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Introduction
Published in T. R. Crompton, Determination of Metals and Anions in Soils, Sediments and Sludges, 2020
The problem in detecting atoms in the ng L−1 or sub-μg L−1 level is basically one of being able to obtain a signal which can be clearly distinguished from the background. The detection limit being given typically as the signal which is equivalent to three times the standard deviation of the background counts for a given unit of time. In energy-dispersive X-ray fluorescence spectrometry the background is essentially caused by interactions of radiation with matter resulting from an intense flux of elastic and Compton scattered photons. The background especially in the low-energy region (0–20 keV) is due in the main to Compton scattering of high-energy Bremsstrahlung photons from the detector crystal itself. In addition, impurities on the specimen support will contribute to the background. The Auger effect does not contribute to an increased background, as the emitted electrons, of different but low energy, are absorbed either in the beryllium foil of the detector entrance windows or in the air path of the spectrometer.
Amperometric Enzyme Electrodes for Substrate and Enzyme Activity Determinations
Published in Loïc J. Blum, Pierre R. Coulet, Biosensor Principles and Applications, 2019
Gilbert Bardeletti, Florence Séchaud, Pierre R. Coulet
The detection limit is defined as the lowest analyte concentration that can be detected with an acceptable signal-to-noise ratio. With a Clark electrode the lowest limit of detection for oxygen is about 0.5 ppm (mg O2/L) for a usually air-saturated solution corresponding to about 8 ppm at room temperature (25°C) and normal pressure (1 atm) (42). With an hydrogen peroxide sensor as previously described the detection limit for H2O2 is in the nanomolar range.
Laboratory Analytical Methods and Data Interpretation
Published in Rong Yue, Fundamentals of Environmental Site Assessment and Remediation, 2018
Now, the concept of detection limit needs to be introduced. From the perspective of site assessment, one would like to know what kinds of compounds are present at the site and at what levels. The detection limit indicates the lowest concentration that this analytical method is able to detect. The detection limit is defined as follows.
Properties of Na2O–SiO2 slags in Doré smelting
Published in Mineral Processing and Extractive Metallurgy Review, 2018
K. Avarmaa, H. O’Brien, M. Valkama, L. Klemettinen, E. Niemi, P. Taskinen
The EPMA analyses were done for selected elements in the alloy (Ag, Au, Cu, Pd, Rh, and Te) and slag (Ag, Ba, Cu, Mg, Na, Si, and Te). The analysis of each sample comprised 10 analysis points from both the alloy and slag, giving the mean and standard deviation for each observation. Analysis areas from the slag were chosen from well-quenched, homogeneous regions avoiding segregations. The Pouchou and Pichoir (1986) ZAF matrix correction procedure was applied for the raw EPMA results, the uncertainty (relative standard deviation) of which is estimated to be ≤2.5% (Lavrent’ev et al. 2004). The detection limit is the lowest concentration of an element present that is statistically above the background continuum level by 3σ (3× standard deviation). In this study, the detection limits were calculated using the method described by Ziebold (1967). The average detection limits for the slag determined by EPMA software were Ba 790 ppm, Cu 380 ppm, Mg 300 ppm, Na 380 ppm, Si 350 ppm, and Te 750 ppm. For precious and platinum group metals in the silver alloy were Ag 630 ppm, Au 5440 ppm, Pd 720, and Rh 450 ppm. Oxygen was also measured in the samples, with the detection limit of 2350 ppm in the slag. Pure metals were used as standards for Ag, Au, Cu, Pd, Pt, and Rh. Synthetic minerals were used for the elements in the slag and Sb2Te3 for Te.
The unfulfilled promise of urban Lake Kleine Melanen (The Netherlands): Diagnostics, experiment on reduction of sediment P-release and in-lake restoration
Published in Lake and Reservoir Management, 2018
Guido Waajen, Miquel Lürling, René van de Sande
Water quality variables (TP, OP, TN, Chl-a, cyanobacterial Chl-a, SDT, O2) of the lake were analyzed with data lumped for the periods before (Jan 2008–Aug 2010; cyanobacterial Chl-a Aug 2007–Jul 2010), during (Sep 2010–Oct 2012; except for cyanobacterial Chl-a), and after (Nov 2012–Oct 2014) the whole-lake measures. As data for ANOVA were violated, the 3 groups for each variable were compared by nonparametric Kruskal–Wallis Test (P < 0.05) and post hoc analyses by pairwise comparisons using Dunn’s (1964) procedure (SigmaPlot). When the concentration was below the detection limit, the value of half the detection limit was used in data analysis.