Quantitative X-ray Fluorescence Analysis of Biomass (Switchgrass

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Quantitative X-ray Fluorescence Analysis of Biomass (Switchgrass, Corn Stover, Eucalyptus, Beech, and Pine Wood) with a Typical Commercial Multi-Element Method on a WD-XRF Spectrometer Trevor J. Morgan, Anthe George, Aikaterini K. Boulamanti, Patricia Alvarez, Ibtissam Adanouj, Charles Dean, Stanislav V. Vassilev, David Baxter, and Lars Klembt Andersen Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef502380x • Publication Date (Web): 03 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Quantitative X-ray Fluorescence Analysis of Biomass (Switchgrass, Corn Stover, Eucalyptus, Beech, and Pine Wood) with a Typical Commercial Multi-Element Method on a WD-XRF Spectrometer Trevor J. Morgan a, Anthe George d , Aikaterini K. Boulamanti a, Patricia Álvarez c, Ibtissam Adanouja, Charles Dean d, Stanislav V. Vassilev a,e, David Baxter a, Lars Klembt Andersen a,* a

Institute for Energy and Transport, Joint Research Centre, European Commission, Westerduinweg 3,

1755 LE Petten, The Netherlands b

Sandia National Laboratories, 7011 East Avenue, Livermore, CA 94550, USA

c

Instituto Nacional del Carbόn, INCAR-CSIC, C/Francisco Pintado Fe 26, La Corredoria, 33011

Oviedo, Spain d

Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK

e

Institute of Mineralogy and Crystallography, Bulgarian Academy of Sciences, Acad. G. Bonchev

Street, Block 107, 1113 Sofia, Bulgaria

ABSTRACT: Quick and reliable inorganic elemental chemical analysis of biomass (incl. solid biofuels) is of importance in the increasing utilization and trade of biomass. In particular, it is important for the exploitation of contaminated / dirty biomass / biomass waste, and potentially also as a tool in ascertaining the type/origin of biomass. X-ray fluorescence (XRF) spectrometry

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performed directly on the raw biomass with limited prior sample preparation is an attractive method for performing such inorganic elemental analysis. In the present study we therefore carefully investigate the performance of a commercial multi-element standardless XRF method by analyzing 5 common biomass types (switchgrass, corn stover, eucalyptus, beech, and pine wood). Sample preparation involves milling the raw biomass using cutter and rotor mills (avoiding ball-milling) and cold-pressing the powdered samples into pellets using binder (vax). XRF users often rely on this type of commercial pre-calibrated or 'standardless' methods delivered with their XRF spectrometer. However, these methods are often sold without any guarantee on performance. We recently demonstrated the quite good performance of a typical commercial pre-calibrated / standardless method when analyzing biomass in the ideal form of certified reference material. In the present article, we report now on analysis of common raw biomass using the same method purchased with a 4 kW wavelength dispersive (WD) XRF spectrometer. The accuracy (trueness and precision) is determined by comparing the XRF data with the elemental composition obtained by standard elemental analysis (ICP-OES and ionchromatography). The certified elements consistently detected by the XRF are: Na, Mg, Al, Si, P, S, Cl, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Sr, Mo. For elements above 25 ppm, the XRF data show a relative systematic error (bias, trueness) typically better than ±15% independent of the concentration. The elements present with > 1000 ppm (Mg, Si, Cl, K, Ca) consistently show a positive bias of 3 – 18% relative. The relative precision (measured as the relative standard error) is better than ±5% (typically ±1%) for concentrations > 25 ppm (obtained with 10 – 30 measurements). Quantifying elements below 25 ppm (Co, Ni, Cu, Zn, Sr, Mo) is possible in some cases, but it requires a more detailed study for each specific element. E.g. Cu can be determined down to a few ppm with an appropriate correction for the method bias. Occasionally, larger relative biases of up to 45% to 90% can occur for certain elements (Cl, Si) in certain


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samples, so care should be taken to carefully test the applied method for the particular samples and elements of interest. Quantification of silicon (Si) by XRF works well for concentrations > 100 ppm. The XRF method can further be used to estimate the ash yield from biomass combustion with a relative bias better than ±10%. It is shown that the errors on the elemental composition are dominated by systematic errors (biases) and therefore measuring the two sides of a single pellet combined with correction for any bias is the optimum approach. The 5 biomass types employed here, combined with the 13 certified reference materials employed in our previous study, span a broad range of biomass types with the XRF method generally producing reliable results (keeping in mind the limitations and needed bias corrections) with errors comparable to the standard reference methods. This suggests that typical standardless / pre-calibrated XRF methods works well in elemental analysis of raw biomass (keeping in mind the limitations), and therefore could be considered for general usage in e.g. industrial analytical laboratories requiring fast elemental analysis of biomass.

1. INTRODUCTION With the increasing utilization of biomass (incl. solid biofuels) there is a growing need for quick and reliable quantitative inorganic elemental chemical analysis of biomass e.g. with producers, traders, and end-users for quality assurance, and potentially also as a tool in ascertaining the type and origin of the biomass. Quick inorganic analysis could also help in promoting the usage of under-utilized biomass resources e.g. dirty / contaminated biomass and biomass waste.


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X-ray fluorescence (XRF) spectrometry performed directly on the raw biomass with limited prior sample preparation – or automated sample preparation - is potentially attractive for simultaneous quantitative elemental analysis of many elements including ash-forming and nutrient elements (e.g. Na, Mg, Al, Si, P, S, K, Ca, Mn, Fe) and environmentally important elements (e.g. S, Cl) including heavy metals (e.g. Tl, Pb, Cd, As, Hg). (Note: We have published compilations of the composition of various types of biomass based on our own work and many helpful references.1-4) When performing quantitative chemical analysis with XRF spectrometry one of the fundamental issues is the calibration method used to convert measured fluorescence intensity to calculated concentrations of each element.5-8 Due to so-called 'sample matrix effects' including inter-element interferences there is often not a simple proportionality between concentration of a particular element and intensity of the X-ray fluorescence emitted from this element. One approach to solving this problem involves setting up a calibration based on standard reference materials of similar biomass origin to match the sample matrix. Such a 'fully calibrated' method is generally preferred if only a single or a few elements are to be determined. The other approach is the so-called 'standardless' method based on fundamental physical parameters. Combinations of the two methods also seem possible. Modern instruments are often sold with commercial pre-calibrated 'semi-quantitative' analysis programs consisting of fundamental parameters methods, maybe combined with calibrations by reference materials. These are intended for analysis of nearly the whole periodic table (fluorine to uranium) in several different types of samples e.g. biomass, coal, minerals, soils, metals, plastics etc.. The term 'semi-quantitative' is here used by the manufactures for


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methods (believed to) giving only estimated (uncertain) values – or without guaranties on their accuracy. Recently, we demonstrated the actually good performance of a typical commercial precalibrated 'semi-quantitative' method for analysis of biomass on a 4 kW wavelength dispersive (WD) XRF spectrometer.9 In that study, we investigated the performance by analyzing the certified elements in 13 certified reference materials (CRMs) of diverse vegetal/plant origin. The method performance was surprisingly good for the 21 elements detected by XRF, with relative systematic errors (bias, trueness) better than ±20% for elements in the range 25 to 100 ppm, better than ±15% for the range 100 to 1000 ppm, and better than ±10% for concentrations above 1000 ppm. In that investigation, the XRF method was tested with certified reference materials (CRMs) to avoid any doubt on the reference values. However, CRMs are also ideal since they are generally finely powdered (< 100 µm) and homogeneous – not the typical state of real biomass (solid biofuels) encountered in e.g. power plants and biorefineries. The CRMs available to us (commercially available at that time) were typical environmental biomass CRMs and arguably not really representative of typical biomass / solid biofuels. The present study therefore investigates the performance of the same semi-quantitative standardless method when used to analyze common raw biomass (i.e. not biomass ash) specifically switchgrass, corn stover, eucalyptus wood, beech wood, and pine wood (Table 1). The biomass was received as coarse powder (1-5 mm) or flakes (2-3 cm) and was subjected to cutter-milling and rotary-milling to produce fine powder suitable for pressing the pellets analyzed by XRF. Notably, we avoid using the fairly time-consuming ball-milling which is normally used as a last step to reduce the grain sizes below 100 µm in general XRF analysis of pressed pellets. Therefore, the analyzed powder samples contain significant amounts (around 50%) of larger


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particles (up to 700 µm). This investigation is thus also a test if the ball-milling can be omitted in practice. Finally, in the previous investigation we could not investigate if silicon (Si) can be reliably analyzed by this XRF method (no certified reference values for Si given in the CRMs). However, silicon is important because it will generally be present as quartz causing abrasion / attrition in transport installations, or as silicates which can contribute to slagging during biomass combustion. Therefore, we now also investigate the determination of Si by this XRF method. The specific XRF method investigated here is based on a combination of calibration with reference materials and fundamental physical parameters, and we also use the term 'standardless' for such ‘combined methods’ to indicate that they are different from the 'fully calibrated' methods. The application of both fully calibrated methods (using reference standards) and standardless methods in multi-element XRF analysis of vegetal and plant materials has been also discussed in two illustrative publications.10-11 The scientific literature holds relatively few publications with XRF investigations on raw biomass (i.e. not biomass ash) and specifically investigations of method performance by analysis of reference materials of raw biomass, plant, or vegetal samples are fairly rare.9-13 These investigations show that sometimes systematic errors (biases) can be quite pronounced (> 50 – 100% relative) for some elements in some samples. In general though, biases in the range ±10% to ±20% relative are found, which appears quite satisfactory for many applications. For comparison, current standard methods for biomass elemental analysis are acidic bomb digestion followed by analysis using atomic spectroscopic methods (ICP-OES, ICP-MS, or AAS) e.g. according to EN 15290 and EN 15297 – or alternatively bomb combustion followed


