Measuring Lanthanide Concentrations in Molten Salt Using Laser-Induced Breakdown Spectroscopy (LIBS) Arel Weisberg,a,* Rollin E. Lakis,b Michael F. Simpson,c Leo Horowitz,a Joseph Craparoa a b c
Energy Research Company, 1250 South Avenue, Plainfield, NJ 07062 USA Los Alamos National Laboratory, Los Alamos, NM 87545 USA University of Utah, Department of Metallurgical Engineering, 135 S 1460 E, Salt Lake City, UT 84112 USA
The versatility of laser-induced breakdown spectroscopy (LIBS) as an analytical method for high-temperature applications was demonstrated through measurement of the concentrations of the lanthanide elements europium (Eu) and praseodymium (Pr) in molten eutectic lithium chloride–potassium chloride (LiCl-KCl) salts at a temperature of 500 8C. Laser pulses (1064 nm, 7 ns, 120 mJ/ pulse) were focused on the top surface of the molten salt samples in a laboratory furnace under an argon atmosphere, and the resulting LIBS signals were collected using a broadband Echelletype spectrometer. Partial least squares (PLS) regression using leave-one-sample-out cross-validation was used to quantify the concentrations of Eu and Pr in the samples. The root mean square error of prediction (RMSEP) for Eu was 0.13% (absolute) over a concentration range of 0–3.01%, and for Pr was 0.13% (absolute) over a concentration range of 0–1.04%. Index Headings: Laser-induced breakdown spectroscopy; LIBS; Lanthanides; Lithium potassium chloride salt; Molten salt; Europium; Praseodymium.
INTRODUCTION A unique strength of laser-induced breakdown spectroscopy (LIBS), compared to other analytical methods for determining the elemental makeup of materials, is its versatility. Because the laser pulses that create LIBS plasmas and the light emitted by the plasmas can be projected using telescopic optics and/or transmitted through fiber optic cables, researchers have found many LIBS applications where it is advantageous to position the laser, spectrometer, and other relatively expensive components of the LIBS system some distance from the sample being analyzed. These applications include the detection of explosives;1–6 the analysis of molten steel,7–9 aluminum,10 and glass;11,12 the analysis of radioactive materials;13–19 and planetary science.20–25 Lanthanides are increasingly of interest in manufacturing and other applications due to their unique properties, and they have therefore been the subjects of studies to determine their concentrations in different matrices using a variety of technologies, including in a number of LIBS efforts. Most recently, LIBS was used to identify La, Ce, Pr, Nd, Yb, Gd, Dy, and Er in monazite sands.26 Laser-induced breakdown spectroscopy was also used to simultaneously measure concentrations of Sm, Eu, and Gd in aqueous solutions.27 Other efforts Received 19 November 2013; accepted 31 March 2014. * Author to whom correspondence should be sent. E-mail: aweisberg@ er-co.com. DOI: 10.1366/13-07390
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included analyses of europium oxide in powder mixtures28 and aqueous solutions.29 Early work was done by Ishizuka on Eu and Yb.30 In this study, the concentrations of two lanthanide elements, Eu and Pr, in molten lithium chloride–potassium chloride (LiCl-KCl) eutectic salts were measured using LIBS. The 500 8C temperature of the salts precludes the use of most analytical techniques to perform this measurement without allowing the salts to freeze. The compositional measurement of molten materials without the need for solidifying the samples is of interest to many industries, such as secondary (i.e., recycled) metal producers.31,32 This is because the ability to make an in situ compositional measurement of a melt will save considerable time compared to retrieving a sample, solidifying it, and preparing it for laboratory measurement. The ability to project laser pulses suitable for LIBS onto the surface of the molten salt, combined with the ability to use optics to collect the LIBS plasma light from a standoff distance, allows the LIBS measurements to be taken from the molten salts. In this study, we provide evidence that accurate measurements of the concentrations of Eu and Pr can be performed from outside the furnace, protecting the laser, spectrometer, and optics from the elevated temperature inside the furnace.
EXPERIMENTAL Samples. Eutectic lithium chloride (44 wt%)–potassium chloride (56 wt%) salts doped with europium(III) chloride (EuCl3) and praseodymium(III) chloride (PrCl3) were prepared specifically for this study. Each salt mixture was prepared by weighing out and mixing dry powders in an argon atmosphere (water (H2O) and oxygen gas (O2) ,10 parts per million (ppm)) glove box. The mixtures were heated to 500 8C for at least an hour to homogenize the mixture and then cooled and allowed to freeze in alumina crucibles. Samples of each salt mixture were taken in the molten state by quickly dipping a stainless steel pan into the salt and scooping out less than a gram of molten salt. This salt was later crushed, dissolved, and analyzed using inductively coupled plasma mass spectrometry (ICP-MS) to determine the elemental composition. A total of 12 samples were analyzed; the compositions are given in Table I. Each sample weighed approximately 50 g and was stored in a glass jar under an argon atmosphere. Care was taken with the samples to minimize exposure to atmospheric air to minimize the absorption of moisture. Table I contains both the nominal composition of the samples and the composition measured using the ICP–
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TABLE I. Sample compositions.a Nominal (wt%) Sample number 1 3 4 5 6 7 8 9 10 11 12 14
ICP-MS measured (wt%)
Eu
Pr
Eu
Pr
0.15 0.59 1.18 2.94 0.00 0.00 0.00 0.00 0.00 0.59 1.18 0.00
0.00 0.00 0.00 0.00 0.14 0.29 0.57 1.14 2.85 0.57 0.57 0.00
0.15 0.56 1.11 3.01 0.16 0.00 0.00 0.00 0.00 0.57 1.19 0.00
0.00 0.00 0.00 0.00 0.14 0.29 0.56 1.04 1.88 0.57 0.58 0.00
a Concentrations in boldface indicate nominal and ICP-MS concentrations differ by 0.1% or greater.
MS instrument. The concentrations of samples for which there was a difference between these two values of 0.1% or greater are shown in boldface. The largest discrepancy is seen in sample 10, which has nearly a 1% difference between the nominal and measured Pr compositions. The cause of this discrepancy is not known for certain at this time, but it is probably due to experimenter error during the weighing out and mixing of the salt components; as discussed in the Results section, the LIBS analysis of this sample also indicates that this sample is atypical. The ICP-MS values are used throughout this study in the quantitative analyses. Apparatus. A schematic of the LIBS apparatus used in this study is shown in Fig. 1 and photographs of the apparatus are in Fig. 2. A small alumina crucible containing a sample was placed in a bed of sand inside a larger crucible inside an electric furnace under an argon blanket. The laser was a neodymium-doped yttrium aluminum garnet (Nd : YAG) model CFR400 (Quantel USA) operating at a wavelength of 1064 nm with 7 ns pulses and set to an energy of 120 mJ/pulse. The laser pulses were directed downward by being reflected off a mirror (M1) and were then focused using a 25.4 mm diameter lens (L1) with a 343 mm focal length. The pulses passed through the center opening in a 50.8 mm diameter 458 pierced mirror (M2) before reaching their focus on the surface of the salt. The sample was placed at the nominal focus of the laser pulses, which was approximately 152 mm below the top of a furnace cover, which had an 82.6 mm diameter cutout. For the last samples measured, samples 3 and 4, we used a furnace cover with a furnace cutout that was reduced from the original 82.6 mm to a 28.6 mm diameter aperture to determine whether air infiltration may have been occurring through the large 82.6 mm opening. No differences were seen when we used the smaller opening. The pierced mirror (M2) reflected the LIBS plasma light through two lenses (L2 and L3) that focused the light onto the tip of a fiber optic cable. These lenses were 50.8 mm in diameter and had focal lengths of 203 and 114 mm. A 600 lm core, 0.22 numerical aperture (NA), 6 m long optical fiber (FO) transmitted the LIBS
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FIG. 1. Schematic of the LIBS apparatus.
