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Acoustic Sensors
Published in J. David, N. Cheeke, Fundamentals and Applications of Ultrasonic Waves, 2017
Acoustic microsensors may be configured as one- or two-port devices. A one-port device, which is active, contains a feedback loop that converts the device into an oscillator. The external perturbation is then manifested as a frequency shift, as shown for a gas sensor in Figure 16.1. The one-port device has the advantage of simplicity, but some information is lost. This situation is rectified in the two-port passive device, which has an input and output. In this case, amplitude and phase can be measured, but the disadvantage is that extra, bulky instrumentation is needed to convert this into a practical sensor. For this reason, most practical sensors are configured in the oscillator mode. Figure 16.1 shows the response curve of a typical gas sensor. Some of the more important parameters characterizing the sensor can be appreciated from this figure. It is desirable to operate within the linear range for simplicity although a nonlinear response could be handled by adding a look-up calibration table. The resolution of the sensor, the smallest signal that can be measured, is specified by the minimum detectable mass (MDM); it is often determined by the electrical noise in the measuring system. It is essential to distinguish the resolution from the sensitivity; the latter is proportional to the slope of Figure 16.1. The sensitivity of acoustic microsensors will be treated in detail later in this chapter. Finally, parameters such as reversibility and cyclability will be important practical considerations.
Chemical and elemental analysis of the edible fruit of five Carpobrotus species from South Africa: assessment of nutritional value and potential metal toxicity
Published in International Journal of Environmental Health Research, 2020
Neal Keith Broomhead, Roshila Moodley, Sreekantha Babu Jonnalagadda
The linear range (including the limit of quantitation {LOQ}) and limit of detection (LOD) of each of the elements are presented in Table 3. The R-squared coefficient of linearity of the calibrations was greater than 0.995 within the linear range for all the elements. The accuracy of the analytical method was assessed by comparing mean experimental values of the CRM (white clover, BCR-402) with certified values and acceptable ranges (within ± 5% of the certified value). Precision was assessed by comparing the per cent relative standard deviation (% RSD) of the CRM experimental results against the acceptable limit of RSD (<5%). The elements reported were Cr, Fe, Ni and Zn (Table 4). The mean experimental values of the CRM fell within the acceptable ranges and the corresponding % RSDs were below the acceptable limits for all the elements reported (Table 4). The results confirm the accuracy and precision of the analytical method.
A Review of CMOS Variable Gain Amplifiers and Programmable Gain Amplifiers
Published in IETE Technical Review, 2019
Chunfeng Bai, Jianhui Wu, Xiaoying Deng
Both (11) and (12) approximate exponential function when , and deviate significantly otherwise. The second-order Taylor approximation provides only 12-dB linear range for a linearity error of less than -dB [40]. In contrast, the pseudo-exponential function shown in (12) exhibits a linear range of 15-dB even though only first-order polynomial is involved. Furthermore, it is easily compatible to the source-coupled pair with diode-connected load, as shown in Figure 10. When a differential control voltage signal is applied on and, a dB-linear VGA is obtained [40,43–44]. The gain can be given by (13), where.
Removal of five fluoroquinolone antibiotics during broiler manure composting
Published in Environmental Technology, 2018
Bing Yang, Lei Meng, Nandong Xue
The HPLC was calibrated with each analyte by means of a five-point calibration curve. The calibration standards were dissolved in methanol/water (1:3, V/V) with 0.1% formic acid. Linear regression with a weighting factor of 1/x was used for the calibration of all analytes. Linearity was tested at concentrations ranging from its instrumental detection limit (IQL) to 500 ng/mL for each analyte. Data collection, peak integration, and linear regression were performed using Labsolutions version 5.54 SP5 software from Shimadzu International Trading Co., Ltd. (Shanghai, China). Recovery was evaluated in five replicates by spiking 100 μL of 2 mg/L native standard mixtures into blank compost samples. Linear range, linearity, recovery, and method detection and quantification limits (MDL and MQL) for the target analytes are presented in Table 2. Excellent linearity was achieved for each analyte calibration (R2 = 0.9997–0.9999, p < .001). Analyte recovery in compost sample was in the range of 79.2–109.8%, with relative standard deviations (%RSD) across all analytes of below 15% (2.3–14.4%), which falls in the USEPA recommendations (Bialk-Bielinska et al. 2009). The MDL ranged from 4.3 to 20 μg/kg DW, whereas the MQL from 14 to 66 μg/kg DW.