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Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix
Accepted Manuscript Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix
E.S. Austria, E.M. Fuentes, G.M. Nuesca, R.B. Lamorena PII: DOI: Reference:
S0584-8547(16)30155-0 doi: 10.1016/j.sab.2017.07.001 SAB 5274
To appear in:
Spectrochimica Acta Part B: Atomic Spectroscopy
Received date: Revised date: Accepted date:
15 August 2016 25 April 2017 5 July 2017
Please cite this article as: E.S. Austria, E.M. Fuentes, G.M. Nuesca, R.B. Lamorena , Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix, Spectrochimica Acta Part B: Atomic Spectroscopy (2017), doi: 10.1016/j.sab.2017.07.001
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ACCEPTED MANUSCRIPT Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix E. S. Austria Jr.1,2 , E. M. Fuentes1 , G. M. Nuesca1 and R. B. Lamorena1,*
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Institute of Chemistry, College of Science, University of the Philippines, Diliman, Quezon City,
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Philippines
Materials Science and Engineering Program, College of Science, University of the Philippines,
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Diliman, Quezon City, Philippines
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*corresponding author
ACCEPTED MANUSCRIPT Abstract The dependence of laser-induced breakdown spectroscopy (LIBS) to the matrix of the sample remains an important consideration in performing quantitative analysis. In this study, a new matrix was introduced in the preparation of solid powder calibration curves. Heat-treated natural zeolite
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and KBr were mixed separately into high purity metal powders to generate calibration curves using
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a univariate approach. A LIBS technique was used in the detection and quantitative analysis of Cr, Cu and Pb in river sediment samples. The relative percent difference (RPD) was calculated to
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describe the variability measurements made using ICP/OES and LIBS as well as to evaluate the accuracy of the method. Calculated limits of detection in the matrices prepared were comparable
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with literature values and ranged from 0.41 to 6.1 ppm. The resulting metal concentrations indicate
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that the natural zeolite matrix were closer to the reference values compared to the KBr matrix. By employing principal component analysis (PCA), heat treated zeolite was shown as a potential
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diluent or binder for generating calibration curves and could provide matrix-matched standards in
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identification of target metals from unknown sediment samples. The natural zeolite appeared to closely simulate the ablation behavior and property of the samples, and it is found to be a potential
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suitable matrix for the quantitative LIBS analysis of sediments.
Keywords: laser-induced breakdown spectroscopy; matrix-match standard; principal component analysis; heat-treated natural zeolite; sediment samples
ACCEPTED MANUSCRIPT 1. Introduction Laser-induced breakdown spectroscopy (LIBS) makes it possible to perform rapid qualitative and quantitative analysis of trace, major, and minor elements in sediment and soil samples. In this technique, laser pulses ablate and ionize the sample surface. Atomic and ionic
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emissions from the plasma-material interaction provide fingerprint lines in the spectra that can be
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used for elemental analysis [1-4]. Compositional analysis using LIBS has been carried out in a wide range of samples such as rocks, sediments, sludge, aerosols, liquids, alloys and biological
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samples [5-11]. LIBS analysis in sediments offers several advantages in comparison with classical methods of analysis like atomic absorption spectroscopy (AAS) and inductively coupled
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plasma/optical emission spectrometry (ICP/OES). The advantages of LIBS include a fast
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turnaround time, the in situ analysis of multiple elements in virtually any phase, the potential in obtaining depth-profile composition , and minimal sample amount, treatment, and preparation.
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However, LIBS also suffers from several drawbacks due to the nature of analysis. The ablation
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mechanism is heavily dependent on instrumental parameters such as laser energy, spot size and spectrometer delay. Repeatability of the analysis becomes challenging because of the laser energy
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fluctuations that happen from shot-to-shot [12]. Moreover, since the laser firing is localized, the
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spatial distribution of the target analyte in the sample is influenced by the homogeneity of the sample itself. The biggest disadvantage of quantitative LIBS analysis is the dependence of the calibration standards with the sample matrix, also known as the matrix effect [13]. The matrix plays a significant role in the plasma formation process of LIBS and methods to reduce the uncertainties of each pulse measurement have been a focus of various research works [14-16]. Sample-related matrix effects may significantly affect the accuracy of LIBS quantitative analysis. Emission lines and matrix effects occur simultaneously, leading to LIBS signals that are either
ACCEPTED MANUSCRIPT under or over quantified. There is a need for alternative or potentially better matrices that can be used for the detection of metals in sediment samples, especially the ones that provide lower detection limits. LIBS analyses with univariate or multivariate calibration models are commonly coupled to
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perform quantitative and classification analyses on many environmental samples, such as sediment
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and soil samples [17-18]. Commonly used multivariate techniques include principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Nonetheless, both
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univariate and multivariate techniques utilize calibration samples and/or standards which must be closely matched with the matrix of the target sample. The use of internal standard and/or
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background normalization is commonly done in order to correlate the calibration curves with those
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from the sample for quantitative analysis, especially for those samples with no suitable standard matrices [19]. Both methods are done to offset the matrix effects of samples and standards. Internal
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standard utilizes the intensity of the major element emission line that is independent from the
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concentration. In the normalization with background procedure, the intensity of the analyte is normalized with the continuum radiation that is produced from the radiative recombination of ions
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with electrons to create atoms. The target analyte emission becomes normalized because the
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continuum radiation is directly related to the plasma density and the plasma luminosity [20]. There were several studies reported regarding the analysis of metals in solids utilizing normalization with different calibration matrices to improve the sensitivity and accuracy of the LIBS technique [21-24]. However, the matrices of the binders used in these studies were mostly boric acid (H3BO3) and potassium bromide (KBr) which do not closely emulate the same plasma-material interaction of their respective samples.
ACCEPTED MANUSCRIPT The aim of this study is to evaluate an alternative, inexpensive and stable matrix modifier to improve detection limits in sediment samples. In this work, we performed the detection and quantification of three contaminant metals (Cr, Cu and Pb) in river sediment samples. The prepared standard diluent used was a naturally-occurring zeolite; thus, it potentially matched the matrix of
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the samples. For this study, the zeolite material acted as both a diluent and the sample binder. The
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effects of varying matrices will be highlighted by comparing the natural zeolite with an established
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KBr binder matrix.
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2. Materials and methods
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2.1. Sample collection and preparation
Three representative mining areas were used as sediment collection sites in this study.
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These mining areas run along the Acupan Creek located at Benguet, Philippines. The Acupan
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Creek flows through the mining and residential areas of Acupan town , where the livelihood is
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primarily based on gold mining operations near the river. The origin, depth, elevation and relevant
Table 1
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properties are listed in Table 1.