light to the spectrometer, an Echelle type broadband spectrometer (ESA3000A; LLA, Germany). This spectrometer has a wavelength range of 200–780 nm and a resolution of k/dk full width half-maximum (FWHM) 20 000. It uses an intensified charge-coupled-device (ICCD) camera to capture the spectra. In addition to amplifying the plasma light, the intensifier temporally gates the light that reaches the camera, which improves the spectra’s signal-to-noise ratio. The settings used in this study were a gate delay of 3.6 ls from the laser pulse and a gate window duration of 10 ls. We arrived at these settings through a series of tests in which we attempted to maximize the signal-to-noise ratio of the peaks of interest, with a focus on the Pr lines because they have lower amplitudes than the Eu lines. Two programmable electrical furnaces were used in this study to melt the samples, one a front-loading model (not shown) and the other a top- and bottomloading model seen in Fig. 2. A constant stream of argon was flowing in each furnace while the samples were melting. The furnaces were programmed to ramp the temperature up from room temperature to 500 6 10 8C at a rate of 5 8C/min. Samples melted in the front-loading furnace were manually transferred to the furnace shown in Fig. 2 for analysis as quickly as possible to minimize their exposure to the atmosphere. The samples were positioned so that the surface of the molten salt was approximately 356 mm below the LIBS apparatus shown in the figure. The relatively coarse positioning controls available on the furnace led to 5 mm variability in the distance from the pierced mirror to the surface of the molten salt from sample to sample, and this was probably a source of signal variability across samples in this study. Although the likelihood of atmospheric moisture contaminating the samples was very low due to the elevated temperature, nonetheless we continuously flowed argon into the furnace throughout the experiments. The LIBS plasmas ejected salt droplets from the molten samples that reached the 458 pierced mirror. To prevent damage to the mirror from the salt, a glass microscope slide was placed immediately below the mirror. The glass slide was periodically cleaned, as necessary, of the salt buildup. A steel washer was placed on the small crucible that narrowed the opening for salt spray to be ejected without blocking the laser pulses or the plasma light reaching the 458 mirror, but
FIG. 2. The LIBS experimental setup for molten salts. (a) Zoomed-out view illustrating the placement of the laser and optics relative to the furnace. (b) Zoomed-in view illustrating the placement of the optics and the sample.
this was only mildly effective in reducing the salt particle spray. Procedure and Observations. After we positioned a sample at the nominally optimal vertical position in the furnace, we collected 300 LIBS spectra from each molten sample. The sequence of spectral measurements from the molten samples was periodically interrupted to clean the glass slide protecting the system optics from the salt spray, as previously mentioned. The amplitudes of the spectra from the molten samples varied widely and unpredictably for reasons that are not definitively known at this time; however, this may be due in part to the formation of a film on many of the molten samples. On occasion, successive laser pulses resulted in particularly weak spectra, which were followed by very strong spectra, only to revert again to weak signals. We originally attributed this behavior to the salt spray, but this hypothesis appeared to be incorrect because the frequent cleaning of the protective slide did not mitigate this variability. An example of the film that was observed floating on the molten samples is shown in Fig. 3a. The color and sheen of the film were consistent across the samples exhibiting the film, but the spatial extent of the film varied considerably. Typically, the extent of the film was
much smaller after the 300 LIBS spectra were collected, as shown in the Fig. 3b. The film appears to be due to the presence of Eu and/or Pr in the salt because no film was seen in the pure LiCl-KCl sample (sample 14, in which Eu = Pr = 0%), as seen in Fig. 4. The apparatus used in this study could not determine whether a particular laser pulse had struck the film or the underlying molten salt; therefore, the data represent a combination of spectra from the salt and the film, with the fraction of each being unknown.
RESULTS: SAMPLE SPECTRA AND ANALYTICAL LINES The averaged LIBS spectrum from sample 11, from 370–500 nm, is shown in Fig. 5. This spectrum was generated by removing from the set of 300 collected spectra those spectra that exhibited saturation or had unusually low signal amplitudes and then computing the average of the remaining spectra. Saturation was defined as a maximum amplitude in the spectrum (200– 780 nm) greater than 45 000 counts. The spectrometer’s response begins to saturate at this count level. Unusually low signals were defined as those having a maximum amplitude in the spectrum below 10 000
FIG. 3. Photographs of molten sample 3 exhibiting the floating film. (a) Before collection of the LIBS spectra. (b) After collection of 300 LIBS spectra.
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FIG. 4. Photographs of molten sample 14. (a) Before collection of the LIBS spectra. (b) After collection of the LIBS spectra.
counts. We chose this threshold by taking the mean background level near 400 nm plus 20 times the standard deviation of this background region. A factor of 20 was chosen because the intensity ratio between the most intense lines used in the study and the least intense lines is approximately 15. Adding the minimum signal-to-noise ratio of three for detection to the intensity ratio leads to a rounded-up threshold value of 20. The number of spectra that fell within the allowable range for each sample is given in Table II. The vertical lines in Fig. 5 are spectral peaks, and the range shown in the figure contained all the spectral peaks that were used to calculate the concentrations of Eu and Pr in the salts. Figure 6 shows the same spectrum but only in the range of 390–394 nm, illustrating the narrow width of three spectral peaks, two for Eu and one for Pr. Also present in Fig. 5 are the Li and K emission lines, but these did not prove to be useful in the quantitative analysis of the samples. We added calcium chloride in small controlled amounts to
some of the molten samples to determine whether having an internal standard would increase the accuracy and precision of the quantitative concentration measurements. Therefore, Ca peaks are seen in some of the spectra. However, we determined that the Ca peaks did not improve the quantitative analysis, so they were not used. The presence of the calcium chloride in some of the samples did not affect the Eu or Pr emissions because the Ca lines are at different wavelengths. All the emission lines in this study were identified using two publicly available atomic emission databases: the National Institute of Standards and Technology (NIST) database33 and the Harvard–Smithsonian Center for Astrophysics database.34 An example of the effect of concentration on the amplitude of LIBS peaks is seen in Fig. 7, where the same peaks in Fig. 6 are plotted for samples 1 and 3, which differ in their Eu content by approximately a factor of 4. The Eu peak amplitudes reflect this difference, with the Eu peaks in sample 3 having a much greater amplitude than the peaks in sample 1. Also, the Pr peak that is visible in Fig. 6 immediately to the right of the leftmost Eu peak is absent in Fig. 7 because there is no Pr in samples 1 and 3. Sample 1 had the lowest Eu (nonzero) concentration of 0.15%, and the peak amplitude is more than sufficient to accurately measure the emission from the line. Similarly, sample 6 has the lowest (nonzero) Pr concentration of 0.14%. Figure 8 illustrates two of the higher-amplitude peaks in the averaged sample 6 spectrum. TABLE II. Number of spectra out of 300 analyzed for each molten sample. Sample
FIG. 5. Averaged LIBS spectrum from molten sample 11.
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1 3 4 5 6 7 8
Number of spectra analyzed 89 133 51 94 73 97 125
Sample
Number of spectra analyzed
9a 9b 10 11 12 14
224 195 167 57 66 83
FIG. 6. Spectral range from 390 to 394 nm in the spectrum in Fig. 5.
To quantify the peak amplitudes, we calculated the area under each peak by integrating from the peak point on the spectrum and four points on either side using cubic spline integration. This is illustrated in Fig. 9 for the average molten sample 11 spectrum. As is visible in Figs. 6 and 8, the intensity of the spectrum between the peaks is not at zero counts. We subtracted two background regions, one for Eu (Fig. 10a) and one for Pr (Fig. 10b), from the peak areas to account for the fluctuating baseline intensities from spectrum to spectrum. Each background region was chosen so it was near
FIG. 7. Sections of averaged LIBS spectra from samples 1 and 3 illustrating the effect of concentration on peak amplitude.
FIG. 8. Two Pr emission peaks from the averaged sample 6 LIBS spectrum, illustrating the signal-to-background ratio of Pr peaks at the lowest Pr concentration (0.14%).
the majority of the spectral lines used to measure that element. The final peak signal is calculated as: Peak signal ¼
ðPeak area Background areaÞ ðBackground areaÞ
ð1Þ
The reason we normalized the peak areas using the background area was to factor out the changes in intensity due to the LIBS spark moving slightly, which affects the optical efficiency with which the light is
FIG. 9. Peak area measurement showing the data-point locations in the spectrum. The amplitude of the spectral peak was computed as the hatched area under curve bounded by the solid vertical lines.
