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A Plasma-Image-Assisted Method for Matrix Effect Correction in Laser-Induced Breakdown Spectroscopy
Journal Pre-proof A Plasma-Image-Assisted Method for Matrix Effect Correction in Laser-Induced Breakdown Spectroscopy Deng Zhang, Yanwu Chu, Shixiang Ma, Siyu Zhang, Haohao Cui, Zhenlin Hu, Feng Chen, Ziqian Sheng, Lianbo Guo, Yongfeng Lu PII:
S0003-2670(20)30144-6
DOI:
https://doi.org/10.1016/j.aca.2020.02.003
Reference:
ACA 237435
To appear in:
Analytica Chimica Acta
Received Date: 2 December 2019 Revised Date:
31 December 2019
Accepted Date: 1 February 2020
Please cite this article as: D. Zhang, Y. Chu, S. Ma, S. Zhang, H. Cui, Z. Hu, F. Chen, Z. Sheng, L. Guo, Y. Lu, A Plasma-Image-Assisted Method for Matrix Effect Correction in Laser-Induced Breakdown Spectroscopy, Analytica Chimica Acta, https://doi.org/10.1016/j.aca.2020.02.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
Zhang Deng: Conceptualization, Methodology, Software, Writing-Original draft preparation. Chu Yanwu: Software. Ma Shixiang: Visualization, Investigation. Zhang Siyu: Validation. Cui Haohao: Software, Validation. Hu Zhenlin: Software. Chen Feng: Writing-Reviewing and Editing. Sheng Ziqian: Writing-Original Draft. Guo Lianbo: Writing-Reviewing and Editing, Supervision. Lu Yongfeng: Writing-Reviewing and Editing, Supervision.
A Plasma-Image-Assisted Method for Matrix Effect Correction in Laser-Induced Breakdown Spectroscopy Deng Zhang1, Yanwu Chu1, Shixiang Ma1, Siyu Zhang1, Haohao Cui1, Zhenlin Hu1, Feng Chen1, Ziqian Sheng1, Lianbo Guo1,*,Yongfeng Lu2 1 Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China 2 Department of Electrical Engineering, University of Nebraska-Lincoln, Lincoln, NE 685880511 *Corresponding author: [email protected] Abstract: The matrix effect is one of the main bottlenecks for the laser-induced breakdown spectroscopy (LIBS) technique. In this work, image-assisted, laserinduced breakdown spectroscopy (IA-LIBS) based on the Lomakin-Scherbe formula was put forward as a correction to the matrix effect. The brightness and area information in the plasma image was extracted to correct the spectral line intensities among which the brightness information characterizes the plasma temperature, and the area information characterizes the ablative mass. To verify the feasi-
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bility of this method, the experiment was conducted on metal samples and pressed samples. The method was applied for quantitative analysis of copper (Cu), magnesium (Mg) in metal samples and chromium (Cr), manganese (Mn) in pressed samples. For the metal samples, after correcting the matrix effect by IA-LIBS, the determination coefficient R squared (R2) of Cu I 510.55 nm and Mg I 518.36 nm calibration curves were increased from 0.726 and 0.942 to 0.992 and 0.988, respectively. The root-mean-square-error of cross-validation (RMSECV) and the average relative error (ARE) decreased by 75.10% and 77.18%, respectively. For the pressed samples, R2 of Cr I 520.84 nm and Mn I 403.07 nm calibration curves corrected by IA-LIBS increased from 0.364 and 0.098 to 0.975 and 0.980; and RMSECV and ARE decreased by 77.88% and 83.83%, respectively. The experimental results showed that IA-LIBS had an obvious improvement on elimination of the matrix effect for the different samples and the different elements. Therefore, IA-LIBS will become a promising technology and will greatly promote the development of LIBS in various fields. Keywords: laser-induced breakdown spectroscopy; matrix effect; plasma image; 1. Introduction Laser-induced breakdown spectroscopy (LIBS) is an atomic and ion emission spectroscopy technique based on the laser ablation mechanism. A pulse laser is focused on the surface of the sample, instantly ablating the surface material to form the laser-induced plasma. The material composition and content of the sample are determined by analyzing the plasma emission spectroscopy obtained [1, 2]. Laser-induced breakdown spectroscopy has been praised as the “future super star” [3] in analytical chemistry due to its advantages of simple preparation, microdamage 2 / 28
and in situ detection [4, 5]. In recent years, it has been widely used in many fields, such as metallurgy [6], food analysis [7, 8], energy [9], biomedicine [10], environmental monitoring [11], and other fields [12-14]. However, LIBS also has some accuracy limitations, such as the matrix effect [15-17], which limit the further promotion of LIBS in the industry. The matrix effect, which leads to the discrepant spectral intensities of elements with the same content in different samples, is caused by the different ablation mechanism between different substances and the laser as well as the disparate plasma characteristics under the same experimental conditions [18, 19]. The physical (surface roughness, grain size, thermal conductivity) [20, 21] and chemical (element type, element concentration) [22, 23] properties of the samples are all causes of the matrix effect. Meanwhile, plasma formation dynamics, sample ablation, and associated processes are highly nonlinear [24]. Thus, correcting the matrix effect of LIBS remains a significant challenge. Many studies have focused on correcting the influence of the matrix effect. C. Chaleard et al. [25] came up with an approach by studying the physical model of spectral emission, which corrects the matrix effect by vaporized mass and plasma temperature. The vaporized mass was characterized by ultrasonic signal, and the plasma temperature was calculated by the two-line method. Bret C. Windom and David W. Hahn [26] eliminated the matrix effect by ablating samples to produce plasma plumes, blowing the ablative plume through a carrier gas into a secondary chamber, further exciting the plume with another laser to detect the spectral signals. Y. Tian et al. [27] proposed a method, which may be generically called surface-assisted thin film LIBS analysis and is actually a sensitive method free of the matrix effect, for elemental determination. A lot of other methods have been proposed to correct the matrix effect [28-30]. Although these methods can reduce the matrix effect to some extent, there are still some limitations that prevent
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them from being applied to industrial monitoring, such as cumbersome pretreatment, sample type restrictions, etc. The plasma image is a direct presentation of the plasma condition, which contains a lot of information, such as its size, shape, brightness, and so on. In recent studies, some researchers tried to use the plasma image as a reference signal to improve the stability of LIBS spectra and achieved good results [31-34]. Z. B. Ni et al. [31] proposed a method to reduce the spectral fluctuations by counting the number of pixels in the plasma region. P. Zhang et al. [33] improved the spectral stability by extracting the plasma position information as the reference signal. The plasma image is also a promising reference signal for matrix effect correction. However, to our knowledge, the application of plasma images in matrix effect correction has not been previously reported. In this work, a new method of image-assisted, laser-induced breakdown spectroscopy (IALIBS) was proposed to correct the matrix effect using the information in the plasma images. The plasma images were collected perpendicular to the laser beam and parallel to the sample surface. After obtaining the plasma images and spectra concurrently, we extracted the information on brightness and area from the plasma images, with brightness information representing plasma temperature and area information representing ablative mass. Subsequently, we corrected the spectra using corresponding brightness and area information to compensate the influence of the plasma temperature and ablative mass. In order to evaluate the effect of the matrix effect correction, we used leave-one-out cross validation (LOOCV) to evaluate the model. The results of the comparison proved that IA-LIBS can effectively eliminate the influence of the matrix effect and improve the accuracy of the calibration curve.
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2. Experimental setup and samples 2.1 Experimental Setup A schematic diagram of the LIBS setup used in our work is shown in Figure 1. The laser beam was generated by a Q-switched Nd:YAG laser (wavelength: 532nm; repetition rate: 10 Hz; pulse width: 8 ns; French Quantel, Brilliant B). It was reflected by a mirror and then focused onto the surface of the samples by a focal lens (f = 150 mm) to generate plasma for detection. The sample was placed on a three-dimensional (3D) electric displacement platform so that the laser pulse would impinge on a fresh location on the sample each time. The plasma emission was collected by a light collector (Ocean Optics, 84-UV-25, wavelength range: 200-2000 nm), and the emission spectra were obtained using a Czerny-Turner spectrometer (United Kingdom, Andor Tech., Shamrock 500i, grating of 3600 lines per mm) coupled with an intensified chargecoupled device (ICCD) camera (United Kingdom, Andor Tech., iStar DH-334T). Simultaneously, the plasma images were captured by an ICCD camera (United Kingdom, Andor Tech., iStar DH-334T) which was placed parallel to the sample surface. The laser, spectrometer, and ICCD were simultaneously controlled by a digital delay generator (SRS, DG535) to achieve simultaneous acquisition of spectra and plasma images. Both ICCDs were operated in the gated mode; and the gate delay, gate width, and exposure time were set to be 2 µs, 2 µs, and 2 s to obtain the high signal-to-noise ratio spectra and high-quality images. 2.2 Samples and Sample Preparation In order to verify the wide applicability of IA-LIBS, metal and pressed samples were selected as the experimental samples. The metal samples we selected were aluminum alloy (GSB 041661-2004, Fushun Steel Plant) and cast iron (GBW01131a–01137a, Central Iron & Steel Re-
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search Institute) with certified reference materials. The analyzed elements were copper (Cu) and magnesium (Mg). Because the element content in metal samples is fixed, only parts of the samples were selected for the analysis; and their contents are shown in Table 1. The second kind of sample we analyzed was pressed samples. Two types of certified soil samples (GBW07408 and GBW07446) and one type of certified rock samples (GBW(E)070164), approved by the State Administration of Quality Supervision, Inspection, and Quarantine of China, were used to prepare the pressed samples with different chromium (Cr) and manganese (Mn) concentrations. By adding appropriate amounts of cadmium chloride (CrCl3) and manganese chloride (MnCl2) solutions in the soil samples and rock samples, several new samples of Cr and Mn elements with different gradient concentrations were prepared. The concentrations of the Cr and Mn elements in the pressed samples are listed in Table 2. The pressed sample preparation mainly included the following steps, as shown in Figure 2. The samples we used are shown in Figure 3. a.
