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MODÌ THE FIRST MOBILE INSTRUMENT FOR DOUBLE PULSE LASER INDUCED BREAKDOWN SPECTROSCOPY (LIBS) A N A L Y S I S O F M A T E R I A L

FAST ANALYSIS OF COMPLEX METALLIC ALLOYS BY DOUBLE-PULSE TIME-INTEGRATED LASER-INDUCED BREAKDOWN SPECTROSCOPY

We here report on the application of double-pulse Laser-Induced Breakdown Spectroscopy (LIBS) for fast analysis of complex metallic alloys. The approach followed for the determination of the composition of the alloys is based on the time-integrated acquisition of LIBS spectra emitted by plasmas induced by collinear double-pulse laser excitation. The spectra are analysed using the Partial Least Squares method, which allows the determination of sample composition even in the presence of strong spectral interferences. The results shown indicate the possibility of measuring the composition of complex metallic alloys in very short times and using relatively cheap LIBS instrumentation.

1. Introduction

The LIBS analysis of complex metallic alloys, such as steel, is made difficult by the presence of a number of interfering lines emitted by the main matrix element (iron). The current approach for quantitative determination of the alloy composition, in these cases, is based on the combined use of high-resolution spectrometers, time-resolved spectral acquisition and sophisticated spectral analysis, with the aim of reducing the effect of such interference [1]. This approach results in high costs of the instrumentation and time-consuming elaboration of the acquired spectra. However, it has been demonstrated that the use of time-integrated spectrometers for the acquisition of the LIBS signal may result in better calibration curves with respect to time-resolved approach [2]. This possibility is clearly interesting in view of a wider diffusion of the LIBS technique to 'real world' applications, since time-integrated spectrometers are cheaper and smaller than time-resolved systems. Although limited to relatively low resolution, time-integrated compact spectrometers could also offer broadband acquisition capability, making their use particularly appropriate for in-situ LIBS analysis. On the other hand, the application of powerful data-mining algorithms may also be helpful in discriminating the actual information present in the spectra from the interference of the matrix elements, without having recourse to spectral correction or line fitting which, although useful, take time and potentially introduce errors in the resulting calibration curves [3]. Statistical approaches such as the Partial Least Squares method may produce good results without using the so-called internal standard normalization, i.e. the normalization of the LIBS signal of the elements of interest with the same spectral signal produced by an element of know concentration in the sample (in general the major matrix element). In the case of steel analysis, being iron the major matrix element, the choice of the spectral line for normalization is not trivial, because of the strong self-absorption which affects most of the measurable iron lines in the spectrum [4]. Moreover, for the application of the internal standard method the iron concentration must be know or, if not available, it must be estimated. In the latter case, the experimental errors could increase dramatically, resulting in a poor estimation of the composition of unknown samples.

2 - Experimental approach

The approach presented in this communication is based on the use of a modified version of the Modì double-pulse LIBS spectrometer [5], especially devoted to the reduction of costs, size and time of analysis for fast on-line analysis of materials (see Figure 1). To this purpose, the high spectral resolution, time-resolved Echelle broadband spectrometer fitted in Modì was replaced with a low resolution, time integrated matchbox spectrometer (AvaSpec-2048, Avantes). This spectrometer covers the spectral band between 230 and 440 nm, with a spectral resolution of 0.1 nm. Although its performances are definitely worst with respect to the Echelle, its cost (about 20 times lower than the Echelle) and size (10 times smaller) make the choice interesting for the specific application to fast analysis of metallic alloys. As for

the laser excitation of the material, we used the same double-pulse laser used in Modì, although in principle time-integrated spectral analysis should not be particularly appropriate in association with double-pulse excitation. In fact, it has been demonstrated that a proper choice of time delay and gating of the spectrometer is very useful for the optimization of the spectral signal [6]. However, our measurements demonstrate that a substantial improvement of the LIBS signal can be obtained using the double-pulse LIBS approach even with time-integrated spectral acquisition. The acquisition of the LIBS signal in time-integrated mode also guarantees an excellent reproducibility of the resulting spectra [2]. For the analysis of the steel samples here reported, we set the laser pulse energy to 60 mJ per pulse (12 ns FWHM) with a 1 ms delay between the two pulses. The repetition rate of the laser was set to 10 Hz. The LIBS signal was integrated on the spectrometer for 20 seconds (corresponding to 200 couples of shots on the sample); the sample was not moved during the measurement, since the depth of the laser-induced crater on the surface was rather limited and, moreover, the effect of the crater was approximately the same on all the samples used for building the calibration curve and on the unknown samples to be analysed. The measurements were repeated on three different spots of the sample for checking its homogeneity and then averaged for improving the signal to noise ratio. The LIBS signal was collected, as in the standard version of Modì [4], using an optical fibre placed at 45 degrees, 2 cm from the sample surface.