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by ion-chromatography, e.g. according to EN 15289 and ISO 10304. Note that these reference methods also have significant uncertainty associated with them as discussed in this paper. In the present study, these reference methods were used to determine the reference elemental composition of the 5 biomass samples. During our work we became aware of the Biornorm Projects and the follow-up work in the technical committees under the standardization bodies ISO/TC 238 and CEN/TC 335 working on Chemical Test Methods for Solid Biofuels, including a technical specification for Xray analysis of biomass (ISO 16996).14 This work is very relevant, but unfortunately the reports from the Bionorm Projects are not readily available online. However, we were able to obtain some of the information contained in these reports and include it to provide more complete information.14 The pre-calibrated / standardless method investigated here, Quant Express, was delivered with our 4 kW wavelength-dispersive XRF (WD-XRF) instrument (model Tiger S8, Bruker AXS) in 2009. Given the close similarity with comparable XRF instruments from other manufacturers, it is likely that the results presented here are representative for this class of highend 4 kW WD-XRF instruments. It is important to state that the choice of manufacturer does not constitute in any way an endorsement or a preference. Several other manufacturers of high-end WD-XRF spectrometers exist. We also like to stress that the data presented here are as measured: No data have been omitted, and there has been no 'cherry-picking' of particularly good runs, etc. The data represent the actual performance of the equipment in our hands working independently from the manufacturer.9


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2. EXPERIMENTAL SECTION 2.1. Sample preparation. Table 1 describes the 5 biomass types used in this study. The samples were sourced mainly from the USA, and beech from Europe. Typically, the samples were first pulverized in a cutter mill (Fritsch, Pulverisette 15) with a sieve of 1 or 2 mm. From this, 200 grams of fine powder was produced in a rotor mill (Fritsch, Pulverisette 14) equipped with a 200 µm sieve. Care was taken to make representative sampling of the initial material and also to transfer all material along the whole process, i.e. there was no selection of size-fractions. This was also observed in the Bionorm II project.14 The cutter mill operates with a rotor with 4 knifes with a 5 mm clearance to the sieve surrounding the rotating knifes. The 1 mm sieve e.g. will produce course powder with grains of 0.5 – 1 mm × 1 – 2 mm (pine). The rotor mill has a rotor with wedges with 2 mm clearance to the surrounding sieve. The 200 µm sieve produces the grain sizes given in Table 1. From the grain sizes it is clear that both mills work by comminuting the material until one dimension of the particle is small enough for the particle to pass the sieve. For the rotor mill the resultant grains are thus often elongated objects with the shortest dimension being 200 µm. Besides this, there is a significant amount of round-grained powder with diameter < 50 µm – likely debris from prolonged comminuting prior to passing the sieve. Both mills have a high throughput and sufficient material for running one sample on the XRF can be produced in a few minutes. The mills were cleaned (compressed air) between different (types of) biomass samples. The mills and sieves are made of steel which might contaminate the biomass with some elements (e.g. Fe, Cr, V, Mo, Ni, Co). However, the biomass being fairly soft and the rotating parts not touching the sieves would suggest the contamination to be low. Pellets for XRF analysis were made as follows: First, 8.00 g of pulverized samples was mixed carefully with 2.00 g of Hoechst Wax C powder (Bruker AXS, bis-stearoyl-ethylendiamin,


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C38H76N2O2, grain size approx. CVr. For the interpretation of these figures, the two norms refer to EN 15296. EN 15296 proposes that each laboratory can calculate the expUrel for their measurements by measuring the relative standard deviation (SDrel) on their results and combining them with the tabulated CVR, thus: expUrel = 2 × urel with urel2 = CVR2 +


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SDrel2. This equation combines the overall uncertainty (systematic and random errors associated with the digestion and analytical methods etc.) with random errors in that particular laboratory. Now, if SDrel is not available one might even think to use CVr as an estimate of SDrel, thus urel2 = CVR2 + CVr2. This would probably lead to some double counting – but, could be used 'to be on the safe side' and not to underestimate expUrel. However, in another very readable reference discussing these concepts, it is argued that CVR is the maximum variation possible.19 This seems plausible since CVR should include all variations in systematic and random errors between laboratories and methods and also the variations from random errors within each laboratory. In Table 4, we therefore give the half-width of the 95% confidence interval from the norms EN 15290:2011 and EN 15297:2011 as expUrel = 2 × urel with urel = CVR. Further, we make a final choice (Final expUrel) which we believe to be the best estimate of the half-width of the 95% confidence interval valid to our specific data. For example Na: The external lab provides expUrel = 6% for Na concentration of 4400 ppm in BCR-063 (skim milk powder). However, the Na concentrations in our 5 samples (0 – 180 ppm) are much closer to the range (13 – 170 ppm) reported in EN 15290 and the expUrel = 2 × CVR = 96% to 46% reported in EN 15290 for this range seems more plausible for such low concentrations. Further, the samples (wood chips and olive residues) analyzed in EN 15290 are likely more similar to our samples in terms of digestion behavior and matrix effects than BCR-063 (skim milk powder) used by our external reference laboratory for their method validation. On the other hand, we should take into account that our external reference laboratory has probably done a particularly careful effort in validating their method/instrument – and more importantly; our reference data derive specifically from that very method/instrument. Therefore, their expUrel = 6% should be regarded as very important. Also, CVR could be an overestimate for our data because we use one analytical method (ICP-OES)


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carefully validated for Na, and not a whole host of methods as in EN 15290:2011 and EN 15297:2011. Because of these arguments we take both values into account and finally settle for the average: (6% + 46%) / 2 = 27% ( = Final expUrel ) as the best estimate for the half-width of the 95% CI on our Na data. This approach may arguably seem a bit arbitrary, but it also illustrates that it is very hard to get good data for expUrel. In Table 4, in a similar fashion, we estimate the half-width of the 95% CI (Final expUrel ) for the other elements taking into account differences in sample types and concentration ranges, and by being conservative, and rather err by overestimating expUrel. We end up with half-width 95% CIs typically in the range 10 to 30% with the most common values around 20±5% and only few extreme values below 10% and above 30%, the maximum being 58% on Ni due to its low concentration (3 ppm) in biomass. Incidentally, we were also informed by practitioners in the field that a half-width 95% CI of 10-30 % is quite typical for the total uncertainty (including from sampling and digestion) for ICP-OES analysis with acid bomb digestion of biomass (also consistent with Table 4). Also, it is clear - when comparing the two sets for the 95% CI - that the external laboratory used by us does a good job: Except for the low concentration of Ni, their uncertainties are within the generally accepted uncertainty of these methods. In Table 5, we report the analysis data for sulfur in the 5 biomass samples. Due to the characterizations performed in this study, we obtained sulfur values by 3 different methods (ICPOES, ion-chromatography, and organic combustion analysis) and it is interesting to compare the values from different methods. Notably the determination(s) by ICP-OES and the determinations by ion-chromatography give close results with overlapping 95% CIs. Although the ICP-OES result has a lower estimated uncertainty it is noted that the ion-chromatography was optimized specifically for the precise analysis of anions formed from e.g. S during the sample combustion.


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The ion-chromatographic results are thus likely very good. Therefore, we combined one (or two) determination by ICP-OES with the two determinations by ion-chromatography in a singular reference value by using standard formulas for averaging and combining standard uncertainties. This rather lengthy discussion of the uncertainty of the reference method not only serves to establish the uncertainty on our reference data, but also to discuss the uncertainty of these standard reference methods used for analysis of biomass. This is relevant when we ultimately would like to compare the XRF method against these reference methods. 3.2.

XRF analysis and repeatability. To obtain a good measure on the statistical

variation on the XRF data we measured both sides of between 5 and 15 pellets for each of the 5 biomass types. Specifically, preparing samples for analysis starting from the finely pulverized sample (Table 1), weighing, and pressing pellets, and extracting the final data averages and standard deviations was performed by three students under careful instruction and supervision by one of the authors. The number of pellets analyzed (always both sides): Beech: 5 by one student (N = 10); Switchgrass, Corn stover, and Pine: each 5 by two students (N = 20); Eucalyptus: each 5 by 3 students (N = 30). The pellets were analyzed over a two week period. Figure 2 shows a typical example of results obtained for the 3 students each analyzing 5 pellets. To illustrate the typical performance we look at the elements Fe, Cl, S, Si, and Na in eucalyptus. First, it is noticed that the individual measurements generally deviate less than ±10% from the mean value, and there is no pattern indicating that analyzing the second side of the pellet gives results that are systematically different from the first side. Between the students there is some variation in the mean value and standard deviation, and this could have to do with how carefully they worked. Probably, it is less likely due to variation in the XRF instrument since our experience is that day-to-day variation is minimal and even long-term drift is also not