Configure the required gradient of the standard solution, and mix 3 ml of standard solution with 2 g of powder sample in the reagent bottles.
b. Place the reagent bottles containing the mixed solution in the ultrasonic cleaning machine, and apply the ultrasonic oscillation for 10 minutes. c.
After the ultrasonic oscillation, place the reagent bottles on the heating device to completely evaporate the solution.
d. Transfer the dried powder into mortar and grind evenly. e.
Press the powder sample into pellets with a diameter of 40 mm under pressure of 30 Mpa.
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3. Corrected principle description 3.1 Physical Model For a LIBS plasma under the local thermal equilibrium (LTE) condition, the intensity of an emitted line of a given element i can be expressed by the Lomakin-Scherbe formula [35]: Ii = KCi Anm hvβ e − E / kT
(1)
we can simplify the Lomakin-Scherbe formula [25]: Ii = KCi M pl e− E / kT
(2)
where K is a constant factor affected by the chosen spectral line and the collection efficiency of the device, Ci represents the content of element i in the plasma, which is the same as that in the sample to be analyzed, A mn is the transition probability, hν represents the energy of a photon, M pl represents the total mass of laser ablation, and e − E / kT represents the excitation temperature
under the assumption that the plasma is in the local thermal equilibrium ( k is Boltzmann’s constant). Figure 4 is the function diagram of Eq. (2). We can draw the conclusion that the discrepancy of the ablative mass and the plasma temperature of different substrates are the main cause of the matrix effect in LIBS. Hence, the matrix effect can be eliminated if the plasma temperature and ablative mass can be measured simultaneously. However, both of these parameters cannot be accurately and easily obtained. The magnitude of the ablative mass is usually at the nanogram level, thus it’s almost impossible for traditional weighting methods to obtain it. Meanwhile, the plasma temperature cannot be directly obtained. The plasma temperature is usually estimated by the Boltzmann method and some other methods. However, those methods require the selection of spectral lines and cannot satisfy the needs of online detection. In this work, the ablative mass and 7 / 28
plasma temperature were characterized by area and brightness information in the plasma images to eliminate the matrix effect. 3.2 Plasma Temperature Diagnostic
Laser-induced plasma is a heterogeneous medium; and its temperature spans several orders of magnitude from 1000 to 100000 K, making it difficult to directly measure. There are several plasma temperature calculation methods in LIBS, among which the Boltzmann method is the most accurate and commonly used. The Boltzmann equation used for plasma temperature calculation is as follows [20]:
ln
I ij λij cB gi Aij
=−
Ei Ns + ln kT U s (T )'
(3)
where c B is the instrument calibration coefficient. According to Eq. (3), ln I ij λij cB gi Aij and E i satisfy the linear relationship. The Boltzmann diagram can be established by obtaining the intensity and parameters of spectral lines ( λij , E i , g i , Aij ). Then the plasma temperature can be calculated. This method is not suitable for online detection because different spectral lines are needed to obtain more accurate plasma temperature. So we hoped to find a reference signal that can quickly and accurately characterize the plasma temperature. The plasma image contains a lot of information, such as the shape and size of the plasma. However, for a long time, researchers only used the change of plasma images to analyze the cause of spectral fluctuation but did not take the plasma image as the reference signal of spectral correction. Recently, some researchers have applied the plasma image as a reference signal to spectral correction to improve the stability of the spectrum [31-33]. Therefore, the plasma image was a potential reference signal for correcting the spectra. In this study, we chose the average
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brightness of the plasma region in the plasma images as the reference signal to eliminate the influence of the plasma temperature. This is a very interesting challenge because a plasma image is easy to obtain and well adapted to in situ measurements. A series of experiments was conducted to verify that the brightness information in the plasma images can characterize the plasma temperature. To achieve different plasma temperatures, we used lasers of different energies to excite the samples. Take cast iron as an example. We used lasers of different energies (20, 30, 40, 50, 60, and 70 mJ) to ablate cast iron samples and collected the full-band spectra and the plasma images synchronously. The spectra and plasma images collected are shown in Figure 5(b). In order to obtain more accurate plasma temperatures, 21 iron (Fe) I spectral lines with weak self-absorption effect were selected to calculate the plasma temperature. The determination coefficients of the Boltzmann diagrams at all energies were over 0.98; therefore, they provided a reliable estimate of the plasma temperature with considerable accuracy. We analyzed the correlation between the calculated plasma temperature and the average brightness of the plasma images. The linear relationship between them is shown in Figure 6. The same linear behavior was also observed for the aluminum alloy under 50 mJ in our work. These results demonstrate the effectiveness of using the brightness information in the plasma images as a diagnostic tool to evaluate the plasma temperature. Moreover, the slope of the linear relationship between the brightness information in the plasma image and the plasma temperature is large. Therefore, it is very sensitive to characterize the plasma temperature by the brightness of the plasma images.
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3.3 Ablative Mass Diagnostics
Laser-induced breakdown spectroscopy is a microdamage detection technology. As the ablative mass of a laser is very small, it cannot be directly measured by the traditional weighing technology. Therefore it’s necessary to find a reference signal to characterize the ablative mass of a laser. As we all know, the plasma image is the statistics of luminescent particles in the plasma. It’s assumed that the total number of luminous particles in the plasma is related to the ablative mass, and the total number of luminous particles is also related to the area information of the plasma images. Therefore, in our work the area information in the plasma images has been used to characterize the ablative mass. Again, take cast iron as an example. We used lasers with different energies (20, 30, 40, 50, 60, and 70 mJ) to ablate cast iron samples and calculated the ablative mass. The macroscopic and microscopic morphology of the ablative crater are shown in Figure 7. By calculating the plasma area in images and the mass of the ablative crater at different laser energies, the relationship between them can be obtained. The variation of the plasma area of images as a function of the ablative mass is shown in Figure 8. The R2 of the calibration curve was as high as 0.9839, indicating that there is a relatively reliable linear relationship between the area information in the plasma images and the ablative mass. The same linear behavior was observed for the aluminum alloy under 50 mJ in our work. These results validate that the use of area information in the plasma images as a diagnostic tool for evaluating the mass of material denuded by the ablation process is viable. Moreover, it was noticed that the slope giving the area information in plasma images as a function of ablative mass is large. This proves that our method of characterizing the ablative mass has good sensitivity.
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3.4 Spectral Correction Model
We previously verified the feasibility of representing the ablative mass and the plasma temperature through the brightness and area information in the plasma images. Therefore, we can express the ablative mass and the plasma temperature by the following formulas:
M pl = a1S + b1
(4)
T = a2 B + b2
(5)
where S is the area occupied by the plasma in images, B is the average brightness of the area occupied by the plasma, and a1 , a2 , b1 and b2 are the parameters that we obtained from the experiment. Due to the error in the previous experiment, the parameters obtained by the experiment deviated from the actual situation, so we needed to optimize the parameters to get better results. Meanwhile, the surface of the pressed sample was relatively rough; and the ablative mass was difficult to measure. So in this experiment, we directly obtained the parameters of the pressed samples through parameter optimization. Finally, we corrected the spectrum according to Eq. (2), (3), and (4); and the corrected spectrum is as follows:
Ii' =
Ii = M pl e− E / kT
Ii (a1S + b1 )e
−
E k ( a2 B +b2 )
= KCi
(6)
According to Eq. (6), we can eliminate the influence of the ablative mass and the plasma temperature on the spectrum and realize the correction of the matrix effect in LIBS. 3.5 Evaluation Indexes
The evaluation of the matrix effect correction model in this work was based on LOOCV. To estimate the performance of this method, the determination coefficient (R2), the root-mean-
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square-error of cross-validation (RMSECV), and the average relative error (ARE) were used as the evaluation parameters. Their expressions are as follows [36]: n
R
2
( yˆ i − yi ) i =1
∑ = 1− ∑
RMSECV=
ARE(%) =
2
n
( yi − y ) 2 i =1
∑
n i =1
( yˆi − yi )2 n
100 n yˆi − yi ∑ n i =1 yi
(7)
(8)
(9)
where n is the number of samples, y i is the certified concentration of the sample i , y is the average value of y i over n sample, and y i is the predicted concentration of sample i . 4. Results and discussion
Previously, we verified the theoretical feasibility of IA-LIBS correcting the matrix effect. We further verified the feasibility of this method in this experiment. We used metal and pressed samples to conduct the experimental study of the matrix effect correction. 4.1 Results of the Metal Samples
Aluminum alloy and cast iron were selected as the experimental samples. The Cu and Mg in these samples were studied, and their contents are shown in Table 1. The spectra and plasma images of these samples were obtained simultaneously, and the brightness and area information in the plasma images was extracted for the spectral correction. To avoid spectral interference, the spectral lines at 510.55 and 518.36 nm were selected as the analytical lines of Cu and Mg, respectively. Partial spectra of the aluminum alloy and cast iron are shown in Figure 9. Partial plasma images of the aluminum alloy and cast iron are shown in Figure 10. 12 / 28
According to Eq. (6), the brightness and area information in the plasma images was used to correct the selected spectral lines. Most of the matrix effect correction methods proposed at present cannot be realized in this experiment as they are applicable to specific samples and require device improvement, etc. Therefore, only the results of the original data and the corrected data using IA-LIBS are compared here. The concentration calibration curves of Cu and Mg using the linear fitting are shown in Figure 11. Figures 11(a) and (c) correspond to the calibration curves with the original data for Cu and Mg. Figures 11(b) and (d) correspond to the calibration curves with the corrected data using IA-LIBS for Cu and Mg. The evaluation parameters of the calibration curve are shown in Table 3. As shown in the table, the calibration curves based on the original data had severe matrix effect; and the evaluation parameters (R2, RMSECV, and ARE) of the multimatrix calibration curve were poor. After the normalization of the net line intensity by the area and brightness information in plasma images, the R2, RMSECV, and ARE of the calibration curve improved greatly. For Cu, the value of R2 for the spectral line at 510.55 nm improved from 0.726 to 0.992. The value of RMSECV for the spectral line at 510.55 nm decreased from 0.222 to 0.048. The value of ARE for the spectral line at 510.55 nm decreased from 294.52% to 47.25%. For Mg, the value of R2 for the spectral line at 518.36 nm improved from 0.942 to 0.988. The value of RMSECV for the spectral line at 518.36 nm decreased from 0.019 to 0.012. The value of ARE for the spectral line at 518.36 nm decreased from 30.42% to 24.96%. From the above data, after IA-LIBS correction, R2 of the two spectral lines increased to around 0.99, RMSECV and ARE decreased by 75.10% and 77.78%, respectively. Noticeably, the data points of the different matrix samples all fell on the same calibration curve, so the spectra corrected by IA-LIBS effectively overcame the matrix effect of the different metal samples.