3 - Experimental results

The LIBS spectra were collected on 10 certified and two unknown samples, provided by BAM, Berlin (D) in the framework of the LIBS 2008 Contest. The Contest was aimed to the assessment of current analytical capabilities of the LIBS technique; a comprehensive comparison of the results obtained by more than 20 LIBS laboratories worldwide distributed would be the subject of a specific publication [7]. For the purpose of this communication, it's sufficient to know that the composition of the certified samples was provided by BAM as reported in Table I. The information on iron concentration was not provided, and it was not easily calculable by difference, due to the presence in non-negligible concentration of unreported elements (such as Titanium) in some samples (see Figure 2 for an example).

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Figure 3 shows a comparison of the LIBS spectra obtained in single pulse mode (60 mJ) and double pulse mode (60+60 mJ). An increase of the LIBS signal of about a factor of 10 is observed for all the

Table I

Ni Mn Cr C Si Mo Co

C1 12.55 0.74 12.35 0.092 0.46 -- --

C2 6.124 0.686 14.727 0.0103 0.374 0.0138 --

C3 12.85 0.722 11.888 0.0345 0.463 0.0304 --

C4 10.2 1.4 18.46 0.019 0.27 0.265 0.116

C5 20.05 0.791 25.39 0.086 0.57 -- 0.054

C6 9.24 1.38 17.31 0.066 0.405 0.092 0.053

C7 10.2 1.311 17.84 0.0141 0.48 2.776 0.0184

C8 8.9 1.7 17.96 0.143 1.41 -- 0.018

C9 5.66 0.89 14.14 0.05 0.21 1.59 0.22

C10 10.72 1.745 16.811 0.0201 0.537 2.111 0.0525

Certified composition of the reference steel samples. Concentrations are in mass %.

Figure 1The Modì spectrometer used for the fast analysis of steel samples.

samples; it is evident that - despite the time integration on the spectrometer (both the LIBS signals corresponding to the first and second pulse are acquired) - the second pulse produces a LIBS signal much higher that the first. Therefore, we are in the position to say that the benefits of double-pulse LIBS approach can be exploited also using a time-integrated acquisition scheme.

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Figure 2: Evidence of strong Ti II emission lines in samples C6 (black), C10 (light grey) and in the unknown sample S1 (grey). The presence of Titanium in these samples was not reported in the data provided by BAM.

Figure 3: Comparison of the LIBS spectra obtained in single pulse mode (60 mJ, black) and double pulse mode (60+60 mJ, grey) on sample C1. The intensity of the LIBS spectrum corresponding to single pulse is multiplied by a factor of 10.

