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Sensing technology drives innovation in digital technology, especially in data acquisition [...]

Currently, heavy metal ion pollution in water is becoming more and more common, especially As (III), which is a serious threat to human health. In this experiment, a glassy carbon electrode modified with Fe3O4/MoS2 nanocomposites was used to select the square wave voltammetry (SWV) electrochemical detection method for the detection of trace As (III) in water. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) showed that Fe3O4 nanoparticles were uniformly attached to the surface of MoS2 and were not easily agglomerated. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) showed that Fe3O4/MoS2 has higher sensitivity and conductivity. After optimizing the experimental conditions, the Fe3O4/MoS2-modified glassy carbon electrode exhibited high sensitivity (3.67 μA/ppb) and a low detection limit (0.70 ppb), as well as excellent interference resistance and stability for As (III).

Gamma rays from HESS J1849−000, a middle-aged TeV pulsar wind nebula (PWN), are observed by the Tibet air shower array and the muon detector array. The detection significance of gamma rays reaches 4.0 σ and 4.4 σ levels above 25 TeV and 100 TeV, respectively, in units of the Gaussian standard deviation σ . The energy spectrum measured between 40 TeV < E < 320 TeV for the first time is described with a simple power-law function of ${dN}/{dE}={(2.86\pm 1.44)\times {10}^{-16}(E/40\,\mathrm{TeV})}^{-2.24\pm 0.41}\,{\mathrm{TeV}}^{-1}\,{\mathrm{cm}}^{-2}\,{{\rm{s}}}^{-1}$ . The gamma-ray energy spectrum from the sub-TeV ( E < 1 TeV) to sub-PeV (100 TeV < E < 1 PeV) ranges, including the results of previous studies, can be modeled with the leptonic scenario, i.e., inverse Compton scattering by high-energy electrons accelerated by the PWN of PSR J1849−0001. On the other hand, the gamma-ray energy spectrum can also be modeled with the hadronic scenario in which gamma rays are generated from the decay of neutral pions produced by collisions between accelerated cosmic-ray protons and the ambient molecular cloud found in the gamma-ray-emitting region. The cutoff energy of cosmic-ray protons E _p,cut is estimated as ${\mathrm{log}}_{10}({E}_{{\rm{p}},\mathrm{cut}}/\mathrm{TeV})={3.73}_{-0.66}^{+2.98}$ , suggesting that protons are accelerated up to the PeV energy range. Our study thus proposes that HESS J1849−000 should be further investigated as a new candidate as a Galactic PeV cosmic-ray accelerator, or “PeVatron.”

Abstract Raman spectrum, as a kind of scattering spectrum, has been widely used in many fields because it can characterize the special properties of materials. However, Raman signal is so weak that the noise distorts the real signals seriously. Polynomial fitting has been proved to be the most convenient and simplest method for baseline correction. It is hard to choose the order of polynomial because it may be so high that Runge phenomenon appears or so low that inaccuracy fitting happens. This paper proposes an improved approach for baseline correction, namely the piecewise polynomial fitting (PPF). The spectral data are segmented, and then the proper orders are fitted, respectively. The iterative optimization method is used to eliminate discontinuities between piecewise points. The experimental results demonstrate that this approach improves the fitting accuracy.

Arsenic is extremely abundant in the Earth’s crust and is one of the most common environmental pollutants in nature. In the natural water environment and surface soil, arsenic exists mainly in the form of trivalent arsenite (As(III)) and pentavalent arsenate (As(V)) ions, and its toxicity can be a serious threat to human health. In order to manage the increasingly serious arsenic pollution in the living environment and maintain a healthy and beautiful ecosystem for human beings, it is urgent to conduct research on an efficient sensing method suitable for the detection of As(III) ions. Electrochemical sensing has the advantages of simple instrumentation, high sensitivity, good selectivity, portability, and the ability to be analyzed on site. This paper reviews various electrode systems developed in recent years based on nanomaterials such as noble metals, bimetals, other metals and their compounds, carbon nano, and biomolecules, with a focus on electrodes modified with noble metal and metal compound nanomaterials, and evaluates their performance for the detection of arsenic. They have great potential for achieving the rapid detection of arsenic due to their excellent sensitivity and strong interference immunity. In addition, this paper discusses the relatively rare application of silicon and its compounds as well as novel polymers in achieving arsenic detection, which provides new ideas for investigating novel nanomaterial sensing. We hope that this review will further advance the research progress of high-performance arsenic sensors based on novel nanomaterials.

To achieve low cost and compactness, small dc capacitance in dc-link is preferred in the cascaded H-bridge (CHB) static synchronous compensator (STATCOM). However, small dc capacitance will definitely result in high dc-link ripples among three clusters, and thus cause severe third-order harmonic currents under the asymmetric grid. In this study, to mitigate the third-order harmonic currents, a selective component compensation approach for the CHB STATCOM is proposed. To study the effect of the dc-link ripples on the phase currents, sequence component decomposition is employed to derive the relationship between the dc-link ripples and the converter voltages. The relationship reveals that the converter voltages contain positive, negative, and zero sequence components in both fundamental and third-order under the asymmetric grid, while only the positive and negative sequence components in third-order converter voltages result in third-order harmonic currents due to the three-wire system. Therefore, the third-order harmonic currents suppression method is proposed by only compensating the positive and negative components in third-order converter voltages. A 380 V/15 kVA STATCOM prototype is built to verify the proposed control method.