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pronounced. (Note: The XRF was drift-corrected - hence calibrated - by the manufacturer just before this analysis series.) Clearly, there is some variation from sample preparation which could derive from how careful the pellets are prepared and handled, but also from factors which are (partially) outside the control of the person preparing the pellets. Specifically, as discussed in our previous study, Na analysis by XRF can be subject to problems – especially with the low concentration in these biomass samples - due to the low penetration depth of the low-energy Na Kα line (Table 2).9 For Na, the emitted Na Kα radiation only originates from a thin top-layer of the pellet, and the density and smoothness of this layer thus influences the detected intensity (and hence the measured Na concentration) significantly. Further, the Na results will easily reflect any inhomogeneity in the pellet – and especially if the top-layer has areas (grains) with a different Na concentration than the bulk pellet. This segregation of Na was also observed in the Bionorm Project.14 The same applies to elements like Si: Sand grains rich in Si (quartz, silicates) could render the pellet inhomogeneous – or could even settle at the pellet surface. Therefore, one should indeed expect more variation in the Na and Si results than for other elements as also evident in Figure 2. Nevertheless, as can be seen, the average of 5 measurements (on one side of 5 pellets) of any elements rarely exceeds 10% deviation from the global mean (30 measurements). In Figure 2, we also give the global sample mean with sample standard deviation (SDN) and sample standard error (SEN = SDN/√N, N=30). It is briefly recalled that the sample average ( XN ) and sample standard deviation ( SDN ) are only estimates of the population mean ( X ) and the population standard deviation (SD); the 'population values' being the result of performing an infinite number of measurements. The experimental sample average ( XN ) and the sample standard deviation ( SDN ) are calculated


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from the data set using standard formulas. The standard error on the mean is calculated as SEN = SDN /√N and is a measure how well XN estimates X. The population mean ( X ), i.e. the true experimental mean from an infinite number of measurements, now lies in the confidence interval given by XN ± t × SEN where t is calculated using Student's t-distribution available in tables for different confidence levels and degrees of freedom ( N – 1 ).20 Specifically, for N = 10, 20, and 30 the values are t = 2.26, 2.09, and 2.04 and we simply use t = 2 for these numbers. A central question to be asked would be: How many pellets would one have to analyze to get an average XRF result which deviates no more than e.g. 10% or 20%-rel. from the true value? (Assuming here that a true concentration (µ) exists for each element.) In the end, we will see how this question can be answered. Here it is noted that the question has to be formulated differently: As the number of measurements ( N ) increases the average ( XN) does generally not converge towards the true value ( µ ); it converges towards the experimental population mean ( X ). X deviates from µ by the systematic error (bias) Z ≡ X - µ. So the question can better to be formulated like, e.g.: By measuring the two sides of one pellet and taking the average, will this average deviate less than e.g. 10% from the experimental population mean? I.e., if one can get a good estimate of the experimental population mean (X) and also has information available the bias ( Z ), e.g. tabulated from analysis of reference materials like in this and our previous study, then it is also possible to make a good estimate of the true value µ = X –Z. Clearly, the true concentration (µ) is in the end what we would like to know – however, all measurements will only be estimates of this true (but unknown) concentration – and by correcting for biases to the extent possible one would obtain the best possible estimate of µ.


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Figure 3 illustrates these points using sulfur in eucalyptus as an example. The graph shows the evolution in the calculated sample mean (XN), sample standard deviation (SDN), and sample standard error (SEN = SDN /√N), and 95%CI (XN ± tN × SEN with Student's t depending on N) with increasing number of measurements (N). The graph also includes the reference value with 95% CI (139 ±15 ppm) as calculated in Table 5 and the global sample mean with 95% CI (157±2 ppm) determined by XRF for N = 30 (Table 6). As expected, SEN drops as N increases and along with SEN the 95%CIN narrows as well – especially in the beginning (small N) where there is a rapid reduction in Student's t. This reduction in Student's t simply reflects that SDN becomes a better estimate of SD. Briefly to explain, when measuring a quantity repeatedly the process could look like this: The first two measurements give two numbers very close. Does this closeness just happen by chance? Or does it reflect that that all future measurements will be close as well? That is, in the beginning (small N) - without any other information available – there is no way to know just from looking at the data if the observed small scatter of data points just happen by chance, or if it is already an reflection that the whole population of data (i.e. including all the subsequent future data points) do not scatter much. Any truly low scatter in the population of measurements first becomes certain with more measurements. Therefore Student’s t has to be huge for small N, and as N increases the increased certainty in the scattering is reflected in Student's t becoming smaller, i.e. SDN becomes better estimate of SD. This means that the large Student’s t - and the very wide calculated 95%CI interval - for N=2 is only valid if there is no other information available than the experimental data themselves. However, experience presented here and in our previous study has given us more information, specifically that data obtained from this XRF method generally scatter very little.9


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Therefore, we can immediately say that the huge 95%CI for N=2 is an exaggeration not reflecting a good estimate of the 95%CI (also evident in Figure 3). This is an important point: We know the XRF data scatter little (SD is small) so only a few measurements are in reality needed for XN to be a good estimate of the population mean (X). In Figure 3, it is noted that with just two measurements (each side of one pellet, N=2) the mean (163.5 ppm) is already quite close to the global sample mean (157 ppm), although it is not inside the 95%CI (157±2 ppm = XN ± 2 × SEN, N = 30) of the experimental data. Clearly, the reference value (139 ppm) is further away, and even with increasing N the experimental sample mean does not converge towards the reference value. As can be seen, as N grows, the experimental sample mean (XN) clearly converges to a different value, i.e. the experimental population mean (X) for infinite N, approximated by the ‘global mean’ for N = 30. In Figure 4, the sulfur results for all individual eucalyptus pellets are shown. The data fall in the range 140 to 170 ppm and the pellet mean values fall generally in the range 153.5 to 162.5 ppm with one somewhat outlying number (146.5 ppm). Clearly, measuring the two sides of one pellet generally returns a mean value which is less than ±5 ppm (or 3%-rel) from the global mean (157 ppm). Note also, there is no systematic trend whereby the second side gives higher or lower results than the first side. This is very convincing: The mean value calculated from two sides of the same pellet is a very good estimate of the large sample mean (XN, N =30) and, by extrapolation, also a good estimate of the experimental population mean (X). Further, the bias (Z = 157 – 139 = 18 ppm, or 18/139 = 13%-rel) is larger than the range of XN=2 values (146.5 to 163.5 ppm, or ±6%-rel), and the bias thus dominates (or is as large as) the random error. Therefore, focus should be on correcting for this bias rather than increasing the number of analyses (and analysis time).


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Specifically, if a good estimate of the bias (Z) is known then a very good estimate of the true value (µ) can be calculated from the mean value of the two sides of one pellet by subtracting the bias (µ ≈ XN=2 – Z). Clearly, information on the bias (Z) one would have to record from analysis of set of independent reference materials in a way like illustrated in the present study (i.e. use the equation the other way round (Z ≈ XN - µ ≈ XN - mref). With the discussion above, exemplified by sulfur, we hope to have made a reasonable argument that to obtain – within a minimal amount of analysis time - a reasonably good estimate of the experimental concentration (X) it is sufficient to analyze the two sides of a single pellet. True, we did not conduct the detailed analysis above for each element. However, as shown below, all XRF data scatter very little for most elements, i.e. for all (most) elements the random error on XN=2 is generally less than the bias (systematic error). Therefore, the overall error of the analysis does not reduce much by increasing the number of analyses beyond N = 2. The results obtained from analyzing the two sides of one pellet are thus already quite good, and if there is no information available on the systematic errors (biases) the the best estimate of the true concentration is µ ≈ XN=2. To further improve the result one has to investigate the biases (Z) and tabulate these, or express them as mathematical functions where possible (see below). Information on the bias (Z) one would have to record from analysis of set of independent reference materials. When information on Z is available, a better estimate of the true concentration (µ) can be obtained as µ ≈ XN=2 – Z. Again, this approach is a good choice because: 1) with only two measurements the overall error on the mean value (XN=2) has a large component from (dominated by) the bias and,


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2) reducing the random error further will cost significant extra experimental time and more samples.

3.3.

Comparing XRF data with reference values. The performance of the XRF

analysis is assessed by comparing XRF data with the reference value (assumed best estimate of true value) determined with the reference methods. The XRF analysis was performed on pressed pellets produced from the same batch of pulverized (200 µm) sample used to obtain the reference data. Specifically, we are looking for any bias in the XRF method. As discussed above: With a good (tabulated) estimate of the method bias on each element concentration determined by XRF, combined with the low random variation in the XRF data, it would be possible to calculate a good estimate of the true value with just measuring the two sides of one sample pellet. The 'paradigm' used here, and in our previous study, assumes that there is an absolute true value (µ) for the concentration of an element in a sample.9,19 The value of µ is unknown, and both the reference value and the experimental XRF result are estimates of µ. In reality, we are comparing these two estimates, with the reference value (mref) being the best estimate (µ ≈ mref), and the XRF value (XN) potentially having a bias, Z ≡ X - µ ≈ XN - mref. We want to know this bias on the XRF value since - along with the random error – it is a direct measure of the XRF methods performance, and furthermore the bias can potentially be tabulated and corrected for. Table 6 reports the XRF results with standard error (XN ± SEN) along with the reference mean value (mref , dry base) with the associated half-width ( CI/2 ) of the 95% confidence interval calculated from the expanded uncertainty 'Final expUrel' in Table 4. Obviously, the best


