A Comprehensive Study of Three Different Portable XRF Scanners to Assess the Soil Geochemistry of An Extensive Sample Dataset

Declercq, Ynse; Delbecque, Nicole; Grave, Johan De; Smedt, Philippe De; Finke, Peter; Mouazen, Abdul Mounem; Nawar, Said; Vandenberghe, Dimitri; Meirvenne, Marc Van; Verdoodt, Ann

doi:10.3390/rs11212490

Cited by 31 publications

(21 citation statements)

References 33 publications

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“…For instance, ICP requires the complete digestion of samples using a proper medium which is usually a highly corrosive solution, for example, HF 44,45 . In the case of XRF, it suffers from poor detection limits of light ( Z < 16, Z : atomic number) elements, 46 and quantitative analysis using EDX is known to provide inaccurate data for the samples having multiphase(s) with common elements.…”

Section: Resultsmentioning

confidence: 99%

Hydrothermal synthesis of potassium–sodium niobate powders

et al. 2022

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Potassium–sodium niobates (KxNa1−xNbO3, 0 < x < 1, KNN) were hydrothermally synthesized under varying alkaline ratios (K+/Na+), total hydroxide concentration, reaction temperature, and time. Compositional surveys were developed by using Rietveld analyses derived quantitative volume fractions. The data demonstrated that phase pure KNN synthesis can be achieved by reacting the niobium source with the hydroxide solution having 6 M total hydroxide concentration, cation ratio (K+/Na+) of above 6 at temperatures ≥200°C for 24 h. Dissolution–precipitation events through intermediate products including hexaniobates were postulated as a plausible formation mechanism. It was shown also that the single‐phase KNN approaching the morphotropic phase boundary (MPB) could be obtained by further incorporation of sodium ions into the crystal via post‐annealing at 800°C/2 h, following the hydrothermal synthesis.

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Section: Resultsmentioning

confidence: 99%

Hydrothermal synthesis of potassium–sodium niobate powders

et al. 2022

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“…It may be that the pXRF systematically overestimates S content of the samples and further calibration should have been applied. Systematic overestimation of S (along with P, Cu, and Mg) by pXRF has been observed with field analysis of soils [30]. However, Roman Alday et al [16] found that for samples from the Elatsite porphyry-type deposit, pXRF overestimates Fe and K, and underestimates Mg compared to more intensive laboratory methods.…”

Section: Causes Of Poor Correlationmentioning

confidence: 99%

A Comparative Study of Porphyry-Type Copper Deposit Mineralogies by Portable X-ray Fluorescence and Optical Petrography

Gray

Rythoven

2020

Minerals

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Porphyry-type deposits are crucial reserves of Cu and Mo. They are associated with large haloes of hydrothermal alteration that host particular mineral assemblages. Portable X-ray fluorescence analysis (pXRF) is an increasingly common tool used by mineral prospectors to make judgments in the field during mapping or core logging. A total of 31 samples from 13 porphyry copper deposits of the Western Cordillera were examined. Whole-rock composition was estimated over three points of analysis by pXRF. This approach attempts to capture the rapid and sometimes haphazard application of pXRF in mineral exploration. Modes determined by optical petrography were converted into bulk rock compositions and compared with those determined by pXRF. The elements S, Si, Ca, and K all were underestimated by optical mineralogy, and the elements Cu, Mo, Al, Fe, Mg, and Ti were overestimated by optical mineralogy when compared with pXRF results. Most of these porphyry samples occur in veined porphyritic quartz monzonite that is characteristic of these deposits. Sulfide and silicate vein stockworks are pervasive in most of the samples as well as dissemination of sulfides outwards from veinlets. Ore minerals present include chalcopyrite and molybdenite with lesser bornite. Chalcocite, digenite, and covellite are secondary. Potential sources of analytical bias are discussed.

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“…Moreover, thematic geochemical databases have also been established in many countries [14][15][16]. A variety of spatial methods can be applied to produce geochemical patterns using original data stored in a geospatial database [17][18][19]. Shapefile is a geographic information system (GIS) file format developed by the Environmental Systems Research Institute (ESRI), and is the most widely used vector data format to store the location, shape, and attributes of geographical features [20].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods

et al. 2021

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The spatial calculation of vector data is crucial for geochemical analysis in geological big data. However, large volumes of geochemical data make for inefficient management. Therefore, this study proposed a shapefile storage method based on MongoDB in GeoJSON form (SSMG) and a shapefile storage method based on PostgreSQL with open location code (OLC) geocoding (SSPOG) to solve the problem of low efficiency of electronic form management. The SSMG method consists of a JSONification tier and a cloud storage tier, while the SSPOG method consists of a geocoding tier, an extension tier, and a storage tier. Using MongoDB and PostgreSQL as databases, this study achieved two different types of high-throughput and high-efficiency methods for geochemical data storage and retrieval. Xinjiang, the largest province in China, was selected as the study area in which to test the proposed methods. Using geochemical data from shapefile as a data source, several experiments were performed to improve geochemical data storage efficiency and achieve efficient retrieval. The SSMG and SSPOG methods can be applied to improve geochemical data storage using different architectures, so as to achieve management of geochemical data organization in an efficient way, through time consumed and data compression ratio (DCR), in order to better support geological big data. The purpose of this study was to find ways to build a storage method that can improve the speed of geochemical data insertion and retrieval by using excellent big data technology to help us efficiently solve problem of geochemical data preprocessing and provide support for geochemical analysis.

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A Comprehensive Study of Three Different Portable XRF Scanners to Assess the Soil Geochemistry of An Extensive Sample Dataset

Cited by 31 publications

References 33 publications

Hydrothermal synthesis of potassium–sodium niobate powders

Hydrothermal synthesis of potassium–sodium niobate powders

A Comparative Study of Porphyry-Type Copper Deposit Mineralogies by Portable X-ray Fluorescence and Optical Petrography

A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods

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