3.1 - Spectral analysis

The emission lines of many elements of interest in the samples are superimposed or, in any case, very close to the emission lines of iron, the major matrix element. Usually, for avoiding these interferences a multi-peak line fitting is considered essential; baseline correction is also needed [8]. However, for a proper application of the method of line separation, the resolution of the spectrometer should be high enough for reducing the spectral interferences as much as possible. In our case, the resolution of the matchbox spectrometer was too low for allowing any useful application of multi-peak fitting. Moreover, the process of fitting is time-consuming and it may introduce additional uncertainties on the results.The approach we decided to use is based on the method of Partial Least Squares Multiple Regression [9]. According to this method, no attempt is done to exclude the interfering lines from the spectrum; on the contrary, once suitable spectral regions, encompassing one or more emission lines of the element of interest, are individuated, the discrimination among 'good' and 'bad' spectral points is left completely to the PLS algorithm. In practice, all the spectral portions of the reference samples are provided to the software, along with the certified concentrations of the element of interest. The PLS approach produces a vector of 'weights' which is scalarly multiplied by the LIBS spectrum in order to reproduce the certified concentration. The same vector of weights is then used for the unknown samples; by scalarly multiplying this vector by the corresponding LIBS spectra portions we obtain a number, which corresponds to the estimated concentration of the element of interest. A clear advantage of PLS approach is its speed - all the reference samples are processed at the same time, no fitting is required - and the fact that both information and interfering 'noise' are processed at the same time; the discrimination between information and noise is left to the PLS algorithm. Moreover, the results does not depend on the normalization of the spectra by an internal standard, which is interesting in this case since the information on the iron concentration in the samples was not easily obtainable. On the other hand, a 'black-box' approach as the one just described may be extremely precise in reproducing the concentrations of the standards but, at the same time, may be largely wrong in predicting the concentrations of the unknown samples (see below). A careful check of the results provided by the PLS algorithm is thus necessary for a correct analysis of unknown samples.

3.2 Results

The participants to the LIBS 2008 Contest were provided with 10 standards samples of steel and were asked to give the concentration of five elements (Carbon, Chromium, Manganese, Nickel and Silicon) on two unknown samples. For the application of PLS analysis, we chose a set of spectral regions containing significant lines of these elements, as reported in Table II.

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Range 1 (nm) Range 2 (nm) Range 3 (nm) Range 4 (nm)

Ni 341.2 - 341.8 349.175 - 349.43 351.29 -351.67 352.3 - 352.75

Mn 257.5 - 260.85 402.95 - 403.69

Cr 424.875 - 425.75 426.65 - 427.85

C 247.77 - 248.1

Si 287.1 - 288.6

Table II: Spectral ranges used for the PLS analysis of the steel samples.

An example of the analytical procedure used for the determination of Chromium is given in figures 4a-4c. Figure 4a shows the portions of the LIBS spectrum provided to the PLS algorithm, for the two standards with the highest and lowest concentration of Chromium, respectively. Figure 4b shows the vector of weights produced by the PLS algorithm. It is interesting to note that, as expected, the relevant data (Cr lines) correspond to positive weights, while the Fe lines are associated to negative values. However, the peaks of Cr lines are less significant (i.e. are associated to lower positive weights) than the tails. This is probably associated to the effect of self-absorption, which is more marked at the peak of the line with respect to the tails [10]. Once the LIBS spectra of the unknown samples are acquired, the corresponding portions of the spectra are scalarly multiplied with the vector of weights shown in figure 4b, obtaining the measured concentration of Cr in the samples. These concentrations are compared with the nominal values (provided after the conclusion of the LIBS 2008 Contest) in Table III. The measured concentrations of the elements considered are in general in good agreement with the nominal values; however, without a check of the reliability of the method it's not possible assessing the effective reliability of the results obtained. In fact, the PLS algorithm could give almost exact matching of the concentration of the standards, and at the same time produce largely wrong predictions. To this purpose, we chose one of the standard samples, considering it as

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Figure 4: a) Portions of LIBS spectrum used for the determination of Chromium concentration. Sample C5 (highest concentration of Cr among the standards, 25.39 %, black), sample C3 (lowest concentration of Cr among the standards, 11.888 %, grey); b) Vector of weights determined processing the data of the 10 standards (3 components). The position and attribution of the main spectral lines in the spectral range considered is marked by the arrows.

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'unknown', and used the remaining nine standards as reference. The predicted concentration is then checked against the nominal one, and the procedure repeated for all the standards. The final result of this procedure is a sort of calibration curve, reported for the elements of interest in figures 5a-5d. The scatter of the points with respect to the ideal 45 degrees line is a measure of the reliability of the results. It's worth noting that the application of this procedure allowed us to correct the wrong results submitted for the Contest regarding the Silicon concentration of the unknown samples; a more appropriate choice of the spectral range used for the calculation resulted in a much better agreement of the measurements with the nominal results. As expected, the check of the results described above also reveals that the estimation of Carbon concentration in the samples is not possible. Indeed, the low concentration of Carbon in the samples, the presence of a Fe II line (247.857 nm) exactly superimposed to the only intense Carbon line (247.856 nm) available and the interference of the carbon deriving from the atmospheric CO2, makes the estimation of Carbon concentration of the samples practically impossible, at least with the present experimental apparatus (see figure 6).