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estimate of the true value is the mean value (midpoint) of the reference range, thus: µ ≈ mref. It is also clear that the XRF standard error (SEN) is quite small due to the relative large N (= 10 - 30) and because XRF in general has little random scatter: SEN/XN < 5% and typically < 1 % for these large N. This low random error was also found in our previous study.9 For the lowest concentrations, typically less than approx. 10 to 25 ppm, the relative uncertainty can be higher, and when approaching the detection limit (LOD) SEN/XN can increase to 100% because the element is not detected in all measurements. In Table 6, if an element is detected by XRF in only some, but not all measurements, the result is stated in parenthesis as '(XN ± SEN)' where the 'not detected' measurements have been assigned the value zero in the calculation of XN and SEN. If an element is never detected in any XRF measurement of a particular biomass sample then this is stated as 'not det'. This would typically mean that the concentration is below the detection limit (Table 2), e.g. < 90 ppm for Na in switchgrass. In our previous study, we observed - with some exceptions – that below 25 ppm (or below LOD) not all elements are consistently detected in all repeated XRF measurements.9 The results of the current study are consistent with this observation. Note that some of the lightest elements are not detected consistently by the XRF method close to 25 ppm and somewhat above 25 ppm: Ti in Corn Stover with reference value of 22 ppm and XRF value of (34 ± 3) ppm, Cl in Beech with reference 38 ppm and XRF value (22 ± 9) ppm, and Al in Monterey Pine with reference 34 ppm and XRF value (19 ± 3) ppm. Here, the reference values are low and close to the rather high LOD for XRF analysis of these light elements (Table 2). Note for information: For the elements in Table 6 which have been consistently detected one can compare each individual XRF result with the reference value by mathematical methods as discussed elsewhere.21


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In Figure 5, we use a simple method with the advantage that it allows comparing all data for each element together. Here, for each element we only incorporate measurements down to the lowest concentration where they are still consistently detected in all repeated XRF measurements (i.e. we exclude 'not detected' and results in parenthesis in Table 6). The XRF results for all elements are plotted with 95% confidence interval (XN ± 2 × SEN) against the reference values with 95% confidence interval (mCRM ± CI/2). The horizontal and vertical error bars indicating the 95% confidence interval form a small conceptual box ('95% confidence interval box'). The shown straight line through the origin (0,0) has a slope of unity (1). Ideally, the mean values should be right on this line, or the '95% confidence interval box' should include (or touch) the line implying that the two 95% confidence intervals of the XRF data and reference value overlap. In Figure 5, in several cases the mean value points lie close to the unity line and in most cases the '95% confidence interval box' includes the unity line. The data thus indicate that the XRF data are close to the reference data within the uncertainty on each value. When plotted this way, the data indicate that the XRF method performs very well without a pronounced systematic error (bias) for many elements (Na, Mg, Al, Si P, S, Cl, K, Ca, Mn, Fe) down to approx. 100 ppm or lower concentrations. In some cases, the unity line is clearly outside the box which indicates that there is a pronounced bias on this particular XRF measurement. Chlorine specifically is clearly over-determined by XRF in Switchgrass and Pine (around 100 ppm). A blank (pure Hoechst Wax C) showed 81 ppm Cl so the over-determination is likely caused by an instrument offset. However, we recall that for Cl in Beech (38 ppm reference value) the XRF gives (22 ± 9) ppm with Cl not detected in all measurements. The XRF Beech value is thus far below the 81 ppm ‘blank’ measured with Hoechst Wax C and this seems strange. Therefore, we cannot exclude that in fact the Hoechst Wax C has some chlorine contamination


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and we probably should be careful in interpreting the origin of the errors on the Cl concentrations below 100 ppm. However, for chlorine there also seem to be a real over-determination increasing with the concentration (up to 0.4 %). Looking at the XRF spectrum (Figure 1), on the high-energy side of the Cl peak there is another peak which could maybe explain this overestimation, e.g. by causing baseline problems for the Cl peak at least for low concentrations of Cl. Indeed, looking at the XRF spectra from Switchgrass and Pine, the Cl peaks are just tiny peaks on the shoulder of this much larger peak. For silicon (Si) an over-determination by XRF in Beech and Pine (concentration less than 100 ppm) is also observed in Figure 5. Again, as visible in Figure 1, the Si peaks in the XRF spectra are not high (but still sharp peaks of about 10 times the noise) and located somewhat at the end of the recorded range. This may be the reason for the discrepancy, or it may be some effect of the inter-element correction in the spectrum evaluation. Also, as already mentioned, it could also be that occasionally grains of silicates (feldspars and quartz) could segregate to the surface during pellet pressing causing an over-determination by XRF. Finally, we cannot completely exclude that there is an error on the reference values for these low concentrations of Si due to under-determination by ICP-OES (e.g. partial sample digestion and adsorption of silicates to the ICP-OES sample feed system). Regarding silicon (Si): XRF is particularly interesting for direct quantification of Si due to these complications associated with determination of Si by standard methods (ICP-OES / MS) requiring dissolution of SiO2 and silicates with hydrofluoric acid. Further, quartz ( SiO2 ) and silicates are important in the processing of biomass, e.g. in ash formation, and in abrasion and attrition of equipment transporting the biomass (pipes, reactor vessels etc.).22-23 Therefore, a reliable and quick quantification of Si as part of a multi-element XRF analysis would be of


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practical interest. The present XRF method shows that a reliable determination of Si within the typical error of 10 – 20 %-relative is possible for concentrations above approx. 100 ppm. For many applications this would be quite acceptable. Below 100 ppm this XRF method appears to significantly over-estimate Si. For some elements with concentrations < 25 ppm (Co, Ni, Cu, Zn, Sr) the data indicate that the XRF method gives useful results for these elements below 25 ppm (Figure 5). This is consistent with our previous study.9 Molybdenum (Mo) is only present significantly in Corn Stover ( 4 ppm reference value) and detected with 25 ppm by the XRF. This gross error is characteristic for Mo analysis by this particular XRF method and was associated with interferences or baseline problems in our previous study.9 In fact, the 17.5 keV Mo Kα line is located on the shoulder of the Rh Kα Compton line (Figure 1) and this may cause some problems in determining a good baseline for the very low concentration of Mo. For the other investigated elements with concentrations below 25 ppm (Co, Ni, Cu, Zn, Sr) it appears that Co, Sr, and maybe Ni and Zn, can be determined quite well down to around 10 - 15 ppm, although it appears Zn was not determined well in Corn Stover at 20 ppm reference value. Cupper (Cu) is consistently over-determined by XRF in this concentration range consistent with findings in our previous study.9 However, both in that study (Figure 4 in that study) and in the current study (Figure 5) the measurement points actually form a straight line offset by close to 9 ppm corresponding exactly to the 9 ppm recorded in a blank sample (pure Hoechst Wax C). The bias on the Cu results are thus caused by an instrument offset which is stable and can be corrected for. Correcting for this offset would therefore give reliable Cu results down to just a few ppm.


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This illustrates that the systematic error for the lowest concentration range (e.g. < 25 ppm) likely has a significant contribution from offsets rather than factors proportional in the concentration.9 Thus, for this range (< 25 ppm) it would make most sense to give the bias as an absolute value in ppm. For the higher concentrations the systematic error will often be dominated by factors proportional in the concentration and it makes more sense to report the bias relative to the concentration. In Figure 6, the relative bias (ZREL = Z / mref × 100%) on the individual XRF data points is plotted. It is (reasonably) assumed that the best possible estimate of this bias (Z) is given as Z ≈ XN - mref (see above). This analysis only includes data where the reference value is ≥ 25 ppm and above the LOD (Table 4) and where the element is consistently detected in all repeated XRF measurements of a sample. Here, to get a better view of the systematic errors, it makes sense to group the data according to reference values: mref ≥ 1000 ppm = 0.1 % go in one group, those with 100 ppm ≤ mref < 1000 ppm in another group, and those with 25 ppm ≤ mref < 100 ppm in a third group. Figure 6 gives a somewhat different impression of the XRF results than does Figure 5: The actual biases are much more visible in Figure 6 when expressed as relative errors. It is further notable that a positive bias is much more common than a negative bias. This tendency towards a positive bias was already observed in our previous study.9 But, in the present study it is more pronounced and we speculate if this may have to do with the generally larger sample grain size used in the present study. For the range ≥ 1000 ppm (■) the relative biases are 3 – 18%, i.e. less than 20%, and notably all positive. This is only slightly different from our previous study with errors typically in the range ±10% for concentrations ≥ 1000 ppm.9


For the two lower

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ranges, 25 to 100 ppm (× ×) and 100 to 1000 ppm (○), the relative errors are generally within the range ±15%, slightly better than or comparable to our previous study.9 The larger errors found for Cl are clearly visible in the range +45% to +90%. As discussed above, we cannot say exactly the origin of this, however, it could due to an interfering peak next to the Cl peak in the X-ray emission spectrum. Likely, it is not a contamination of the sample with NaCl during handling and sample preparation for the XRF e.g. due to the laboratory being located a few 100 meters from the ocean. Because, in that case, samples used for reference ICP-OES measurements would likely also be contaminated, and moreover, the Na concentration would be higher and Na would have been detected in at least some on the XRF measurements on these samples. These observations are consistent with those in our previous study as well.9 For aluminum (Al), the large negative error (-39%) found in the previous study in one sample was assigned to a bias in the XRF method (for that particular sample).9 In the present study, no such pronounced error was found for Al – although, all data for both studies taken together suggest that Al generally has a negative bias in this XRF method. This is discussed further in the previous study.9 There we also discussed the fact that this XRF method, at times, for some specific elements in some specific samples, can produce larger errors exceeding the typical say ±15%. As discussed, these larger errors (more than ±20%, sometimes ±40% to ±90%) seem to be caused by problems with interfering neighboring / overlapping peaks, and by the general problem of defining the proper baseline around a peak to properly establish its exact intensity. We cannot say if these are problems specific to this method and if they can be easily solved – or if they are fundamental and general in character. In any case, when analyzing an unknown sample this occasional large error could be a strong limitation. In practice however, a laboratory may be analyzing limited types of samples and therefore build their own experience