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4.2 Results of the Pressed Samples
To verify the wide applicability of IA-LIBS in the test samples, the pressed samples were further used in the following test. As shown in Table 2, 23 soil and fluorite samples were prepared for verification in this experiment, including six soil samples and six fluorite samples with different Cr concentrations and five soil samples and six fluorite samples with different Mn concentrations. The spectra and plasma images of these samples were obtained at the same time. Then the brightness and area information in the images was extracted for the spectral correction. To avoid the spectral interference, the spectral line of 520.84 nm was selected as the analysis line of Cr; and the spectral line of 403.07 nm was selected as the analysis line of Mn. The results with and without correction by IA-LIBS were compared. Similar to the phenomenon of the metal samples, the original data showed serious matrix effect. The evaluation parameters (R2, RMSECV, and ARE) of the multimatrix calibration curves were poor. Among them, the R2 of the calibration curve of Mn was as low as 0.098, indicating the unreliability of the results. After the correction of the net line intensity by the brightness and area information in the plasma images, the R2, RMSECV, and ARE of the multimatrix calibration curve were greatly improved. For the spectral line at 520.84 nm of Cr, the value of R2 improved from 0.364 to 0.975. The value of RMSECV decreased from 445.304 to 108.160. The value of ARE decreased from 83.70% to 16.80%. For the spectral line at 403.07 nm of Mn, the value of R2 improved from 0.098 to 0.980. The value of RMSECV decreased from 439.854 to 87.674. The value of ARE decreased from 152.40% to 21.38%. From the experimental results, R2 of the calibration curves increased to over 0.97 after IA-LIBS correction, while RMSECV and ARE, on average, decreased by 77.88% and 83.83%, respectively.
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It can be concluded that the IA-LIBS proposed in this paper can also achieve good correction effect for the pressed samples. Therefore, IA-LIBS has a significant effect on eliminating the matrix effect for different samples and different elements. All of the data processing, modeling, and other calculations in this paper were implemented in MATLAB® 2018b (MathWorks Corp, USA). 5. Conclusions
As the chemical and physical components of the different substrates are disparate, the matrix effect exists and affects the detection results in LIBS. In order to meet the requirements of industrial monitoring, IA-LIBS was proposed to correct the matrix effect. This method uses the brightness and area information in plasma images to characterize the ablative mass and the plasma temperature and further correct the spectra. Two categories of samples were used: metal and pressed. The concentrations of Cu and Mg in the metal samples and the concentrations of Cr and Mn in the pressed samples were studied. For the spectral lines at 510.55 nm (Cu) and 518.36 nm (Mg) in metal samples, after correcting the matrix effect by IA-LIBS, R2 increased from 0.726 and 0.942 to 0.992 and 0.988, RMSECV decreased from 0.222 and 0.019 to 0.048 and 0.012; and ARE decreased from 294.52% and 30.42% to 47.25% and 24.96%, respectively. For the pressed samples, R2 of the calibration curves for Cr I 520.84 nm and Mn I 403.07 nm corrected by IA-LIBS increased from 0.364 and 0.098 to 0.975 and 0.980, RMSECV and ARE, on average, decreased by 77.88% and 83.83%, respectively. These results demonstrate that the correction of the intensity by the brightness and area information in the plasma images can help attain a multimatrix calibration curve with satisfactory precision. Therefore, IA-LIBS can effectively eliminate the matrix effect between different samples, which can help promote the industrial application of LIBS. 15 / 28
Acknowledgements
This research was financially supported by National Natural Science Foundation of China (No. 61575073). Notes and references
[1] J. Li, Z. Zhu, R. Zhou, N. Zhao, R. Yi, X. Yang, X. Li, L. Guo, X. Zeng, Y. Lu, Determination of Carbon Content in Steels Using Laser-Induced Breakdown Spectroscopy Assisted with Laser-Induced Radical Fluorescence, Anal Chem, 89 (2017) 8134-8139. [2] J. Bergevin, T.H. Wu, J. Yeak, B.E. Brumfield, S.S. Harilal, M.C. Phillips, R.J. Jones, Dualcomb spectroscopy of laser-induced plasmas, Nat Commun, 9 (2018) 1273. [3] J.D. Winefordner, I.B. Gornushkin, T. Correll, E. Gibb, B.W. Smith, N. Omenetto, Comparing several atomic spectrometric methods to the super stars: special emphasis on laser induced breakdown spectrometry, LIBS, a future super star, Journal of Analytical Atomic Spectrometry, 19 (2004) 1061. [4] S.S. Harilal, B.E. Brumfield, N.L. LaHaye, K.C. Hartig, M.C. Phillips, Optical spectroscopy of laser-produced plasmas for standoff isotopic analysis, Applied Physics Reviews, 5 (2018) 021301. [5] R. Yi, X. Yang, R. Zhou, J. Li, H. Yu, Z. Hao, L. Guo, X. Li, Y. Lu, X. Zeng, Determination of Trace Available Heavy Metals in Soil Using Laser-Induced Breakdown Spectroscopy Assisted with Phase Transformation Method, Anal Chem, 90 (2018) 7080-7085. [6] F.-Z. Dong, X.-L. Chen, Q. Wang, L.-X. Sun, H.-B. Yu, Y.-X. Liang, J.-G. Wang, Z.-B. Ni, Z.-H. Du, Y.-W. Ma, J.-D. Lu, Recent progress on the application of LIBS for metallurgical online analysis in China, Frontiers of Physics, 7 (2012) 679-689.
16 / 28
[7] Y.G. Mbesse Kongbonga, H. Ghalila, M.B. Onana, Z. Ben Lakhdar, Classification of vegetable oils based on their concentration of saturated fatty acids using laser induced breakdown spectroscopy (LIBS), Food Chem, 147 (2014) 327-331. [8] S. Moncayo, J.D. Rosales, R. Izquierdo-Hornillos, J. Anzano, J.O. Caceres, Classification of red wine based on its protected designation of origin (PDO) using Laser-induced Breakdown Spectroscopy (LIBS), Talanta, 158 (2016) 185-191. [9] C.K. Kim, J.H. In, S.H. Lee, S. Jeong, Influence of plasma conditions on the intensity ratio calibration curve during laser induced breakdown spectroscopy analysis, Opt Lett, 39 (2014) 3818-3821. [10] Y.W. Chu, F. Chen, Y. Tang, T. Chen, Y.X. Yu, H.L. Jin, L.B. Guo, Y.F. Lu, X.Y. Zeng, Diagnosis of nasopharyngeal carcinoma from serum samples using hyperspectral imaging combined with a chemometric method, Opt Express, 26 (2018) 28661-28671. [11] E.C. Ferreira, E.J. Ferreira, P.R. Villas-Boas, G.S. Senesi, C.M. Carvalho, R.A. Romano, L. Martin-Neto, D.M.B.P. Milori, Novel estimation of the humification degree of soil organic matter by laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 99 (2014) 76-81. [12] J. Peng, F. Liu, F. Zhou, K. Song, C. Zhang, L. Ye, Y. He, Challenging applications for multi-element analysis by laser-induced breakdown spectroscopy in agriculture: A review, TrAC Trends in Analytical Chemistry, 85 (2016) 260-272. [13] S. Eto, J. Tani, K. Shirai, T. Fujii, Measurement of concentration of chlorine attached to a stainless-steel canister material using laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 87 (2013) 74-80. [14] R. Gaudiuso, M. Dell'Aglio, O. De Pascale, G.S. Senesi, A. De Giacomo, Laser induced
17 / 28
breakdown spectroscopy for elemental analysis in environmental, cultural heritage and space applications: a review of methods and results, Sensors (Basel), 10 (2010) 7434-7468. [15] A. Segnini, A.A.P. Xavier, P.L. Otaviani-Junior, E.C. Ferreira, A.M. Watanabe, M.A. Sperança, G. Nicolodelli, P.R. Villas-Boas, P.P.A. Oliveira, D.M.B.P. Milori, Physical and Chemical Matrix Effects in Soil Carbon Quantification Using Laser-Induced Breakdown Spectroscopy, American Journal of Analytical Chemistry, 05 (2014) 722-729. [16] J.M. Anzano, M.A. Villoria, A. Ruiz-Medina, R.J. Lasheras, Laser-induced breakdown spectroscopy for quantitative spectrochemical analysis of geological materials: effects of the matrix and simultaneous determination, Anal Chim Acta, 575 (2006) 230-235. [17] J.-S. Huang, C.-B. Ke, K.-C. Lin, Matrix effect on emission/current correlated analysis in laser-induced breakdown spectroscopy of liquid droplets, Spectrochimica Acta Part B: Atomic Spectroscopy, 59 (2004) 321-326. [18] H.J. Hakkanen, J.E.I. Korppi-Tommola, Laser-induced plasma emission spectrometric study of pigments and binders in paper coatings: Matrix effects, Analytical Chemistry, 70 (1998) 4724-4729. [19] Z. Wang, L. Li, L. West, Z. Li, W. Ni, A spectrum standardization approach for laserinduced breakdown spectroscopy measurements, Spectrochimica Acta Part B: Atomic Spectroscopy, 68 (2012) 58-64. [20] M.A. Ismail, H. Imam, A. Elhassan, W.T. Youniss, M.A. Harith, LIBS limit of detection and plasma parameters of some elements in two different metallic matrices, Journal of Analytical Atomic Spectrometry, 19 (2004) 489. [21] B.S. Marangoni, K.S.G. Silva, G. Nicolodelli, G.S. Senesi, J.S. Cabral, P.R. Villas-Boas, C.S. Silva, P.C. Teixeira, A.R.A. Nogueira, V.M. Benites, D.M.B.P. Milori, Phosphorus
