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  • Factor de Impacto: 0,280(2015)
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© Thomson Reuters, Journal Citation Reports, 2015

Bol Soc Esp Ceram Vidr 2016;55:185-96 - DOI: 10.1016/j.bsecv.2016.06.003
Determination of minor and trace elements in geological materials used as raw ceramic materials
Determinación de elementos minoritarios y traza en materiales geológicos utilizados como materias primas cerámicas
María Fernanda Gazulla Barreda, , Marta Rodrigo Edo, Mónica Orduña Cordero, María Jesús Ventura Vaquer
Instituto de Tecnología Cerámica, Asociación de Investigación de las Industrias Cerámicas, Universitat Jaume I, Castellón, Spain
Recibido 14 enero 2016, Aceptado 13 junio 2016
Abstract

A study has been undertaken to develop a methodology to determine minor and trace elements in geological ceramic raw materials by wavelength-dispersive X-ray fluorescence (WD-XRF) spectrometry. The set up of the methodology has been done either by optimising not only the sample preparation process but also optimising the measurement with the aid of the software Pro-Trace, and also by making an exhaustive compilation of reference materials for calibration and validation.

The developed method is precise and accurate and allows the analysis of Ba, Ce, Co, Cr, Cu, Fe, La, Mn, Ni, Pb, Rb, S, Sr, Ta, Th, U, V, Y, Zn and Zr present in the sample as minor or trace elements in geological materials used as raw ceramic material in a relatively short period of time. Besides, the method is more environmentally friendly than other methodologies as it does not require the use of solvents or reagents.

Resumen

Se ha llevado a cabo un estudio para el desarrollo de una metodología para la determinación de elementos minoritarios y traza en materias primas geológicas cerámicas mediante espectrometría de fluorescencia de rayos X por dispersión de longitudes de onda (WD-FRX). La puesta en marcha se ha llevado a cabo no solo mediante la optimización del proceso de preparación de muestra sino mediante la optimización de la medida con la ayuda del software Pro-Trace y mediante una exhaustiva recopilación de materiales de referencia para calibración y validación.

El método desarrollado es preciso y exacto, y permite el análisis de Ba, Ce, Co, Cr, Cu, Fe, La, Mn, Ni, Pb, Rb, S, Sr, Ta, Th, U, V, Y, Zn y Zr presentes en la muestra como elementos minoritarios y traza en materiales geológicos utilizados como materias primas cerámicas en un tiempo relativamente corto. Además, el método es más respetuoso con el medio ambiente que otras metodologías ya que no requiere el uso de disolventes o reactivos.

Keywords
Minor elements, Trace elements, WD-XRF, Geological ceramic raw materials
Palabras clave
Elementos minoritarios, Elementos traza, WD-FRX, Materias primas geológicas cerámicas
Introduction

The development of new analysis methods capable of determining minor and trace elements in ceramic raw materials has been demanded because of the emergence of new ceramic products with technical characteristics and novel functionalities demands, as some elements present in very low concentrations can generate defects in the final product.

The presence of compounds such as pyrites and other sulfur compounds that can decompose at elevated temperatures during the firing process of ceramic materials originates defects in the final product; other elements such as Ti and Fe compounds generate colouring problems, and the presence of U and Th in materials such as zirconium silicates can cause high levels of radioactivity.

Trace elements in rocks have often been determined using atomic absorption spectrometry (GFAAS or FIAS-AAS), inductively coupled plasma atomic emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), which are extremely sensitive but require a tedious pretreatment, including decomposition with acid, due which implies the conduction of digestions, entailing the ensuing increase of the uncertainty and long analysis times, for that reason, analyses of numerous samples are difficult by these methods [1]. Bennett, in his book “XRF analysis of ceramics, minerals, and allied materials” [2], gives a general idea of how to characterise ceramics, minerals, and allied materials by WD-XRF, but does not refer to the analysis of trace elements.

The use of XRF in the analysis of geological samples is increasing, mainly because of the precision and accuracy with which the major elements and a wide range of trace elements may be determined. Although it is an old and well-established technique, it continues to find widespread use in the analysis of soils and other environmental samples. One reason for the continuing popularity of the technique is the simple sample preparation [3]. Its contribution to a substantial extent to the complete elemental characterisation allows the elucidation of its geological origin or the study of the evolution of mineral deposition with time. Furthermore, XRF is frequently used for the verification of the quality and the physical characteristics of industrial mineral processes. Across the years, many authors have pointed out the new applications of XRF in the field of geological minerals [4,5]. In the field of nanotechnology and the development of catalysts and new ceramic materials, the XRF technique continues to be one of the favourable analytical tools routinely applied in the characterisation process of these materials [6,7]. Another advantage of XRF against classical techniques is the analysis of U and Th, present in geological samples in very low concentrations. Techniques such as spectrophotometry, spectrofluorimetry, flame and graphite furnace AAS, ICP-OES, or neutron activation analysis (NAA) present different interferences and/or low sensitive which increase their detection limit of U and Th, which entail the necessity of a tedious sample preparation to concentrate these analytes [8].

This study has been undertaken to obtain such a methodology for the determination of minor and trace elements in materials such as sands, clays, kaolins, feldspars and feldspathoids, calcites, dolomites, etc., by wavelength dispersive X-ray fluorescence spectrometry (WD-XRF), and making an exhaustive compilation of reference materials to calibrate and validate the methodology. The following elements were analysed: Ba, Ce, Co, Cr, Cu, Fe, La, Mn, Ni, Pb, Rb, S, Sr, Ta, Th, U, V, Y, Zn y Zr.