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for these particular samples and thereby judge what samples can be reliably analyzed and what constitute reliable results. Still, one obviously has to be very careful with such an approach and ensure sufficient scrutiny of the obtained data. Comparing the error observed for the XRF method with those of the reference methods (ICP-OES and ion-chromatography) we realize that the XRF method actually performs at least equally well for the elements investigated here: The XRF method generally has relative errors better than 10 - 20% depending on element and concentration. The reference methods typically have relative errors in the range 10-30% (Table 4). The XRF method has the draw-back that it will occasionally produce large (gross) errors for some elements in some samples. Further, below 25 ppm the XRF method has problems with quantifying some elements. However, it may be possible to resolve some of these problems with further refinement of the XRF method and correcting for systematic errors (biases). Figure 7 illustrates plotting the bias (Z) against the concentration of some elements (P, S, Cl, K). Clearly, in some cases Z is ‘stable’ in the sense that the biases fall systematically on a curve which can be tabulated or approximated with a mathematical function. Chlorine (Cl) performs very well with all data recorded in this study and in our previous study (2013 study, data recorded one year before the present data)9 falling on an approximately straight line. For chlorine one can therefore establish a function to describe and subtract the bias in the XRF method. For potassium (K) the data in this study lie on a straight line but the 2013 data are more scattered and only some fall on a (different) line. Apparently, for K there is some method/instrument instability causing the bias-function not to be stable over time. The same holds true to an even higher degree for sulfur (S). For phosphorus (P) the calculated bias scatter randomly, but in this case the calculated bias is very small ( typically < 1- 3%-rel) and therefore


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close to the random error on the measurements. Therefore, when calculating the bias as Z ≈ XN mref we have probably been ‘spilling’ the random error on XN (i.e. the random error, XN – X ) into Z. This is not a problem; it simply indicates that the bias is equal or smaller than the random error on the phosphorus (P) determinations. Figure 7 gives an indication that there is room for further refining the XRF method to ensure that the ‘bias function’ for each element is more stable over time. Figure 7, and also the results for cupper (discussed above), show that stable ‘bias functions’ are possible, and in these cases one could develop a procedure to subtract the bias. This would then include checking the ‘bias function’ of these elements during periodical instrument check-ups, i.e. through the analysis of a set of reference materials. Overall, the performance of the method for this range of 5 real biomass samples is quite comparable to the findings in the previous study using a range of 13 biomass reference materials with their very fine grain-size typically below 100 µm.9 One interesting finding, demonstrated clearly in the present study, is that it is not necessary to pulverize to a grain-size below 100 µm using e.g. time-consuming ball-milling. Instead, using a rotary mill with a 200 µm sieve and achieving typical grains (needles) with one dimension < 200 µm appears sufficient for achieving a comparable performance of the method. These samples also produced mechanically stable pressed pellets. This shows one possible practical approach to biomass analysis by XRF, and the method may well be optimized with e.g. automated sample preparation. In the Bionorm Project it was shown that using a sample grain-size of 0.5 to 1 mm is sufficient in combination with hot-pressing of sample pellets at 140 degC.14 Using large grainsizes obviously potentially has the advantage of completely avoiding fine milling in the rotary mill and also avoiding any possible fractionation by size. This is very interesting and should certainly be considered further. However, hot-pressing potentially may have the disadvantage of


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removing volatile fractions and elements. Unfortunately, for this large grain size, obtaining mechanically stable pellets with cold-pressing with binder (wax) is probably not possible. Method improvement could come from making a standardless method specifically for biomass. That is, the current method is a universal method for all kinds of materials and it may be possible to refine the method for the specific area of biomass samples by resolving the specific problems for this type of samples. Here, it is encouraging that the generally good performance of this method has been confirmed now for a total of 13 + 5 = 18 biomass types of very diverse origin. In our opinion the performance for biomass is quite acceptable for many applications, and the overall errors are comparable to those found in the standard reference methods. Therefore, the method can be regarded as quantitative for biomass analysis. However, the occasional large (gross) errors on specific elements in specific samples should be kept in mind, although this problem may not be present if a laboratory always analyses the same type of sample (e.g. in quality control) and the samples are known to not have this problem. These occasional large (gross) errors may be one reason manufacturers prefer not to give guaranties on the accuracy of these methods. Finally, the whole time-consuming process of sample milling and pellet-pressing might be avoided by using hand-held XRF directly on the sample. Hand-held XRF is thus potentially interesting for routine analysis of biomass since it can deliver results quickly directly at the location of the biomass (field, storage, processing line). The actual performance of hand-held XRF for biomass analysis is being investigated.24-27 Further developments in this area and in other areas of XRF, e.g. methods and spectrum evaluation, will likely improve the detection


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limits and matrix and inter-element interferences. Here, new developments can be found a relevant annual review of developments in the XRF field.28 3.4.

Comparison of XRF ash content with proximate analysis ash yield. Proximate

analysis by combustion is the standard procedure for determining ash yield of biomass (Table 3, ASTM E1755-01, at 575±25°C). However, an estimate of the ash yield can also be obtained from the XRF analysis on the raw biomass itself. If the XRF is used for elemental analysis anyways it would of course be rational to use the XRF ash yield rather than performing a separate ash yield determination by combustion. Further, in those cases where there is only a limited amount of sample available, the 8 grams used by the XRF is sufficient to give both elemental data and ash yield. (Note: We have also published data on biomass ash composition based on our own work and many helpful references.3-4) The determination of ash yield by XRF is done by calculating the sum of the elements expressed as oxides: Specifically, based on the XRF data for the elemental composition (ppm mass fractions) of the raw biomass one can calculate the concentration of the corresponding oxide (ppm mass fractions) by multiplying with the oxide-to-element stoichiometric factors included for information in Table 6. We also used this approach in our previous study of 13 biomass certified reference materials.9 Thus assuming the ash from combustion of the biomass would consist of the most common oxides (or common form if not bound to oxygen, see Table 6) the sum of oxides calculated by XRF would be a direct measure of the ash yield from combustion. If so, the XRF method would not only provide elemental composition, but also the ash yield (and ash composition). Obviously, ash yield and composition are important parameters for many applications including biomass combustion and gasification. (Note: Formally, assuming the ash to consist of oxides is a good approximation as long as only oxides and oxo-anion salts


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are present. The oxo-anion salts have the stoichiometry as mixed oxides, e.g. Na2SiO3 = Na2O + SiO2, CaSO4 = CaO + SO3 etc.) In Figure 8, the ash yield calculated from the XRF data (Table 6) – as the sum of the oxides - are plotted against the reference ‘as received’ ash yield determined by combustion (Table 3) and also plotted against the combustion ‘dry’ ash yield, i.e. the ash yield one would have obtained from combustion of a moisture-free biomass (calculated from data in Table 3). Clearly, when plotted as in Figure 7 the XRF data look very good for the shown range up to 6% ash. The XRF data are generally fairly close to both the ‘as received’ and ‘dry’ combustion ash yield data. This is consistent with our previous study.9 In that study, however, the XRF data were closer to the ‘as received’ combustion ash yield data for higher ash yields ( >15% approx.). In fact, the data shown in that study show a curvature, i.e. for the higher ash yields the XRF will underestimate the ash yield. As discussed there, this is likely due to the formation of carbonates and hydroxides (i.e. the ash is not only oxides) and the inclusion of e.g. char (unburned carbon) in the ash particles. This would nevertheless not explain why for around 5 to 15% ash yield one observes a slight overestimation by the XRF. This might instead be caused by the generally observed slightly more common positive bias in the XRF data, and might therefore be corrected with further refinement of the XRF method. In any case, one can obtain quite acceptable results with the XRF method for up to 5 to 10% ash yield with absolute errors (vertical distance to unity line) generally less than 0.5 to 1%, or less than ±10% relative error, and with both the ‘as received’ and ‘dry’ ash yield being included in this error range.


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4. CONCLUSIONS The presented data constitute a comprehensive and independent test on the performance of a typical commercial pre-calibrated / standardless XRF method for multi-element analysis performed on 5 common biomass samples (switchgrass, corn stover, eucalyptus wood, beech wood, and pine wood). The method involves cutter-milling and rotary-milling (200 µm sieve), pressing the biomass into pellets, and recording the full X-ray emission spectrum. The good results show that there is no need to apply time-consuming ball-milling to grain-sizes below 100 µm as is commonly recommended for XRF analysis of general samples. Data recording and evaluation takes about 40 min per sample ( 2 sides), manually milling takes approx. 15 min, and manually pellet pressing and weighing the sample takes another 10 min. Total analysis – manually performed on the raw biomass – thus takes about 1 hour. The whole procedure can also be automated which would reduce the associated labor costs and probably also the time needed to mill and press the pellet. The elements consistently detected by the XRF are: Na, Mg, Al, P, S, Cl, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Sr, Mo. For elements above 25 ppm, the XRF data show a relative systematic error (bias, trueness) typically better than ±15% independent of the concentration. The elements present with > 1000 ppm (Mg, Cl, K, Ca) show consistently a positive bias of 3 – 18% relative. The relative precision (measured as the relative standard error) is better than ±5% (typically ±1%) for concentrations > 25 ppm (obtained with 10 – 30 measurements). Quantifying elements below 25 ppm (Co, Ni, Cu, Zn, Sr, Mo) is possible in some cases, but it requires a more detailed study for each specific element. E.g. Cu can be determined down to a few ppm if applying an appropriate correction for the method bias. Occasionally, larger relative biases of up to 50% to