18 / 28
quantification in fertilizers using laser induced breakdown spectroscopy (LIBS): a methodology of analysis to correct physical matrix effects, Analytical Methods, 8 (2016) 7882. [22] J.A. Aguilera, C. Aragón, V. Madurga, J. Manrique, Study of matrix effects in laser induced breakdown spectroscopy on metallic samples using plasma characterization by emission spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 64 (2009) 993-998. [23] A.S. Eppler, D.A. Cremers, D.D. Hickmott, M.J. Ferris, A.C. Koskelo, Matrix Effects in the Detection of Pb and Ba in Soils Using Laser-Induced Breakdown Spectroscopy, Applied Spectroscopy, 50 (1996) 1175-1181. [24] N.B. Zorov, A.A. Gorbatenko, T.A. Labutin, A.M. Popov, A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction, Spectrochimica Acta Part B: Atomic Spectroscopy, 65 (2010) 642-657. [25] C. Chaleard, P. Mauchien, N. Andre, J. Uebbing, J.L. Lacour, C. Geertsen, Correction of matrix effects in quantitative elemental analysis with laser ablation optical emission spectrometry, Journal of Analytical Atomic Spectrometry, 12 (1997) 183-188. [26] B.C. Windom, D.W. Hahn, Laser ablation—laser induced breakdown spectroscopy (LALIBS): A means for overcoming matrix effects leading to improved analyte response, Journal of Analytical Atomic Spectrometry, 24 (2009) 1665. [27] Y. Tian, H.C. Cheung, R. Zheng, Q. Ma, Y. Chen, N. Delepine-Gilon, J. Yu, Elemental analysis of powders with surface-assisted thin film laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 124 (2016) 16-24. [28] D. Díaz, D.W. Hahn, A. Molina, Evaluation of Laser-Induced Breakdown Spectroscopy (LIBS) as a Measurement Technique for Evaluation of Total Elemental Concentration in
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Soils, Applied Spectroscopy, 66 (2012) 99-106. [29] L. Zheng, F. Cao, J. Xiu, X. Bai, V. Motto-Ros, N. Gilon, H. Zeng, J. Yu, On the performance of laser-induced breakdown spectroscopy for direct determination of trace metals in lubricating oils, Spectrochimica Acta Part B: Atomic Spectroscopy, 99 (2014) 1-8. [30] T. Takahashi, B. Thornton, Quantitative methods for compensation of matrix effects and self-absorption in Laser Induced Breakdown Spectroscopy signals of solids, Spectrochimica Acta Part B: Atomic Spectroscopy, 138 (2017) 31-42. [31] Z.-B. Ni, X.-L. Chen, H.-B. Fu, J.-G. Wang, F.-Z. Dong, Study on quantitative analysis of slag based on spectral normalization of laser-induced plasma image, Frontiers of Physics, 9 (2014) 439-445. [32] P. Zhang, L. Sun, H. Yu, P. Zeng, L. Qi, Y. Xin, An Image Auxiliary Method for Quantitative Analysis of Laser-Induced Breakdown Spectroscopy, Anal Chem, 90 (2018) 4686-4694. [33] P. Zhang, L.X. Sun, H.B. Yu, P. Zeng, L.F. Qi, Y. Xin, An intensity correction method combined with plasma position information for laser-induced breakdown spectroscopy, Journal of Analytical Atomic Spectrometry, 32 (2017) 2371-2377. [34] F. Anabitarte, L. Rodriguez-Cobo, J.M. Lopez-Higuera, A. Cobo, Normalization of laserinduced breakdown spectroscopy spectra using a plastic optical fiber light collector and acoustic sensor device, Appl. Optics, 51 (2012) 8306-8314. [35] R. Yuan, Y. Tang, Z.H. Zhu, Z.Q. Hao, J.M. Li, H.Y. Yu, Y.X. Yu, L.B. Guo, X.Y. Zeng, Y.F. Lu, Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration, Anal. Chim. Acta, 1064 (2019) 1116. [36] Y. Tang, L. Guo, S. Tang, Y. Chu, Q. Zeng, X. Zeng, J. Duan, Y. Lu, Determination of
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potassium in ceramic raw materials using laser-induced breakdown spectroscopy combined with profile fitting, Appl Opt, 57 (2018) 6451-6455.
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Fig. 1. Schematic diagram of the experimental setup.
Table 1. Element content (wt.%) in metal samples.
Sample label 1 2 3 4 5
Cu Aluminum alloy 0.280 0.200 0.100 0.047 0.010
Cast iron 0.571 1.120 0.846 0.536 1.730
Mg Aluminum alloy 0.290 0.190 0.100 -
Cast iron 0.038 0.014 0.077 0.034 -
Table 2. Element content (mg kg-1) in pressed samples. Sample label 1 2 3 4 5 6
Cr Soil 192 358 692 1025 1358 1692
Fluorite 167 333 667 1000 1333 1667
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Mn Soil 650 817 983 1317 1650 -
Fluorite 49 216 382 716 1049 1716
Fig. 2. Pressed sample preparation process.
Fig. 3. The experimental samples: (a) cast iron, (b) aluminum alloy, (c) pressed soil sample, and (d) pressed fluorite sample.
Fig. 4. Function diagram of spectral intensity changing with the ablative mass and the plasma temperature.
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Fig. 5. (a) The full-band spectra, and (b) the plasma images of cast iron samples at different laser energies.
Fig. 6. The functional relationship between plasma temperature and brightness information in plasma images.
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Fig. 7. The macroscopic and microscopic morphology of the ablative crater.
Fig. 8. The functional relationship between ablative mass and area information in a plasma image.
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Fig. 9. Part of the spectra of aluminum alloy and cast iron.
Fig. 10. Part of the plasma images of aluminum alloy and cast iron.
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Table 3. Evaluation parameters of the calibration curve of the metal samples established by using the original data and calibration data.
Sample Metal samples Pressed samples
Element (Wavelength nm) Cu (510.55 nm) Mg (518.36 nm) Cr (520.84 nm) Mn (403.07 nm)
R2
RMSECV
ARE
LIBS
IA-LIBS
LIBS
IA-LIBS
LIBS
IA-LIBS
0.726 0.942 0.364 0.098
0.992 0.988 0.975 0.980
0.222 0.019 455.304 439.854
0.048 0.012 108.160 87.674
294.52% 30.42% 83.70% 152.40%
47.25% 24.96% 16.80% 21.38%
Fig. 11. Calibration curve of (a) Cu with the spectral line at 510.55 nm using the original data, and (b) the correction data, (c) Mg with the spectral line at 518.36 nm using the original data, and (d) the correction data.
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Fig. 12. Calibration curve of (a) Cr with the spectral line at 520.84 nm using the original data, and (b) the correction data, (c) Mn with the spectral line at 403.07 nm using the original data, and (d) the correction data.
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Table 1.
Element content (wt.%) in metal samples. Cu
Mg
Sample label
Aluminum alloy
Cast iron
Aluminum alloy
Cast iron
1
0.280
0.571
0.290
0.038
2
0.200
1.120
0.190
0.014
3
0.100
0.846
0.100
0.077
4
0.047
0.536
-
0.034
5
0.010
1.730
-
-
Table 2. Sample label
Table 3.
Element content (mg kg-1) in pressed samples. Cr
Mn
Soil
Fluorite
Soil
Fluorite
1
192
167
650
49
2
358
333
817
216
3
692
667
983
382
4
1025
1000
1317
716
5
1358
1333
1650
1049
6
1692
1667
-
1716
Evaluation parameters of the calibration curve of the metal samples established by using the original data and calibration data. R2
Element
RMSECV
ARE
Sample
(Wavelength nm)
LIBS
IA-LIBS
LIBS
IA-LIBS
LIBS
IA-LIBS
Metal
Cu (510.55 nm)
0.726
0.992
0.222
0.048
294.52%
47.25%
samples
Mg (518.36 nm)
0.942
0.988
0.019
0.012
30.42%
24.96%
Pressed
Cr (520.84 nm)
0.364
0.975
455.304
108.160
83.70%
16.80%
samples
Mn (403.07 nm)
0.098
0.980
439.854
87.674
152.40%
21.38%
Fig. 1.
Schematic diagram of the experimental setup.
Fig. 2.
Fig. 3.
Pressed sample preparation process.
The experimental samples: (a) cast iron, (b) aluminum alloy, (c) pressed soil sample, and (d) pressed fluorite sample.
Fig. 4.
Function diagram of spectral intensity changing with the ablative mass and the plasma temperature.
Fig. 5.
(a) The full-band spectra, and (b) the plasma images of cast iron samples at different laser energies.
Fig. 6.
The functional relationship between plasma temperature and brightness information in plasma images.
Fig. 7.
The macroscopic and microscopic morphology of the ablative crater.
Fig. 8.
The functional relationship between ablative mass and area information in a plasma image.
Fig. 9.
Part of the spectra of aluminum alloy and cast iron.
Fig. 10.
Fig. 11.
Part of the plasma images of aluminum alloy and cast iron.
Calibration curve of (a) Cu with the spectral line at 510.55 nm using the
original data, and (b) the correction data, (c) Mg with the spectral line at 518.36 nm using the original data, and (d) the correction data.
Fig. 12.
Calibration curve of (a) Cr with the spectral line at 520.84 nm using the
original data, and (b) the correction data, (c) Mn with the spectral line at 403.07 nm using the original data, and (d) the correction data.