Code
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Location of the sampling sites along the Acupan Creek. Description
Elevation (m)
Depth (m)
Texture
818
0.2
Silt
Midstream section located at the
community, main tributary of the creek S1 system, flows directly from the upstream
ACCEPTED MANUSCRIPT Midstream section located at the S2
community, secondary tributary of the
821
0.2
Silt
828
0.2
Silt
creek system Midstream section located at the S3
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community, confluence of S1 and S2
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Sediment samples collected in this study were sent to a service laboratory for reference
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measurements. The reference concentrations of Cr (0.01g/mL), Cu (0.02 g/mL) and Pb (0.02 g/mL) were obtained through ICP/OES analysis using Agilent 720 Series ICP-OES (Agilent
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Technologies, Santa Clara, CA, USA). ediment samples were initially digested in 20 mL-50 mL of HNO3, HCl and HF mixture, and were subsequently filtered. All solutions were prepared using
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deionized water. Table 2 shows the metal concentrations of the samples obtained via ICP/OES.
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Table 2
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Metal concentrations (mg kg-1) in sediment samples analyzed via ICP/OES*
Samples
ICP/OES values (ppm) Cu
Pb
17
147
216
S2
44
200
640
S3
28
165
293
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S1
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Cr
* Complete elemental composition of the sediment samples are found in Supporting Data
For the preparation of sediment samples for LIBS analysis, the samples were initially oven-dried at 50°C for 2 days. The oven-dried samples were sieved into a stainless steel mesh (0.707 mm, US
ACCEPTED MANUSCRIPT Sieve No. 24). The resulting samples were ground into finer particles using a mortar and pestle. The sediments were further crushed and ground using an electrical ball mill. The homogenized samples were pelletized by subjecting the samples into a 4-ton load for 10 seconds in a uniaxial hydraulic press.
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Calibration curve preparation
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Two binders, as constituents of the calibrating materials, namely, potassium bromide (KBr), and natural zeolite (NZ) mined from Pangasinan, Philippines, were examined in order to
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choose the most suitable matrix for the calibration standards. NZ powders were calcined and
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referred to as heat-treated NZ in the study. The calcination involved the heating of the NZ powder for 2 hours at 100°C and for 6 hours at 700°C in order to remove aluminum, volatile organic
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materials and residual metal content. High purity metal standard reference material (SRM)
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powders (99.99% purity, Sigma-Aldrich) were used as metal source. The target metals for the study were Cr, Cu and Pb. Calibration standards were prepared by mixing the standards with the
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matrices in terms of mass. The calibration standard powders were mixed initially with either KBr
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or NZ using mortar and pestle before the resultant mixture was homogenized in an electrical ball mill. The homogenized calibration standards with the binder were then pelletized using a 4-ton
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load for 10 seconds in a uniaxial hydraulic press, similar to that used for sediment samples. 2.2. Optimization of instrument parameters Four instrument parameters were optimized in terms of intensity prior to analysis. Laser energy, laser spot size, spectrometer delay and type of firing were optimized. The main atomic emission lines used for elemental identification are 520.6 nm, 324.7 nm and 405.8 nm for Cr, Cu, and Pb, respectively.
ACCEPTED MANUSCRIPT A commercial LIBS set-up (J200, Applied Spectra, Fremont, CA, USA) was used in this study. Axiom software (Applied Spectra, Fremont, CA, USA) was used in operating the LIBS setup. The laser was a Q-switched Nd:YAG laser operated at the fundamental wavelength of 1064 nm with repetition rate of 20 Hz and pulse width of 6 ns. The laser was fired into the pelletized
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calibration standards and samples at atmospheric pressure. Analyses were conducted under
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optimized parameters, namely: 0.5 µs (gate delay), 45 mJ (laser energy), 75 µm (spot size), and
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raster firing. A 5x5 grid with a total area of approximately 65 mm2 was ablated through raster patterns with the speed of 10 mm/s totaling to 219 shots. Light coming from the plasma was
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collected and recorded into a high resolution 6-channel CCD spectrometer through a high
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transmission optical fiber collection system. The built-in spectrometer had a spectral range of 190 nm to 1040 nm, and full spectrum analysis had been performed within this range to get complete
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information on the elements of interest. The plasma temperature was obtained using Boltzmann
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plot method, while the electron density was obtained using the Saha-Boltzmann equation. Table 3 shows the Fe emission lines used for the estimation of plasma parameters. Fe I and II were used
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due to the high percent abundance of Fe in the sediment samples. The plasma temperature reached
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10808.2 K at a gate delay time of 500 ns. On the other hand, the estimated electron density at 500
Table 3
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ns was 4.20634 x1027/cm3.
Spectroscopic parameters used for Tplasma determination Upper Transition energy Wavelength Element probability level (nm) (1x107 s-1) (1x104 cm-1) Fe I
246.26
4.06
5.85
296.69
3.37
2.72
Fe I
344.06
2.91
1.71
Fe I
344.10
2.95
1.24
Fe I
347.55
2.95
0.98
Fe I
349.06
2.91
0.61
Fe I
358.12
3.48
10.20
Fe I
360.89
3.59
8.13
Fe I
361.88
3.56
7.22
Fe I
367.99
2.72
0.14
Fe I
374.56
2.74
1.15
Fe I
374.83
2.76
0.92
Fe I
374.95
3.40
Fe I
375.82
3.43
6.34
Fe I
376.38
3.45
5.44
Fe I
381.58
Fe I
382.04
Fe I Fe I
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7.63
11.20
3.31
6.67
385.64
2.63
0.46
385.99
2.59
0.97
387.86
2.65
0.62
Fe I
388.63
2.61
0.53
Fe I
404.58
3.67
8.62
Fe I
406.36
3.72
6.65
Fe I
432.58
3.61
5.16
Fe I
438.36
3.48
5.00
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3.82
Fe I
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Fe I
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34.40
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249.06
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Fe I
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ACCEPTED MANUSCRIPT Fe II
249.12
4.10
29.10
Fe II
259.94
3.85
23.50
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2.3. PCA Analysis
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The Aurora software (Applied Spectra, Fremont, CA, USA) was used to determine the spectral
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line information for the target elements. The preliminary evaluation was done with Principal Component Analysis (PCA) in order to get an understanding on existing trends and to recognize
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property variations in the chemical data for each sample. PCA was also employed to validate the
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emission lines used and to qualitatively distinguish the uniformity of the binder with the sediment samples. A total of 12,288 spectral data points were analyzed and were used as input variables for
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the PCA method. No pre-processing technique for each LIBS spectrum was employed before
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performing PCA analysis. The constructed PCA models were used only for visualization purposes
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and no optimization were done for determining the number of principal components.
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3. Results and Discussion
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3.1. Calibration curve
The performance of the two types of binders employed in this study has been primarily evaluated by linearity and limit of detection. In order to examine the capability of LIBS for singleelement quantitative analysis in sediments, a calibration set was prepared for each target metal wherein known amounts of metal targets were added with KBr or natural zeolite matrix.. Both univariate and multivariate techniques need calibration standards that are capable of simulating
ACCEPTED MANUSCRIPT the laser-material interaction of the sample. However, exact replication of the behavior and particle interaction between the laser and sample material is not possible. Thus, finding a suitable matrix with nearly the same ablation threshold is necessary for the quantitative analysis of the target metals in the sediments. The first matrix used was potassium bromide (KBr). Along with boric
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acid (H3BO3), the KBr binder has been used in most of the LIBS-based analyses because of its
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good binding ability and the uncomplicated matrix that it forms with calibration samples [25-29].