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FIG. 10. Background regions used to calculate the peak areas. (a) For Eu. (b) For Pr. The amplitude of the background was computed as the hatched area under curve bounded by the solid vertical lines.
collected and, therefore, scales the entire spectrum’s amplitude up and down. There are also other effects contributing to this scaling, such as fluctuations in the laser pulse energy and variability in the absorbance of the laser light by the sample, potentially in part due to the film already mentioned. Peak areas were determined for a total of seven Eu emission lines and seven Pr emission lines, as shown in Table III. All the emission lines are from singly ionized atoms. The basis for measuring the Eu and Pr concentrations in a salt is the correlation of the peak signal measurements with concentration. Example scatter plots are shown in for one Eu emission line (Fig. 11a) and one Pr line (Fig. 11b). Although the trends in the plots are generally linear, the error bars, which represent one standard deviation, are too large for the measurement to be useful for quantitative analysis. Because the LIBS spectra are collected very quickly, with modern spectrometers being capable of 10 Hz operation, averaging LIBS spectra is a common method for improving the precision of a measurement. The procedure used in this study was to sort the spectra according to the maximum intensity data point in the spectrum and then to calculate a moving average of 21 spectra. We sorted the spectra because the process that creates LIBS emissions is highly nonlinear. Averaging spectra with vastly different peak amplitudes can result in a spectrum that does not resemble the typical spectrum that is sought. By sorting before averaging, we ensure that the spectra going into each average are likely to differ much less, minimizing the effect of the nonlinearities in the LIBS signals. We used TABLE III. Atomic emission lines measured in this study. Eu II (nm) 372.494 381.966 393.048 412.969 443.556 452.257 462.722 Pr II (nm) 400.470 406.281 410.072 417.939 422.293 430.576 440.882
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the following averaging process to ensure that each averaged spectrum was unique: (1) For the 10 lowest-amplitude spectra, we took the average in a forward manner (i.e., the average over spectrum i to spectrum i þ 20). (2) For the middle-amplitude spectra, we took the averages over 610 spectra (i.e., the average over i 10 to i þ 10). (3) For the 10 greatest-amplitude spectra, we took a backward-looking average (i.e., the average over i 20 to i). When we applied this process to the data used to generate Fig. 11, the result was as shown in Fig. 12, with an expected reduction in the standard deviation because the standard deviation in Fig. 12 is calculated over the averaged spectra instead of over single-shot spectra. The average relative standard deviation (RSD = r/l) in Fig. 11 for Eu is 155% and for Pr is 240%. In Fig. 12, the RSD for Eu is reduced to one-tenth, to only 14%, and for Pr it reduced to one-fourth, to only 62%. It is notable that the reduction in RSD for Pr was commensurate with that predicted if the experimental variability was Gaussian (i.e., 211/2 = 4.6), but it is much greater for Eu. The cause for this discrepancy is not known at this time. Calibration plots for the remaining Eu emission lines are shown in Fig. 13. The outlier at the 1.1% concentration in several of the plots is the data from sample 4. At this time, we do not know why this sample is an outlier. Calibration plots for the remaining Pr emission lines are shown in Fig. 14. In many of these plots, sample 10, with the 1.88% Pr concentration, has anomalously low intensities, so it appears that many of the plots would be improved by removing this point. Sample 10 is also the sample that had a vastly different Pr measurement using the ICP-MS analyzer from the nominal concentra-
FIG. 11. Calibration plots. (a) For Eu (413 nm). (b) For Pr (422 nm). The error bars represent one standard deviation.
tion, as shown in Table I. Whether these two phenomena are related is left for future studies. Even though many of the plots show that LIBS signals track with concentration, the fits of the data to best-fit lines are not promising for accurate quantitative measurements. For this reason, we pursued multivariate calibration methods that use data from multiple emission lines in concert for the calibration. We employed the multivariate calibration method partial least squares (PLS) regression, which incorporates all the emission lines from the element of interest (Eu or Pr) in the analysis. The PLS method finds the best correlations between the amplitudes of multiple emission lines for an element and that element’s concentration while discarding noise by reducing the
dimensionality of the data from the original seven variables (i.e., peak wavelengths) for each element. The optimal number of retained variables in this study for Eu was six and for Pr was three when we used the root mean square error of prediction (RMSEP) metric to optimize the results. Using the same RMSEP metric, we optimized the results by normalizing the Eu peak areas by demeaning the areas for each peak wavelength and then dividing the results by the standard deviation of the peak areas for the same wavelength. For the Pr data, this normalization was effective only if sample 10 was left out of the analysis. Whether or not sample 10 was included, normalizing the concentrations by demeaning and dividing by their standard deviation did improve the results.
FIG. 12. Calibration plots using moving averages. (a) For Eu (413 nm). (b) For Pr (422 nm). The error bars represent one standard deviation.
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FIG. 13. Calibration plots for Eu using the moving-average spectra. (a) The 327 nm peak. (b) The 382 nm peak. (c) The 393 nm peak. (d) The 444 nm peak. (e) The 452 nm peak. (f) The 463 nm peak. The error bars represent one standard deviation.
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FIG. 14. Calibration plots for Pr using the moving-average spectra. (a) The 400 nm peak. (b) The 406 nm peak. (c) The 410 nm peak. (d) The 418 nm peak. (e) The 431 nm peak. (f) The 441 nm peak. The error bars represent one standard deviation.
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FIG. 15. The PLS regression results for Eu using leave-one-sample-out cross-validation. The 458 dotted line represents perfect prediction (i.e., actual concentration equals predicted concentration). Error bars represent one standard deviation.
FIG. 17. The PLS regression results for Pr using leave-one-sample-out cross-validation omitting sample 10. The 458 dotted line represents perfect prediction (i.e., actual concentration equals predicted concentration). Error bars represent one standard deviation.
To perform a regression analysis in this study, the PLS model (i.e., the optimal multivariate correlation) was computed using the spectra from the samples but leaving one sample out. The spectra from the sample left out of the model were then tested against the PLS model. If the model is accurate, an accurate concentration would be predicted. Each sample was left out of the PLS model in turn and tested against the model to
complete the analysis of all the samples. This validation method is termed leave-one-sample-out cross-validation. It estimates accuracy and precision in the field because it measures a model’s performance when it encounters a sample it has never seen.35 This PLS approach was highly successful with the molten samples, resulting in low measurement errors. In Fig. 15, the x-axis is the actual Eu concentration and the y-axis is the predicted Eu concentration using the LIBS PLS model. If the PLS model were perfect, all the data points would fall on the 458 line shown (i.e., the actual would equal the predicted concentration). That all the data points in fact fall close to the 458 line demonstrates that the model is accurate and that the Eu concentrations in molten salts at different concentrations can be measured in new samples using the model. To quantify this result, we computed the RMSEP: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X ðLIBS Actual Þ2 i i RMSEP ¼ ð2Þ N
FIG. 16. The PLS regression results for Pr using leave-one-sample-out cross-validation. The 458 dotted line represents perfect prediction (i.e., actual concentration equals predicted concentration). Error bars represent one standard deviation.
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where i = 1, . . ., N is the number of points and can be interpreted as the average expected error in a measurement, LIBSi is the LIBS-predicted concentration using PLS regression, and Actuali is the actual concentration of the element for that sample. The RMSEP for Eu is only 0.13% (absolute), or approximately 4% of the full scale of concentrations. In other words, we can expect a concentration error of 0.13%, on average, when measuring a sample never before seen by the PLS model. Figure 16 contains analogous results for Pr, where the RMSEP is 0.26% (absolute), or approximately 13% of the full scale of concentrations. Once again, sample 10 stands out in the Pr results as an outlier. If we assume that sample 10 is anomalous and remove it from the PLS
regression model, the results for Pr improve dramatically, as shown in Fig. 17. In this case, the RMSEP is only 0.13% (absolute), or 11% of the full scale of concentrations.