1. Correcting the matrix effect is always a big challenge in LIBS. 2. A new
simple
method
called
image-assisted
laser-induced
breakdown
spectroscopy (IA-LIBS) based on the Lomakin-Scherbe formula was proposed to correct the matrix effect. 3. The plasma image is easily acquired and can directly reflect the shape, brightness and other information of the plasma. 4. IA-LIBS can effectively eliminate the matrix effect without cumbersome pretreatment, expensive device, it is suitable for different samples and different elements.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0003-2670(20)30144-6
DOI:
https://doi.org/10.1016/j.aca.2020.02.003
Reference:
ACA 237435
To appear in:
Analytica Chimica Acta
Received Date: 2 December 2019 Revised Date:
31 December 2019
Accepted Date: 1 February 2020
Please cite this article as: D. Zhang, Y. Chu, S. Ma, S. Zhang, H. Cui, Z. Hu, F. Chen, Z. Sheng, L. Guo, Y. Lu, A Plasma-Image-Assisted Method for Matrix Effect Correction in Laser-Induced Breakdown Spectroscopy, Analytica Chimica Acta, https://doi.org/10.1016/j.aca.2020.02.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
Zhang Deng: Conceptualization, Methodology, Software, Writing-Original draft preparation. Chu Yanwu: Software. Ma Shixiang: Visualization, Investigation. Zhang Siyu: Validation. Cui Haohao: Software, Validation. Hu Zhenlin: Software. Chen Feng: Writing-Reviewing and Editing. Sheng Ziqian: Writing-Original Draft. Guo Lianbo: Writing-Reviewing and Editing, Supervision. Lu Yongfeng: Writing-Reviewing and Editing, Supervision.
A Plasma-Image-Assisted Method for Matrix Effect Correction in Laser-Induced Breakdown Spectroscopy Deng Zhang1, Yanwu Chu1, Shixiang Ma1, Siyu Zhang1, Haohao Cui1, Zhenlin Hu1, Feng Chen1, Ziqian Sheng1, Lianbo Guo1,*,Yongfeng Lu2 1 Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China 2 Department of Electrical Engineering, University of Nebraska-Lincoln, Lincoln, NE 685880511 *Corresponding author: [email protected] Abstract: The matrix effect is one of the main bottlenecks for the laser-induced breakdown spectroscopy (LIBS) technique. In this work, image-assisted, laserinduced breakdown spectroscopy (IA-LIBS) based on the Lomakin-Scherbe formula was put forward as a correction to the matrix effect. The brightness and area information in the plasma image was extracted to correct the spectral line intensities among which the brightness information characterizes the plasma temperature, and the area information characterizes the ablative mass. To verify the feasi-
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bility of this method, the experiment was conducted on metal samples and pressed samples. The method was applied for quantitative analysis of copper (Cu), magnesium (Mg) in metal samples and chromium (Cr), manganese (Mn) in pressed samples. For the metal samples, after correcting the matrix effect by IA-LIBS, the determination coefficient R squared (R2) of Cu I 510.55 nm and Mg I 518.36 nm calibration curves were increased from 0.726 and 0.942 to 0.992 and 0.988, respectively. The root-mean-square-error of cross-validation (RMSECV) and the average relative error (ARE) decreased by 75.10% and 77.18%, respectively. For the pressed samples, R2 of Cr I 520.84 nm and Mn I 403.07 nm calibration curves corrected by IA-LIBS increased from 0.364 and 0.098 to 0.975 and 0.980; and RMSECV and ARE decreased by 77.88% and 83.83%, respectively. The experimental results showed that IA-LIBS had an obvious improvement on elimination of the matrix effect for the different samples and the different elements. Therefore, IA-LIBS will become a promising technology and will greatly promote the development of LIBS in various fields. Keywords: laser-induced breakdown spectroscopy; matrix effect; plasma image; 1. Introduction Laser-induced breakdown spectroscopy (LIBS) is an atomic and ion emission spectroscopy technique based on the laser ablation mechanism. A pulse laser is focused on the surface of the sample, instantly ablating the surface material to form the laser-induced plasma. The material composition and content of the sample are determined by analyzing the plasma emission spectroscopy obtained [1, 2]. Laser-induced breakdown spectroscopy has been praised as the “future super star” [3] in analytical chemistry due to its advantages of simple preparation, microdamage 2 / 28
and in situ detection [4, 5]. In recent years, it has been widely used in many fields, such as metallurgy [6], food analysis [7, 8], energy [9], biomedicine [10], environmental monitoring [11], and other fields [12-14]. However, LIBS also has some accuracy limitations, such as the matrix effect [15-17], which limit the further promotion of LIBS in the industry. The matrix effect, which leads to the discrepant spectral intensities of elements with the same content in different samples, is caused by the different ablation mechanism between different substances and the laser as well as the disparate plasma characteristics under the same experimental conditions [18, 19]. The physical (surface roughness, grain size, thermal conductivity) [20, 21] and chemical (element type, element concentration) [22, 23] properties of the samples are all causes of the matrix effect. Meanwhile, plasma formation dynamics, sample ablation, and associated processes are highly nonlinear [24]. Thus, correcting the matrix effect of LIBS remains a significant challenge. Many studies have focused on correcting the influence of the matrix effect. C. Chaleard et al. [25] came up with an approach by studying the physical model of spectral emission, which corrects the matrix effect by vaporized mass and plasma temperature. The vaporized mass was characterized by ultrasonic signal, and the plasma temperature was calculated by the two-line method. Bret C. Windom and David W. Hahn [26] eliminated the matrix effect by ablating samples to produce plasma plumes, blowing the ablative plume through a carrier gas into a secondary chamber, further exciting the plume with another laser to detect the spectral signals. Y. Tian et al. [27] proposed a method, which may be generically called surface-assisted thin film LIBS analysis and is actually a sensitive method free of the matrix effect, for elemental determination. A lot of other methods have been proposed to correct the matrix effect [28-30]. Although these methods can reduce the matrix effect to some extent, there are still some limitations that prevent
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them from being applied to industrial monitoring, such as cumbersome pretreatment, sample type restrictions, etc. The plasma image is a direct presentation of the plasma condition, which contains a lot of information, such as its size, shape, brightness, and so on. In recent studies, some researchers tried to use the plasma image as a reference signal to improve the stability of LIBS spectra and achieved good results [31-34]. Z. B. Ni et al. [31] proposed a method to reduce the spectral fluctuations by counting the number of pixels in the plasma region. P. Zhang et al. [33] improved the spectral stability by extracting the plasma position information as the reference signal. The plasma image is also a promising reference signal for matrix effect correction. However, to our knowledge, the application of plasma images in matrix effect correction has not been previously reported. In this work, a new method of image-assisted, laser-induced breakdown spectroscopy (IALIBS) was proposed to correct the matrix effect using the information in the plasma images. The plasma images were collected perpendicular to the laser beam and parallel to the sample surface. After obtaining the plasma images and spectra concurrently, we extracted the information on brightness and area from the plasma images, with brightness information representing plasma temperature and area information representing ablative mass. Subsequently, we corrected the spectra using corresponding brightness and area information to compensate the influence of the plasma temperature and ablative mass. In order to evaluate the effect of the matrix effect correction, we used leave-one-out cross validation (LOOCV) to evaluate the model. The results of the comparison proved that IA-LIBS can effectively eliminate the influence of the matrix effect and improve the accuracy of the calibration curve.
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2. Experimental setup and samples 2.1 Experimental Setup A schematic diagram of the LIBS setup used in our work is shown in Figure 1. The laser beam was generated by a Q-switched Nd:YAG laser (wavelength: 532nm; repetition rate: 10 Hz; pulse width: 8 ns; French Quantel, Brilliant B). It was reflected by a mirror and then focused onto the surface of the samples by a focal lens (f = 150 mm) to generate plasma for detection. The sample was placed on a three-dimensional (3D) electric displacement platform so that the laser pulse would impinge on a fresh location on the sample each time. The plasma emission was collected by a light collector (Ocean Optics, 84-UV-25, wavelength range: 200-2000 nm), and the emission spectra were obtained using a Czerny-Turner spectrometer (United Kingdom, Andor Tech., Shamrock 500i, grating of 3600 lines per mm) coupled with an intensified chargecoupled device (ICCD) camera (United Kingdom, Andor Tech., iStar DH-334T). Simultaneously, the plasma images were captured by an ICCD camera (United Kingdom, Andor Tech., iStar DH-334T) which was placed parallel to the sample surface. The laser, spectrometer, and ICCD were simultaneously controlled by a digital delay generator (SRS, DG535) to achieve simultaneous acquisition of spectra and plasma images. Both ICCDs were operated in the gated mode; and the gate delay, gate width, and exposure time were set to be 2 µs, 2 µs, and 2 s to obtain the high signal-to-noise ratio spectra and high-quality images. 2.2 Samples and Sample Preparation In order to verify the wide applicability of IA-LIBS, metal and pressed samples were selected as the experimental samples. The metal samples we selected were aluminum alloy (GSB 041661-2004, Fushun Steel Plant) and cast iron (GBW01131a–01137a, Central Iron & Steel Re-
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search Institute) with certified reference materials. The analyzed elements were copper (Cu) and magnesium (Mg). Because the element content in metal samples is fixed, only parts of the samples were selected for the analysis; and their contents are shown in Table 1. The second kind of sample we analyzed was pressed samples. Two types of certified soil samples (GBW07408 and GBW07446) and one type of certified rock samples (GBW(E)070164), approved by the State Administration of Quality Supervision, Inspection, and Quarantine of China, were used to prepare the pressed samples with different chromium (Cr) and manganese (Mn) concentrations. By adding appropriate amounts of cadmium chloride (CrCl3) and manganese chloride (MnCl2) solutions in the soil samples and rock samples, several new samples of Cr and Mn elements with different gradient concentrations were prepared. The concentrations of the Cr and Mn elements in the pressed samples are listed in Table 2. The pressed sample preparation mainly included the following steps, as shown in Figure 2. The samples we used are shown in Figure 3. a.
Configure the required gradient of the standard solution, and mix 3 ml of standard solution with 2 g of powder sample in the reagent bottles.
b. Place the reagent bottles containing the mixed solution in the ultrasonic cleaning machine, and apply the ultrasonic oscillation for 10 minutes. c.