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The normalized intensity of the target metals, Cr (520.6 nm), Cu (324.7 nm) and Pb (405.8 nm), were plotted versus the known metal concentration. The metal concentrations used in all
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calibration sets were 0, 10, 25, 50, 100 and 200 ppm. Figure 1 (a-c) shows the calibration curves
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for Cr, Cu and Pb in the KBr matrix.
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The three calibration curves prepared using KBr (Figure 1) have satisfactory values of coefficient of variation (R2 = 0.900 - 0.973). This indicates that the LIBS signal intensities of the
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calibration standards were linearly correlated at low concentrations. Even though these R2 values
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are not as high compared to those obtained from standards used in ICP-OES analysis, it should be noted that the matrix phase used for LIBS analysis was solid which induces inherent problems
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with homogeneity. Nonetheless, the linear correlations of these calibration curves were
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comparable to previous LIBS literature [7, 25, 30-31]. The second matrix used was a natural zeolite obtained from a mining quarry in the province of Pangasinan, Philippines. The composition of the natural zeolite after calcination showed negligible concentrations of the target metals as shown in Table 4. The total metal concentrations for the three metals were below the method limit of detection (MDL). Table 4 Metal concentration of natural zeolite.
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Concentration (ppm)
Cr
<5.0*
Cu
<1.0*
Pb
<5.0*
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* metal concentration values fall below the method detection limit
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The NZ binder was heat treated to be free from metal impurities but may have caused misleading
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interpretation of the results. Pre-treatment step allowed the natural zeolite to be a suitable binder in the preparation of the calibration curves. Figure 2 shows the calibration curves for Cr, Cu and
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Pb using the natural zeolite. The standard deviation values are found to be high and this can be
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associated to the innate heterogenous composition nature of the zeolite in contrast to the relative
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homogenous nature of KBr.
Similar with the R2 values for the KBr matrix, the calibration curves using the NZ heat-
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treated binder have generated high R2 values (R2 = 0.96 - 0.99). These values were comparable
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with literature values cited previously. Considering that multiple minerals/elements constitute the natural zeolite, the linear correlation was still comparable with the much simpler KBr matrix. This
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positive correlation may come from two reasons, (1) homogeneity in the calibration sample preparation, and (2) output data normalization. Since the calibration samples were solid pellets, the preparation was largely affected by the homogeneity of the powder mixture. The mixing process was initially done by a mortar and pestle set-up. These powders were further mixed in an electrical ball mill that was set to 100 rpm. This two-step mixing process ensured uniformity of the powder samples for ablation. Another factor that helped to improve homogeneity is by increasing the sample area during the ablation process. By averaging the spectral intensities
ACCEPTED MANUSCRIPT coming from a much larger firing area, better representative data were collected. The second reason for the comparable linearity between the natural zeolite matrix and KBr was the normalization of the spectra with the background. In quantitative LIBS analysis, adding an internal standard to the calibration standards is a common practice in order to correlate the calibration curves with the
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sample. This is particularly necessary for those samples with no matching matrices [32]. The
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intensity of the target metal will then be normalized with the intensity of the internal standard
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placed. Another common approach of data processing technique is normalization with the background, with or without the presence of an internal standard. The intensity of the target metal
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is normalized with the continuum radiation that is produced when ions interact with electrons
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through radiative recombination forming atoms [33]. In this study, normalization with the background was done in order to offset the effects of the varying matrices between the samples
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3.2. Limit of detection
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and the calibration standards.
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The sensitivity of the procedure was evaluated using the prepared calibration curves.
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Equation 1 was used for the calculation of the limit of detection (LOD) of the calibration curves. The σ is the standard deviation of the blank (0 ppm) and s is the slope of the line of the calibration
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curve.
𝐿𝑂𝐷 (𝑝𝑝𝑚) =
3𝜎 𝑠
Equation 1. Limit of detection. For the calculation of the LOD, 25 trials using the variability of signals from the blank sample were done for each metal in both matrices. Each trial consisted of 219 shots equivalent to 219 spectra. The peak areas of intensities of the target analyte in all spectra were averaged and
ACCEPTED MANUSCRIPT were normalized with the continuum background intensity. Table 5 summarizes the results obtained from the analysis of limit of detection for each metal considered in a either KBR or NZ matrix. Table 5
LOD in KBr (ppm)
Cr
1.43 ± 0.23
Cu
0.41 ± 0.01
Pb
2.47 ± 0.02
LOD in NZ (ppm) 6.10 ± 0.02 4.26 ± 0.01 2.65 ± 0.01
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Metal analyte
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Limit of detection of each metal in KBr and natural zeolite (NZ) matrix.
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The values presented in Table 5 were comparable with the LOD values using LIBS
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reported in previous literature [7, 30, 34-36]. Comparison between the two matrices shows that use of KBr provided a lower detection limit compared to the natural zeolite. This trend was
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expected since the KBr presented a less complicated matrix compared to the natural zeolite. Aside
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from the aluminosilicate framework, elements such as calcium, magnesium and iron were also present in the natural zeolite. The heat-treatment was performed to eliminate organic and more
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volatile metal components from the zeolite. This pre-treatment step did not produce significant changes in the framework of the zeolite as seen in XRD data (Supporting Data). 3.3. LIBS vs. ICP/OES analysis Three midstream sediment samples (S1, S2 and S3) were used as real samples for the study. ICP/OES was used in validating the results of the LIBS analysis. The representative LIBS spectra of the three samples, along with the integration peaks used per element, were illustrated in Figure
ACCEPTED MANUSCRIPT 3. The results from the ICP/OES analysis were averaged and used as the reference values in this study. Tables 6-8 summarizes the reported composition of the S1 sediment sample using LIBS and ICP/OES analysis as well as the relative percent difference (RPD) between these two analytical methods. The RPD was calculated to describe the variability between ICP/OES and LIBS. RPD
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was not determined for values less than the laboratory reporting level or method detection limit.
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Table 6 Comparison of LIBS and ICP/OES values for S1.
Matrix
Analyte
LIBS value (ppm)
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ICP/OES value
RPD (%) #
(ppm) NA
Cu
121 ± 8.18
147
19.4
Pb
<2.47*
216
NA
Cr
16.9 ± 0.63
17
0.590
Cu
125 ± 16.1
147
16.2
216
125
Pb
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17
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<1.43*
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NZ
Cr
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KBr
938 ± 27.0
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* metal concentrations values fall below the method detection limit
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# % Relative percent difference was calculated by taking the absolute value of the difference over the mean times 100 NA: not applicable
Several values were not determined (indicated as NA) for S1 using the calibration curves generated for Cr and Pb measurements in the KBr matrix as shown in Table 6. These results may be caused by the difference in material ablation threshold of the KBr and the S1 sediment sample. Moreover, it should be noted that the percent difference observed for Cu was 18.6% and considered
ACCEPTED MANUSCRIPT to be a relatively low value. Results of LIBS analysis for the natural zeolite were comparable with the ICP/OES analysis and can be calculated using the generated calibration curves. Interpolation of concentration for Cr and Cu yielded results close to the reference value. In comparison to KBr, the zeolite provided a matrix with comparable laser threshold value with sediment. However, the
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extrapolated concentration of Pb was far from the ICP/OES value. The calculated error in
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extrapolation could be due to the low linear correlation of signal-to-noise ratio with metal
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concentration at higher concentration. Similar trend were obtained for S2 (Table 7) and S3 (Table
Table 7
ICP/OES value (ppm)
RPD (%)#
<1.43*
44
NA
Cu
166 ± 30.9
200
18.6
Pb
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Comparison of LIBS and ICP/OES values for S2.