SUMMARY AND CONCLUSION The results presented in this article, especially the PLS regression results, reveal great promise for LIBS in this application. The fact that these results were achieved under a number of non-ideal conditions—such as (i) imprecise control over the laser focal-point position relative to the sample surface, (ii) the presence of a film of unknown composition, and (iii) a persistent salt spray—makes these results more significant. The combination of all these factors led to a tremendous amount of variability in the signals. Despite this variability, the final results, as illustrated in the PLS regression figures, are measurements that can be expected to be accurate to within approximately 0.1– 0.3% (absolute, mean RMSEP) in concentration. We expect even greater accuracy and precision once the issues driving the variability have been addressed in a future iteration of the apparatus. For example, we are investigating the use of an optical probe capable of being placed close to the surface of the molten salt, in place of the optical setup described here. The probe would be coupled to the laser and spectrometer via fiber optic cables. One benefit of using a probe placed close to the molten salt will be that lower laser pulse energies can be used. We project that the lower pulse energies, in combination with a small aperture at the probe’s forward tip, will greatly mitigate the salt spray issue. 1. F.C. De Lucia, Jr., J.L. Gottfried, C.A. Munson, A.W. Miziolek. ‘‘Multivariate Analysis of Standoff Laser-Induced Breakdown Spectroscopy Spectra for Classification of Explosive-Containing Residues’’. Appl. Opt. 2008. 47(31): G112-G121. 2. C. Lo´pez-Moreno, S. Palanco, J.J. Laserna, F.C. DeLucia, Jr., A.W. Miziolek, J. Rose, R.A. Walters, A.I. Whitehouse. ‘‘Test of a StandOff Laser-Induced Breakdown Spectroscopy Sensor for the Detection of Explosive Residues on Solid Surfaces’’. J. Anal. Atom. Spectrom. 2006. 21(1): 55-60. 3. R. Gonza´lez, P. Lucena, L.M. Tobaria, J.J. Laserna. ‘‘Standoff LIBS Detection of Explosive Residues Behind a Barrier’’. J. Anal. Atom. Spectrom. 2009. 24(8): 1123-1126. 4. F.C. DeLucia, Jr., A.C. Samuels, R.S. Harmon, R.A. Walters, K.L. McNesby, A. Lapointe, R.J. Winkel, Jr., A.W. Miziolek. ‘‘LaserInduced Breakdown Spectroscopy (LIBS): A Promising Versatile Chemical Sensor Technology for Hazardous Material Detection’’. IEEE Sens. J. 2005. 5(4): 681-689. 5. F.C. De Lucia, Jr., R.S. Harmon, K.L. McNesby, R.J. Winkel, A.W. Miziolek. ‘‘Laser-Induced Breakdown Spectroscopy Analysis of Energetic Materials’’. Appl. Opt. 2003. 42(30): 6148-6152. 6. J.L. Gottfried, F.C. De Lucia, Jr., C.A. Munson, A.W. Miziolek. ‘‘Double-Pulse Standoff Laser-Induced Breakdown Spectroscopy for Versatile Hazardous Materials Detection’’. Spectrochim. Acta, Part B. 2007. 62(12): 1405-1411. 7. C. Arago´n, J.A. Aguilera, J. Campos. ‘‘Determination of Carbon Content in Molten Steel Using Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 1993. 47(5): 606-608. 8. R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Mo¨nch, L. Peter, V. Sturm. ‘‘Laser-Induced Breakdown Spectrometry—Applications for Production Control and Quality Assurance in the Steel Industry’’. Spectrochim. Acta, Part B. 2001. 56(6): 637-649. 9. G. Hubmer, R. Kitzberger, K. Mo¨rwald. ‘‘Application of LIBS to the In-Line Process Control of Liquid High-Alloy Steel Under Pressure’’. Anal. Bioanal. Chem. 2006. 385(2): 219-224.
10. A.K. Rai, F.-Y. Yueh, J.P. Singh. ‘‘Laser-Induced Breakdown Spectroscopy of Molten Aluminum Alloy’’. Appl. Opt. 2003. 42(12): 2078-2084. 11. J.-I. Yun, R. Klenze, J.-I. Kim. ‘‘Laser-Induced Breakdown Spectroscopy for the On-Line Multielement Analysis of Highly Radioactive Glass Melt Simulants. Part II: Analyses of Molten Glass Samples’’. Appl. Spectrosc. 2002. 56(7): 852-858. 12. U. Panne, C. Haisch, M. Clara, R. Niessner. ‘‘Analysis of Glass and Glass Melts During the Vitrification Process of Fly and Bottom Ashes by Laser-Induced Plasma Spectroscopy. Part I: Normalization and Plasma Diagnostics’’. Spectrochim. Acta, Part B. 1998. 53(14): 1957-1968. 13. A.I. Whitehouse, J. Young, I.M. Botheroyd, S. Lawson, C.P. Evans, J. Wright. ‘‘Remote Material Analysis of Nuclear Power Station Steam Generator Tubes by Laser-Induced Breakdown Spectroscopy’’. Spectrochim. Acta, Part B. 2001. 56(6): 821-830. 14. D.A. Cremers, A. Beddingfield, R. Smithwick, R.C. Chinni, C.R. Jones, B. Beardsley, L. Karch. ‘‘Monitoring Uranium, Hydrogen, and Lithium and Their Isotopes Using a Compact Laser-Induced Breakdown Spectroscopy (LIBS) Probe and High-Resolution Spectrometer’’. Appl. Spectrosc. 2012. 66(3): 250-261. 15. R.C. Chinni, D.A. Cremers, L.J. Radziemski, M. Bostian, C. NavarroNorthrup. ‘‘Detection of Uranium Using Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 2009. 63(11): 1238-1250. 16. P. Fichet, P. Mauchien, C. Moulin. ‘‘Determination of Impurities in Uranium and Plutonium Dioxides by Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 1999. 53(9): 1111-1117. 17. A. Sarkar, D. Alamelu, S.K. Aggarwal. ‘‘Determination of Thorium and Uranium in Solution by Laser-Induced Breakdown Spectrometry’’. Appl. Opt. 2008. 47(31): G58-G64. 18. C.A. Smith, M.A. Martinez, D.K. Veirs, D.A. Cremers. ‘‘Pu-239/Pu240 Isotope Ratios Determined Using High Resolution Emission Spectroscopy in a Laser-Induced Plasma’’. Spectrochim. Acta, Part B. 2002. 57(5): 929-937. 19. F.R. Doucet, G. Lithgow, R. Kosierb, P. Bouchard, M. Sabsabi. ‘‘Determination of Isotope Ratios Using Laser-Induced Breakdown Spectroscopy in Ambient Air at Atmospheric Pressure for Nuclear Forensics’’. J. Anal. Atom. Spectrom. 2011. 26(3): 536-541. 20. F. Colao, R. Fantoni, V. Lazic, A. Paolini, F. Fabbri, G.G. Ori, L. Marinangeli, A. Baliva. ‘‘Investigation of LIBS Feasibility for In Situ Planetary Exploration: An Analysis on Martian Rock Analogues’’. Planet. Space Sci. 2004. 52(1-3): 117-123. 21. N.L. Lanza, R.C. Wiens, S.M. Clegg, A.M. Ollila, S.D. Humphries, H.E. Newsom, J.E. Barefield. ‘‘Calibrating the ChemCam LaserInduced Breakdown Spectroscopy Instrument for Carbonate Minerals on Mars’’. Appl. Opt. 2010. 49(13): C211-C217. 22. J. Lasue, R.C. Wiens, T.F. Stepinski, O. Forni, S.M. Clegg, S. Maurice. ‘‘Nonlinear Mapping Technique for Data Visualization and Clustering Assessment of LIBS Data: Application to ChemCam Data’’. Anal. Bioanal. Chem. 2011. 400(10): 3247-3260. 23. S.M. Clegg, E. Sklute, M.D. Dyar, J.E. Barefield, R.C. Wiens. ‘‘Multivariate Analysis of Remote Laser-Induced Breakdown Spectroscopy Spectra Using Partial Least Squares, Principal Component Analysis, and Related Techniques’’. Spectrochim. Acta, Part B. 2009. 64(1): 79-88. 24. B. Salle´, J.-L. Lacour, P. Mauchien, P. Fichet, S. Maurice, G. Manhe`s. ‘‘Comparative Study of Different Methodologies for Quantitative Rock Analysis by Laser-Induced Breakdown Spectroscopy in a Simulated Martian Atmosphere’’. Spectrochim. Acta, Part B. 2006. 61(3): 301-313. 25. J.A.F. Lasue, R.C. Wiens, S.M. Clegg, D.T. Vaniman, K.H. Joy, S. Humphries, A. Mezzacappa, N. Melikechi, R.E. McInroy, S. Bender. ‘‘Remote Laser-Induced Breakdown Spectroscopy (LIBS) for Lunar Exploration’’. J. Geophys. Res. 2012. 117(E1). doi: 10.1029/ 2011JE003898. 26. K.M. Abedin, A.F.M.Y. Haider, M.A. Rony, Z.H. Khan. ‘‘Identification of Multiple Rare Earths and Associated Elements in Raw Monazite Sands by Laser-Induced Breakdown Spectroscopy’’. Opt. Laser Technol. 2011. 43(1): 45-49. 27. D. Alamelu, A. Sarkar, S.K. Aggarwal. ‘‘Laser-Induced Breakdown Spectroscopy for Simultaneous Determination of Sm, Eu and Gd in Aqueous Solution’’. Talanta. 2008. 77(1): 256-261. 28. L.C. Jensen, S.C. Langford, J.T. Dickinson, R.S. Addleman. ‘‘Mechanistic Studies of Laser-Induced Breakdown Spectroscopy of Model Environmental Samples’’. Spectrochim. Acta, Part B. 1995. 50(12): 1501-1519.
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29. J.-I. Yun, T. Bundschuh, V. Neck, J.-I. Kim. ‘‘Selective Determination of Europium (III) Oxide and Hydroxide Colloids in Aqueous Solution by Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 2001. 55(3): 273-278. 30. T. Ishizuka. ‘‘Laser Emission Spectrography of Rare Earth Elements’’. Anal. Chem. 1973. 45(3): 538-541. 31. A.K. Rai, H. Zhang, F.Y. Yueh, J.P. Singh, A. Weisberg. ‘‘Parametric Study of a Fiber-Optic Laser-Induced Breakdown Spectroscopy Probe for Analysis of Aluminum Alloys’’. Spectrochim. Acta, Part B. 2001. 56(12): 2371-2383. 32. R. De Saro, A. Weisberg, J. Craparo. ‘‘In-Situ, Real Time Measurement of Aluminum Melt Chemistry’’. In: P. Crepeau, editor. Light Metals. Warrendale, PA: TMS, 2003. Pp. 1103-1110.
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33. National Institute of Standards and Technology. . ‘‘NIST Atomic Spectra Database Lines Form’’. http://physics.nist.gov/ PhysRefData/ASD/lines_form.html [accessed May 27 2014]. 34. P.L. Smith, C. Heise, J.R. Esmond, R.L. Kurucz. ‘‘Atomic Spectral Line Database from CD-ROM 23 of R.L. Kurucz’’. Harvard University. http://www.cfa.harvard.edu/amp/ampdata/kurucz23/ sekur.html [accessed May 27 2014]. 35. R. Kohavi. ‘‘A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection’’. In: C.S. Mellish, editor. IJCAI Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann, 1995. Vol. 2, pp. 1137-1143.
Energy Research Company, 1250 South Avenue, Plainfield, NJ 07062 USA Los Alamos National Laboratory, Los Alamos, NM 87545 USA University of Utah, Department of Metallurgical Engineering, 135 S 1460 E, Salt Lake City, UT 84112 USA
The versatility of laser-induced breakdown spectroscopy (LIBS) as an analytical method for high-temperature applications was demonstrated through measurement of the concentrations of the lanthanide elements europium (Eu) and praseodymium (Pr) in molten eutectic lithium chloride–potassium chloride (LiCl-KCl) salts at a temperature of 500 8C. Laser pulses (1064 nm, 7 ns, 120 mJ/ pulse) were focused on the top surface of the molten salt samples in a laboratory furnace under an argon atmosphere, and the resulting LIBS signals were collected using a broadband Echelletype spectrometer. Partial least squares (PLS) regression using leave-one-sample-out cross-validation was used to quantify the concentrations of Eu and Pr in the samples. The root mean square error of prediction (RMSEP) for Eu was 0.13% (absolute) over a concentration range of 0–3.01%, and for Pr was 0.13% (absolute) over a concentration range of 0–1.04%. Index Headings: Laser-induced breakdown spectroscopy; LIBS; Lanthanides; Lithium potassium chloride salt; Molten salt; Europium; Praseodymium.
INTRODUCTION A unique strength of laser-induced breakdown spectroscopy (LIBS), compared to other analytical methods for determining the elemental makeup of materials, is its versatility. Because the laser pulses that create LIBS plasmas and the light emitted by the plasmas can be projected using telescopic optics and/or transmitted through fiber optic cables, researchers have found many LIBS applications where it is advantageous to position the laser, spectrometer, and other relatively expensive components of the LIBS system some distance from the sample being analyzed. These applications include the detection of explosives;1–6 the analysis of molten steel,7–9 aluminum,10 and glass;11,12 the analysis of radioactive materials;13–19 and planetary science.20–25 Lanthanides are increasingly of interest in manufacturing and other applications due to their unique properties, and they have therefore been the subjects of studies to determine their concentrations in different matrices using a variety of technologies, including in a number of LIBS efforts. Most recently, LIBS was used to identify La, Ce, Pr, Nd, Yb, Gd, Dy, and Er in monazite sands.26 Laser-induced breakdown spectroscopy was also used to simultaneously measure concentrations of Sm, Eu, and Gd in aqueous solutions.27 Other efforts Received 19 November 2013; accepted 31 March 2014. * Author to whom correspondence should be sent. E-mail: aweisberg@ er-co.com. DOI: 10.1366/13-07390
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included analyses of europium oxide in powder mixtures28 and aqueous solutions.29 Early work was done by Ishizuka on Eu and Yb.30 In this study, the concentrations of two lanthanide elements, Eu and Pr, in molten lithium chloride–potassium chloride (LiCl-KCl) eutectic salts were measured using LIBS. The 500 8C temperature of the salts precludes the use of most analytical techniques to perform this measurement without allowing the salts to freeze. The compositional measurement of molten materials without the need for solidifying the samples is of interest to many industries, such as secondary (i.e., recycled) metal producers.31,32 This is because the ability to make an in situ compositional measurement of a melt will save considerable time compared to retrieving a sample, solidifying it, and preparing it for laboratory measurement. The ability to project laser pulses suitable for LIBS onto the surface of the molten salt, combined with the ability to use optics to collect the LIBS plasma light from a standoff distance, allows the LIBS measurements to be taken from the molten salts. In this study, we provide evidence that accurate measurements of the concentrations of Eu and Pr can be performed from outside the furnace, protecting the laser, spectrometer, and optics from the elevated temperature inside the furnace.
EXPERIMENTAL Samples. Eutectic lithium chloride (44 wt%)–potassium chloride (56 wt%) salts doped with europium(III) chloride (EuCl3) and praseodymium(III) chloride (PrCl3) were prepared specifically for this study. Each salt mixture was prepared by weighing out and mixing dry powders in an argon atmosphere (water (H2O) and oxygen gas (O2) ,10 parts per million (ppm)) glove box. The mixtures were heated to 500 8C for at least an hour to homogenize the mixture and then cooled and allowed to freeze in alumina crucibles. Samples of each salt mixture were taken in the molten state by quickly dipping a stainless steel pan into the salt and scooping out less than a gram of molten salt. This salt was later crushed, dissolved, and analyzed using inductively coupled plasma mass spectrometry (ICP-MS) to determine the elemental composition. A total of 12 samples were analyzed; the compositions are given in Table I. Each sample weighed approximately 50 g and was stored in a glass jar under an argon atmosphere. Care was taken with the samples to minimize exposure to atmospheric air to minimize the absorption of moisture. Table I contains both the nominal composition of the samples and the composition measured using the ICP–
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TABLE I. Sample compositions.a Nominal (wt%) Sample number 1 3 4 5 6 7 8 9 10 11 12 14
ICP-MS measured (wt%)
Eu
Pr
Eu
Pr
0.15 0.59 1.18 2.94 0.00 0.00 0.00 0.00 0.00 0.59 1.18 0.00
0.00 0.00 0.00 0.00 0.14 0.29 0.57 1.14 2.85 0.57 0.57 0.00
0.15 0.56 1.11 3.01 0.16 0.00 0.00 0.00 0.00 0.57 1.19 0.00
0.00 0.00 0.00 0.00 0.14 0.29 0.56 1.04 1.88 0.57 0.58 0.00
a Concentrations in boldface indicate nominal and ICP-MS concentrations differ by 0.1% or greater.