After the ultrasonic oscillation, place the reagent bottles on the heating device to completely evaporate the solution.
d. Transfer the dried powder into mortar and grind evenly. e.
Press the powder sample into pellets with a diameter of 40 mm under pressure of 30 Mpa.
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3. Corrected principle description 3.1 Physical Model For a LIBS plasma under the local thermal equilibrium (LTE) condition, the intensity of an emitted line of a given element i can be expressed by the Lomakin-Scherbe formula [35]: Ii = KCi Anm hvβ e − E / kT
(1)
we can simplify the Lomakin-Scherbe formula [25]: Ii = KCi M pl e− E / kT
(2)
where K is a constant factor affected by the chosen spectral line and the collection efficiency of the device, Ci represents the content of element i in the plasma, which is the same as that in the sample to be analyzed, A mn is the transition probability, hν represents the energy of a photon, M pl represents the total mass of laser ablation, and e − E / kT represents the excitation temperature
under the assumption that the plasma is in the local thermal equilibrium ( k is Boltzmann’s constant). Figure 4 is the function diagram of Eq. (2). We can draw the conclusion that the discrepancy of the ablative mass and the plasma temperature of different substrates are the main cause of the matrix effect in LIBS. Hence, the matrix effect can be eliminated if the plasma temperature and ablative mass can be measured simultaneously. However, both of these parameters cannot be accurately and easily obtained. The magnitude of the ablative mass is usually at the nanogram level, thus it’s almost impossible for traditional weighting methods to obtain it. Meanwhile, the plasma temperature cannot be directly obtained. The plasma temperature is usually estimated by the Boltzmann method and some other methods. However, those methods require the selection of spectral lines and cannot satisfy the needs of online detection. In this work, the ablative mass and 7 / 28
plasma temperature were characterized by area and brightness information in the plasma images to eliminate the matrix effect. 3.2 Plasma Temperature Diagnostic
Laser-induced plasma is a heterogeneous medium; and its temperature spans several orders of magnitude from 1000 to 100000 K, making it difficult to directly measure. There are several plasma temperature calculation methods in LIBS, among which the Boltzmann method is the most accurate and commonly used. The Boltzmann equation used for plasma temperature calculation is as follows [20]:
ln
I ij λij cB gi Aij
=−
Ei Ns + ln kT U s (T )'
(3)
where c B is the instrument calibration coefficient. According to Eq. (3), ln I ij λij cB gi Aij and E i satisfy the linear relationship. The Boltzmann diagram can be established by obtaining the intensity and parameters of spectral lines ( λij , E i , g i , Aij ). Then the plasma temperature can be calculated. This method is not suitable for online detection because different spectral lines are needed to obtain more accurate plasma temperature. So we hoped to find a reference signal that can quickly and accurately characterize the plasma temperature. The plasma image contains a lot of information, such as the shape and size of the plasma. However, for a long time, researchers only used the change of plasma images to analyze the cause of spectral fluctuation but did not take the plasma image as the reference signal of spectral correction. Recently, some researchers have applied the plasma image as a reference signal to spectral correction to improve the stability of the spectrum [31-33]. Therefore, the plasma image was a potential reference signal for correcting the spectra. In this study, we chose the average
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brightness of the plasma region in the plasma images as the reference signal to eliminate the influence of the plasma temperature. This is a very interesting challenge because a plasma image is easy to obtain and well adapted to in situ measurements. A series of experiments was conducted to verify that the brightness information in the plasma images can characterize the plasma temperature. To achieve different plasma temperatures, we used lasers of different energies to excite the samples. Take cast iron as an example. We used lasers of different energies (20, 30, 40, 50, 60, and 70 mJ) to ablate cast iron samples and collected the full-band spectra and the plasma images synchronously. The spectra and plasma images collected are shown in Figure 5(b). In order to obtain more accurate plasma temperatures, 21 iron (Fe) I spectral lines with weak self-absorption effect were selected to calculate the plasma temperature. The determination coefficients of the Boltzmann diagrams at all energies were over 0.98; therefore, they provided a reliable estimate of the plasma temperature with considerable accuracy. We analyzed the correlation between the calculated plasma temperature and the average brightness of the plasma images. The linear relationship between them is shown in Figure 6. The same linear behavior was also observed for the aluminum alloy under 50 mJ in our work. These results demonstrate the effectiveness of using the brightness information in the plasma images as a diagnostic tool to evaluate the plasma temperature. Moreover, the slope of the linear relationship between the brightness information in the plasma image and the plasma temperature is large. Therefore, it is very sensitive to characterize the plasma temperature by the brightness of the plasma images.
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3.3 Ablative Mass Diagnostics
Laser-induced breakdown spectroscopy is a microdamage detection technology. As the ablative mass of a laser is very small, it cannot be directly measured by the traditional weighing technology. Therefore it’s necessary to find a reference signal to characterize the ablative mass of a laser. As we all know, the plasma image is the statistics of luminescent particles in the plasma. It’s assumed that the total number of luminous particles in the plasma is related to the ablative mass, and the total number of luminous particles is also related to the area information of the plasma images. Therefore, in our work the area information in the plasma images has been used to characterize the ablative mass. Again, take cast iron as an example. We used lasers with different energies (20, 30, 40, 50, 60, and 70 mJ) to ablate cast iron samples and calculated the ablative mass. The macroscopic and microscopic morphology of the ablative crater are shown in Figure 7. By calculating the plasma area in images and the mass of the ablative crater at different laser energies, the relationship between them can be obtained. The variation of the plasma area of images as a function of the ablative mass is shown in Figure 8. The R2 of the calibration curve was as high as 0.9839, indicating that there is a relatively reliable linear relationship between the area information in the plasma images and the ablative mass. The same linear behavior was observed for the aluminum alloy under 50 mJ in our work. These results validate that the use of area information in the plasma images as a diagnostic tool for evaluating the mass of material denuded by the ablation process is viable. Moreover, it was noticed that the slope giving the area information in plasma images as a function of ablative mass is large. This proves that our method of characterizing the ablative mass has good sensitivity.
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3.4 Spectral Correction Model
We previously verified the feasibility of representing the ablative mass and the plasma temperature through the brightness and area information in the plasma images. Therefore, we can express the ablative mass and the plasma temperature by the following formulas:
M pl = a1S + b1
(4)
T = a2 B + b2
(5)
where S is the area occupied by the plasma in images, B is the average brightness of the area occupied by the plasma, and a1 , a2 , b1 and b2 are the parameters that we obtained from the experiment. Due to the error in the previous experiment, the parameters obtained by the experiment deviated from the actual situation, so we needed to optimize the parameters to get better results. Meanwhile, the surface of the pressed sample was relatively rough; and the ablative mass was difficult to measure. So in this experiment, we directly obtained the parameters of the pressed samples through parameter optimization. Finally, we corrected the spectrum according to Eq. (2), (3), and (4); and the corrected spectrum is as follows:
Ii' =
Ii = M pl e− E / kT
Ii (a1S + b1 )e
−
E k ( a2 B +b2 )
= KCi
(6)
According to Eq. (6), we can eliminate the influence of the ablative mass and the plasma temperature on the spectrum and realize the correction of the matrix effect in LIBS. 3.5 Evaluation Indexes
The evaluation of the matrix effect correction model in this work was based on LOOCV. To estimate the performance of this method, the determination coefficient (R2), the root-mean-
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square-error of cross-validation (RMSECV), and the average relative error (ARE) were used as the evaluation parameters. Their expressions are as follows [36]: n
R
2
( yˆ i − yi ) i =1
∑ = 1− ∑
RMSECV=
ARE(%) =
2
n
( yi − y ) 2 i =1
∑
n i =1
( yˆi − yi )2 n
100 n yˆi − yi ∑ n i =1 yi
(7)
(8)
(9)
where n is the number of samples, y i is the certified concentration of the sample i , y is the average value of y i over n sample, and y i is the predicted concentration of sample i . 4. Results and discussion
Previously, we verified the theoretical feasibility of IA-LIBS correcting the matrix effect. We further verified the feasibility of this method in this experiment. We used metal and pressed samples to conduct the experimental study of the matrix effect correction. 4.1 Results of the Metal Samples
Aluminum alloy and cast iron were selected as the experimental samples. The Cu and Mg in these samples were studied, and their contents are shown in Table 1. The spectra and plasma images of these samples were obtained simultaneously, and the brightness and area information in the plasma images was extracted for the spectral correction. To avoid spectral interference, the spectral lines at 510.55 and 518.36 nm were selected as the analytical lines of Cu and Mg, respectively. Partial spectra of the aluminum alloy and cast iron are shown in Figure 9. Partial plasma images of the aluminum alloy and cast iron are shown in Figure 10. 12 / 28
According to Eq. (6), the brightness and area information in the plasma images was used to correct the selected spectral lines. Most of the matrix effect correction methods proposed at present cannot be realized in this experiment as they are applicable to specific samples and require device improvement, etc. Therefore, only the results of the original data and the corrected data using IA-LIBS are compared here. The concentration calibration curves of Cu and Mg using the linear fitting are shown in Figure 11. Figures 11(a) and (c) correspond to the calibration curves with the original data for Cu and Mg. Figures 11(b) and (d) correspond to the calibration curves with the corrected data using IA-LIBS for Cu and Mg. The evaluation parameters of the calibration curve are shown in Table 3. As shown in the table, the calibration curves based on the original data had severe matrix effect; and the evaluation parameters (R2, RMSECV, and ARE) of the multimatrix calibration curve were poor. After the normalization of the net line intensity by the area and brightness information in plasma images, the R2, RMSECV, and ARE of the calibration curve improved greatly. For Cu, the value of R2 for the spectral line at 510.55 nm improved from 0.726 to 0.992. The value of RMSECV for the spectral line at 510.55 nm decreased from 0.222 to 0.048. The value of ARE for the spectral line at 510.55 nm decreased from 294.52% to 47.25%. For Mg, the value of R2 for the spectral line at 518.36 nm improved from 0.942 to 0.988. The value of RMSECV for the spectral line at 518.36 nm decreased from 0.019 to 0.012. The value of ARE for the spectral line at 518.36 nm decreased from 30.42% to 24.96%. From the above data, after IA-LIBS correction, R2 of the two spectral lines increased to around 0.99, RMSECV and ARE decreased by 75.10% and 77.78%, respectively. Noticeably, the data points of the different matrix samples all fell on the same calibration curve, so the spectra corrected by IA-LIBS effectively overcame the matrix effect of the different metal samples.
