scholarly journals Portable X-ray fluorescence (pXRF) calibration for analysis of nutrient concentrations and trace element contaminants in fertilisers

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262460
Author(s):  
Gifty E. Acquah ◽  
Javier Hernandez-Allica ◽  
Cathy L. Thomas ◽  
Sarah J. Dunham ◽  
Erick K. Towett ◽  
...  
Keyword(s):  
Trace Elements ◽  
Trace Element ◽  
Nutrient Concentrations ◽  
Good Prediction ◽  
X Ray ◽  
Accuracy And Precision ◽  
Wide Range ◽  
High Throughput Detection ◽  
Characterisation Methods

With the increasing popularity of local blending of fertilisers, the fertiliser industry faces issues regarding quality control and fertiliser adulteration. Another problem is the contamination of fertilisers with trace elements that have been shown to subsequently accumulate in the soil and be taken up by plants, posing a danger to the environment and human health. Conventional characterisation methods necessary to ensure the quality of fertilisers and to comply with local regulations are costly, time consuming and sometimes not even accessible. Alternatively, using a wide range of unamended and intentionally amended fertilisers this study developed empirical calibrations for a portable handheld X-ray fluorescence (pXRF) spectrometer, determined the reliability for estimating the macro and micro nutrients and evaluated the use of the pXRF for the high-throughput detection of trace element contaminants in fertilisers. The models developed using pXRF for Mg, P, S, K, Ca, Mn, Fe, Zn and Mo had R2 values greater or equal to 0.97. These models also performed well on validation, with R2 values greater or equal to 0.97 (except for Fe, R2val = 0.55) and slope values ranging from 0.81 to 1.44. A second set of models were developed with a focus on trace elements in amended fertilisers. The R2 values of calibration for Co, Ni, As, Se, Cd and Pb were greater than or equal to 0.80. At concentrations up to 1000 mg kg-1, good validation statistics were also obtained; R2 values ranged from 0.97–0.99, except in one instance. The regression coefficients of the validation also had good prediction in the range of 0–100 mg kg-1 (R2 values were from 0.78–0.99), but not as well at lower concentrations up to 20 mg kg-1 (R2 values ranged from 0.10–0.99), especially for Cd. This study has demonstrated that pXRF can measure several major (P, Ca) and micro (Mn, Fe, Cu) nutrients, as well as trace elements and potential contaminants (Cr, Ni, As) in fertilisers with high accuracy and precision. The results obtained in this study is good, especially considering that loose powders were scanned for a maximum of 90 seconds without the use of a vacuum pump.

Counterfeit Parts Recognition and Detection for Failure Analysts

2010 ◽  
Author(s):  
Katherine V. Whittington
Keyword(s):  
Thermal Cycle ◽  
Visual Inspection ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy ◽  
3D Measurement ◽  
X Ray ◽  
Wide Range ◽  
Electronic Parts ◽  
Testing Awareness

Abstract The electronics supply chain is being increasingly infiltrated by non-authentic, counterfeit electronic parts, whose use poses a great risk to the integrity and quality of critical hardware. There is a wide range of counterfeit parts such as leads and body molds. The failure analyst has many tools that can be used to investigate counterfeit parts. The key is to follow an investigative path that makes sense for each scenario. External visual inspection is called for whenever the source of supply is questionable. Other methods include use of solvents, 3D measurement, X-ray fluorescence, C-mode scanning acoustic microscopy, thermal cycle testing, burn-in technique, and electrical testing. Awareness, vigilance, and effective investigations are the best defense against the threat of counterfeit parts.

scholarly journals Multivariate Statistical Analysis of Trace Elements in Pyrite: Prediction, Bias and Artefacts in Defining Mineral Signatures

Minerals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 61 ◽  
Author(s):  
Marija Dmitrijeva ◽  
Nigel J. Cook ◽  
Kathy Ehrig ◽  
Cristiana L. Ciobanu ◽  
Andrew V. Metcalfe ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Analysis ◽  
Trace Element ◽  
South Australia ◽  
Large Datasets ◽  
Ore Deposits ◽  
Partial Replacement ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Olympic Dam

