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.
Abstract: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/m 2 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.
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.
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.
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