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4.2 Results of the Pressed Samples
To verify the wide applicability of IA-LIBS in the test samples, the pressed samples were further used in the following test. As shown in Table 2, 23 soil and fluorite samples were prepared for verification in this experiment, including six soil samples and six fluorite samples with different Cr concentrations and five soil samples and six fluorite samples with different Mn concentrations. The spectra and plasma images of these samples were obtained at the same time. Then the brightness and area information in the images was extracted for the spectral correction. To avoid the spectral interference, the spectral line of 520.84 nm was selected as the analysis line of Cr; and the spectral line of 403.07 nm was selected as the analysis line of Mn. The results with and without correction by IA-LIBS were compared. Similar to the phenomenon of the metal samples, the original data showed serious matrix effect. The evaluation parameters (R2, RMSECV, and ARE) of the multimatrix calibration curves were poor. Among them, the R2 of the calibration curve of Mn was as low as 0.098, indicating the unreliability of the results. After the correction of the net line intensity by the brightness and area information in the plasma images, the R2, RMSECV, and ARE of the multimatrix calibration curve were greatly improved. For the spectral line at 520.84 nm of Cr, the value of R2 improved from 0.364 to 0.975. The value of RMSECV decreased from 445.304 to 108.160. The value of ARE decreased from 83.70% to 16.80%. For the spectral line at 403.07 nm of Mn, the value of R2 improved from 0.098 to 0.980. The value of RMSECV decreased from 439.854 to 87.674. The value of ARE decreased from 152.40% to 21.38%. From the experimental results, R2 of the calibration curves increased to over 0.97 after IA-LIBS correction, while RMSECV and ARE, on average, decreased by 77.88% and 83.83%, respectively.
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It can be concluded that the IA-LIBS proposed in this paper can also achieve good correction effect for the pressed samples. Therefore, IA-LIBS has a significant effect on eliminating the matrix effect for different samples and different elements. All of the data processing, modeling, and other calculations in this paper were implemented in MATLAB® 2018b (MathWorks Corp, USA). 5. Conclusions
As the chemical and physical components of the different substrates are disparate, the matrix effect exists and affects the detection results in LIBS. In order to meet the requirements of industrial monitoring, IA-LIBS was proposed to correct the matrix effect. This method uses the brightness and area information in plasma images to characterize the ablative mass and the plasma temperature and further correct the spectra. Two categories of samples were used: metal and pressed. The concentrations of Cu and Mg in the metal samples and the concentrations of Cr and Mn in the pressed samples were studied. For the spectral lines at 510.55 nm (Cu) and 518.36 nm (Mg) in metal samples, after correcting the matrix effect by IA-LIBS, R2 increased from 0.726 and 0.942 to 0.992 and 0.988, RMSECV decreased from 0.222 and 0.019 to 0.048 and 0.012; and ARE decreased from 294.52% and 30.42% to 47.25% and 24.96%, respectively. For the pressed samples, R2 of the calibration curves for Cr I 520.84 nm and Mn I 403.07 nm corrected by IA-LIBS increased from 0.364 and 0.098 to 0.975 and 0.980, RMSECV and ARE, on average, decreased by 77.88% and 83.83%, respectively. These results demonstrate that the correction of the intensity by the brightness and area information in the plasma images can help attain a multimatrix calibration curve with satisfactory precision. Therefore, IA-LIBS can effectively eliminate the matrix effect between different samples, which can help promote the industrial application of LIBS. 15 / 28
Acknowledgements
This research was financially supported by National Natural Science Foundation of China (No. 61575073). Notes and references
[1] J. Li, Z. Zhu, R. Zhou, N. Zhao, R. Yi, X. Yang, X. Li, L. Guo, X. Zeng, Y. Lu, Determination of Carbon Content in Steels Using Laser-Induced Breakdown Spectroscopy Assisted with Laser-Induced Radical Fluorescence, Anal Chem, 89 (2017) 8134-8139. [2] J. Bergevin, T.H. Wu, J. Yeak, B.E. Brumfield, S.S. Harilal, M.C. Phillips, R.J. Jones, Dualcomb spectroscopy of laser-induced plasmas, Nat Commun, 9 (2018) 1273. [3] J.D. Winefordner, I.B. Gornushkin, T. Correll, E. Gibb, B.W. Smith, N. Omenetto, Comparing several atomic spectrometric methods to the super stars: special emphasis on laser induced breakdown spectrometry, LIBS, a future super star, Journal of Analytical Atomic Spectrometry, 19 (2004) 1061. [4] S.S. Harilal, B.E. Brumfield, N.L. LaHaye, K.C. Hartig, M.C. Phillips, Optical spectroscopy of laser-produced plasmas for standoff isotopic analysis, Applied Physics Reviews, 5 (2018) 021301. [5] R. Yi, X. Yang, R. Zhou, J. Li, H. Yu, Z. Hao, L. Guo, X. Li, Y. Lu, X. Zeng, Determination of Trace Available Heavy Metals in Soil Using Laser-Induced Breakdown Spectroscopy Assisted with Phase Transformation Method, Anal Chem, 90 (2018) 7080-7085. [6] F.-Z. Dong, X.-L. Chen, Q. Wang, L.-X. Sun, H.-B. Yu, Y.-X. Liang, J.-G. Wang, Z.-B. Ni, Z.-H. Du, Y.-W. Ma, J.-D. Lu, Recent progress on the application of LIBS for metallurgical online analysis in China, Frontiers of Physics, 7 (2012) 679-689.
16 / 28
[7] Y.G. Mbesse Kongbonga, H. Ghalila, M.B. Onana, Z. Ben Lakhdar, Classification of vegetable oils based on their concentration of saturated fatty acids using laser induced breakdown spectroscopy (LIBS), Food Chem, 147 (2014) 327-331. [8] S. Moncayo, J.D. Rosales, R. Izquierdo-Hornillos, J. Anzano, J.O. Caceres, Classification of red wine based on its protected designation of origin (PDO) using Laser-induced Breakdown Spectroscopy (LIBS), Talanta, 158 (2016) 185-191. [9] C.K. Kim, J.H. In, S.H. Lee, S. Jeong, Influence of plasma conditions on the intensity ratio calibration curve during laser induced breakdown spectroscopy analysis, Opt Lett, 39 (2014) 3818-3821. [10] Y.W. Chu, F. Chen, Y. Tang, T. Chen, Y.X. Yu, H.L. Jin, L.B. Guo, Y.F. Lu, X.Y. Zeng, Diagnosis of nasopharyngeal carcinoma from serum samples using hyperspectral imaging combined with a chemometric method, Opt Express, 26 (2018) 28661-28671. [11] E.C. Ferreira, E.J. Ferreira, P.R. Villas-Boas, G.S. Senesi, C.M. Carvalho, R.A. Romano, L. Martin-Neto, D.M.B.P. Milori, Novel estimation of the humification degree of soil organic matter by laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 99 (2014) 76-81. [12] J. Peng, F. Liu, F. Zhou, K. Song, C. Zhang, L. Ye, Y. He, Challenging applications for multi-element analysis by laser-induced breakdown spectroscopy in agriculture: A review, TrAC Trends in Analytical Chemistry, 85 (2016) 260-272. [13] S. Eto, J. Tani, K. Shirai, T. Fujii, Measurement of concentration of chlorine attached to a stainless-steel canister material using laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 87 (2013) 74-80. [14] R. Gaudiuso, M. Dell'Aglio, O. De Pascale, G.S. Senesi, A. De Giacomo, Laser induced
17 / 28
breakdown spectroscopy for elemental analysis in environmental, cultural heritage and space applications: a review of methods and results, Sensors (Basel), 10 (2010) 7434-7468. [15] A. Segnini, A.A.P. Xavier, P.L. Otaviani-Junior, E.C. Ferreira, A.M. Watanabe, M.A. Sperança, G. Nicolodelli, P.R. Villas-Boas, P.P.A. Oliveira, D.M.B.P. Milori, Physical and Chemical Matrix Effects in Soil Carbon Quantification Using Laser-Induced Breakdown Spectroscopy, American Journal of Analytical Chemistry, 05 (2014) 722-729. [16] J.M. Anzano, M.A. Villoria, A. Ruiz-Medina, R.J. Lasheras, Laser-induced breakdown spectroscopy for quantitative spectrochemical analysis of geological materials: effects of the matrix and simultaneous determination, Anal Chim Acta, 575 (2006) 230-235. [17] J.-S. Huang, C.-B. Ke, K.-C. Lin, Matrix effect on emission/current correlated analysis in laser-induced breakdown spectroscopy of liquid droplets, Spectrochimica Acta Part B: Atomic Spectroscopy, 59 (2004) 321-326. [18] H.J. Hakkanen, J.E.I. Korppi-Tommola, Laser-induced plasma emission spectrometric study of pigments and binders in paper coatings: Matrix effects, Analytical Chemistry, 70 (1998) 4724-4729. [19] Z. Wang, L. Li, L. West, Z. Li, W. Ni, A spectrum standardization approach for laserinduced breakdown spectroscopy measurements, Spectrochimica Acta Part B: Atomic Spectroscopy, 68 (2012) 58-64. [20] M.A. Ismail, H. Imam, A. Elhassan, W.T. Youniss, M.A. Harith, LIBS limit of detection and plasma parameters of some elements in two different metallic matrices, Journal of Analytical Atomic Spectrometry, 19 (2004) 489. [21] B.S. Marangoni, K.S.G. Silva, G. Nicolodelli, G.S. Senesi, J.S. Cabral, P.R. Villas-Boas, C.S. Silva, P.C. Teixeira, A.R.A. Nogueira, V.M. Benites, D.M.B.P. Milori, Phosphorus
