Comparative photocatalytic performance and therapeutic applications of zinc oxide (ZnO) and neodymium-doped zinc oxide (Nd–ZnO) nanocatalysts against Acid Yellow-3 dye: kinetic and thermodynamic study of the reaction and effect of various parameters

Zinc oxide (ZnO) nanoparticles (NPs) were synthesized hydrothermally and doped with 4% Neodymium (Nd). The produced NPs were characterized using UV–Vis spectroscopy, X-ray diffraction (XRD), Energy dispersive X-ray analysis, Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Thermogravimetric analysis (TGA) and Photoluminescence (PL) spectroscopy. With the addition of 4% Nd, the bandgap reduced from 3.20 to 3.00 eV which confirmed successful doping with Nd which also evident from FTIR study. The XRD study showed hexagonal structure of the synthesized material, while SEM study confirmed that Nd-doped ZnO (Nd–ZnO) NPs are well dispersed as compare to ZnO. TGA study revealed that synthesized NPs were much stable to temperature and only 11.3% and 7.2% the total loss occurred during heating range (40–600 °C) in case of ZnO and Nd–ZnO NPs, respectively. The PL intensity of the visible peaks of ZnO reduced after doping with Nd. The degradation of Acid yellow-3 over both the catalysts followed first-order kinetics. The activation energy calculated for the photodegradation reaction was 43.8 and 33.7 kJ/mol using pure ZnO and Nd–ZnO NPs, respectively. About 91% and 80% dye was degraded at the time interval of 160 min using Nd–ZnO and ZnO NPs, respectively. High percent degradation of dye was found at low concentration (10 ppm) and at optimal dosage (0.035 g) of the catalyst. The rate of Acid yellow-3 dye degradation was found to increase with increase in temperature (up to 50 °C) and pH(8) of the medium. The recyclability study showed that both pure ZnO and Nd–ZnO NPs could be reused for the degradation of the given dye. With the addition of H₂O₂ up to 5 µL, the rate of reaction increased clearly indicating the effect of OH^· generation during photocatalysis. When compared with Nd–ZnO NPs at low concentrations, ZnO NPs at higher concentrations were found to be less hazardous. Both the NPs showed best antibacterial activities against Staphylococcus aureus. The hemolytic study indicated that at low concentration, pure ZnO was non-hemolytic as compared to Nd–ZnO.

     

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.     

Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAM-ResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research.     

Recent progress in the modification of carbon materials and their application in composites for electromagnetic interference shielding

With the development in the modern technologies such as telecommunication instruments and scientific electronic devices, large amount of the electromagnetic radiations are produced, which lead to harmful effect on the highly sensitive electronic devices as well as on the health of human beings. To minimize the effect of electromagnetic radiations produced by different technologies, more efficient shielding materials are required which must be cost-effective, lightweight and good corrosion resistive. In this review, we focused on the shielding materials based on composites of carbon nanotubes and graphene. The typical surface modification of carbon nanotubes and graphene to optimize their interactions with polymers matrix has also summarized. It was found that the composites based on these carbon fillers were more efficient for electromagnetic interference shielding due to their unique properties (i.e., superior electrical, mechanical and thermal) together with lightweight, easy processing. Hence, the carbon nanotubes and graphene-based composites are excellent shielding materials against the electromagnetic radiations.     

Micro-capacitors based on electrochemically grown vertical arrays of gold nanowires as electrodes

This paper describes the development of micro-capacitors with electrodes based on electrochemically grown vertical gold nanowire arrays. A high aspect-ratio anodized aluminum oxide template integrated on silicon dioxide/silicon substrates was exploited for fabricating a vertical array of nanowires with a high surface to volume ratio. Bismuth ferric oxide thin films were deposited to create high dielectric material between the electrodes using room temperature electrodeposition. This nanofabrication process may be compatible with a complementary metal-oxide-semiconductor (CMOS) process, therefore, this capacitor can be used for protecting and regulating the surge voltage biased to the CMOS circuits. This capacitor achieved a high density capacitance of 3.1 μF/m² at 1 MHz, which was measured using a parallel plate set-up.     

Metal–organic frameworks for electronics: emerging second order nonlinear optical and dielectric materials

Metal–organic frameworks (MOFs) have been intensively studied over the past decade because they represent a new category of hybrid inorganic–organic materials with extensive surface areas, ultrahigh porosity, along with the extraordinary tailorability of structure, shape and dimensions. In this highlight, we summarize the current state of MOF research and report on structure–property relationships for nonlinear optical (NLO) and dielectric applications. We focus on the design principles and structural elements needed to develop potential NLO and low dielectric (low-κ) MOFs with an emphasis on enhancing material performance. In addition, we highlight experimental evidence for the design of devices for low-dielectric applications. These results motivate us to develop better low-dielectric and NLO materials and to perform in-depth studies related to deposition techniques, patterning and the mechanical performance of these materials in the future.     

Selective potentiometric determination of uric acid with uricase immobilized on ZnO nanowires

In this study, a potentiometric uric acid biosensor was fabricated by immobilization of uricase onto zinc oxide (ZnO) nanowires. Zinc oxide nanowires with 80-150 nm in diameter and 900 nm to 1.5 μm in lengths were grown on the surface of a gold coated flexible plastic substrate. Uricase was electrostatically immobilized on the surface of well aligned ZnO nanowires resulting in a sensitive, selective, stable and reproducible uric acid biosensor. The potentiometric response of the ZnO sensor vs Ag/AgCl reference electrode was found to be linear over a relatively wide logarithmic concentration range (1-650 μM) suitable for human blood serum. By applying a Nafion^® membrane on the sensor the linear range could be extended to 1-1000 μM at the expense of an increased response time from 6.25 s to less than 9 s. On the other hand the membrane increased the sensor durability considerably. The sensor response was unaffected by normal concentrations of common interferents such as ascorbic acid, glucose, and urea.     

Linguistic frequent pattern mining using a compressed structure

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.