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90% can occur for certain elements (Si and Cl) in certain samples, so care has to be taken to carefully test the applied method for the particular samples and elements of interest. Determination of silicon (Si) by XRF which appears to work well (< 15%-rel. error) for higher Si concentrations ( > 100 ppm). A limited amount of moisture (< 8% tested) in the sample is acceptable and will produce results within the stated accuracy with the XRF data reflecting the dry state. It is further demonstrated that the XRF method can be used to estimate the amount of ash yield from biomass combustion with a bias typically better than ±10%-rel (as received base, or dry base for moisture < 8% tested). Finally, it is argued that the XRF data scatter little and that systematic errors often dominate. Therefore it is possible to obtain acceptable data as the average obtained from analyzing just the two sides of one single pellet. With further refinement of the XRF method it should be possible to obtain ‘bias functions’ to correct the data for the bias in the XRF method. The 5 biomass types combined with the 13 CRMs employed in our previous study span a broad range of biomass types with the XRF method generally producing quite reliable results (keeping in mind the limitations and needed bias corrections) with overall errors comparable to the reference methods. This suggests that typical standardless / pre-calibrated XRF methods can work well (keeping in mind the limitations) in elemental analysis of raw biomass, and therefore could be considered for general usage in e.g. industrial analytical laboratories requiring fast multi-elemental analysis of biomass. AUTHOR INFORMATION Corresponding Author


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*Tel.: +31-22456-5029; Fax: +31-22456-5626; E-mail address: [email protected] Notes The authors declare no competing financial (or other) interest ACKNOWLEDGMENTS The present work was carried out in part within the European Commission's research programme. Stanislav Vassilev would like to express his gratitude to the Joint Research Centre (European Commission) for the possibility to perform studies at the Institute for Energy and Transport (Petten, The Netherlands) as a Detached National Expert. Patricia Alvarez would like to thank the Spanish Science and Innovation Ministry for her Ramon y Cajal contract and research project MAT2010-16194. We gratefully acknowledge the encouragement received from working group WG 5 of the standardization bodies ISO/TC 238 and CEN/TC 335 working on Chemical Test Methods for Solid Biofuels, including a technical specification for X-ray analysis of biomass (ISO TS 16996). We thank Eoin Oude Essink, Jakub Bielewski, and Jesse Ang from the European School in Bergen (NL) for their help with preparing the biomass samples for analysis and their help with data extraction and evaluation. We thank Peter Rensen and Maurice Heimstra, Energy Research Centre of the Netherlands (ECN), for discussion of the results obtained by ICP-OES and ion-chromatography.


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REFERENCES (1) Vassilev, S. V.; Baxter, D.; Andersen, L. K.; Vassileva, C. G., An overview of the chemical composition of biomass, Fuel 2010, 89, 913-933. (and references therein) (2) Vassilev, S. V.; Baxter, D.; Andersen, L. K.; Vassileva, C. G.; Morgan, T. J., An overview of the organic and inorganic phase composition of biomass, Fuel 2012, 94, 1-33. (and references therein) (3) Vassilev, S. V.; Baxter, D.; Andersen, L. K.; Vassileva, C. G., An overview of the composition and application of biomass ash. Part 1. Phase-mineral and chemical composition and classification, Fuel 2013, 105, 40-76. (and references therein) (4) Vassilev, S. V.; Vassileva, C. G.; Baxter D., Trace element concentrations and associations in some biomass ashes, Fuel 2014, 129, 292-313. (and references therein) (5) Handbook of Practical X-ray Fluorescence Analysis, Beckhoff, B.; Kanngiesser, B.; Langhoff, N.; Wedell, R.; Wolff, H., Eds.; Springer-Verlag: Berlin Heidelberg, 2006. (6) Handbook of X-Ray Spectrometry (2nd ed.), Van Grieken, R. E.; Markowicz A. A., Eds.; Marcel Dekker: New York, 2002. (7) Jenkins, R.; Gould, R. W.; Gedcke, D., Quantitative X-ray Spectrometry (2nd ed.), Marcel Dekker: New York, 1995. (8) Jenkins, R., X-Ray Fluorescence Spectrometry (2nd ed.), John Wiley & Sons: New York, 1999. (9) Andersen, L. K.; Morgan, T. J.; Boulamanti, A. K.; Alvarez, P.; Vassilev, S. V.; Baxter, D., Quantitative X-ray Fluorescence Analysis of Biomass: Objective Evaluation of a Typical Commercial Multi-Element Method on a WD-XRF Spectrometer. Energy Fuels 2013, 27, 74397454. (10) Margui, E.; Hidalgo, M.; Queralt, I., Multielemental fast analysis of vegetation samples by wavelength dispersive X-ray fluorescence spectrometry: Possibilities and drawbacks. Spectrochimica Acta Part B 2005, 60, 1363–1372.


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Energy & Fuels

(11) Margui, E.; Queralt, I.; Hidalgo, M., Application of X-ray fluorescence spectrometry to determination and quantitation of metals in vegetal material. Trends in Analytical Chemistry 2009, 28(3), 362–372. (12) Nielson, K. K.; Mahoney, A. W.; Williams, L. S.; Rogers, V. C., X-Ray Fluorescence Measurements of Mg, P, S, Cl, K, Ca, Mn, Fe, Cu, and Zn in Fruits, Vegetables, and Grain Products, Journal of Food Composition and Analysis 1991, 4, 39–51. (13) Shaltout, A. A,; Moharram, M. A.; Mostafa, N. Y., Wavelength dispersive X-ray fluorescence analysis using fundamental parameter approach of Catha edulis and other related plant samples, Spectrochmica Acta Part B 2012, 67, 74-78. (14) Personal communication from Dr. Martin Englisch (Bionorm Project and ISO TS 16996) (15) Lachance, G. R., Tutorial review: Correction procedures using influence coefficients in Xray fluorescence spectrometry, Spectrochimica Acta 1993, 48B(3), 343-357. (16) Broll, N.; Tertian, R., Quantitative X-Ray Fluorescence Analysis by Use of Fundamental Influence Coefficients, X-Ray Spectrometry 1983, 12(1), 30-37. (17) Broll, N., Quantitative X-Ray Fluorescence Analysis – Theory and Practice of the Fundamental Coefficient Method, X-Ray Spectrometry 1986, 15(4), 271-285. (18) Broll, N.; Caussin, P.; Peter, M., Matrix Correction in X-ray Fluorescence Analysis by the Effective Coefficient Method, X-Ray Spectrometry 1992, 21(1), 43-49. (19) Uncertainty of Measurement: Implications of Its Use in Analytical Science, Analytical Methods Committee; The Royal Society of Chemistry, Analyst, 1992, 120, 2303-2308. (20) Taylor, B. N.; Kuyatt, C. E., Guidelines for Evaluating and Expressing the Uncertainty of NIST measurement Results, NIST Technical Note 1297 (1994 ed.), The National Institute of Standards and Technology (NIST): Gaithersburg, 1994. Available online at: http://physics.nist.gov/Pubs/guidelines/TN1297/tn1297s.pdf (21) Linsinger, T., Comparison of a measurement result with a certified value, ERM European Reference Materials Application Note 1, European Commission – Joint Research Centre –


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Institute for Reference Materials and Measurements (IRMM): Geel (Belgium), 2010. Available online at: http://irmm.jrc.ec.europa.eu/reference_materials_catalogue/user_support (22) Vassilev, S. V.; Baxter, D.; Andersen, L. K.; Vassileva, C. G., An overview of the composition and application of biomass ash. Part 2. Potential utilization, technological and ecological advantages and challenges, Fuel 2013, 105, 19-39. (and references therein) (23) Vassilev, S. V.; Baxter, D.; Vassileva, C. G., An overview of the behaviour of biomass during combustion: Part II. Ash fusion and ash formation mechanisms of biomass types, Fuel 2014, 117, 152-183. (and references therein) (24) McLaren, T. I.; Guppy, C. N.; Tighe, M. K., A Rapid and Nondestructive Plant Nutrient Analysis using Portable X-Ray Fluorescence, Soil Sci. Soc. Am. J. 2012, 76, 1446 – 1453. (25) Guerra, M. B. B.; de Almeida, E.; Carvalho, G. G. A.; Souza, P. F.; Nunes, L. C.; Junior, D. S.; Krug, F. J., Comparison of analytical performance of benchtop and handheld energy dispersive X-ray fluorescence systems for the direct analysis of plant materials, J. Anal. At. Spectrom. 2014, 29, 1667-1674. (26) Reidinger, S.; Ramsey, M. H.; Hartley, S. E., Rapid and accurate analyses of silicon and phosphorus in plants using a portable X-ray fluorescence spectrometer, New Phytologist 2012, 195, 699-706. (27) Tighe, M.; Forster, N., Rapid, Nondestructive Elemental Analysis of Tree and Shrub Litter, Communications in Soil Science and Plant Analysis 2014, 45, 53-60. (28) West, M.; Ellis, A. T.; Potts, P. J.; Streli, C., Vanhoof, C.; Wobrauschek, P., 2014 Atomic Spectrometry Update – a review of advances in X-ray fluorescence spectrometry, J. Anal. At. Spectrom. 2014, 29, 1516-1563.