18 / 28
quantification in fertilizers using laser induced breakdown spectroscopy (LIBS): a methodology of analysis to correct physical matrix effects, Analytical Methods, 8 (2016) 7882. [22] J.A. Aguilera, C. Aragón, V. Madurga, J. Manrique, Study of matrix effects in laser induced breakdown spectroscopy on metallic samples using plasma characterization by emission spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 64 (2009) 993-998. [23] A.S. Eppler, D.A. Cremers, D.D. Hickmott, M.J. Ferris, A.C. Koskelo, Matrix Effects in the Detection of Pb and Ba in Soils Using Laser-Induced Breakdown Spectroscopy, Applied Spectroscopy, 50 (1996) 1175-1181. [24] N.B. Zorov, A.A. Gorbatenko, T.A. Labutin, A.M. Popov, A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction, Spectrochimica Acta Part B: Atomic Spectroscopy, 65 (2010) 642-657. [25] C. Chaleard, P. Mauchien, N. Andre, J. Uebbing, J.L. Lacour, C. Geertsen, Correction of matrix effects in quantitative elemental analysis with laser ablation optical emission spectrometry, Journal of Analytical Atomic Spectrometry, 12 (1997) 183-188. [26] B.C. Windom, D.W. Hahn, Laser ablation—laser induced breakdown spectroscopy (LALIBS): A means for overcoming matrix effects leading to improved analyte response, Journal of Analytical Atomic Spectrometry, 24 (2009) 1665. [27] Y. Tian, H.C. Cheung, R. Zheng, Q. Ma, Y. Chen, N. Delepine-Gilon, J. Yu, Elemental analysis of powders with surface-assisted thin film laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 124 (2016) 16-24. [28] D. Díaz, D.W. Hahn, A. Molina, Evaluation of Laser-Induced Breakdown Spectroscopy (LIBS) as a Measurement Technique for Evaluation of Total Elemental Concentration in
19 / 28
Soils, Applied Spectroscopy, 66 (2012) 99-106. [29] L. Zheng, F. Cao, J. Xiu, X. Bai, V. Motto-Ros, N. Gilon, H. Zeng, J. Yu, On the performance of laser-induced breakdown spectroscopy for direct determination of trace metals in lubricating oils, Spectrochimica Acta Part B: Atomic Spectroscopy, 99 (2014) 1-8. [30] T. Takahashi, B. Thornton, Quantitative methods for compensation of matrix effects and self-absorption in Laser Induced Breakdown Spectroscopy signals of solids, Spectrochimica Acta Part B: Atomic Spectroscopy, 138 (2017) 31-42. [31] Z.-B. Ni, X.-L. Chen, H.-B. Fu, J.-G. Wang, F.-Z. Dong, Study on quantitative analysis of slag based on spectral normalization of laser-induced plasma image, Frontiers of Physics, 9 (2014) 439-445. [32] P. Zhang, L. Sun, H. Yu, P. Zeng, L. Qi, Y. Xin, An Image Auxiliary Method for Quantitative Analysis of Laser-Induced Breakdown Spectroscopy, Anal Chem, 90 (2018) 4686-4694. [33] P. Zhang, L.X. Sun, H.B. Yu, P. Zeng, L.F. Qi, Y. Xin, An intensity correction method combined with plasma position information for laser-induced breakdown spectroscopy, Journal of Analytical Atomic Spectrometry, 32 (2017) 2371-2377. [34] F. Anabitarte, L. Rodriguez-Cobo, J.M. Lopez-Higuera, A. Cobo, Normalization of laserinduced breakdown spectroscopy spectra using a plastic optical fiber light collector and acoustic sensor device, Appl. Optics, 51 (2012) 8306-8314. [35] R. Yuan, Y. Tang, Z.H. Zhu, Z.Q. Hao, J.M. Li, H.Y. Yu, Y.X. Yu, L.B. Guo, X.Y. Zeng, Y.F. Lu, Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration, Anal. Chim. Acta, 1064 (2019) 1116. [36] Y. Tang, L. Guo, S. Tang, Y. Chu, Q. Zeng, X. Zeng, J. Duan, Y. Lu, Determination of
20 / 28
potassium in ceramic raw materials using laser-induced breakdown spectroscopy combined with profile fitting, Appl Opt, 57 (2018) 6451-6455.
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Fig. 1. Schematic diagram of the experimental setup.
Table 1. Element content (wt.%) in metal samples.
Sample label 1 2 3 4 5
Cu Aluminum alloy 0.280 0.200 0.100 0.047 0.010
Cast iron 0.571 1.120 0.846 0.536 1.730
Mg Aluminum alloy 0.290 0.190 0.100 -
Cast iron 0.038 0.014 0.077 0.034 -
Table 2. Element content (mg kg-1) in pressed samples. Sample label 1 2 3 4 5 6
Cr Soil 192 358 692 1025 1358 1692
Fluorite 167 333 667 1000 1333 1667
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Mn Soil 650 817 983 1317 1650 -
Fluorite 49 216 382 716 1049 1716
Fig. 2. Pressed sample preparation process.
Fig. 3. The experimental samples: (a) cast iron, (b) aluminum alloy, (c) pressed soil sample, and (d) pressed fluorite sample.
Fig. 4. Function diagram of spectral intensity changing with the ablative mass and the plasma temperature.
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Fig. 5. (a) The full-band spectra, and (b) the plasma images of cast iron samples at different laser energies.
Fig. 6. The functional relationship between plasma temperature and brightness information in plasma images.
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Fig. 7. The macroscopic and microscopic morphology of the ablative crater.
Fig. 8. The functional relationship between ablative mass and area information in a plasma image.
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Fig. 9. Part of the spectra of aluminum alloy and cast iron.
Fig. 10. Part of the plasma images of aluminum alloy and cast iron.
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Table 3. Evaluation parameters of the calibration curve of the metal samples established by using the original data and calibration data.
Sample Metal samples Pressed samples
Element (Wavelength nm) Cu (510.55 nm) Mg (518.36 nm) Cr (520.84 nm) Mn (403.07 nm)
R2
RMSECV
ARE
LIBS
IA-LIBS
LIBS
IA-LIBS
LIBS
IA-LIBS
0.726 0.942 0.364 0.098
0.992 0.988 0.975 0.980
0.222 0.019 455.304 439.854
0.048 0.012 108.160 87.674
294.52% 30.42% 83.70% 152.40%
47.25% 24.96% 16.80% 21.38%
Fig. 11. Calibration curve of (a) Cu with the spectral line at 510.55 nm using the original data, and (b) the correction data, (c) Mg with the spectral line at 518.36 nm using the original data, and (d) the correction data.
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Fig. 12. Calibration curve of (a) Cr with the spectral line at 520.84 nm using the original data, and (b) the correction data, (c) Mn with the spectral line at 403.07 nm using the original data, and (d) the correction data.
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Table 1.
Element content (wt.%) in metal samples. Cu
Mg
Sample label
Aluminum alloy
Cast iron
Aluminum alloy
Cast iron
1
0.280
0.571
0.290
0.038
2
0.200
1.120
0.190
0.014
3
0.100
0.846
0.100
0.077
4
0.047
0.536
-
0.034
5
0.010
1.730
-
-
Table 2. Sample label
Table 3.
Element content (mg kg-1) in pressed samples. Cr
Mn
Soil
Fluorite
Soil
Fluorite
1
192
167
650
49
2
358
333
817
216
3
692
667
983
382
4
1025
1000
1317
716
5
1358
1333
1650
1049
6
1692
1667
-
1716
Evaluation parameters of the calibration curve of the metal samples established by using the original data and calibration data. R2
Element
RMSECV
ARE
Sample
(Wavelength nm)
LIBS
IA-LIBS
LIBS
IA-LIBS
LIBS
IA-LIBS
Metal
Cu (510.55 nm)
0.726
0.992
0.222
0.048
294.52%
47.25%
samples
Mg (518.36 nm)
0.942
0.988
0.019
0.012
30.42%
24.96%
Pressed
Cr (520.84 nm)
0.364
0.975
455.304
108.160
83.70%
16.80%
samples
Mn (403.07 nm)
0.098
0.980
439.854
87.674
152.40%
21.38%
Fig. 1.
Schematic diagram of the experimental setup.
Fig. 2.
Fig. 3.
Pressed sample preparation process.
The experimental samples: (a) cast iron, (b) aluminum alloy, (c) pressed soil sample, and (d) pressed fluorite sample.
Fig. 4.
Function diagram of spectral intensity changing with the ablative mass and the plasma temperature.
Fig. 5.
(a) The full-band spectra, and (b) the plasma images of cast iron samples at different laser energies.
Fig. 6.
The functional relationship between plasma temperature and brightness information in plasma images.
Fig. 7.
The macroscopic and microscopic morphology of the ablative crater.
Fig. 8.
The functional relationship between ablative mass and area information in a plasma image.
Fig. 9.
Part of the spectra of aluminum alloy and cast iron.
Fig. 10.
Fig. 11.
Part of the plasma images of aluminum alloy and cast iron.
Calibration curve of (a) Cu with the spectral line at 510.55 nm using the
original data, and (b) the correction data, (c) Mg with the spectral line at 518.36 nm using the original data, and (d) the correction data.
Fig. 12.
Calibration curve of (a) Cr with the spectral line at 520.84 nm using the
original data, and (b) the correction data, (c) Mn with the spectral line at 403.07 nm using the original data, and (d) the correction data.
1. Correcting the matrix effect is always a big challenge in LIBS. 2. A new
simple
method
called
image-assisted
laser-induced
breakdown
spectroscopy (IA-LIBS) based on the Lomakin-Scherbe formula was proposed to correct the matrix effect. 3. The plasma image is easily acquired and can directly reflect the shape, brightness and other information of the plasma. 4. IA-LIBS can effectively eliminate the matrix effect without cumbersome pretreatment, expensive device, it is suitable for different samples and different elements.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: