Recognition of coal from other minerals in powder form using terahertz spectroscopy.

Currently a significant fraction of the world energy is still produced from the combustion of mineral coal. The extraction of coal from mines is a relatively complex and dangerous activity that still requires the intervention of human miners, and therefore in order to minimize risks, automation of the coal mining process is desirable. An aspect that is still under investigation is potential techniques that can recognize on-line if the mineral being extracted from the mine is coal or if it is the surrounding rock. In this contribution we present the proof of concept of a method that has potential for recognition of the extraction debris from mining based on their terahertz transmission.

A Complete Feasible and Nodes-Grouped Scheduling Algorithm for Wireless Rechargeable Sensor Networks in Tunnels.

Limited energy in each node is the major design constraint in wireless sensor networks (WSNs), especially in mine tunnel scenario where the WSNs are required to work perpetually. To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. To keep the sensor nodes working perpetually, one fundamental question is how to design the charging scheme. Considering the special tunnel scenario, this paper proposes a Complete Feasible Charging Strategy (CFCS) to ensure the whole WRSNs is working perpetually. We divide the whole WRSN into several subnetworks and use several mobile chargers (MCs) to charge every subnetwork periodically and orderly. For a subnetwork, we formulate the main problem as a charging time distribution problem. A series of theorems are deduced to restrict the charging configurations, and a group nodes mechanism is proposed to expand the scale of the WRSNs. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results demonstrate which of the CFCS boundary theorems is correct and that our proposed CFCS can keep the WRSNs working perpetually. Furthermore, our Nodes-Grouped mechanism can support more nodes in WRSN compared to the state-of-the-art baseline methods.

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform.

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert-Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500-800 and a m range of 50-300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.

Advances in the detection of ß-lactamase: A review.

ß-lactamase, an enzyme secreted by bacteria, is the main resistant mechanism of Gram-negative bacteria to ß-lactam antibiotics. The resistance of bacteria to ß-lactam antibiotics can be evaluated by testing the activity of ß-lactamase. Traditional phenotypic detection is a golden principle, but it is time-consuming. In recent years, many new methods have emerged, which improve the efficiency by virtue of their sensitivity, low cost, easy operation, and other advantages. In this paper, we systematically review these researches and emphasize their limits of detection, sample operation, and test duration. Noteworthily, some detection systems can identify the ß-lactamase subtype conveniently. We mainly divide these tests into three categories to elaborate their characteristics and application status. Both advantages and disadvantages of these methods are discussed. Additionally, we analyze the recent 5 years published researches to predict the trend of development in this field.

Tailored Synthesis of Catalytically Active Cerium Oxide for N, N-Dimethylformamide Oxidation.

Cerium oxide nanopowder (CeOx) was prepared using the sol-gel method for the catalytic oxidation of N, N-dimethylformamide (DMF). The phase, specific surface area, morphology, ionic states, and redox properties of the obtained nanocatalyst were systematically characterized using XRD, BET, TEM, EDS, XPS, H2-TPR, and O2-TPO techniques. The results showed that the catalyst had a good crystal structure and spherelike morphology with the aggregation of uniform small grain size. The catalyst showed the presence of more adsorbed oxygen on the catalyst surface. XPS and H2-TPR have confirmed the reduction of Ce4+ species to Ce3+ species. O2-TPR proved the reoxidability of CeOx, playing a key role during DMF oxidation. The catalyst had a reaction rate of 1.44 mol g-1cat s-1 and apparent activation energy of 33.30 ± 3 kJ mol-1. The catalytic performance showed ~82 ± 2% DMF oxidation at 400 °C. This work's overall results demonstrated that reducing Ce4+ to Ce3+ and increasing the amount of adsorbed oxygen provided more suitable active sites for DMF oxidation. Additionally, the catalyst was thermally stable (~86%) after 100 h time-on-stream DMF conversion, which could be a potential catalyst for industrial applications.

Production of metal oxides nanoparticles based on poly-alanine/chitosan/reduced graphene oxide for photocatalysis degradation, anti-pathogenic bacterial and antioxidant studies.

A novel AgO-CoO-CdO/Poly(alanine)-chitosan-reduced graphene oxide (PACSGO) nanocomposite was developed to study the degradation efficiency under visible light irradiation. The AgO, CoO, CdO nanoparticles and AgO-CoO-CdO heterometal oxides were prepared by using the chemical method. The crystallite structure and phase studies were studied by the X-ray diffraction assay. The SEM images were evaluated to explore the morphology of the prepared materials. EDS analysis and FTIR spectra confirmed the formation of nano-materials with high purity. The optical bandgap values were measured via Kubelka-Munk plot showing that the metal oxides produced a new energy state in the electronic level for high photocatalysis efficiency. The incorporation of AgO-CoO-CdO in PACSGO showed a novel nano-photocatalyst for substantial degradation of dye in low process time. The catalysis data displayed that PACSGO based AgO-CoO-CdO nanocomposites ensured a strong potential to degradation of organic dye compounds from water in during photocatalysis reaction. The beneficial anti-pathogenic bacterial performance of the AgO-CoO-CdO/PACSGO nanocomposites was further demonstrated by a substantial reduction in the amount of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and B. cereus medium and increase in inhibition zone value with the addition of the AgO-CoO-CdO/PACSGO nanocomposites.

Electromagnetic Spectrum Analysis and Its Influence on the Photoelectric Conversion Efficiency of Solar Cells.

The electromagnetic spectrum and the photoelectric conversion efficiency of the silicon hexagonal nanoconical hole (SiHNH) arrays based solar cells is systematically analyzed according to Rigorous Coupled Wave Analysis (RCWA) and Modal Transmission Line (MTL) theory. An ultimate efficiency of the optimized SiHNH arrays based solar cell is up to 31.92% in consideration of the absorption spectrum, 4.52% higher than that of silicon hexagonal nanoconical frustum (SiHNF) arrays. The absorption enhancement of the SiHNH arrays is due to its lower reflectance and more supported guided-mode resonances, and the enhanced ultimate efficiency is insensitive to bottom diameter (D(bot)) of nanoconical hole and the incident angle. The result provides an additional guideline for the nanostructure surface texturing fabrication design for photovoltaic applications.