Nowadays, heavy metal ion pollution in water is becoming more and more common, especially arsenic, which seriously threatens human health. In this work, we used Fe3O4–rGO nanocomposites to modify a glassy carbon electrode and selected square wave voltametric electrochemical detection methods to detect trace amounts of arsenic in water. Field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) showed that Fe3O4 nanoparticles were uniformly distributed on the rGO sheet, with a particle size of about 20 nm. Raman spectroscopy and electrochemical impedance spectroscopy (EIS) showed that rGO provides higher sensitivity and conductive substrates. Under optimized experimental conditions, Fe3O4–rGO-modified glassy carbon electrodes showed a higher sensitivity (2.15 µA/ppb) and lower limit of detection (1.19 ppb) for arsenic. They also showed good selectivity, stability, and repeatability.

Detecting the defects of a battery on the surface and edge has always been difficult, especially for concave and convex ones, thereby seriously affecting its quality. Thus, sub-regional Gaussian and moving average filtering are innovatively proposed in this study considering the effect of the nonuniform background illumination of the battery edge and the difference between the edge background and the internal surface defects of the battery. The battery surface image is divided into two areas, namely, edge area W 1 and inner area W 2 . Gaussian and moving average filtering are carried out row-by-row and column-by-column in the inner area W 2 and the edge area W 1 , respectively. The algorithm is tested on 600 battery samples that mainly possess concave and convex defects. The proposed method has higher detection accuracy and lower omission detection rate than the traditional unpartitioned processing method, especially in detecting the accuracy of edge defects. The accuracy rates were approximately 20% higher than that obtained by the traditional processing algorithm. The proposed method has remarkable real-time performance that can process four 8192 × 10,240 pixel battery images per second, thereby meeting the industrial production line speed requirements while satisfying accuracy. The proposed method has been applied in actual production for defect inspection.

We present a hybrid electromagnetic generator (EMG) and triboelectric nanogenerator (TENG) using a multi-impact approach for broad-bandwidth-frequency (10–45 Hz) energy harvesting. The TENG and the EMG were located at the middle and the free end of the cantilever beam, respectively. When the system was subjected to an external vibration, the cantilever beam would be in a nonlinear response with multiple impacts from a low frequency oscillator. The mathematical model included a TENG oscillator which can have multiple impacts on the cantilever, and the nonlinear Lorenz force which comes from the motion of the coil in the electromagnetic field. Due to the strong nonlinearity of the impacts from the TENG oscillator and the limited space for the free tip of the cantilever, the dynamic response of the cantilever presented a much broader bandwidth, with a frequency range from 10–45 Hz. We also found that the average generated power from TENG and EMG can reach up to 30 μW/m2 and 53 μW, respectively. Moreover, the dynamic responses of the hybrid EMG and TENG were carefully analyzed, and we found that the measured experimental results and the numerical simulations results were in good agreement.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a severe lung condition, and finding better ways to diagnose and treat the disease is crucial for improving patient outcomes. Our study sought to develop an artificial neural network (ANN) model for IPF and determine the immune cell types that differed between the IPF and control groups. Methods From the Gene Expression Omnibus (GEO) database, we first obtained IPF microarray datasets. To conduct protein-protein interaction (PPI) networks and enrichment analyses, differentially expressed genes (DEGs) were screened between tissues of patients with IPF and tissues of controls. Afterward, we identified the important feature genes associated with IPF using random forest (RF) analysis, and then constructed and validated a prediction ANN mode. In addition, the proportions of immune cells were quantified using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, which was performed on microarray datasets based on gene expression profiling. Results A total of 11 downregulated and 36 upregulated DEGs were identified. PPI networks and enrichment analyses were carried out; the immune system and extracellular matrix were the subjects of the enrichments. Using RF analysis, the significant feature genes LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were identified. The nine feature gene scores were integrated into the ANN to develop a diagnostic prediction model. The receiver operating characteristic (ROC) curves demonstrated the strong diagnostic ability of the ANN in predicting IPF in the training and testing sets. An analysis of IPF tissues in comparison to normal tissues revealed a reduction in the infiltration of natural killer cells resting, monocytes, macrophages M0, and neutrophils; conversely, the infiltration of T cells CD4 memory resting, mast cells, and macrophages M0 increased. Conclusion LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were determined as key feature genes for IPF. The nine feature genes in the ANN model will be extremely important for diagnosing IPF. It may be possible to use differentiated immune cells from IPF samples in comparison to normal samples as targets for immunotherapy in patients with IPF.