<2.47*
640
NA
45.1 ± 1.82
44.0
2.47
191 ± 19.8
200
4.61
630 ± 19.2
640
1.57
Analyte
LIBS value (ppm)
KBr
Cr
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Matrix
Cr Cu
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Pb
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NZ
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8) sediment samples.
* metal concentrations values fall below the method detection limit # % Relative percent difference was calculated by taking the absolute value of the difference over the mean times 100 NA: not applicable
Table 8
ACCEPTED MANUSCRIPT Comparison of LIBS and ICP/OES values for S3. Analyte
LIBS value (ppm)
ICP/OES value (ppm)
RPD (%) #
KBr
Cr
<1.43*
28
NA
Cu
120 ± 9.16
165
31.6
Pb
<2.47*
293
NA
Cr
28.2 ± 0.99
28
Cu
141 ± 20.0
165
15.7
Pb
693 ± 115
293
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0.712
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NZ
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Matrix
* metal concentrations values fall below the method detection limit
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# % Relative percent difference was calculated by taking the absolute value of the difference over the mean times 100
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NA: not applicable
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Measurements done on samples prepared in NZ matrix were all determined and can be
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compared with ICP/OES results. Calculated concentration values of Cr and Pb in KBr were lower than the corresponding calculated value for the S2 and S3 samples prepared in NZ matrix. Results
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for Cu concentration using LIBS remained satisfactory with respect to ICP/OES values for the
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KBr matrix. On the other hand, both Cr and Cu have low deviations from the ICP/OES values in NZ matrix. Extrapolated LIBS result of S2 sample for Pb in zeolite was close with the reference value. However, the S3 sample produced erroneous result which was far from the reference value for Pb in the zeolite matrix. 3.4. Principal component analysis
ACCEPTED MANUSCRIPT In order to evaluate and visualize the difference in material property of the matrices and the samples, principal component analysis (PCA) was utilized. The full LIBS spectra of the three samples (S1, S2 and S3), along with the two matrices (KBr and NZ), were transformed into several principal components (PCs) through PCA. The first 5 PCs explained 96.04% of the total variation
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in the data. The loading plot of the first five components (Figure 4) validates the presence of the
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chosen emission lines (Cu 324.7, Pb 405.8 and Cr 520.6) in the five PCA samples. However, the
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loading values for the three elements were relatively low (from -0.1 to 0.1) and can be directly
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related to the total amount of the respective elements in the sample & matrices. Moreover, the degree of variation between the samples and binders were further analyzed
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using the same PCA run. Figure 5 shows the orthographic view of the PCA plot of the samples
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and matrices. The first two components explained 94.17% (PC 1: 92.59%; PC 2: 1.58%) of the variations of the spectral information presented. Each circle corresponds to the linear combination
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of the LIBS spectra of the first two principal components. The preparation of the calibration curves
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involved the use of binders that act as the diluent of the high purity metal powders. A homogeneous matrix that produces signals similar with the sample is needed in order to obtain results that are
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comparable with classical analytical methods. Interpretation of the PC score plot suggests that
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discrimination between the two matrices and the samples can be characterized in terms of the lasermaterial interaction. A pattern can be recognized, as seen in the PCA plot (Fig. 5), wherein for the natural zeolite the pattern is clustered closer to the sediment samples compared to the KBr matrix. It can also be observed from the plot that that the sediment samples and natural zeolite are identified in proximity to PC 2, while the KBr matrix approached PC 1.The PCA plot was generated from the laser intensity signals of each of the matrices and the samples. The plot showed the natural distribution of samples indicating the similar chemical composition among the
ACCEPTED MANUSCRIPT sediment samples and natural zeolite. The KBr matrix was clearly separated from the zeolite matrix and sediment samples. This signifies that the natural zeolite simulated closer the ablation threshold and emission intensity of the sediment samples compared to KBr. This would indicate that laser and matrix would have a better interaction and both the zeolite and sediment samples
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would be much better recognized as homogeneous or uniform.
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4. Conclusion
The utilization of PCA showed that heat-treated zeolite is a potential diluent or binder for
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generating calibration curves and provide matrix-matched standards in the identification of target
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metals from unknown sediment samples. The score plot of PCs was used to reveal the similarity of the natural zeolite and sediment samples based on their respective LIBS spectra. The results
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showed that the detection and quantification of Cr, Cu and Pb in sediments via LIBS can be
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comparable to an established analytical technique like ICP/OES by using an appropriate matrix, such as heat-treated natural zeolite as reported in this study. Single-element calibration curves
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with good linear correlation for each metal target were generated. However, extrapolation of data
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from the curve did not produce satisfactory results which signified the non-linearity of concentration and signal-to-noise ratio at higher concentrations. Limit of detections for each curve
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produced comparable results with previous literature. The accuracy of the LIBS method was evaluated by relating the values to ICP/OES analysis. The similarity between the material properties of the natural zeolite matrix and the sediment samples produced comparable statistical values in comparison with the KBr matrix and other LIBS literature.
ACCEPTED MANUSCRIPT Acknowledgments The research was supported and funded by the Department of Science & Technology - Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOSTPCIEERD). The study was under Project 1 of the Chemical Sensors for Mine Site Monitoring
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Program implemented at the Institute of Chemistry, University of the Philippines – Diliman.
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List of Figures
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Figure 1. Calibration curves of (a) Cr, (b) Cu and (c) Pb prepared with KBr binder.
Figure 2. Calibration curves of (a) Cr, (b) Cu and (c) Pb prepared with natural zeolite binder.
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Figure 3. Overlapped LIBS spectra of S1, S2 and S3. Integration peaks (red boxed region)
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highlights the respective emission lines used (Cu 324.7, Pb 405.8 and 520.6).
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Figure 4. Loading plot of the first five principal components from the PCA of the LIBS spectra of
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the three sediment samples and two matrices. Emission lines used for Cr (520.6 nm), Cu (324.7 nm) and Pb (405.8 nm) were identified.
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Figure 5. Scatter plot of two principal components (PC1 and PC2) for KBr, heat-treated natural zeolite, and sediment samples S1, S2 and S3.
ACCEPTED MANUSCRIPT GRAPHICAL ABSTRACT
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Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix
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HIGHLIGHTS Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix Heat-treated natural zeolite is a suitable matrix for the quantitative LIBS analyses of sediments
Use of an alternative and inexpensive binder that act as diluent of high purity metal powders in preparation for the calibration curves
Detection and quantification of Cu, Cr, Pb in river sediment samples using a potentially matrix-matched standard
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E.S. Austria, E.M. Fuentes, G.M. Nuesca, R.B. Lamorena PII: DOI: Reference:
S0584-8547(16)30155-0 doi: 10.1016/j.sab.2017.07.001 SAB 5274
To appear in:
Spectrochimica Acta Part B: Atomic Spectroscopy
Received date: Revised date: Accepted date:
15 August 2016 25 April 2017 5 July 2017
Please cite this article as: E.S. Austria, E.M. Fuentes, G.M. Nuesca, R.B. Lamorena , Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix, Spectrochimica Acta Part B: Atomic Spectroscopy (2017), doi: 10.1016/j.sab.2017.07.001
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ACCEPTED MANUSCRIPT Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix E. S. Austria Jr.1,2 , E. M. Fuentes1 , G. M. Nuesca1 and R. B. Lamorena1,*
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Institute of Chemistry, College of Science, University of the Philippines, Diliman, Quezon City,
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2
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Philippines
Materials Science and Engineering Program, College of Science, University of the Philippines,
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Diliman, Quezon City, Philippines
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*corresponding author
ACCEPTED MANUSCRIPT Abstract The dependence of laser-induced breakdown spectroscopy (LIBS) to the matrix of the sample remains an important consideration in performing quantitative analysis. In this study, a new matrix was introduced in the preparation of solid powder calibration curves. Heat-treated natural zeolite
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and KBr were mixed separately into high purity metal powders to generate calibration curves using
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a univariate approach. A LIBS technique was used in the detection and quantitative analysis of Cr, Cu and Pb in river sediment samples. The relative percent difference (RPD) was calculated to
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describe the variability measurements made using ICP/OES and LIBS as well as to evaluate the accuracy of the method. Calculated limits of detection in the matrices prepared were comparable
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with literature values and ranged from 0.41 to 6.1 ppm. The resulting metal concentrations indicate
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that the natural zeolite matrix were closer to the reference values compared to the KBr matrix. By employing principal component analysis (PCA), heat treated zeolite was shown as a potential
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diluent or binder for generating calibration curves and could provide matrix-matched standards in
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identification of target metals from unknown sediment samples. The natural zeolite appeared to closely simulate the ablation behavior and property of the samples, and it is found to be a potential
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suitable matrix for the quantitative LIBS analysis of sediments.
Keywords: laser-induced breakdown spectroscopy; matrix-match standard; principal component analysis; heat-treated natural zeolite; sediment samples
ACCEPTED MANUSCRIPT 1. Introduction Laser-induced breakdown spectroscopy (LIBS) makes it possible to perform rapid qualitative and quantitative analysis of trace, major, and minor elements in sediment and soil samples. In this technique, laser pulses ablate and ionize the sample surface. Atomic and ionic
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emissions from the plasma-material interaction provide fingerprint lines in the spectra that can be
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used for elemental analysis [1-4]. Compositional analysis using LIBS has been carried out in a wide range of samples such as rocks, sediments, sludge, aerosols, liquids, alloys and biological
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samples [5-11]. LIBS analysis in sediments offers several advantages in comparison with classical methods of analysis like atomic absorption spectroscopy (AAS) and inductively coupled
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plasma/optical emission spectrometry (ICP/OES). The advantages of LIBS include a fast
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turnaround time, the in situ analysis of multiple elements in virtually any phase, the potential in obtaining depth-profile composition , and minimal sample amount, treatment, and preparation.
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However, LIBS also suffers from several drawbacks due to the nature of analysis. The ablation
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mechanism is heavily dependent on instrumental parameters such as laser energy, spot size and spectrometer delay. Repeatability of the analysis becomes challenging because of the laser energy
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fluctuations that happen from shot-to-shot [12]. Moreover, since the laser firing is localized, the
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spatial distribution of the target analyte in the sample is influenced by the homogeneity of the sample itself. The biggest disadvantage of quantitative LIBS analysis is the dependence of the calibration standards with the sample matrix, also known as the matrix effect [13]. The matrix plays a significant role in the plasma formation process of LIBS and methods to reduce the uncertainties of each pulse measurement have been a focus of various research works [14-16]. Sample-related matrix effects may significantly affect the accuracy of LIBS quantitative analysis. Emission lines and matrix effects occur simultaneously, leading to LIBS signals that are either
ACCEPTED MANUSCRIPT under or over quantified. There is a need for alternative or potentially better matrices that can be used for the detection of metals in sediment samples, especially the ones that provide lower detection limits. LIBS analyses with univariate or multivariate calibration models are commonly coupled to
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perform quantitative and classification analyses on many environmental samples, such as sediment
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and soil samples [17-18]. Commonly used multivariate techniques include principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Nonetheless, both
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univariate and multivariate techniques utilize calibration samples and/or standards which must be closely matched with the matrix of the target sample. The use of internal standard and/or
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background normalization is commonly done in order to correlate the calibration curves with those
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from the sample for quantitative analysis, especially for those samples with no suitable standard matrices [19]. Both methods are done to offset the matrix effects of samples and standards. Internal
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standard utilizes the intensity of the major element emission line that is independent from the
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concentration. In the normalization with background procedure, the intensity of the analyte is normalized with the continuum radiation that is produced from the radiative recombination of ions
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with electrons to create atoms. The target analyte emission becomes normalized because the
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continuum radiation is directly related to the plasma density and the plasma luminosity [20]. There were several studies reported regarding the analysis of metals in solids utilizing normalization with different calibration matrices to improve the sensitivity and accuracy of the LIBS technique [21-24]. However, the matrices of the binders used in these studies were mostly boric acid (H3BO3) and potassium bromide (KBr) which do not closely emulate the same plasma-material interaction of their respective samples.
ACCEPTED MANUSCRIPT The aim of this study is to evaluate an alternative, inexpensive and stable matrix modifier to improve detection limits in sediment samples. In this work, we performed the detection and quantification of three contaminant metals (Cr, Cu and Pb) in river sediment samples. The prepared standard diluent used was a naturally-occurring zeolite; thus, it potentially matched the matrix of
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the samples. For this study, the zeolite material acted as both a diluent and the sample binder. The
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effects of varying matrices will be highlighted by comparing the natural zeolite with an established
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KBr binder matrix.
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2. Materials and methods
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2.1. Sample collection and preparation
Three representative mining areas were used as sediment collection sites in this study.
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These mining areas run along the Acupan Creek located at Benguet, Philippines. The Acupan
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Creek flows through the mining and residential areas of Acupan town , where the livelihood is
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primarily based on gold mining operations near the river. The origin, depth, elevation and relevant
Table 1
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properties are listed in Table 1.