Pyrite is the most common sulphide in a wide range of ore deposits and well known to host numerous trace elements, with implications for recovery of valuable metals and for generation of clean concentrates. Trace element signatures of pyrite are also widely used to understand ore-forming processes. Pyrite is an important component of the Olympic Dam Cu–U–Au–Ag orebody, South Australia. Using a multivariate statistical approach applied to a large trace element dataset derived from analysis of random pyrite grains, trace element signatures in Olympic Dam pyrite are assessed. Pyrite is characterised by: (i) a Ag–Bi–Pb signature predicting inclusions of tellurides (as PC1); and (ii) highly variable Co–Ni ratios likely representing an oscillatory zonation pattern in pyrite (as PC2). Pyrite is a major host for As, Co and probably also Ni. These three elements do not correlate well at the grain-scale, indicating high variability in zonation patterns. Arsenic is not, however, a good predictor for invisible Au at Olympic Dam. Most pyrites contain only negligible Au, suggesting that invisible gold in pyrite is not commonplace within the deposit. A minority of pyrite grains analysed do, however, contain Au which correlates with Ag, Bi and Te. The results are interpreted to reflect not only primary patterns but also the effects of multi-stage overprinting, including cycles of partial replacement and recrystallisation. The latter may have caused element release from the pyrite lattice and entrapment as mineral inclusions, as widely observed for other ore and gangue minerals within the deposit. Results also show the critical impact on predictive interpretations made from statistical analysis of large datasets containing a large percentage of left-censored values (i.e., those falling below the minimum limits of detection). The treatment of such values in large datasets is critical as the number of these values impacts on the cluster results. Trimming of datasets to eliminate artefacts introduced by left-censored data should be performed with caution lest bias be unintentionally introduced. The practice may, however, reveal meaningful correlations that might be diluted using the complete dataset.

Multipoint Background Analysis: Gaining Precision and Accuracy in Microprobe Trace Element Analysis

2019 ◽  
Vol 25 (1) ◽  
pp. 30-46 ◽  
Author(s):  
Julien M. Allaz ◽  
Michael L. Williams ◽  
Michael J. Jercinovic ◽  
Karsten Goemann ◽  
John Donovan
Keyword(s):  
Mass Spectrometry ◽  
Trace Elements ◽  
Trace Element ◽  
Spectral Region ◽  
Trace Element Analysis ◽  
Background Intensity ◽  
Element Analysis ◽  
Accuracy And Precision ◽  
Background Measurement ◽  
Background Curvature

AbstractElectron microprobe trace element analysis is a significant challenge. Due to the low net intensity of peak measurements, the accuracy and precision of such analyses relies critically on background measurements, and on the accuracy of any pertinent peak interference corrections. A linear regression between two points selected at appropriate background positions is a classical approach for electron probe microanalysis (EPMA). However, this approach neglects the accurate assessment of background curvature (exponential or polynomial), and the presence of background interferences, a hole in the background, or an absorption edge can dramatically affect the results if underestimated or ignored. The acquisition of a quantitative wavelength-dispersive spectrometry (WDS) scan over the spectral region of interest remains a reasonable option to determine the background intensity and curvature from a fitted regression of background portions of the scan, but this technique can be time consuming and retains an element of subjectivity, as the analyst has to select areas in the scan which appear to represent background. This paper presents a new multi-point background (MPB) method whereby the background intensity is determined from up to 24 background measurements from wavelength positions on either side of analytical lines. This method improves the accuracy and precision of trace element analysis in a complex matrix through careful regression of the background shape, and can be used to characterize the background over a large spectral region covering several elements to be analyzed. The overall efficiency improves as systematic WDS scanning is not required to assess background interferences. The method is less subjective compared to methods that rely on WDS scanning, including selection of two interpolation points based on WDS scans, because “true” backgrounds are selected through an exclusion method of possible erroneous backgrounds. The first validation of the MPB method involves blank testing to ensure the method can accurately measure the absence of an element. The second validation involves the analysis of U-Th-Pb in several monazite reference materials of known isotopic age. The impetus for the MPB method came from efforts to refine EPMA monazite U-Th-Pb dating, where it was recognized that background errors resulting from interference or strong background curvature could result in errors of several tens of millions of years on the calculated date. Results obtained on monazite reference materials using two different microprobes, a Cameca SX-100 Ultrachron and a JEOL JXA-8230, yield excellent agreement with ages obtained by isotopic methods (Thermal Ionization Mass Spectrometry [TIMS], Sensitive High-Resolution Ion MicroProbe [SHRIMP], or Secondary Ion Mass Spectrometry [SIMS]). Finally, the MPB method can be used to model the background over a large spectrometer range to improve the accuracy of background measurement of minor and trace elements acquired on a same spectrometer, a method called the shared background measurement. This latter significantly improves the accuracy of minor and trace element analysis in complex matrices, as demonstrated by the analysis of Rare Earth Elements (REE) in REE-silicates and phosphates and of trace elements in scheelite.

Direct Comparison of (Anthropometric) Methods for the Assessment of Body Composition

2020 ◽  
Vol 76 (3) ◽  
pp. 183-192
Author(s):  
Petra Golja ◽  
Tatjana Robič Pikel ◽  
Katja Zdešar Kotnik ◽  
Matjaž Fležar ◽  
Samo Selak ◽  
...  
Keyword(s):  
Body Composition ◽  
Healthy Subjects ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Regression Equations ◽  
X Ray ◽  
Accuracy And Precision ◽  
Wide Range ◽  
Assess Body Composition ◽  
Extensive List

Objective: Several methods for the assessment of body composition exist, yet they yield different results. The present study aimed to assess the extent of these differences on a sample of young, healthy subjects. We hypothesised that differences in body composition results obtained with different methods will vary to the extent that a subject can be misclassified into different nutritional categories. Research Methods and Procedures: Underwater weighing (UWW), bioelectrical impedance analysis (BIA), anthropometry (ANT), and dual-energy X-ray absorptiometry (DXA) were used to assess body composition. An extensive list of ANT regression equations (or sets of equations) was analysed in terms of accuracy and precision relative to DXA. Results: When DXA-determined body fat (BF) values were taken as a reference, UWW overestimated BF in both genders. In contrast, BIA (measured with a given bioimpedance analyser) underestimated BF in females, although BIA-determined BF did not differ from DXA in males. A huge difference in BF estimates (8–29% for females and 6–29% for males, for DXA-determined BF of 25.5% and 13.9% for females in males, respectively) was observed across a number of ANT regression equations; yet, ANT proved not to be inferior to DXA, provided that regression equations with the highest combinations of accuracy and precision were chosen. Conclusions: The study proved grounds for comparison of body composition results of young, healthy subjects, obtained with different methods and across a wide range of ANT regression equations. It also revealed a list of the most appropriate ANT regression equations for the selected sample and reported their accuracy and precision.

DETERMINATION OF TRACE ELEMENTS IN HUMAN SALIVA BY PIXE TECHNIQUE

1996 ◽  
Vol 06 (01n02) ◽  
pp. 273-276 ◽  
Author(s):  
P. HATAMI ◽  
H. PEYROVAN ◽  
H. AFARIDEH ◽  
S. SHOJAEI
Keyword(s):  
Trace Elements ◽  
Healthy Volunteers ◽  
Trace Element ◽  
Element Concentration ◽  
Trace Element Concentration ◽  
Human Saliva ◽  
Normal Range ◽  
Pixe Analysis ◽  
X Ray

Mineral trace element concentration of human saliva from healthy volunteers, were investigated by Proton-Induced X-ray Emission (PIXE) analysis. The specimens were bombarded with Proton beams of 2 MeV from a Van de Graaff accelerator at NRC . The object of this measurement was to find a normal range of trace element concentration for Iranian people.

Deconvolution of the composition of fine-grained pyrite in sedimentary matrix by regression of time-resolved LA-ICP-MS data

2020 ◽  
Vol 105 (6) ◽  
pp. 820-832 ◽  
Author(s):  
Aleksandr S. Stepanov ◽  
Leonid V. Danyushevsky ◽  
Ross R. Large ◽  
Indrani Mukherjee ◽  
Irina A. Zhukova
Keyword(s):  
Trace Elements ◽  
Regression Analysis ◽  
Trace Element ◽  
Sedimentary Rocks ◽  
Trace Element Composition ◽  
Element Composition ◽  
Silicate Matrix ◽  
Time Resolved ◽  
Icp Ms ◽  
Wide Range

Abstract Pyrite is a common mineral in sedimentary rocks and is the major host for many chalcophile trace elements utilized as important tracers of the evolution of the ancient hydrosphere. Measurement of trace element composition of pyrite in sedimentary rocks is challenging due to fine-grain size and intergrowth with silicate matrix and other sulfide minerals. In this contribution, we describe a method for calculation of trace element composition of sedimentary pyrite from time-resolved LA-ICP-MS data. The method involves an analysis of both pyrite and pyrite-free sediment matrix, segmentation of LA-ICP-MS spectra, normalization to total, regression analysis of dependencies between the elements, and calculation of normalized composition of the mineral. Sulfur is chosen as an explanatory variable, relative to which all regressions are calculated. The S content value used for calculation of element concentrations from the regressions is calculated from the total, eliminating the need for independent constraints. The algorithm allows efficient measurement of concentrations of multiple chalcophile trace elements in pyrite in a wide range of samples, including quantification of detection limits and uncertainties while excluding operator bias. The data suggest that the main sources of uncertainties in pyrite composition are sample heterogeneity and counting statistics for elements of low abundance. The analysis of regression data of time-resolved LA-ICP-MS measurements could provide new insights into the geochemistry of the sedimentary rocks and minerals. It allows quantification of ratios of elements that do not have reference material available (such as Hg) and provides estimates on the content of non-sulfidic Fe in the silicate matrix. Regression analysis of the mixed LA-ICP-MS signal could be a powerful technique for deconvolution of phase compositions in complex multicomponent samples.

PIXE ANALYSIS OF TRACE ELEMENTS IN CLAM SHELLS MARKED WITH IRON RUSTING

1996 ◽  
Vol 06 (03n04) ◽  
pp. 517-522 ◽  
Author(s):  
YOSHINORI KOSHIKAWA ◽  
NOBUAKI ARAI ◽  
WATARU SAKAMOTO ◽  
KOJI YOSHIDA
Keyword(s):  
Trace Elements ◽  
Trace Element ◽  
Intensity Ratio ◽  
Ruditapes Philippinarum ◽  
Pixe Analysis ◽  
X Ray ◽  
Elemental Concentrations ◽  
Element Concentrations ◽  
Pixe Method

Trace element concentrations in short necked clam Ruditapes philippinarum marked shells with iron rusting were determined by particle induced X-ray emission (PIXE) method. Element such as Ca, Mn, Fe, Zn, Sr, and Br were detected in the shells. The Fe/Ca X-ray intensity ratio decreased exponentially on the day after marking. It was concluded that the higher Fe concentration on marked clams was caused by attached iron rusting. The concentrations of Fe, Br, and Sr differed among the 3 stations (Kamaya, Shigaki, and Iwatani), suggesting that elemental concentrations may be related to the growth of clams.

scholarly journals TRACE ELEMENT ANALYSIS OF TROPICAL WOODS USING PARTICLE INDUCED X–RAY EMISSION (PIXE) METHODS FROM WESTERN NIGERIA

Wood Research ◽  
2021 ◽  
Vol 66 (4) ◽  
pp. 595-605
Author(s):  
ONUMEJOR CHARITY ADAEZE ◽  
BALOGUN FATAI AKINTUNDE ◽  
SEJLO TEMIDAYO GBENU ◽  
MOJISOLA RACHAEL USIKALU ◽  
THEOPHILUS AANUOLUWA ADAGUNODO ◽  
...  
Keyword(s):  
Trace Elements ◽  
Trace Element ◽  
Calcium Concentration ◽  
Concentration Level ◽  
Trace Element Analysis ◽  
Health Concern ◽  
Element Analysis ◽  
X Ray ◽  
Tropical Woods

Trace element investigation and its corresponding concentration level in selected tropical woods from western Nigeria was done using PIXE-particle induced X-ray emission methods. Fifteen selected tropical woods were analyzed and twenty-seventrace elements were identified and quantified. The identified trace elements are Na,Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Se, Br, Rb, Sr, Y, Zr, Ba, Pb and Bi. Calcium concentration were2835, 3195, 4923, 5608, 7770, 5110, 2743, 5092 and 3451ppm in samples 2, 5, 6, 10, 11, 12, 13, 14 and 15, respectively. Potassium recorded2838, 4811, 3184, and 2021ppm in samples 1, 3, 8 and 9, respectively. Silicon recorded5206ppm for sample 4 and 5253ppm for sample 7. Calcium and potassium were observed to have concentration level that is greater than 1000ppm in all the studied samples, hence it can be said that calcium and potassium are major trace element of wood. The concentrations of the elements identified have no immediate health concern on environment and human, therefore the studied tropical woodssafe for use as fuel and other purposes.

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