The developed method is precise and accurate and allows the analysis of minor and trace elements in geological materials used as raw ceramic material in a relatively short period of time. The use of a great number of standards has yield a huge concentration range for all the analysed elements. Besides, the method is more environmentally friendly than other methodologies as it does not require the use of solvents or reagents due to the lack of any sample digestion process; reducing in this way the adverse environmental impact of analytical methodologies [9].

ExperimentalMaterials and equipment

The importance of “reliable” analyses of rocks reference materials in the calibration of modern instrumental techniques has already been stressed. In this respect, compilations of data for all available silicate samples are very valuable. However, these lists of data do have one drawback: they give little indication of the error limits in quoted values apart from a crude classification into “usable”, “proposed”, or “recommended” values as opposed to “for information” or “order of magnitudes values” depending on the favoured terminology of the compiler. The calculation of statistically meaningful uncertainty limits from such data is not simple since interlaboratory bias cannot readily be quantified on a statistical basis [10].

The results of many geological reference materials indicate that there are few major elements whose values are known with a confidence better than 1% (one sigma). Furthermore, for several elements, coefficients of variation exceed 5%, sometimes substantially so, even though the concentration of the element is significantly above the expected detection limit of modern analytical techniques. And so we have the contradiction that many modern instrumental methods are capable of achieving instrument precisions often exceeding 0.1% relative. Uncertainties in analyses of individual reference materials used for calibrating instruments can be overcome by incorporating a large number of such samples (often over 20) in the calibration data set do that discrepancies will cancel out. However, the only way in which the accuracy of a calibration can be satisfactory tested is by the analysis of individual reference and comparing analysed results with data [10].

In the case of trace elements, with a few notable exceptions, error in the analyses of reference materials usually exceeds 5% relative (one sigma). The problems mentioned for setting up and assessing the accuracy of major element calibrations are even more serious for trace element data. An associated difficulty is that is often necessary to determine these elements down to detection limit levels. Such data cannot be achieved unless the calibration line passes through the origin, and in instruments that are calibrated directly from reference materials, this is not always easy to achieve without a highly critical evaluation of the reliability of individual datum points [10].

The preparation of the calibration curves and validation of the measurements were carried out with materials coming from different origins:

  • Reference materials from different certification bodies:

    • -

      National Research Centre for Certified Reference Materials GBW (China): GBW07401 Soil, GBW07402 Soil, GBW07403 Soil, GBW07404 Soil, GBW07405 Soil, GBW07406 Soil, GBW07407 Soil, GBW07408 Soil, GBW03122 Kaolin, GBW07152 Lithium Ore, and GBW07153 Lithium Ore.

    • -

      Bureau of Analysed Samples – BAS (United Kingdom): BCS-CRM No. 313/1 High Purity Silica, and BCS-CRM No. 3751/1 Soda Feldspar.

    • -

      Canadian Centre for Mineral and Energy Technology – CANMET (Canada): STDS-1 Stream sediment, STDS-2 Stream sediment, STDS-3 Stream sediment, STDS-4 Stream sediment, SY-2 Syenite, and SY-3 Syenite.

    • -

      Instituto de Pesquisas Tecnologicas (Brazil): IPT-72 Soda Feldspar.

  • Reference materials obtained from the participation in round robin test organised by different associations:

    • -

      GeoPT series of reference materials obtained from the Interlaboratory Test for the Analysis of geological samples (GeoPT) organised by IAG (International Association of Geoanalysts) (United Kingdom): GeoPT-7 Biotite, GeoPT-8 Microdiorite, GeoPT-11 Dolerite, GeoPT-12 Serpentinite, GeoPT-16 Basalt rock, GeoPT-19 Gabbro, GeoPT-20 Ultramafic rock, GeoPT-21 Granite, GeoPT-22 Basalt, GeoPT-23 Lake pegmatite, GeoPT-24 Greywake, GeoPT-25 Basalt, GeoPT-28 Shale, GeoPT-29 Nepheline, GeoPT-30 Syenite, GeoPT-30A Limestone, GeoPT-31 River sediment, GeoPT-34 Basalt, GeoPT-35 Ball clay, and GeoPT-35A Metalliferous sediment.

    • -

      Mercury Soil-2 MS-2 obtained from the interlaboratory organised by the Central Geological Laboratory of Mongolia (CGL) (Mongolia).

Depending on the certification body and certification procedure, data with different quality can be found in the reference materials certificate, such as certified values with assigned uncertainty (combined (u) or expanded (U)), and reference values or information values with no uncertainty. Regarding the reference materials obtained from the participation in the Interlaboratory Test for the Analysis of geological samples (GeoPT) organised by IAG, we can find two types of materials:

  • (a)

    Most of them present an assigned value (Xa) together with a parameter called target standard deviation (Ha), which is calculated from a modified form of the Horwitz function as follows:

    where Xa is the assigned value expressed as a fraction, and the factor k gets the value 0.01 or 0.02 depending on the kind of laboratory that gave the individual result (for example, “pure geochemistry labs”, which are those which analytical results are designed for geochemical research and care is taken to provide data of high precision and accuracy; or “applied geochemistry labs”, which main objective is to provide results on large number of samples collected).

  • (b)

    A few of them are submitted to a subsequent certification process (GeoPT-16, GeoPT-14, and GeoPT-12) and some elements present an assigned value (Xa) accompanied by its uncertainty (U).

As can be seen from the relation of reference materials used in this study, materials of different nature and with a variety of matrix were used in the preparation of the calibration curves. After the calibration was performed, geological materials different from those used in the calibration were analysed and the results compared in other to validate the established methodology.

The study was conducted with a PANalytical model AXIOS wavelength dispersive X-ray fluorescence (WD-XRF) spectrometer with a Rh anode tube, and 4kW power, fitted with flow, scintillation, and sealed detectors, eight analyzing crystals: LiF200, LIF220, Ge, TLAP, InSb, PE, PX1 and PX7, and provided with masks of 37, 30, 27, 10, and 6mm in diameter.

Optimisation of the sample preparation

Although XRF analysis requires only simple preparation techniques, sample preparation is usually necessary to ensure XRF analysis to be truly effective and contribute to the optimisation of X-ray analysis [11]. This sample preparation is much less time consuming than that necessary in other analytical techniques such as ICP-OES, ICP-MS, GFAAS or FIAS-AAS, requiring sample preparation times over 10min versus several hours for the analysis by these last mentioned techniques.

For WD-XRF analysis, the sample needs to be prepared in the form of pellets or beads. When the analyte is present in the sample in very low concentration (minor or trace), the sample is prepared in the form of pressed pellets in order to have lower detection limits as the sample does not suffer any significant dilution during the sample preparation.

There is literature where the analysis of rare earth elements in rocks by WD-XRF was carried out preparing the sample as beads with a very low dilution which obliged them to reheat the glass at 1200°C with its consequent loss of volatile analyte and increase of uncertainty due to the higher manipulation of the sample [1].

The pellet preparation was optimised forming pellets of a soil with different binders and studying the one that gave the best results, that is, better surface, and better reproducibility in the results. Four binders were studied: d-mannitol, stearic acid, n-butyl methacrylate and a mixture of polyvinylpyrrolidone (PVP) and methyl cellulose (MC). Table 1 shows the pellet preparation for each binder used.

Table 1.

Pellet preparation conditions for each binder studied.

Binder  Binder preparation  Pellet preparation 
d-Mannitol  –  10,000g sample with 2000g binder, mixed in a tungsten carbide mill for 40s
Stearic acid  – 
n-Butyl methacrylate  13.7% solution of n-butyl methacrylate in acetone  10,000mg sample with 2.5ml of the solution, mixed manually in an agate mortar 
Polivinylpyrrolidone and methyl cellulose (PVP-MC)40g of MC dissolved in 400ml deionised boiled water mixed with a solution of 70g PVP in 300ml ethanol10,000g sample with 2 drops of the PVP-CM solution per gram, mixed manually in an agate mortar 
After forming the pellet, dried in an oven at 110°C for a minimum of 10min to get the process of binding formed 

All pellets were formed at a pressure of 100kN [11] in a CASMON hydraulic press using a 40-mm diameter die (being this the highest size for which a mask is available in the WD-XRF instrument).

After forming the pellets, their surface was observed, the one with stearic acid being the best. To confirm this, ten pellets were prepared using this binder and measured; the results obtained showing dispersion lower than 5% (relative). So, all pellets were prepared using stearic acid as binder.

As can be seen in Table 1, the sample and stearic acid are mixed in a tungsten carbide ring mill for 40s. Tungsten carbide presents cobalt in its composition, which is one of the analytes of interest. So, to assure that no contamination occurred during sample preparation, the mixture with the binder for pellet preparation was also carried out in an agate ring mill. Cobalt was then analysed in this pellet and the results compared with the pellet prepared in tungsten carbide ring mill, not having any significant difference between both preparations.

Calibration

Empirical calibration curves comparing intensities with concentrations can be used for the analysis of samples with limited variations of the matrix composition. However, a general-purpose calibration procedure that is applicable to a larger variety of matrix types and covering wider ranges of the analyte concentration is usually more desirable. The calibration procedure known as “empirical” compares directly the net intensity of the analyte peaks with their concentrations, without making any correction for the inter-element of matrix effects. It is possible to use this type of calibration only when the analyte concentration range is limited and when the standard and sample matrix compositions are extremely similar. This can occur in certain industrial applications where the standards are normally typical “samples” that have been analysed by a technique other than XRF. With this calibration type, it is assumed that the net intensity is linearly related to concentration. However, the relationship between intensity and concentration becomes non-linear when significant differences in matrix compositions are present between samples and standards. The analyst must be extremely cautious when using empirical coefficients calculated by multiple regression analysis because such an approach contains many potentials pitfalls. Not only do empirical coefficients correct for matrix effects, but they can also conceal other error types that may be present, such as errors on measured intensities, poor standard chemical data, poor sample preparation, variation of particle size effects, of mineralogical effects, of surface effects, and so on. As opposed to empirical coefficients, theoretically determined influence coefficients allow the error sources to be detected, isolated and estimated, thereby giving the analyst greater confidence in the reliability and applicability of the calibration data. When calibrating for an analyte, it must always be kept in mind that a significant intercept value means an error somewhere, and one must try to discover the cause of it and correct for it. In the case of trace element determination, the best method to correct the matrix effects lies in the use of theoretical influence coefficients, calibration curves should be constrained to pass through the origin, and, whenever possible, the use of linear regression analysis is recommended [12,13].

The measurement was undertaken with the aid of an analytical programme called Pro-Trace, supplied by PANalytical, which uses primary and secondary or only secondary mass attenuation coefficients (MAC's) to make matrix corrections or net intensities. Advantages of the use of Pro-Trace are: the more accurate background interpolation, the matrix effect correction thanks to MAC's and finally the smart element selector (SES) which allows the reduction of measurement times with the use of shared background positions [14]. Table 2 shows the measurement conditions.

Table 2.

Measurement conditions by WD-XRF.

Element  Line  Crystal  Detector  Voltage (kV)  Intensity (mA)  Angle (2θ) (°)  Bg1  Bg2  PHD LL  PHD UL  t (s) 
Ba  Lα  LiF 200  Flow  40  90  87.1906  1.4048    30  60  60 
Ce  Lβ1  LiF 220  Duplexa  50  72  111.6862  −1.5356    30  60  60 
Co  Kα  LiF 220  Duplexa  60  60  77.891  1.4262    20  60  60 
Cr  Kα  LiF 220  Duplexa  50  72  107.1524  −1.2458  3.0002  30  60  60 
Cu  Kα  LiF 220  Duplexa  60  60  65.5376  −3.9523  2.7897  20  60  60 
Fe  Kα  LiF 200  Duplexa  60  60  57.4862      15  72  60 
La  Kα  LiF 200  Flow  50  72  82.908  −0.7432    30  60  60 
Mn  Kα  LiF 220  Duplexa  60  60  95.2112  −2.2636  3.1564  15  60  60 
Ni  Kα  LiF 220  Duplexa  60  60  71.238  −2.9334  2.0164  20  60  60 
Pb  Lβ1  LiF 220  Scintillation  60  60  40.3696    1.8335  35  65  60 
Rb  Kα  LiF 220  Scintillation  60  60  37.9316      36  65  50 
Kα  Ge 111  Flow  36  100  110.698  −1.9198  4.9502  30  65  50 
Sr  Kα  LiF 220  Scintillation  60  60  35.8026  −0.9786  0.8565  35  65  40 
Ta  Lα  LiF 220  Duplexa  60  60  64.614      20  60  60 
Th  Lβ1  LiF 220  Scintillation  60  60  37.2914      35  65  60 
Lα  LiF 220  Scintillation  60  60  31.1626      35  65  60 
Kα  LiF 220  Duplexa  50  72  123.1798  3.0796    30  60  60 
Kα  LiF 220  Scintillation  60  60  33.844      35  65  40 
Zn  Kα  LiF 220  Scintillation  60  60  60.55  −1.3669  1.0536  30  70  60 
Zr  Kα  LiF 220  Scintillation  60  60  32.0462  0.7761    35  65  40 
a

Sealed + Flow.

Once the calibration conditions were selected, the reference materials were measured in order to construct the calibration curves. Tables 3–6 show the concentrations of each element analysed for each of the calibration standards (Xa, for values obtained from interlaboratory results, or Ccert, for values obtained from a certificate of analysis), together with its uncertainty (U) or its target standard deviation (Ha) (when coming from a proficiency test).

Table 3.

Reference materials for calibration from GeoPT (GeoPT-7 to GeoPT-24).

Element (mgkg−1GeoPT-7GeoPT-8GeoPT-11GeoPT-12GeoPT-16GeoPT-19
  Xa  H  Xa  H  Xa  H  Xa  Uc  Xa  Ud  Xa  H 
Ba  908  26.1  360.8  11.9  309.2  10.4  8.4a  0.6  200a  –  53.46  2.349 
Ce  103.2  4.1  55.7  2.4  44.17  0.279  H-0.027  13.3a  –  3.42  0.227 
Co  19.5  13.5  0.73  38.6  1.78  106b  3  49.7a  –  35.34  1.653 
Cr  181.4  6.6  54.7  2.4  38.4  1.77  2780b  33  332b  9  39.77a  1.827 
Cu  30  1.4  27.3  1.3  27.3  1.33  –  –  96a  –  593.95  18.168 
Fe (%)  4.21  0.09  4.07  0.06  10.21  0.14  5.59b  0.15  7.24b  0.03  7.52  0.11 
La  52.95  2.33  24.96  1.23  18.1  0.94  0.15a  0.016  5.2  H-0.32  1.38  0.105 
Mn  542  20  1084  31  2401  54  635b  70  1294b  15  775  23 
Ni  59.6  2.6  21  1.06  15  0.8  2296b  120  150a  –  19.65  1.004 
Pb  14.1  0.76  14.1  0.76  4.66  0.3  –  –  3.3b  0.2  4.55a  0.29 
Rb  56.24  2.45  98.5  3.9  19.29  0.99  –  –  1.91b  0.01  –  – 
–  –  –  –  –  –  –  –  –  –  –  – 
Sr  363.5  12  99.9  226.8  7.34b  0.35  169.2b  0.7  786.94  23.073 
Ta  0.4  0.04  0.08  0.546a  0.048  –  –  0.28  H-0.03  –  – 
Th  11.23  0.62  8.42  0.49  2.25  0.159  0.03b  –  0.33b  0.03  –  – 
0.9  0.07  2.19  0.16  0.5  0.044  0.831b  0.068  0.29b  0.03  0.03  0.004 
96.5  3.9  82.7  3.4  447.8  14.3  33.4b  250a  –  452.8  14.428 
18  0.93  47.1  2.1  51.8  2.3  –  –  19.33  H-0.99  4.44  0.284 
Zn  80.3  3.3  69.5  2.9  133.6  5.1  38.6b  3.2  58.0b  –  93.3  3.771 
Zr  231.8  8.2  195.1  7.1  219.9  7.8  –  –  55.1  H-2.4  10.00a  0.566 
Element (mgkg−1GeoPT-20GeoPT-21GeoPT-22GeoPT-23GeoPT-24
  Xa  H  Xa  H  Xa  H  Xa  H  Xa  H 
Ba  –  –  344.08  11.426  755.01  41.44  8.75a  0.505  311  10.486 
Ce  1.33  0.102  63.06  2.703  103.76  5.97  7.24a  0.43  38a  1.758 
Co  86.46  3.534  2.73  0.188  25.65  3.81  –  –  12  0.661 
Cr  2420.7  59.93  186.7  6.797  214.81  21.72  –  –  34  1.6 
Cu  43.65  1.978  0.418  32.19  3.71  –  –  22.3  1.118 
Fe (%)  8.28  0.11  1.69  0.029  6.84  0.15  0.52  0.01  3.44  0.05 
La  0.42  0.038  29.22  1.407  55.88  3.39  2.03a  0.146  18.8  0.967 
Mn  1394  39  465  15  1007  39  852  23  929  23 
Ni  870.62  25.141  5.92  0.362  159.3  12.5  –  –  17.7  0.919 
Pb  –  –  25.42  1.249  8.59  1.94  –  –  26.9  1.309 
Rb  1.04  0.082  271.94  9.356  62.89  2501  61.5  35.9  1.676 
–  –  –  –  –  –  –  –  –  – 
Sr  15.99  0.843  110.75  4.362  920.52  39.92  –  –  174  6.394 
Ta  0.03a  0.004  2.53  0.176  3.08  0.31  124.7a  4.83  0.56  0.049 
Th  0.03  0.004  19.19  0.984  6.84  1.34  5.08  0.318  0.316 
0.01  0.002  5.43  0.337  1.67  0.42  4.37  0.28  1.09  0.086 
167.85  6.281  14.01  0.753  105.03  7.57  –  –  77  3.209 
9.44  0.538  24.67  1.218  20.41  1.89  8.14a  0.475  20.5  1.039 
Zn  61.81  2.658  54.56  2.391  115.47  8.97  28.15  1.362  54  2.382 
Zr  16.85  0.881  168.41  6.227  288  17.38  –  –  123  4.768 
a

Data in italics is either provisional or informative.

b

Data in bold are certified values due to a subsequent certification process of the material.

c

U is the expanded uncertainty corresponding to a level of confidence of 95%.

d

U based on judgement and represents an evaluation of the combined effects of method imprecision, possible systematic errors among methods and material variability.

Table 4.

Reference materials for calibration from GeoPT (GeoPT-25 to GeoPT-35A).

Element (mgkg−1GeoPT-25GeoPT-26GeoPT-28GeoPT-29GeoPT-30
  Xa  Uc  Xa  H  Xa  H  Xa  H  Xa  H 
Ba  555b  7  512  16.002  788a  23.099  741a  27.85  684.1  20.485 
Ce  93.3b  1.2  48.9  2.178  108.2  4.276  124.3a  2.74  252.4  8.781 
Co  37.5b  1.4  21.4  1.079  22.7  1.135  63.7  –  2.75a  0.189 
Cr  12.4b  1  –    109  4.303  438  –  18a  0.932 
Cu  160b  3  23.7a  1.179  31.2  1.487  56.5  –  –  – 
Fe (%)  10.90b  0.06  2.23  0.01  6.79  0.017  9.29a  0.098  2.82  0.009 
La  42.6b  1  25.9  1.271  52.5  2.312  62.6  1.801  145.3  5.493 
Mn  1496b  21  3129  23  1162  1572  178  1239 
Ni  22.1  H-0.364  87.0a  3.552  82.8  3.408  315  –  77.8  3.232 
Pb  5.44  H-0.089  7.2a  0.426  35  1.639  2.88a  –  15.95  0.841 
Rb  35.4  H-0.285  14.7  0.783  147  5.548  31.4  7.97  248.9  8.678 
–  –  –  –  –  –  –  –  –  – 
Sr  481.8  H-2.874  118.2  4.61  178  6.527  1175  1032  302.7  10.248 
Ta  1.93  H-0.027  0.35a  0.033  1.11  0.087  5.14a  0.074  6.62  0.398 
Th  3.98  H-0.259  3.93  0.256  15.8  0.836  7.4  0.318  32.28  1.531 
0.81  H-0.067  0.83  0.068  5.76  0.354  2.2  1.15  8.4  0.488 
392.8  H-2.613  64.0a  2.736  220  7.814  292a  3.17  23  1.148 
39.93  H-0.466  15.5  0.822  36.5  1.697  29.5  2.11  40  1.836 
Zn  141.5  H-1.496  27.8a  1.349  186.8  6.8  117.4  –  61.6  2.651 
Zr  310.1  H-1.816  81.2  3.352  134.3  5.137  292  –  838.5  24.351 
Element (mgkg−1GeoPT-30AGeoPT-31GeoPT-34GeoPT-35GeoPT-35A
  Xa  H  Xa  H  Xa  H  Xa  U  Xa  H 
Ba  27.85a  1.35  733  733  865.9  49.69  733  21.72  865.9  25.02 
Ce  2.74a  0.188  28.3  28.3  89.32  6.181  28.3  1.368  89.32  3.634 
Co  –  –  19.34  19.34  55.59  0.2919  19.34  0.9904  55.59  2.429 
Cr  –  –  –  –  –  0.977  –  –  –  – 
Cu  –  –  20  20  1159  0.8142  20  1.019  1159  32.06 
Fe (%)  0.098a  0.001  5.27  5.27  4.51  0.03  5.27  0.08  4.51  0.013 
La  1.801  0.132  12.56  12.56  44.89  3.7564  12.56  0.6865  44.89  2.025 
Mn  178  894.7  894.7  3989  894.7  25  3989  88 
Ni  –  –  6a  6  230  0.4214  6a  0.3665  230  8.115 
Pb  –  –  14  14  3893  1.4767  14  0.7527  3893  89.73 
Rb  7.97  0.467  60.75  60.75  152.3  6.201  60.75  2.619  152.3  5.716 
–  –  –  –  –  –  –  –  –  – 
Sr  1032  29.04  294.1  294.1  182.2  14.777  294.1  9.999  182.2  6.657 
Ta  0.074a  0.009  0.401  0.401  1.41  0.0773  0.401  0.037  1.41  0.1071 
Th  0.318  0.03  3.92  3.92  17.74  1.2084  3.92  0.2553  17.74  0.9204 
1.15  0.09  1.274  1.274  4.068  0.1528  1.274  0.09828  4.068  0.2634 
3.17a  0.213  145.7  145.7  73.15  1.6688  145.7  5.506  73.15  3.067 
2.11  0.151  23.95  23.95  25.41  0.5553  23.95  1.188  25.41  1.249 
Zn  –  –  89.94  89.94  3684  3.5532  89.94  3.651  3684  85.61 
Zr  –  –  125.5  125.5  257.9a  11.041  125.5  4.851  257.9a  8.942 
a

Data in italics is either provisional or informative.

b

Data in bold are certified values due to a subsequent certification process of the material.

c

U is the expanded uncertainty for 95% confidence and contains a contribution from the characterisation of the laboratory and a contribution from the material heterogeneity

Table 5.

Reference materials for calibration from BAS, CANMET, IPT, and CGL.

Element (mgkg−1MS-2BCS-CRM No. 313/1IPT-72SY-2SY-3STSD-1STSD-2STSD-3STSD-4BCS-CRM No. 375/1
  Xa  U  Ccert  sb  Xa  Uc  Cknown  U  Cknown  U  Cknown  U  Cknown  U  Cknown  U  Cknown  U  Ccert  Uc 
Ba  –  –  –  –  –  –  460a  –  430a  –  630a  –  540a  –  1490a  –  2000a  –  95  – 
Ce  –  –  –  –  –  –  210a  –  2200a  –  51a  –  93a  –  63a  –  44a  –  54  – 
Co  –  –  –  –  –  –  11a  –  12a  –  17a  –  19a  –  16a  –  13a  –  –  – 
Cr  –  –  –  –  –  –  12a  –  10a  –  67a  –  116a  –  80a  –  93a  –  12  – 
Cu  –  –  –  –  –  –  5a  –  16a  –  36a  –  47a  –  39a  –  65a  –  –  – 
Fe (%)  2.95a  –  0.008  0.0006  0.063  0.01  4.39a  –  4.49a  –  4.54a  –  5.24a  –  4.33a  –  3.99a  –  0.203  0.008 
La  –  –  –  –  –  –  88a  –  1350a  –  30a  –  59a  –  39a  –  24a  –  26  – 
Mn  –  –  1.3  0.3  –  –  2479a  –  2479a  –  0.38a  –  775a  –  2324a  –  1550a  –  –  – 
Ni  –  –  –  –  –  –  10a  –  11a  –  24a  –  53a  –  30a  –  30a  –  –  – 
Pb  –  –  –  –  –  –  80a  –  130a  –  35a  –  66a  –  40a  –  16a  –  – 
Rb  –  –  –  –  –  –  220a  –  208a  –  30a  –  104a  –  68a  –  39a  –  52  – 
930a  –  –  –  –  –  110a  –  500a  –  1800a  –  600a  –  1400a  –  900a  –  –  – 
Sr  –  –  –  –  –  –  275a  –  306a  –  170a  –  400a  –  230a  –  350a  –  101  – 
Ta  –  –  –  –  –  –  –  –  –  –  0.4a  –  1.6a  –  0.9a  –  0.6a  –  –  – 
Th  –  –  –  –  –  –  380a  –  990a  –  3.7a  –  17.2a  –  8.5a  –  4.3a  –  10  – 
–  –  –  –  –  –  290a  –  650a  –  8.0a  –  18.6a  –  10.5a  –  3.0a  –  – 
–  –  –  –  –  –  52a  –  51a  –  98a  –  101a  –  134a  –  106a  –  –  – 
–  –  –  –  –  –  130a  –  740a  –  42a  –  37a  –  36a  –  24a  –  18  – 
Zn  –  –  –  –  –  –  250a  –  240a  –  178a  –  246a  –  204a  –  107a  –  – 
Zr  –  –  –  –  –  –  280a  –  320a  –  218a  –  185a  –  196a  –  190a  –  79  – 
a

Data in italics is either provisional or informative.

b

Standard deviation.

c

The uncertainty (U) has been calculated as U=tα⋅s/N, with where α=0.05 (95% confidence), s is the standard deviation, and N is the number of acceptable data.

Table 6.

Reference materials for calibration from the National Research Centre for Certified Reference Materials GBW.

Element (mgkg−1GBW 07401GBW 07402GBW 07403GBW 07404GBW 07405GBW 07406GBW 07408GBW 03122GBW 07152
  Ccert  Ub  Ccert  Ub  Ccert  Ub  Ccert  Ub  Ccert  Ub  Ccert  Ub  Ccert  Ub  Ccert  Sa  Ccert  sa 
Ba  590  15  930  24  1210  30  213  10  296  12  118  480  11  –  –  –  – 
Ce  70  402  10  39  136  91  66  66  –  –  7.3  0.6 
Co  14.2  0.4  8.7  0.3  5.5  0.2  22  0.6  12  0.5  7.6  0.4  12.7  0.4  –  –  –  – 
Cr  62  47  32  370  118  75  68  –  –  –  – 
Cu  21  0.6  16.3  0.4  11.4  0.4  40.5  0.1  144  390  24.3  0.5  –  –  –  – 
Fe (%)  3.63  0.03  2.46  0.02  1.4  0.01  7.2  0.03  8.83  0.05  5.66  0.04  3.13  0.01  0.56  0.03  0.275  0.013 
La  34  164  21  53  35.7  1.8  30  35.5  1.4  –  –  4.3  0.2 
Mn  1760  24  510  304  1420  30  1360  28  1450  32  650  54  11  540  40 
Ni  20.4  0.6  19.4  0.5  12.2  0.4  64.2  1.7  40  53  31.5  0.7  –  –  –  – 
Pb  98  20.2  26  58.5  2.1  552  14  314  21  –  –  –  – 
Rb  140  88  85  75  117  237  96  –  –  0.13%  0.01% 
310  60  210  30  120  10  180  30  410  40  260  30  120  30  480  40  –  – 
Sr  155  187  380  77  41.5  1.9  39  236  –  –  –  – 
Ta  1.4  0.1  (0.8)c  –  (0.8)c  –  3.1  0.2  1.8  0.2  5.3  0.4  1.05  0.16  –  –  40.5  3.8 
Th  11.6  0.7  16.6  0.8  0.5  27  23  23  11.8  0.7  –  –  –  – 
3.3  0.4  1.4  0.3  1.3  0.3  6.7  0.8  6.5  0.7  6.7  0.7  2.7  0.4  –  –  –  – 
86  62  36.5  1.1  247  166  130  81.4  1.8  –  –  –  – 
25  21.7  0.9  15  39  21  18.8  0.8  26  –  –  13.3  1.4 
Zn  680  11  42.3  1.2  31.4  1.1  210  494  11  96.6  2.4  68  –  –  –  – 
Zr  245  219  246  500  21  272  220  229  –  –  –  – 
a

Standard deviation.

b

The uncertainty (U) has been calculated as U=tα⋅s/N, where α=0.01 (99% confidence), s is the standard deviation, and N is the number of data (N>8).

c

Data enclosed in brackets are reference values.

The software of the instrument permits the quality of the value to be defined. In this way, assigned values from GeoPT proficiency test and certified values were defined as high quality, while those provisional, reference or informative where defined as low quality. The software fits the experimental data taking into account the quality of each value, minimising the RMS value (root mean square), obtained from the following equation:

where C* is the known mass fraction, C is the calculated mass fraction, n is the number of calibration standards, and p is the number of calculated regression parameters (slope, ordinate at the origin, and interelement coefficients). The definition of high and poor quality data permitted the improvement of the RMS obtained. Table 7 shows the results of RMS and the working range of all the elements analysed, studying the standards selected in each calibration curve from Tables 3–6 in order to obtain the required range for this study.

Table 7.

RMS value and working range in the measurement of each element analysed by WD-XRF.

Element  Range (mgkg−1RMS (mgkg−1
Ba  8.39–908  19.0 
Ce  1.33–402  14.4 
Co  2.73–106  3.6 
Cr  10–438  5.5 
Cu  5–593.95  6.0 
Fe  84–5600  104 
La  0.42–1500  6.6 
Mn  41.83–2730  47.3 
Ni  5.92–2296  13.6 
Pb  2.88–979.3  5.9 
Rb  1.04–271.9  3.6 
110–1800  41.2 
Sr  7.34–1175  15.7 
Ta  0.03–124.7  1.3 
Th  0.318–32.28  1.0 
0.01–8.40  0.6 
14.01–452.8  6.7 
2.11–130  1.0 
Zn  28.15–680  5.4 
Zr  10–838.5  8.3 

Very low RMS value was obtained for all the analysed elements, which depends on the number and quality of standards, the interelement coefficients calculated, the range and of course the quality of the measurement process.

Figs. 1–4 show the calibration curve obtained for four of the elements as an example. Data in green are the ones defined as high quality whereas data in red in the one defined as low quality (because they are reference or informative values).

Fig. 1.

Calibration curve for vanadium.

Fig. 2.

Calibration curve for nickel.

Fig. 3.

Calibration curve for cupper.

Fig. 4.

Calibration curve for manganese.

Validation

After the calibration was performed, the following reference materials were analysed by WD-XRF in order to validate the developed method: GeoPT-9 Slate, GBW07153 Lithium Ore, and GBW07407 Soil.

Calculation of the detection limit (LD) and quantification limit (LQ)

The LD was calculated from the measurement of a sample with a concentration 0.5 times the concentration of the lowest standard in the calibration curve for each analyte. The sample was measured ten times under reproducibility conditions. The detection limit was obtained in accordance with the International Union of Pure and Applied Chemistry (IUPAC) guidelines from the following expression:

where s=value of the standard deviation of the measurements.

The LQ, which expresses the quantifiability of an analyte, was calculated according to the IUPAC guidelines as ten times the standard deviation of the measurement, for a number of measurements equal to ten [15,16]:

Calculation of the measurement uncertainty

The measurement uncertainty [17] was calculated as U=kumethod, where umethod is the combined uncertainty calculated from the expression:

where uVR is the uncertainty of the certified value of the reference material, uVL is the uncertainty of the measurement of the reference material and uREPRO is the uncertainty of the measurement of the sample.

uVL and uREPRO were calculated from the expression s/n, where s is the standard deviation of the reference material measurement or the standard deviation of the sample measurement under reproducibility conditions, depending on the term calculated, and n is the number of measurements under reproducibility conditions. The coverage factor k is determined from the Student's t-distribution corresponding to the appropriate degrees of freedom and 95% confidence.

ResultsValidation of the methodology

Once the calibrations were performed, the methodology was validated measuring reference materials. The results obtained, together with their uncertainty (U) calculated from expression [5], are presented in Table 8.

Table 8.

Validation of the calibration curves.

Element  GeoPT-9GBW07153GBW07407
  Ccert  Cexp  Ccert  Cexp  Ccert  Cexp 
Ba  480±13  474±13  –  –  180±27  161±18 
Ce  77.1±2.7  75±11  2.12±0.28  2.5±0.9  98±103±10 
Co  29.2±1.1  32±–  –  97±103±
Cr  70.7±2.1  73±–  –  410±23  424±15 
Cu  40.4±4.9  44±–  –  97±92±13 
Fe  –  –  2105±175  2303±153  –  – 
La  33.2±1.8  28±1.79  2.1±0.4  46±45±
Mn  –  –  1952±79  2041±65  1780±113  1871±72 
Ni  40.2±1.3  39±–  –  276±15  285±
Pb  28.80±0.79  27±28  –  –  14±13±
Rb  121.3±3.9  116±6718±198  6880±180  16±14±
–  –  –  –  250±36  270±24 
Sr  131.7±2.6  126±–  –  26±24±
Ta  1.02±0.12  2±98±11  105±10  3.9±0.6  5±
Th  11.3±1.0  11±–  –  9.1±0.7  9±
1.92±0.09  2±–  –  2.2±0.4  2±
129.8±5.1  129±–  –  245±21  243±14 
27.75±0.74  27±2.7±0.6  2.9±0.6  27±27±
Zn  111.4±3.4  112±–  –  142±11  144±
Zr  174.2±5.7  181±–  –  318±37  319±22 

In order to compare the results obtained with the known values of the validation standards, the difference between both was compared, together with the related uncertainty: that is, the combined uncertainty of the known and measured values, as specified in the literature [18].

The absolute value of the difference between the measured value and the known value is calculated as follows:

where Δm=absolute value of the difference between the measured and the known value; cm=measured value; cknown=known or certified value.

The uncertainty of Δm is calculated from the uncertainty of the known/certified value and the uncertainty of the measured value from the following formula:

where uΔm=combined uncertainty of the result and of the known value; um=uncertainty of the measured value; uknown=uncertainty of the known value.

The expanded uncertainty UΔm is obtained by multiplying uΔm by a coverage factor (k), usually equal to two, which corresponds approximately to a 95% level of confidence. Thus:

In order to verify the goodness of the method, Δm is compared with UΔm, such that if ΔmUΔm, there is no significant difference between the measured value and the known value.

The results of this comparison are presented in Table 9. For the comparison of the results obtained in the measurement of the reference material named GBW07153, the uncertainty of the certified values was calculated from the standard deviation and number of data shown in the certificate with a level of confidence of 95%, in order to be able to apply the statistical test.

Table 9.

Comparison of the results of the WD-XRF measurements of the validation standards with the certified values.

Element (mgkg−1GeoPT-9GBW07153GBW07407
  Δm  UΔm  Δm  UΔm  Δm  UΔm 
Ba  18  –  –  19  24 
Ce  2.1  9.3  0.4  0.7  5.2  10.9 
Co  –  –  5.5 
Cr  3.1  –  –  14  20 
Cu  3.2  7.2  –  –  11 
Fe  –  –  198  220  –  – 
La  5.2  6.4  –  –  1.4 
Mn  –  –  89  92  91  100 
Ni  1.5  3.9  –  –  12.8 
Pb  2.1  2.7  –  –  2.6 
Rb  162  259  1.7  2.7 
–  –  –  –  20  31 
Sr  6.8  –  –  1.6  3.6 
Ta  1.1  3.9  6.9  14.4  0.7 
Th  0.2  1.9  –  –  0.5 
0.08  0.7  –  –  0.3  2.1 
0.5  7.3  –  –  2.4  19 
1.1  3.4  0.2  0.7  0.5  3.4 
Zn  0.9  –  –  2.4  9.8 
Zr  7.1  9.6  –  –  1.1  32 

The value of Δm is smaller than UΔm for all the elements analysed which indicates that there is not a significant difference between the results obtained and the certified value, making the developed methodology validated. Nor uncertainty or standard deviation was declared for lanthanum in GBW07153 reference material, so this comparison could not be made for this element, but comparing both the certified and measured value, no significant differences where found.

Calculation of the detection limit (LD) and quantification limit (LQ)

Table 10 presents the results obtained in the calculation of the detection and quantification limits, according to expressions (3) and (4), of each analysed element.

Table 10.

Detection and quantification limits of the elements analysed by WD-XRF.

Element  LD (mgkg−1LQ (mgkg−1
Ba  14  47 
Ce  15  46 
Co  2.4 
Cr  14 
Cu  10  32 
Fe  14  45 
La  15  45 
Mn  2.5 
Ni  2.4 
Pb  1.4 
Rb  0.5 
16  50 
Sr  0.7 
Ta  2.3 
Th  0.6 
13 
0.5 
Zn  1.4 
Zr  0.6 

To be noted are the low detection and quantification limits reached for all analysed elements.

Conclusions

  • 1.

    An exhaustive compilation of geological reference materials has been undertaken which has allowed the achievement of a wide working range for all the elements studied, these materials coming from different sources: round robin tests, certification bodies, etc.

  • 2.

    Low detection limits have been obtained for all the elements analysed owing to the optimisation of the sample preparation as pressed pellets, the optimised measurement conditions, together with the use of the Pro-Trace software, and the use of a WD-XRF instrument that could operate at 4kW power and had scintillation, flow, and sealed detectors, with devoted software for the calibration.

  • 3.

    The developed analytical method is robust, allowing the precise and accurate analysis of trace and minor elements in geological ceramic raw materials.

  • 4.

    Time required to carry out the analysis, including the preparation of the sample and the measurement, is much less than for any other method which uses ICP-OES or ICP-MS, being really suitable to be used as a fast control method.

  • 5.

    The method is environmentally friendly compared with others such as ICP-OES, ICP-MS, etc., because it does not required reagents and high temperatures in the process of sample preparation.

Acknowledgements

This study was cofunded by the Valencian Institute of Business Competitiveness (IVACE) under the Activity programme for the Competitiveness improvement in the Technological Institutes Help Plan through the IMAMCA/2015/1 project, and by the European Regional Development Fund (ERDF), under the ERDF Operative Programme from the Valencian Community 2014–2020.

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Corresponding author.
Copyright © 2016. SECV