Code
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Location of the sampling sites along the Acupan Creek. Description
Elevation (m)
Depth (m)
Texture
818
0.2
Silt
Midstream section located at the
community, main tributary of the creek S1 system, flows directly from the upstream
ACCEPTED MANUSCRIPT Midstream section located at the S2
community, secondary tributary of the
821
0.2
Silt
828
0.2
Silt
creek system Midstream section located at the S3
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community, confluence of S1 and S2
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Sediment samples collected in this study were sent to a service laboratory for reference
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measurements. The reference concentrations of Cr (0.01g/mL), Cu (0.02 g/mL) and Pb (0.02 g/mL) were obtained through ICP/OES analysis using Agilent 720 Series ICP-OES (Agilent
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Technologies, Santa Clara, CA, USA). ediment samples were initially digested in 20 mL-50 mL of HNO3, HCl and HF mixture, and were subsequently filtered. All solutions were prepared using
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deionized water. Table 2 shows the metal concentrations of the samples obtained via ICP/OES.
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Table 2
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Metal concentrations (mg kg-1) in sediment samples analyzed via ICP/OES*
Samples
ICP/OES values (ppm) Cu
Pb
17
147
216
S2
44
200
640
S3
28
165
293
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S1
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Cr
* Complete elemental composition of the sediment samples are found in Supporting Data
For the preparation of sediment samples for LIBS analysis, the samples were initially oven-dried at 50°C for 2 days. The oven-dried samples were sieved into a stainless steel mesh (0.707 mm, US
ACCEPTED MANUSCRIPT Sieve No. 24). The resulting samples were ground into finer particles using a mortar and pestle. The sediments were further crushed and ground using an electrical ball mill. The homogenized samples were pelletized by subjecting the samples into a 4-ton load for 10 seconds in a uniaxial hydraulic press.
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Calibration curve preparation
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Two binders, as constituents of the calibrating materials, namely, potassium bromide (KBr), and natural zeolite (NZ) mined from Pangasinan, Philippines, were examined in order to
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choose the most suitable matrix for the calibration standards. NZ powders were calcined and
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referred to as heat-treated NZ in the study. The calcination involved the heating of the NZ powder for 2 hours at 100°C and for 6 hours at 700°C in order to remove aluminum, volatile organic
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materials and residual metal content. High purity metal standard reference material (SRM)
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powders (99.99% purity, Sigma-Aldrich) were used as metal source. The target metals for the study were Cr, Cu and Pb. Calibration standards were prepared by mixing the standards with the
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matrices in terms of mass. The calibration standard powders were mixed initially with either KBr
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or NZ using mortar and pestle before the resultant mixture was homogenized in an electrical ball mill. The homogenized calibration standards with the binder were then pelletized using a 4-ton
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load for 10 seconds in a uniaxial hydraulic press, similar to that used for sediment samples. 2.2. Optimization of instrument parameters Four instrument parameters were optimized in terms of intensity prior to analysis. Laser energy, laser spot size, spectrometer delay and type of firing were optimized. The main atomic emission lines used for elemental identification are 520.6 nm, 324.7 nm and 405.8 nm for Cr, Cu, and Pb, respectively.
ACCEPTED MANUSCRIPT A commercial LIBS set-up (J200, Applied Spectra, Fremont, CA, USA) was used in this study. Axiom software (Applied Spectra, Fremont, CA, USA) was used in operating the LIBS setup. The laser was a Q-switched Nd:YAG laser operated at the fundamental wavelength of 1064 nm with repetition rate of 20 Hz and pulse width of 6 ns. The laser was fired into the pelletized
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calibration standards and samples at atmospheric pressure. Analyses were conducted under
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optimized parameters, namely: 0.5 µs (gate delay), 45 mJ (laser energy), 75 µm (spot size), and
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raster firing. A 5x5 grid with a total area of approximately 65 mm2 was ablated through raster patterns with the speed of 10 mm/s totaling to 219 shots. Light coming from the plasma was
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collected and recorded into a high resolution 6-channel CCD spectrometer through a high
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transmission optical fiber collection system. The built-in spectrometer had a spectral range of 190 nm to 1040 nm, and full spectrum analysis had been performed within this range to get complete
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information on the elements of interest. The plasma temperature was obtained using Boltzmann
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plot method, while the electron density was obtained using the Saha-Boltzmann equation. Table 3 shows the Fe emission lines used for the estimation of plasma parameters. Fe I and II were used
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due to the high percent abundance of Fe in the sediment samples. The plasma temperature reached
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10808.2 K at a gate delay time of 500 ns. On the other hand, the estimated electron density at 500
Table 3
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ns was 4.20634 x1027/cm3.
Spectroscopic parameters used for Tplasma determination Upper Transition energy Wavelength Element probability level (nm) (1x107 s-1) (1x104 cm-1) Fe I
246.26
4.06
5.85
296.69
3.37
2.72
Fe I
344.06
2.91
1.71
Fe I
344.10
2.95
1.24
Fe I
347.55
2.95
0.98
Fe I
349.06
2.91
0.61
Fe I
358.12
3.48
10.20
Fe I
360.89
3.59
8.13
Fe I
361.88
3.56
7.22
Fe I
367.99
2.72
0.14
Fe I
374.56
2.74
1.15
Fe I
374.83
2.76
0.92
Fe I
374.95
3.40
Fe I
375.82
3.43
6.34
Fe I
376.38
3.45
5.44
Fe I
381.58
Fe I
382.04
Fe I Fe I
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7.63
11.20
3.31
6.67
385.64
2.63
0.46
385.99
2.59
0.97
387.86
2.65
0.62
Fe I
388.63
2.61
0.53
Fe I
404.58
3.67
8.62
Fe I
406.36
3.72
6.65
Fe I
432.58
3.61
5.16
Fe I
438.36
3.48
5.00
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3.82
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249.12
4.10
29.10
Fe II
259.94
3.85
23.50
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2.3. PCA Analysis
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The Aurora software (Applied Spectra, Fremont, CA, USA) was used to determine the spectral
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line information for the target elements. The preliminary evaluation was done with Principal Component Analysis (PCA) in order to get an understanding on existing trends and to recognize
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property variations in the chemical data for each sample. PCA was also employed to validate the
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emission lines used and to qualitatively distinguish the uniformity of the binder with the sediment samples. A total of 12,288 spectral data points were analyzed and were used as input variables for
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the PCA method. No pre-processing technique for each LIBS spectrum was employed before
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performing PCA analysis. The constructed PCA models were used only for visualization purposes
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and no optimization were done for determining the number of principal components.
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3. Results and Discussion
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3.1. Calibration curve
The performance of the two types of binders employed in this study has been primarily evaluated by linearity and limit of detection. In order to examine the capability of LIBS for singleelement quantitative analysis in sediments, a calibration set was prepared for each target metal wherein known amounts of metal targets were added with KBr or natural zeolite matrix.. Both univariate and multivariate techniques need calibration standards that are capable of simulating
ACCEPTED MANUSCRIPT the laser-material interaction of the sample. However, exact replication of the behavior and particle interaction between the laser and sample material is not possible. Thus, finding a suitable matrix with nearly the same ablation threshold is necessary for the quantitative analysis of the target metals in the sediments. The first matrix used was potassium bromide (KBr). Along with boric
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acid (H3BO3), the KBr binder has been used in most of the LIBS-based analyses because of its
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good binding ability and the uncomplicated matrix that it forms with calibration samples [25-29].