MS instrument. The concentrations of samples for which there was a difference between these two values of 0.1% or greater are shown in boldface. The largest discrepancy is seen in sample 10, which has nearly a 1% difference between the nominal and measured Pr compositions. The cause of this discrepancy is not known for certain at this time, but it is probably due to experimenter error during the weighing out and mixing of the salt components; as discussed in the Results section, the LIBS analysis of this sample also indicates that this sample is atypical. The ICP-MS values are used throughout this study in the quantitative analyses. Apparatus. A schematic of the LIBS apparatus used in this study is shown in Fig. 1 and photographs of the apparatus are in Fig. 2. A small alumina crucible containing a sample was placed in a bed of sand inside a larger crucible inside an electric furnace under an argon blanket. The laser was a neodymium-doped yttrium aluminum garnet (Nd : YAG) model CFR400 (Quantel USA) operating at a wavelength of 1064 nm with 7 ns pulses and set to an energy of 120 mJ/pulse. The laser pulses were directed downward by being reflected off a mirror (M1) and were then focused using a 25.4 mm diameter lens (L1) with a 343 mm focal length. The pulses passed through the center opening in a 50.8 mm diameter 458 pierced mirror (M2) before reaching their focus on the surface of the salt. The sample was placed at the nominal focus of the laser pulses, which was approximately 152 mm below the top of a furnace cover, which had an 82.6 mm diameter cutout. For the last samples measured, samples 3 and 4, we used a furnace cover with a furnace cutout that was reduced from the original 82.6 mm to a 28.6 mm diameter aperture to determine whether air infiltration may have been occurring through the large 82.6 mm opening. No differences were seen when we used the smaller opening. The pierced mirror (M2) reflected the LIBS plasma light through two lenses (L2 and L3) that focused the light onto the tip of a fiber optic cable. These lenses were 50.8 mm in diameter and had focal lengths of 203 and 114 mm. A 600 lm core, 0.22 numerical aperture (NA), 6 m long optical fiber (FO) transmitted the LIBS
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FIG. 1. Schematic of the LIBS apparatus.
light to the spectrometer, an Echelle type broadband spectrometer (ESA3000A; LLA, Germany). This spectrometer has a wavelength range of 200–780 nm and a resolution of k/dk full width half-maximum (FWHM) 20 000. It uses an intensified charge-coupled-device (ICCD) camera to capture the spectra. In addition to amplifying the plasma light, the intensifier temporally gates the light that reaches the camera, which improves the spectra’s signal-to-noise ratio. The settings used in this study were a gate delay of 3.6 ls from the laser pulse and a gate window duration of 10 ls. We arrived at these settings through a series of tests in which we attempted to maximize the signal-to-noise ratio of the peaks of interest, with a focus on the Pr lines because they have lower amplitudes than the Eu lines. Two programmable electrical furnaces were used in this study to melt the samples, one a front-loading model (not shown) and the other a top- and bottomloading model seen in Fig. 2. A constant stream of argon was flowing in each furnace while the samples were melting. The furnaces were programmed to ramp the temperature up from room temperature to 500 6 10 8C at a rate of 5 8C/min. Samples melted in the front-loading furnace were manually transferred to the furnace shown in Fig. 2 for analysis as quickly as possible to minimize their exposure to the atmosphere. The samples were positioned so that the surface of the molten salt was approximately 356 mm below the LIBS apparatus shown in the figure. The relatively coarse positioning controls available on the furnace led to 5 mm variability in the distance from the pierced mirror to the surface of the molten salt from sample to sample, and this was probably a source of signal variability across samples in this study. Although the likelihood of atmospheric moisture contaminating the samples was very low due to the elevated temperature, nonetheless we continuously flowed argon into the furnace throughout the experiments. The LIBS plasmas ejected salt droplets from the molten samples that reached the 458 pierced mirror. To prevent damage to the mirror from the salt, a glass microscope slide was placed immediately below the mirror. The glass slide was periodically cleaned, as necessary, of the salt buildup. A steel washer was placed on the small crucible that narrowed the opening for salt spray to be ejected without blocking the laser pulses or the plasma light reaching the 458 mirror, but
FIG. 2. The LIBS experimental setup for molten salts. (a) Zoomed-out view illustrating the placement of the laser and optics relative to the furnace. (b) Zoomed-in view illustrating the placement of the optics and the sample.
this was only mildly effective in reducing the salt particle spray. Procedure and Observations. After we positioned a sample at the nominally optimal vertical position in the furnace, we collected 300 LIBS spectra from each molten sample. The sequence of spectral measurements from the molten samples was periodically interrupted to clean the glass slide protecting the system optics from the salt spray, as previously mentioned. The amplitudes of the spectra from the molten samples varied widely and unpredictably for reasons that are not definitively known at this time; however, this may be due in part to the formation of a film on many of the molten samples. On occasion, successive laser pulses resulted in particularly weak spectra, which were followed by very strong spectra, only to revert again to weak signals. We originally attributed this behavior to the salt spray, but this hypothesis appeared to be incorrect because the frequent cleaning of the protective slide did not mitigate this variability. An example of the film that was observed floating on the molten samples is shown in Fig. 3a. The color and sheen of the film were consistent across the samples exhibiting the film, but the spatial extent of the film varied considerably. Typically, the extent of the film was
much smaller after the 300 LIBS spectra were collected, as shown in the Fig. 3b. The film appears to be due to the presence of Eu and/or Pr in the salt because no film was seen in the pure LiCl-KCl sample (sample 14, in which Eu = Pr = 0%), as seen in Fig. 4. The apparatus used in this study could not determine whether a particular laser pulse had struck the film or the underlying molten salt; therefore, the data represent a combination of spectra from the salt and the film, with the fraction of each being unknown.
RESULTS: SAMPLE SPECTRA AND ANALYTICAL LINES The averaged LIBS spectrum from sample 11, from 370–500 nm, is shown in Fig. 5. This spectrum was generated by removing from the set of 300 collected spectra those spectra that exhibited saturation or had unusually low signal amplitudes and then computing the average of the remaining spectra. Saturation was defined as a maximum amplitude in the spectrum (200– 780 nm) greater than 45 000 counts. The spectrometer’s response begins to saturate at this count level. Unusually low signals were defined as those having a maximum amplitude in the spectrum below 10 000
FIG. 3. Photographs of molten sample 3 exhibiting the floating film. (a) Before collection of the LIBS spectra. (b) After collection of 300 LIBS spectra.
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FIG. 4. Photographs of molten sample 14. (a) Before collection of the LIBS spectra. (b) After collection of the LIBS spectra.
