic species. A short exposure to light and temperature has reduced the metal ions to Cu⁰ and Ag⁰ metallic nanoparticles. The results of XRD analysis let suggest that the interaction of Cu and Ag with carboxylic groups of PAA inhibits the formation of large metallic silver particles. These samples were being studied for their potential as antibacterial agents toward the bacterial strains such as Staphylococcus pneumonia, Bacillus subtilis, Escherichia coli and Pseudomonas aeruginosa as Gram positive and Gram negative bacteria, respectively. Aspergillus fumigatus and Candida albicans as Fungi were also evaluated. The Cu/ZSM-5 and Ag–Cu/ZSM-5 nanocomposites coated with a 10 nm thick PAA layer exhibit significant antibacterial activity.     

Humidity sensing properties of Cu2O-PEPC nanocomposite films

In this study, the blend of copper oxide nanopowder (Cu2O), 3 wt.% and poly-N-epoxypropylcarbazole (PEPC), 2 wt.% in benzol were drop-casted on glass substrates with pre-deposited surface-type silver electrodes for the fabrication of Cu2O-PEPC nanocomposite thin films. The thicknesses of the Cu2O-PEPC films were in the range of 10-13 μm. The effect of humidity on the electrical properties of the nanocomposite films was investigated by measurement of the capacitance and dissipation of the samples at two different frequencies of the applied voltage: 120 Hz and 1 kHz. The AC resistance of the samples was determined from values of dissipation. The DC resistance was measured directly. The effect of ageing on the humidity sensing properties of the nanocomposite was observed. After the ageing it was observed that at 120 Hz and 1 kHz, under humidity of up to 86% RH , the capacitance of the cell increased by 85 and 8 times and resistance decreased by 345 and 157 times, accordingly, with respect to 30% RH conditions. It was found that with increase of the frequency, the capacitance and resistance of the samples decreased. It is assumed that the humidity response of the cell is associated with diffusion of water vapors and doping of the semiconductor nanocomposite by water molecules.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Experimenting with a Question Answering System for the Arabic Language

The World Wide Web (WWW) today is so vast that it has become more and more difficult to find answers to questions using standard search engines. Current search engines can return ranked lists of documents, but they do not deliver direct answers to the user. The goal of Open Domain Question Answering (QA) systems is to take a natural language question, understand the meaning of the question, and present a short answer as a response based on a repository of information. In this paper we present QARAB, a QA system that combines techniques from Information Retrieval and Natural Language Processing. This combination enables domain independence. The system takes natural language questions expressed in the Arabic language and attempts to provide short answers in Arabic. To do so, it attempts to discover what the user wants by analyzing the question and a variety of candidate answers from a linguistic point of view.     

Membrane-hydration-state detection in proton exchange membrane fuel cells using improved ambient-condition-based dynamic model

Proton-exchange-membrane fuel cells (PEMFCs) are a popular source of alternative energy because of their operational reliability and compactness. This paper presents an improved model to represent the semi-empirical voltage of PEMFCs to overcome the limitations of existing models. The proposed model considers variations in ambient conditions, such as the ambient temperature and relative humidity, to obtain the accurate output voltage that corresponds to variations in dynamic and static loads. The proposed model was developed by conducting several experiments on the Horizon PEMFC system under normal, humid, and dry ambient conditions. Subsequently, the model parameters corresponding to each case were optimised using the quantum lightning search algorithm (QLSA). Parameters demonstrating significant variations with ambient conditions were finally represented as a function of the ambient temperature and relative humidity via statistical regression analysis. The voltage obtained using the modified model was verified by conducting experiments on both the Horizon and NEXA PEMFC systems by varying the ambient temperature and relative humidity with root mean square error (RMSE) less than 0.5. As observed, the results we obtained using the modified model closely approximated those obtained using PEMFCs under various operating conditions, and in both cases, the PEMFC voltage was observed to vary with the ambient and load conditions. The inherent advantages of the proposed PEMFC model include its ability to determine the membrane-water content and water pressure inside PEMFCs. The membrane-water content provides clear indications regarding the occurrence of drying and flooding faults. Under normal conditions, this membrane water content ranges from 11 to 7 for both the Horizon and NEXA PEMFC system. The simulation results suggested using the threshold membrane-water-content level as a possible indicator of fault occurrence under extreme ambient conditions. The limits of the said threshold were observed to be useful for fault diagnosis within PEMFC systems.     

Evaluation of hidden H2-consuming pathways using metabolic flux-based analysis for a fermentative side-stream dynamic membrane bioreactor using untreated seed sludge

This study investigates the performance and hidden hydrogen consuming metabolic pathways of a fermentative side stream dynamic membrane (DM) bioreactor using flux balance analysis (FBA). The bioreactor was inoculated with untreated methanogenic seed sludge. It was found that fouling rate aggravated with increasing COD concentration (10–30 g/L) and was positively correlated to it rather than to the applied solid flux on the DM module. Due to increased fouling rate the hydraulic retention time (HRT) could not be reduced less than 0.82 ± 0.02 d. An increase in the organic loading rate (OLR) led to an increase in H₂ yield from 0.01 to 0.76 mol H₂/mol of sucrose. FBA revealed that homoacetogenesis was the main H₂-consuming pathway at lower OLRs (corresponding to 10 and 15 g COD/L), while for the OLR corresponding to 30 g COD/L, homoacetogens were suppressed. More importantly, caproic acid production pathway was identified for the first time as another H₂-consuming pathway at high OLR which was not significant at lower OLRs during fermentative dynamic membrane bioreactor operations.     

Development of ethylene glycol-Cr2AlC nanofluid for thermal management in the automotive sector

The current study is focused on the development of a novel nanofluid for efficient thermal management in the automotive sector. For this, novel Cr₂AlC-based nanofluids were prepared and its properties were compared with conventional nanofluids prepared under similar condition. h-BN, MoS₂, Al₂O₃, and Cr₂AlC powders of <60 nm were prepared by high energy ball mill and were added into the EG fluid in 0.25 and 0.50 wt%. The nanofluids were investigated for viscosity, flash point, fire point, thermal conductivity, stability, and freezing temperature. The flash and fire points of EG increase with the addition of nanocrystalline powders. The viscosity of nanofluids decreases and thermal conductivity increases with increase in temperature. Among all addition, nanofluid containing 0.50 wt% of Cr₂AlC shows maximum enhancement in the thermal conductivity and freezing temperature by 57.91% and 42.15%, respectively. It also shows a good stability up to 20 days.