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The normalized intensity of the target metals, Cr (520.6 nm), Cu (324.7 nm) and Pb (405.8 nm), were plotted versus the known metal concentration. The metal concentrations used in all
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calibration sets were 0, 10, 25, 50, 100 and 200 ppm. Figure 1 (a-c) shows the calibration curves
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for Cr, Cu and Pb in the KBr matrix.
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The three calibration curves prepared using KBr (Figure 1) have satisfactory values of coefficient of variation (R2 = 0.900 - 0.973). This indicates that the LIBS signal intensities of the
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calibration standards were linearly correlated at low concentrations. Even though these R2 values
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are not as high compared to those obtained from standards used in ICP-OES analysis, it should be noted that the matrix phase used for LIBS analysis was solid which induces inherent problems
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with homogeneity. Nonetheless, the linear correlations of these calibration curves were
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comparable to previous LIBS literature [7, 25, 30-31]. The second matrix used was a natural zeolite obtained from a mining quarry in the province of Pangasinan, Philippines. The composition of the natural zeolite after calcination showed negligible concentrations of the target metals as shown in Table 4. The total metal concentrations for the three metals were below the method limit of detection (MDL). Table 4 Metal concentration of natural zeolite.
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Concentration (ppm)
Cr
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Cu
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Pb
<5.0*
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* metal concentration values fall below the method detection limit
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The NZ binder was heat treated to be free from metal impurities but may have caused misleading
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interpretation of the results. Pre-treatment step allowed the natural zeolite to be a suitable binder in the preparation of the calibration curves. Figure 2 shows the calibration curves for Cr, Cu and
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Pb using the natural zeolite. The standard deviation values are found to be high and this can be
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associated to the innate heterogenous composition nature of the zeolite in contrast to the relative
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homogenous nature of KBr.
Similar with the R2 values for the KBr matrix, the calibration curves using the NZ heat-
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treated binder have generated high R2 values (R2 = 0.96 - 0.99). These values were comparable
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with literature values cited previously. Considering that multiple minerals/elements constitute the natural zeolite, the linear correlation was still comparable with the much simpler KBr matrix. This
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positive correlation may come from two reasons, (1) homogeneity in the calibration sample preparation, and (2) output data normalization. Since the calibration samples were solid pellets, the preparation was largely affected by the homogeneity of the powder mixture. The mixing process was initially done by a mortar and pestle set-up. These powders were further mixed in an electrical ball mill that was set to 100 rpm. This two-step mixing process ensured uniformity of the powder samples for ablation. Another factor that helped to improve homogeneity is by increasing the sample area during the ablation process. By averaging the spectral intensities
ACCEPTED MANUSCRIPT coming from a much larger firing area, better representative data were collected. The second reason for the comparable linearity between the natural zeolite matrix and KBr was the normalization of the spectra with the background. In quantitative LIBS analysis, adding an internal standard to the calibration standards is a common practice in order to correlate the calibration curves with the
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sample. This is particularly necessary for those samples with no matching matrices [32]. The
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intensity of the target metal will then be normalized with the intensity of the internal standard
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placed. Another common approach of data processing technique is normalization with the background, with or without the presence of an internal standard. The intensity of the target metal
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is normalized with the continuum radiation that is produced when ions interact with electrons
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through radiative recombination forming atoms [33]. In this study, normalization with the background was done in order to offset the effects of the varying matrices between the samples
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3.2. Limit of detection
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and the calibration standards.
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The sensitivity of the procedure was evaluated using the prepared calibration curves.
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Equation 1 was used for the calculation of the limit of detection (LOD) of the calibration curves. The σ is the standard deviation of the blank (0 ppm) and s is the slope of the line of the calibration
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curve.
𝐿𝑂𝐷 (𝑝𝑝𝑚) =
3𝜎 𝑠
Equation 1. Limit of detection. For the calculation of the LOD, 25 trials using the variability of signals from the blank sample were done for each metal in both matrices. Each trial consisted of 219 shots equivalent to 219 spectra. The peak areas of intensities of the target analyte in all spectra were averaged and
ACCEPTED MANUSCRIPT were normalized with the continuum background intensity. Table 5 summarizes the results obtained from the analysis of limit of detection for each metal considered in a either KBR or NZ matrix. Table 5
LOD in KBr (ppm)
Cr
1.43 ± 0.23
Cu
0.41 ± 0.01
Pb
2.47 ± 0.02
LOD in NZ (ppm) 6.10 ± 0.02 4.26 ± 0.01 2.65 ± 0.01
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Metal analyte
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Limit of detection of each metal in KBr and natural zeolite (NZ) matrix.
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The values presented in Table 5 were comparable with the LOD values using LIBS
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reported in previous literature [7, 30, 34-36]. Comparison between the two matrices shows that use of KBr provided a lower detection limit compared to the natural zeolite. This trend was
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expected since the KBr presented a less complicated matrix compared to the natural zeolite. Aside
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from the aluminosilicate framework, elements such as calcium, magnesium and iron were also present in the natural zeolite. The heat-treatment was performed to eliminate organic and more
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volatile metal components from the zeolite. This pre-treatment step did not produce significant changes in the framework of the zeolite as seen in XRD data (Supporting Data). 3.3. LIBS vs. ICP/OES analysis Three midstream sediment samples (S1, S2 and S3) were used as real samples for the study. ICP/OES was used in validating the results of the LIBS analysis. The representative LIBS spectra of the three samples, along with the integration peaks used per element, were illustrated in Figure
ACCEPTED MANUSCRIPT 3. The results from the ICP/OES analysis were averaged and used as the reference values in this study. Tables 6-8 summarizes the reported composition of the S1 sediment sample using LIBS and ICP/OES analysis as well as the relative percent difference (RPD) between these two analytical methods. The RPD was calculated to describe the variability between ICP/OES and LIBS. RPD
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was not determined for values less than the laboratory reporting level or method detection limit.
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Table 6 Comparison of LIBS and ICP/OES values for S1.
Matrix
Analyte
LIBS value (ppm)
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ICP/OES value
RPD (%) #
(ppm) NA
Cu
121 ± 8.18
147
19.4
Pb
<2.47*
216
NA
Cr
16.9 ± 0.63
17
0.590
Cu
125 ± 16.1
147
16.2
216
125
Pb
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17
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<1.43*
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NZ
Cr
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KBr
938 ± 27.0
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* metal concentrations values fall below the method detection limit
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# % Relative percent difference was calculated by taking the absolute value of the difference over the mean times 100 NA: not applicable
Several values were not determined (indicated as NA) for S1 using the calibration curves generated for Cr and Pb measurements in the KBr matrix as shown in Table 6. These results may be caused by the difference in material ablation threshold of the KBr and the S1 sediment sample. Moreover, it should be noted that the percent difference observed for Cu was 18.6% and considered
ACCEPTED MANUSCRIPT to be a relatively low value. Results of LIBS analysis for the natural zeolite were comparable with the ICP/OES analysis and can be calculated using the generated calibration curves. Interpolation of concentration for Cr and Cu yielded results close to the reference value. In comparison to KBr, the zeolite provided a matrix with comparable laser threshold value with sediment. However, the
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extrapolated concentration of Pb was far from the ICP/OES value. The calculated error in
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extrapolation could be due to the low linear correlation of signal-to-noise ratio with metal
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concentration at higher concentration. Similar trend were obtained for S2 (Table 7) and S3 (Table
Table 7
ICP/OES value (ppm)
RPD (%)#
<1.43*
44
NA
Cu
166 ± 30.9
200
18.6
Pb
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Comparison of LIBS and ICP/OES values for S2.