counts. We chose this threshold by taking the mean background level near 400 nm plus 20 times the standard deviation of this background region. A factor of 20 was chosen because the intensity ratio between the most intense lines used in the study and the least intense lines is approximately 15. Adding the minimum signal-to-noise ratio of three for detection to the intensity ratio leads to a rounded-up threshold value of 20. The number of spectra that fell within the allowable range for each sample is given in Table II. The vertical lines in Fig. 5 are spectral peaks, and the range shown in the figure contained all the spectral peaks that were used to calculate the concentrations of Eu and Pr in the salts. Figure 6 shows the same spectrum but only in the range of 390–394 nm, illustrating the narrow width of three spectral peaks, two for Eu and one for Pr. Also present in Fig. 5 are the Li and K emission lines, but these did not prove to be useful in the quantitative analysis of the samples. We added calcium chloride in small controlled amounts to
some of the molten samples to determine whether having an internal standard would increase the accuracy and precision of the quantitative concentration measurements. Therefore, Ca peaks are seen in some of the spectra. However, we determined that the Ca peaks did not improve the quantitative analysis, so they were not used. The presence of the calcium chloride in some of the samples did not affect the Eu or Pr emissions because the Ca lines are at different wavelengths. All the emission lines in this study were identified using two publicly available atomic emission databases: the National Institute of Standards and Technology (NIST) database33 and the Harvard–Smithsonian Center for Astrophysics database.34 An example of the effect of concentration on the amplitude of LIBS peaks is seen in Fig. 7, where the same peaks in Fig. 6 are plotted for samples 1 and 3, which differ in their Eu content by approximately a factor of 4. The Eu peak amplitudes reflect this difference, with the Eu peaks in sample 3 having a much greater amplitude than the peaks in sample 1. Also, the Pr peak that is visible in Fig. 6 immediately to the right of the leftmost Eu peak is absent in Fig. 7 because there is no Pr in samples 1 and 3. Sample 1 had the lowest Eu (nonzero) concentration of 0.15%, and the peak amplitude is more than sufficient to accurately measure the emission from the line. Similarly, sample 6 has the lowest (nonzero) Pr concentration of 0.14%. Figure 8 illustrates two of the higher-amplitude peaks in the averaged sample 6 spectrum. TABLE II. Number of spectra out of 300 analyzed for each molten sample. Sample
FIG. 5. Averaged LIBS spectrum from molten sample 11.
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1 3 4 5 6 7 8
Number of spectra analyzed 89 133 51 94 73 97 125
Sample
Number of spectra analyzed
9a 9b 10 11 12 14
224 195 167 57 66 83
FIG. 6. Spectral range from 390 to 394 nm in the spectrum in Fig. 5.
To quantify the peak amplitudes, we calculated the area under each peak by integrating from the peak point on the spectrum and four points on either side using cubic spline integration. This is illustrated in Fig. 9 for the average molten sample 11 spectrum. As is visible in Figs. 6 and 8, the intensity of the spectrum between the peaks is not at zero counts. We subtracted two background regions, one for Eu (Fig. 10a) and one for Pr (Fig. 10b), from the peak areas to account for the fluctuating baseline intensities from spectrum to spectrum. Each background region was chosen so it was near
FIG. 7. Sections of averaged LIBS spectra from samples 1 and 3 illustrating the effect of concentration on peak amplitude.
FIG. 8. Two Pr emission peaks from the averaged sample 6 LIBS spectrum, illustrating the signal-to-background ratio of Pr peaks at the lowest Pr concentration (0.14%).
the majority of the spectral lines used to measure that element. The final peak signal is calculated as: Peak signal ¼
ðPeak area Background areaÞ ðBackground areaÞ
ð1Þ
The reason we normalized the peak areas using the background area was to factor out the changes in intensity due to the LIBS spark moving slightly, which affects the optical efficiency with which the light is
FIG. 9. Peak area measurement showing the data-point locations in the spectrum. The amplitude of the spectral peak was computed as the hatched area under curve bounded by the solid vertical lines.
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FIG. 10. Background regions used to calculate the peak areas. (a) For Eu. (b) For Pr. The amplitude of the background was computed as the hatched area under curve bounded by the solid vertical lines.
collected and, therefore, scales the entire spectrum’s amplitude up and down. There are also other effects contributing to this scaling, such as fluctuations in the laser pulse energy and variability in the absorbance of the laser light by the sample, potentially in part due to the film already mentioned. Peak areas were determined for a total of seven Eu emission lines and seven Pr emission lines, as shown in Table III. All the emission lines are from singly ionized atoms. The basis for measuring the Eu and Pr concentrations in a salt is the correlation of the peak signal measurements with concentration. Example scatter plots are shown in for one Eu emission line (Fig. 11a) and one Pr line (Fig. 11b). Although the trends in the plots are generally linear, the error bars, which represent one standard deviation, are too large for the measurement to be useful for quantitative analysis. Because the LIBS spectra are collected very quickly, with modern spectrometers being capable of 10 Hz operation, averaging LIBS spectra is a common method for improving the precision of a measurement. The procedure used in this study was to sort the spectra according to the maximum intensity data point in the spectrum and then to calculate a moving average of 21 spectra. We sorted the spectra because the process that creates LIBS emissions is highly nonlinear. Averaging spectra with vastly different peak amplitudes can result in a spectrum that does not resemble the typical spectrum that is sought. By sorting before averaging, we ensure that the spectra going into each average are likely to differ much less, minimizing the effect of the nonlinearities in the LIBS signals. We used TABLE III. Atomic emission lines measured in this study. Eu II (nm) 372.494 381.966 393.048 412.969 443.556 452.257 462.722 Pr II (nm) 400.470 406.281 410.072 417.939 422.293 430.576 440.882
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the following averaging process to ensure that each averaged spectrum was unique: (1) For the 10 lowest-amplitude spectra, we took the average in a forward manner (i.e., the average over spectrum i to spectrum i þ 20). (2) For the middle-amplitude spectra, we took the averages over 610 spectra (i.e., the average over i 10 to i þ 10). (3) For the 10 greatest-amplitude spectra, we took a backward-looking average (i.e., the average over i 20 to i). When we applied this process to the data used to generate Fig. 11, the result was as shown in Fig. 12, with an expected reduction in the standard deviation because the standard deviation in Fig. 12 is calculated over the averaged spectra instead of over single-shot spectra. The average relative standard deviation (RSD = r/l) in Fig. 11 for Eu is 155% and for Pr is 240%. In Fig. 12, the RSD for Eu is reduced to one-tenth, to only 14%, and for Pr it reduced to one-fourth, to only 62%. It is notable that the reduction in RSD for Pr was commensurate with that predicted if the experimental variability was Gaussian (i.e., 211/2 = 4.6), but it is much greater for Eu. The cause for this discrepancy is not known at this time. Calibration plots for the remaining Eu emission lines are shown in Fig. 13. The outlier at the 1.1% concentration in several of the plots is the data from sample 4. At this time, we do not know why this sample is an outlier. Calibration plots for the remaining Pr emission lines are shown in Fig. 14. In many of these plots, sample 10, with the 1.88% Pr concentration, has anomalously low intensities, so it appears that many of the plots would be improved by removing this point. Sample 10 is also the sample that had a vastly different Pr measurement using the ICP-MS analyzer from the nominal concentra-
FIG. 11. Calibration plots. (a) For Eu (413 nm). (b) For Pr (422 nm). The error bars represent one standard deviation.
tion, as shown in Table I. Whether these two phenomena are related is left for future studies. Even though many of the plots show that LIBS signals track with concentration, the fits of the data to best-fit lines are not promising for accurate quantitative measurements. For this reason, we pursued multivariate calibration methods that use data from multiple emission lines in concert for the calibration. We employed the multivariate calibration method partial least squares (PLS) regression, which incorporates all the emission lines from the element of interest (Eu or Pr) in the analysis. The PLS method finds the best correlations between the amplitudes of multiple emission lines for an element and that element’s concentration while discarding noise by reducing the
dimensionality of the data from the original seven variables (i.e., peak wavelengths) for each element. The optimal number of retained variables in this study for Eu was six and for Pr was three when we used the root mean square error of prediction (RMSEP) metric to optimize the results. Using the same RMSEP metric, we optimized the results by normalizing the Eu peak areas by demeaning the areas for each peak wavelength and then dividing the results by the standard deviation of the peak areas for the same wavelength. For the Pr data, this normalization was effective only if sample 10 was left out of the analysis. Whether or not sample 10 was included, normalizing the concentrations by demeaning and dividing by their standard deviation did improve the results.
FIG. 12. Calibration plots using moving averages. (a) For Eu (413 nm). (b) For Pr (422 nm). The error bars represent one standard deviation.
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FIG. 13. Calibration plots for Eu using the moving-average spectra. (a) The 327 nm peak. (b) The 382 nm peak. (c) The 393 nm peak. (d) The 444 nm peak. (e) The 452 nm peak. (f) The 463 nm peak. The error bars represent one standard deviation.
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FIG. 14. Calibration plots for Pr using the moving-average spectra. (a) The 400 nm peak. (b) The 406 nm peak. (c) The 410 nm peak. (d) The 418 nm peak. (e) The 431 nm peak. (f) The 441 nm peak. The error bars represent one standard deviation.