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Emission Intensity (a.u.) - sqrt axis

FIGURES

Rh Kα - Compton

(a)

Rh Kα Rh Kβ - Compton

Rh Kβ

K Kα Fe Kα

49

Emission Intensity (a.u.) - sqrt axis

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

36 25 9 4 16 Photon Energy (keV) - sqrt axis

(b)

Kα lines

K

1

Ca Si

Cl S

P

Mg Al Na

4.00

3.06

2.25

1.56

1.00

Photon Energy (keV) - sqrt axis

Figure 1. Typical X-ray fluorescence spectrum recorded from corn stover. Note that both x and y axis are linear in the square root. (a) Full spectrum with the Rh excitation and Compton lines. (b) Zoom on low energy part showing the equidistant spacing of the fluorescence lines on the squareroot energy axis (Moseley’s law). The Rh peaks are caused by incident Rh Kα and Kβ X-rays being scattered in the sample via Compton or Rayleigh (elastic) scattering.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

+10% 100% - 10%

Fe

+10% 100% - 10%

Cl

+10% 100% - 10%

S

+10% 100% - 10%

Si

+10% 100% - 10%

Page 44 of 57

Na

Student 1 Student 3 global mean Student 2 Side 1 Side 2 Side 1 Side 2 Side 1 Side 2 ±SD ±SE

Figure 2. XRF data for eucalyptus obtained by 3 students each analyzing 5 pellets on both sides. Data are mean values with sample standard deviation for measurements on 5 pellets and given as %-rel relative to the global sample mean values obtained by XRF (N = 30, in Table 5). Also shown: global sample mean with standard deviation (SDN) and standard error (SEN).


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240 Global mean (N=30) with 95% CI

180 Concentration (ppm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

160 140 95% CIN 120

Reference value with 95% CI

SDN SEN

100

Mean XN Measurement

80 1

2

3

4 5 6 7 8 Measurement No. ( N )

9

10

11

Figure 3. XRF data for sulfur in eucalyptus obtained by student No. 2 (in Fig. 2) analyzing 5 pellets on both sides. Individual data points (■) and evolving mean (○) with evolving sample standard deviation (SDN), standard error (SEN) and 95% confidence interval (CIN) shown evolving as measurement number (N) increases. Data points 1+2, 3+4, etc. are from two sides of the same pellet. Also shown: Global mean and 95% CI for all measurements (N = 30) and the reference value with 95% CI.


45

Energy & Fuels

170 Global mean (N=30) with 95% CI

2nd side mean

165 Sulfur concentration ( ppm )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 46 of 57

1st side

160 155 150 145

Reference value with 95% CI

140 Student 1

Student 2

Student 3

Figure 4. XRF data for sulfur in eucalyptus obtained by 3 students (from Fig. 2) each analyzing 5 pellets on both sides. For each pellet the results of the 1st and 2nd sides are shown along with the mean value. Also shown: Global mean and 95% CI for all measurements (N = 30) and the reference value with 95% CI.


46

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Energy & Fuels 0.4

1.5

1.0

0.5

0

Mg Cl Ca

0.3

XRF ( % )

XRF ( % )

K Si

0.2

0.1

0

0

1.0

0.5

1.5

0

0.1

0.2

400

Fe S P

800

Al Na Cl

( ppm )

300

600

400

100

200

0 0

200

XRF

XRF

( ppm )

1000

0

200

400

600

800

0

1000

100

200

300

400

Reference ( ppm )

Reference ( ppm ) 30

140

Mo

120

Cu Zn

Mn Si

100

20

XRF ( ppm )

( ppm )

0.4

0.3

Reference ( % )

Reference ( % )

XRF

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

80 60

Sr Co Ni

10

40 20 0

0

20

40

60

80

100

120

140

0 0

Reference ( ppm )

10

20

30

Reference ( ppm )

Figure 5. Plot of XRF results against reference values indicating the 95% confidence interval for each value as error bars. The straight line has a slope of unity (1).


47

Energy & Fuels

90 Si

Bias relative to reference value ( % )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 48 of 57

Cl

50 40

25 - 100 ppm 100 - 1000 ppm > 1000 ppm

30 20 10 0 -10 Na Mg Al Si

P

S

Cl K Ca Mn Fe

Figure 6. Plot of the systematic error (bias) in each measured element in Table 5. The bias is plotted as percentage of the reference mean value. The markers indicate the reference value: (■) ≥ 1000 ppm, (○) 1000 ppm> conc. ≥ 100 ppm, (× ×) 100 ppm > conc. ≥ 25 ppm. Elements < 25 ppm are not included.


48

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2000

600

1800 Chlorine (Cl) This study 2013 study

400

Potassium (K) This study 2013 study

1600

Bias on XRF value (ppm)


500

300

200

100

1400 1200 1000 800 600 400 200

0

0

500

1000

1500

2000

2500

3000

0

3500

0

5000

10000 15000 20000 25000 30000 35000

XRF value (ppm)

XRF value (ppm) 60 80

40 60

20

Error on XRF value (ppm)


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Sulfur (S) This study 2013 study

0 -20 -40 -60

Phosphorus This study 2013 study

40 20 0 -20 -40

-80 -60

0

500

1000

1500

2000

2500

3000

0

1000

2000

3000

4000

5000

Reference value (ppm)

XRF value (ppm)

Figure 7. Plot of bias (systematic error) on the XRF value as function of XRF value for chlorine (Cl), potassium (K), sulfur (S), and phosphorus (P). Data from this study (●) and from our 2013 study (○).5


49

Energy & Fuels

6 Switchgrass

% Ash (calculated from XRF)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 50 of 57

5

As-received Dry Corn Stover

4 3 2 Eucalyptus

1 0 0

Beech Monterey Pine

1

2 3 4 % Ash (yield from combustion)

5

6

Figure 8. Plot of the mass fractions of ash: % ash calculated from XRF data versus the % ash yield obtained by combustion (reference). XRF data are plotted against combustion data obtained for the as-received samples (●), and plotted against combustion data corrected to zero moisture in the samples (○). The inserted line has slope of unity (1).


50

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Energy & Fuels

TABLES

Table 1. Biomass used in this study Solid biomass

Milling and final powder grain size*

Switchgrass (Pinacum virgatum)

Received: fine powder < 1 x 1 mm Milling: 200 µm Powder: ≤ 50 µm (40%) + needles (100-200 x 300-700 µm, 60%) Corn stover (Zea mays) Received: flakes 2 x 2 mm containing corn cobs without Milling: 1 mm + 200 µm grains (NK brand N33-J4) Powder: ≤ 50 µm (60%) + needles (200 x 300700 µm, 40%) Eucalyptus Received: flakes 2 x 3 cm (Eucalyptus globulus) Milling: 2 mm + 1 mm + 200 µm wood chips Powder: ≤ 50 µm (50%) + needles (200 x 200700 µm, 50%) Beech Received: flakes 2 x 5 mm (Fagus sylvatica) Milling: 2 mm + 1 mm + 200 µm wood chips (only little bark) Powder: ≤ 50 µm (60%) + needles (100 – 200 x 200-700 µm, 40%) Monterey Pine Received: coarse powder 1 x 2 mm (Pinus radiata) Milling: 2 mm + 1 mm + 200 µm wood chips Powder: fine needles (< 50 x 100 µm, 20%) + coarser needles (100 x 300 µm, 80%) *The grain sizes were estimated under a microscope with the % mass fractions given as very rough estimates based on the area covered. All powders felt like fine powder with some grains when sensed between two fingers.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 52 of 57

Table 2. XRF analysis: Typical detection limits (LOD) and analysis depths for the primary analysis line Kα line Z Element (KeV) 11 Na 1.04 12 Mg 1.25 13 Al 1.49 14 Si 1.74 15 P 2.01 16 S 2.31 17 Cl 2.62 19 K 3.31 20 Ca 3.69 Ti 4.51 22 24 Cr 5.41 25 Mn 5.90 26 Fe 6.40 Co 6.93 27 28 Ni 7.48 29 Cu 8.05 30 Zn 8.64 33 As 10.5 Se 11.2 34 35 Br 11.9 37 Rb 13.4 Sr 14.2 38 39 Y 15.0 Zr 15.8 40 42 Mo 17.5 44 Ru 19.3 46 Pd 21.2 48 Cd 23.2 51 Sb 26.4 53 I 28.6 56 Ba Lα 4.46 80 Hg Lα 10.0 82 Pb Lβ 12.6

LOD (ppm) 90 30 25 25 20 10 15 15 15 10 8 6 5 5 4 4 5 5 5 4 4 3 3 3 3 10 15 15 25 60 35 10 10

Analysis Depth (mm) 0.01 0.01 0.02 0.03 0.04 0.04 0.05 0.1 0.1 0.1 0.4 0.5 0.7 0.9 1.0 1.5 1.5 2 4 5 5 7 7 10 15 20 25 40 60 80 0.3 3 6


52

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Energy & Fuels

Table 3. Proximate and ultimate organic combustion analysis of the biomass in %-mass fractions. Moisture was determined in 2011 and 2013. The 2013 values are used for correction of data to dry base.