Table III: Comparison of the measured concentrations (in mass %) with the nominal ones. The uncertainties on the measured concentrations are evaluated considering the reproducibility of the results over three independent measurements.

S1 PLS S1 Nom. S2 PLS S2 nom.

Ni 24.9±0.5 24.68 14.5±1 12.33

Mn 1.07±0.05 1.016 0.9±0.08 0.897

Cr 17.4±1.5 14.63 17.7±1.5 18.37

Si 0.67±0.01 0.531 0.34±0.05 0.344

C - 0.0489 - 0.0223

Figure 5: Comparison between nominal concentrations and calculated values for a) Cr; b) Ni; c) Mn; d) Si. The continuous line corresponds to the ideal case were the measured concentrations coincide with the nominal ones.

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4. Conclusion

The results presented demonstrate the possibility of performing quantitative analysis of complex metallic alloys (steel, in this case) in short time and using a cheap experimental setup based on the structure of the Modì double-pulse LIBS i n s t r umen t , w i t h t h e E che l l e spectrometer replaced by a time-integrated, low spectral resolution broadband spectrometer. The use of the Partial Least Squares Multivariate Analysis was demonstrated to be effective for the fast and reliable determination of the unknown samples composition.

References

[1] Laser Induced Breakdown Spectroscopy, Eds. A.W. Miziolek, V. Palleschi, I. Schechter, Cambridge University Press, Cambridge, UK (2006)[2] G.Cristoforett i , S.Legnaiol i , V.Palleschi, A.Salvetti, E.Tognoni, P.A.Benedetti, F.Brioschi and F.Ferrario, Quantitative analysis of aluminum alloys by low-energy, high-repetition rate Laser-Induced Breakdown Spectroscopy, Journal of Analytical Atomic Spectrometry, 21 (7) (2006) 697-702[3] S. Laville, M. Sabsabi, and F. R. Doucet, "Multi-elemental analysis of solidified mineral melt samples by laser-induced breakdown spectroscopy coupled with a linear multivariate calibration," Spectrochimica Acta Part B, 62 (12) (2007) 1557-1566[4] M. A. Player, J. Watson and J.M.O. De Freitas, The influence of self-absorption on the performance of Laser-Induced Breakdown Spectroscopy (LIBS), Proc. SPIE Vol. 4076 (2000) 260-268[5] A. Bertolini, G. Carelli, F. Francesconi, M. Francesconi, L. Marchesini, P. Marsili, F. Sorrentino, G. Cristoforetti, S. Legnaioli, V. Palleschi, L. Pardini, A. Salvetti, Modì: a new mobile instrument for in situ double-pulse LIBS analysis, Anal. Bioanal. Chem. 385 (2006) 240-247[6] V. N. Rai, F. Y. Yueh, and J. P. Singh, Time-dependent single and double pulse laser-induced breakdown spectroscopy of chromium in liquid, Appl. Opt. 47 (2008) G21-G29 [7] Wolfram Bremser and Ulrich Panne, communication at LIBS 2008 conference, 22-26 September 2008, Berlin (D)[8] M. Corsi M., V. Palleschi, A. Salvetti, E. Tognoni, Making LIBS Quantitative: a critical review of the current approaches to the problem, Research Advances in Applied Spectroscopy Series, ed. R.M.Mohan (2000)[9] Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Eds. V.E.Vinzi, W. W. Chin, J. Henseler and H.Wang, Springer Handbooks of Computational Statistics, Springer-Verlag, New York (2008)[10] I.B. Gornushkin, J.M. Anzano, L.A. King, B.W. Smith, N. Omenetto and J.D. Winefordner, Curve of growth methodology applied to laser-induced plasma emission spectroscopy, Spectrochimica Acta Part B 54 (3) (1999) 491-503

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Figure 6: Comparison between nominal concentrations and calculated values for Carbon. As explained in the text, the correlation between the two sets of data is very poor. Also in this case the continuous line would correspond to the ideal case were the measured concentrations coincide with the nominal ones.

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