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FIG. 15. The PLS regression results for Eu using leave-one-sample-out cross-validation. The 458 dotted line represents perfect prediction (i.e., actual concentration equals predicted concentration). Error bars represent one standard deviation.
FIG. 17. The PLS regression results for Pr using leave-one-sample-out cross-validation omitting sample 10. The 458 dotted line represents perfect prediction (i.e., actual concentration equals predicted concentration). Error bars represent one standard deviation.
To perform a regression analysis in this study, the PLS model (i.e., the optimal multivariate correlation) was computed using the spectra from the samples but leaving one sample out. The spectra from the sample left out of the model were then tested against the PLS model. If the model is accurate, an accurate concentration would be predicted. Each sample was left out of the PLS model in turn and tested against the model to
complete the analysis of all the samples. This validation method is termed leave-one-sample-out cross-validation. It estimates accuracy and precision in the field because it measures a model’s performance when it encounters a sample it has never seen.35 This PLS approach was highly successful with the molten samples, resulting in low measurement errors. In Fig. 15, the x-axis is the actual Eu concentration and the y-axis is the predicted Eu concentration using the LIBS PLS model. If the PLS model were perfect, all the data points would fall on the 458 line shown (i.e., the actual would equal the predicted concentration). That all the data points in fact fall close to the 458 line demonstrates that the model is accurate and that the Eu concentrations in molten salts at different concentrations can be measured in new samples using the model. To quantify this result, we computed the RMSEP: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X ðLIBS Actual Þ2 i i RMSEP ¼ ð2Þ N
FIG. 16. The PLS regression results for Pr using leave-one-sample-out cross-validation. The 458 dotted line represents perfect prediction (i.e., actual concentration equals predicted concentration). Error bars represent one standard deviation.
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where i = 1, . . ., N is the number of points and can be interpreted as the average expected error in a measurement, LIBSi is the LIBS-predicted concentration using PLS regression, and Actuali is the actual concentration of the element for that sample. The RMSEP for Eu is only 0.13% (absolute), or approximately 4% of the full scale of concentrations. In other words, we can expect a concentration error of 0.13%, on average, when measuring a sample never before seen by the PLS model. Figure 16 contains analogous results for Pr, where the RMSEP is 0.26% (absolute), or approximately 13% of the full scale of concentrations. Once again, sample 10 stands out in the Pr results as an outlier. If we assume that sample 10 is anomalous and remove it from the PLS
regression model, the results for Pr improve dramatically, as shown in Fig. 17. In this case, the RMSEP is only 0.13% (absolute), or 11% of the full scale of concentrations.
SUMMARY AND CONCLUSION The results presented in this article, especially the PLS regression results, reveal great promise for LIBS in this application. The fact that these results were achieved under a number of non-ideal conditions—such as (i) imprecise control over the laser focal-point position relative to the sample surface, (ii) the presence of a film of unknown composition, and (iii) a persistent salt spray—makes these results more significant. The combination of all these factors led to a tremendous amount of variability in the signals. Despite this variability, the final results, as illustrated in the PLS regression figures, are measurements that can be expected to be accurate to within approximately 0.1– 0.3% (absolute, mean RMSEP) in concentration. We expect even greater accuracy and precision once the issues driving the variability have been addressed in a future iteration of the apparatus. For example, we are investigating the use of an optical probe capable of being placed close to the surface of the molten salt, in place of the optical setup described here. The probe would be coupled to the laser and spectrometer via fiber optic cables. One benefit of using a probe placed close to the molten salt will be that lower laser pulse energies can be used. We project that the lower pulse energies, in combination with a small aperture at the probe’s forward tip, will greatly mitigate the salt spray issue. 1. F.C. De Lucia, Jr., J.L. Gottfried, C.A. Munson, A.W. Miziolek. ‘‘Multivariate Analysis of Standoff Laser-Induced Breakdown Spectroscopy Spectra for Classification of Explosive-Containing Residues’’. Appl. Opt. 2008. 47(31): G112-G121. 2. C. Lo´pez-Moreno, S. Palanco, J.J. Laserna, F.C. DeLucia, Jr., A.W. Miziolek, J. Rose, R.A. Walters, A.I. Whitehouse. ‘‘Test of a StandOff Laser-Induced Breakdown Spectroscopy Sensor for the Detection of Explosive Residues on Solid Surfaces’’. J. Anal. Atom. Spectrom. 2006. 21(1): 55-60. 3. R. Gonza´lez, P. Lucena, L.M. Tobaria, J.J. Laserna. ‘‘Standoff LIBS Detection of Explosive Residues Behind a Barrier’’. J. Anal. Atom. Spectrom. 2009. 24(8): 1123-1126. 4. F.C. DeLucia, Jr., A.C. Samuels, R.S. Harmon, R.A. Walters, K.L. McNesby, A. Lapointe, R.J. Winkel, Jr., A.W. Miziolek. ‘‘LaserInduced Breakdown Spectroscopy (LIBS): A Promising Versatile Chemical Sensor Technology for Hazardous Material Detection’’. IEEE Sens. J. 2005. 5(4): 681-689. 5. F.C. De Lucia, Jr., R.S. Harmon, K.L. McNesby, R.J. Winkel, A.W. Miziolek. ‘‘Laser-Induced Breakdown Spectroscopy Analysis of Energetic Materials’’. Appl. Opt. 2003. 42(30): 6148-6152. 6. J.L. Gottfried, F.C. De Lucia, Jr., C.A. Munson, A.W. Miziolek. ‘‘Double-Pulse Standoff Laser-Induced Breakdown Spectroscopy for Versatile Hazardous Materials Detection’’. Spectrochim. Acta, Part B. 2007. 62(12): 1405-1411. 7. C. Arago´n, J.A. Aguilera, J. Campos. ‘‘Determination of Carbon Content in Molten Steel Using Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 1993. 47(5): 606-608. 8. R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Mo¨nch, L. Peter, V. Sturm. ‘‘Laser-Induced Breakdown Spectrometry—Applications for Production Control and Quality Assurance in the Steel Industry’’. Spectrochim. Acta, Part B. 2001. 56(6): 637-649. 9. G. Hubmer, R. Kitzberger, K. Mo¨rwald. ‘‘Application of LIBS to the In-Line Process Control of Liquid High-Alloy Steel Under Pressure’’. Anal. Bioanal. Chem. 2006. 385(2): 219-224.
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29. J.-I. Yun, T. Bundschuh, V. Neck, J.-I. Kim. ‘‘Selective Determination of Europium (III) Oxide and Hydroxide Colloids in Aqueous Solution by Laser-Induced Breakdown Spectroscopy’’. Appl. Spectrosc. 2001. 55(3): 273-278. 30. T. Ishizuka. ‘‘Laser Emission Spectrography of Rare Earth Elements’’. Anal. Chem. 1973. 45(3): 538-541. 31. A.K. Rai, H. Zhang, F.Y. Yueh, J.P. Singh, A. Weisberg. ‘‘Parametric Study of a Fiber-Optic Laser-Induced Breakdown Spectroscopy Probe for Analysis of Aluminum Alloys’’. Spectrochim. Acta, Part B. 2001. 56(12): 2371-2383. 32. R. De Saro, A. Weisberg, J. Craparo. ‘‘In-Situ, Real Time Measurement of Aluminum Melt Chemistry’’. In: P. Crepeau, editor. Light Metals. Warrendale, PA: TMS, 2003. Pp. 1103-1110.
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33. National Institute of Standards and Technology. . ‘‘NIST Atomic Spectra Database Lines Form’’. http://physics.nist.gov/ PhysRefData/ASD/lines_form.html [accessed May 27 2014]. 34. P.L. Smith, C. Heise, J.R. Esmond, R.L. Kurucz. ‘‘Atomic Spectral Line Database from CD-ROM 23 of R.L. Kurucz’’. Harvard University. http://www.cfa.harvard.edu/amp/ampdata/kurucz23/ sekur.html [accessed May 27 2014]. 35. R. Kohavi. ‘‘A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection’’. In: C.S. Mellish, editor. IJCAI Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann, 1995. Vol. 2, pp. 1137-1143.