Switchgrass

As Received Moisture Moisture 2011 2013 Ash 7.4 6.6 5.1

Dry-Ash-Free (daf) – Normal. to 100% C 50.1

H 6.5

N 0.8

S 0.03

O 42.6

Sum 100.0

Corn Stover

8.4

7.1

3.9

49.3

6.4

0.9

0.04

43.3

99.9

Eucalyptus

6.0

6.0

0.7

51.3

6.3

0.4

0.03

42.0

100.0

Beech

7.6

6.5

0.6

49.7

6.4

0.4

0.03

43.4

99.9

Monterey Pine

8.4

7.7

0.3

51.8

6.5

0.3

0.04

41.3

99.9


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Energy & Fuels

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Page 54 of 57

Table 4. Half-width 95% confidence intervals ( expUrel ) in %-rel. for reference methods (ICP-OES and Combustion/Ion-chromatography*), and from EN 15290:2011 and EN 15297:2011. Detection limit (LOD) for reference methods. External Reference Analysis EN 15290/15297 LOD Ref. conc. expUrel conc. range expUrel Z Element (ppm) (ppm) (%) (ppm) (%) 5 B 0.35 3 8 9 F* 10 not available 23* 11 Na 1.5 4400 6 13 - 170 96 - 46 12 Mg 0.15 1100 5 - 30 200 -3200 14 - 16 13 Al 0.15 600 16 50 - 2400 36 - 14 14 Si 15 200000** 5** 300 - 10000 60 - 24 15 P 1.5 1100 - 10000 6 - 20 75 - 1500 14 - 17 16 S* 10 not available 23* S 10 900 - 2000 4-5 17 Cl* 10 not available 17* 19 K 2 3800 - 17500 6 - 11 700 - 24500 22 - 13 20 Ca 8 1900 - 12500 4 - 16 1500 - 14000 13 - 15 22 Ti 0.15 6600** 4** 5 - 140 15 - 16 23 V 0.12 0.5 14 0.08 - 4 46 - 22 24 Cr 0.45 3 27 0.4 - 14 62 - 48 25 Mn 0.05 120 - 650 4-6 40 - 260 13 - 14 26 Fe 1.5 200 - 210 7 – 23 50 - 1600 24 - 20 27 Co 0.15 50** 6** 0.3 - 1 20 - 24 28 Ni 0.3 3 58 0.6 - 13 34 - 13 29 Cu 0.8 3–6 4 – 21 1 - 25 24 - 18 30 Zn 0.25 40-50 5 – 34 14 - 18 28 - 22 33 As 0.7 125** 15** 0.03 – 0.6 128 - 19 34 Se 0.7 12** 20** 35 Br* 10 not available 23* 38 Sr 0.06 3–5 7 – 26 42 Mo 0.4 12** 10** 0.03 – 0.2 104 - 50 48 Cd 0.05 0.25 7 0.03 - 0.3 46 - 14 50 Sn 0.4 7** 20** 51 Sb 1.7 not available n.a. 0.01 – 0.1 62 - 30 56 Ba 0.03 650** 4** 82 Pb 0.3 11 18 0.7 - 4 32 - 30 *Determined by Combustion in Bomb and Ion-chromatography ** Determined only on fly ash (NBS 1633B and BCR-038)


Final expUrel (%) 8 23 27 30 16 24 20 23 5 17 11 16 16 18 27 14 23 24 58 21 34 19 20 23 26 30 14 20 30 4 18

54

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 5. Sulfur content (dry base) determined by ICP-OES or ion-chromatography with half-width of the 95% CI given in parenthesis (5%-rel. for ICP-OES and 23%-rel. for ionchrom.) The reference value is the average of one ICP-OES determination and the two ionchromatography determinations.

Combustion (%)

ICP-OES (ppm)

Ion-Chrom. (ppm)

Switchgrass

0.03

516 (26)

483 (111) 496 (114)

Corn Stover

0.04

787 (39)

Eucalyptus

0.03

140 (7)

Beech

0.03

127 (6) 127 (6)

Monterey Pine

0.04

51 (3)

810 (186) 808 (186) 137 (32) 140 (32) 161 (37) 140 (32) 93 (21) 61 (14)


Ref. value (ppm) 498 (54) 802 (89) 139 (15) 143 (16) 68 (8)

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Energy & Fuels

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Page 56 of 57

Table 6. Reference values and XRF data in ppm mass fractions. Reference data in bold are dry base (d.b.) and given as: mCRM (CI/2) with mean value (mCRM) and half width (CI/2) of the 95% confidence interval. XRF data are given as XN ± SEN with XN and SEN being the average and standard error calculated from between N=10 to 30 measurements. Also given are XN ± SEN for the organic matrix (% mass fraction) and Compton ratio (%). Note that the sum of the elements (not the ash) and the organic matrix (cellulose) is 100%.


56

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Energy & Fuels

Na (11)

Mg (12)

Al (13)

Si (14)

P (15)

S (16)

Cl (17)

K (19)

Ca (20)

Ti (22)

V (23)

Switchgrass (N = 20)

49 (13) not det

1417 (425) 1559 ± 5

249 (40) 225 ± 3

15173 (4400) 15345 ± 52

707 (127) 712 ± 6

498 (54) 527 ± 3

126 (22) 185 ± 2

6534 (719) 7271 ± 10

3023 (483) 3134 ± 6

11.3 (2.0) (9 ± 2)

< 0.25 not det

Corn Stover (N=20) Eucalyptus (N=30) Beech (N=10) Monterey Pine (N=20) Comm.form Oxide/Elem.

101 (27) ( 54 ± 14 ) 181 (49) 168 ± 4 < 3.2 not det

2287 (686) 2667 ± 10 234 (70) 234 ± 2 329 (99) 320 ± 2

413 (66) 410 ± 3 41 (7) (9±2) 10 (2) not det

5813 (1686) 6887 ± 27 136 (39) 124 ± 3 68 (20) 103 ± 3

930 (167) 1016 ± 6 519 (93) 545 ± 3 93 (17) 101 ± 4

802 (89) 853 ± 5 139 (15) 157 ± 1 143 (16) 138 ± 2

3015 (513) 3580 ± 10 810 (138) 950 ± 5 38 (7) ( 22 ± 9 )

7084 (779) 8047 ± 12 2424 (267) 2795 ± 5 1144 (126) 1314 ± 5

2970 (475) 3204 ± 11 992 (159) 1054 ± 3 1689 (270) 1783 ± 7

22 (4) (34 ± 3) 3.0 (0.5) not det < 0.3 not det

0.8 (0.1) (0.5 ± 0.5) < 0.25 not det < 0.25 not det

33 (9) not det Na2O 1.3480

207 (62) 214 ± 3 MgO 1.6583

34 (5) ( 19 ± 3 ) Al2O3 1.8895

< 30 80 ± 2 SiO2 2.1393

42 (8) 48 ± 1 P2O5 2.2914

68 (8) 60 ± 1 SO3 2.4969

66 (12) 125 ± 2 Cl 1

432 (48) 486 ± 2 K2O 1.2046

949 (152) 1009 ± 2 CaO 1.3992

< 0.3 not det TiO2 1.6685

< 0.25 not det V2O5 1.7852

Switchgrass Corn Stover Eucalyptus Beech Monterey Pine Comm. form Oxide/Elem

Cr (24)

Mn (25)

Fe (26)

Co (27)

Ni (28)

Cu (29)

Zn (30)

Br (35)

Sr (38)

Mo (42)

12.1 (3.3) (9±1) 57 (15) ( 15 ± 1 ) 3.3 (0.9) not det < 0.9 not det 2.5 (0.7) not det Cr2O3 1.4616

71 (4) 69 ± 1 37 (2) 33 ± 1 94 (6) 94 ± 1 88 (5) 85 ± 1 88 (5) 87 ± 3 MnO 1.2912

181 (42) 176 ± 1 352 (81) 383 ± 1 40 (9) 35.0 ± 0.4 10 (2) ( 12 ± 1 ) 66 (15) 68 ± 1 Fe2O3 1.4297

< 0.3 not det 7.5 (2.0) (5±1) < 0.3 not det 1.4 (0.4) not det 14.5 (3.8) 12.9 ± 0.2 CoO 1.2715

7.9 (4.6) ( 11 ± 1 ) 22 (13) 12.5 ± 0.3 4.1 (2.4) (8±1) 1.2 (0.7) (5±2) 1.5 (0.9) (3±1) NiO 1.2726

4.1 (0.9) 12.2 ± 0.2 6.7 (1.4) 14.3 ± 0.2 < 1.6 10.1 ± 0.2 < 1.6 8.0 ± 0.9 < 1.6 8.1 ± 0.2 CuO 1.2518

10.0 (3.4) 13.3 ± 0.2 19.5 (6.6) 2.7± 0.1 4.5 (1.5) (8±1) 2.9 (1.0) (6±1) 12.1 (4.1) 15.8 ± 0.2 ZnO 1.2446

< 10 (4±1) < 10 (5±1) < 10 not det < 10 not det < 10 not det Br 1

17.1 (4.5) 16.4 ±0.1 8.3 (2.2) (7±1) 8.3 (2.2) (7±1) 3.6 (1.0) (0.5 ± 0.5) 5.1 (1.3) (2.0 ± 0.5) SrO 1.1826

< 0.8 (22 ± 2) 4.1 (2.2) 25.0 ± 0.3 < 0.8 not det < 0.8 not det < 0.8 not det MoO3 1.5003

Matrix %

Compton %

97.07±0.01

89.7±0.1

5.25

97.21±0.01

89.5±0.2

4.31

96.40±0.00

89.8±0.1

0.85

99.60±0.00

90.1±0.1

0.55

99.80±0.00

90.0±0.1

XRF Ash %

0.32

Note: The following elements were analyzed with the reference method, but were not detected by the XRF in any sample: Li: < 0.3 ppm (Li is not analyzed by XRF), B: 2 – 4 ppm (B is not analyzed by XRF), F: < 10 ppm, As: < 1.4 ppm, Cd: < 0.1 ppm, Sb: < 3.3 ppm, Ba: 12 – 26 ppm, Pb: 1.6 (0.3) ppm in corn stover, < 0.6 ppm in the others. The following elements were analyzed with the reference method and were detected by the XRF in some - but not all - measurements of some sample: Se: < 1.4 ppm except in corn stover with Ref: < 1.4 ppm and XRF: (7 ± 1) ppm The following elements were NOT analyzed with the reference method but were detected by the XRF in some - but not all measurements of some sample (like mostly artifacts): Zr: < 2.5 ppm, Ru: 21-26 ppm (artifact), Pd: 11-17 ppm (artifact), Ag: < 2 ppm, Yb: < 0.5 ppm.


57

Quantitative X-ray Fluorescence Analysis of Biomass (Switchgrass

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