<2.47*
640
NA
45.1 ± 1.82
44.0
2.47
191 ± 19.8
200
4.61
630 ± 19.2
640
1.57
Analyte
LIBS value (ppm)
KBr
Cr
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Matrix
Cr Cu
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Pb
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NZ
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8) sediment samples.
* metal concentrations values fall below the method detection limit # % Relative percent difference was calculated by taking the absolute value of the difference over the mean times 100 NA: not applicable
Table 8
ACCEPTED MANUSCRIPT Comparison of LIBS and ICP/OES values for S3. Analyte
LIBS value (ppm)
ICP/OES value (ppm)
RPD (%) #
KBr
Cr
<1.43*
28
NA
Cu
120 ± 9.16
165
31.6
Pb
<2.47*
293
NA
Cr
28.2 ± 0.99
28
Cu
141 ± 20.0
165
15.7
Pb
693 ± 115
293
134
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NZ
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* metal concentrations values fall below the method detection limit
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# % Relative percent difference was calculated by taking the absolute value of the difference over the mean times 100
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NA: not applicable
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Measurements done on samples prepared in NZ matrix were all determined and can be
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compared with ICP/OES results. Calculated concentration values of Cr and Pb in KBr were lower than the corresponding calculated value for the S2 and S3 samples prepared in NZ matrix. Results
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for Cu concentration using LIBS remained satisfactory with respect to ICP/OES values for the
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KBr matrix. On the other hand, both Cr and Cu have low deviations from the ICP/OES values in NZ matrix. Extrapolated LIBS result of S2 sample for Pb in zeolite was close with the reference value. However, the S3 sample produced erroneous result which was far from the reference value for Pb in the zeolite matrix. 3.4. Principal component analysis
ACCEPTED MANUSCRIPT In order to evaluate and visualize the difference in material property of the matrices and the samples, principal component analysis (PCA) was utilized. The full LIBS spectra of the three samples (S1, S2 and S3), along with the two matrices (KBr and NZ), were transformed into several principal components (PCs) through PCA. The first 5 PCs explained 96.04% of the total variation
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in the data. The loading plot of the first five components (Figure 4) validates the presence of the
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chosen emission lines (Cu 324.7, Pb 405.8 and Cr 520.6) in the five PCA samples. However, the
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loading values for the three elements were relatively low (from -0.1 to 0.1) and can be directly
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related to the total amount of the respective elements in the sample & matrices. Moreover, the degree of variation between the samples and binders were further analyzed
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using the same PCA run. Figure 5 shows the orthographic view of the PCA plot of the samples
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and matrices. The first two components explained 94.17% (PC 1: 92.59%; PC 2: 1.58%) of the variations of the spectral information presented. Each circle corresponds to the linear combination
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of the LIBS spectra of the first two principal components. The preparation of the calibration curves
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involved the use of binders that act as the diluent of the high purity metal powders. A homogeneous matrix that produces signals similar with the sample is needed in order to obtain results that are
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comparable with classical analytical methods. Interpretation of the PC score plot suggests that
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discrimination between the two matrices and the samples can be characterized in terms of the lasermaterial interaction. A pattern can be recognized, as seen in the PCA plot (Fig. 5), wherein for the natural zeolite the pattern is clustered closer to the sediment samples compared to the KBr matrix. It can also be observed from the plot that that the sediment samples and natural zeolite are identified in proximity to PC 2, while the KBr matrix approached PC 1.The PCA plot was generated from the laser intensity signals of each of the matrices and the samples. The plot showed the natural distribution of samples indicating the similar chemical composition among the
ACCEPTED MANUSCRIPT sediment samples and natural zeolite. The KBr matrix was clearly separated from the zeolite matrix and sediment samples. This signifies that the natural zeolite simulated closer the ablation threshold and emission intensity of the sediment samples compared to KBr. This would indicate that laser and matrix would have a better interaction and both the zeolite and sediment samples
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would be much better recognized as homogeneous or uniform.
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4. Conclusion
The utilization of PCA showed that heat-treated zeolite is a potential diluent or binder for
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generating calibration curves and provide matrix-matched standards in the identification of target
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metals from unknown sediment samples. The score plot of PCs was used to reveal the similarity of the natural zeolite and sediment samples based on their respective LIBS spectra. The results
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showed that the detection and quantification of Cr, Cu and Pb in sediments via LIBS can be
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comparable to an established analytical technique like ICP/OES by using an appropriate matrix, such as heat-treated natural zeolite as reported in this study. Single-element calibration curves
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with good linear correlation for each metal target were generated. However, extrapolation of data
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from the curve did not produce satisfactory results which signified the non-linearity of concentration and signal-to-noise ratio at higher concentrations. Limit of detections for each curve
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produced comparable results with previous literature. The accuracy of the LIBS method was evaluated by relating the values to ICP/OES analysis. The similarity between the material properties of the natural zeolite matrix and the sediment samples produced comparable statistical values in comparison with the KBr matrix and other LIBS literature.
ACCEPTED MANUSCRIPT Acknowledgments The research was supported and funded by the Department of Science & Technology - Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOSTPCIEERD). The study was under Project 1 of the Chemical Sensors for Mine Site Monitoring
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Program implemented at the Institute of Chemistry, University of the Philippines – Diliman.
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List of Figures
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Figure 1. Calibration curves of (a) Cr, (b) Cu and (c) Pb prepared with KBr binder.
Figure 2. Calibration curves of (a) Cr, (b) Cu and (c) Pb prepared with natural zeolite binder.
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Figure 3. Overlapped LIBS spectra of S1, S2 and S3. Integration peaks (red boxed region)
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highlights the respective emission lines used (Cu 324.7, Pb 405.8 and 520.6).
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Figure 4. Loading plot of the first five principal components from the PCA of the LIBS spectra of
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the three sediment samples and two matrices. Emission lines used for Cr (520.6 nm), Cu (324.7 nm) and Pb (405.8 nm) were identified.
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Figure 5. Scatter plot of two principal components (PC1 and PC2) for KBr, heat-treated natural zeolite, and sediment samples S1, S2 and S3.
ACCEPTED MANUSCRIPT GRAPHICAL ABSTRACT
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Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix
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HIGHLIGHTS Laser-induced breakdown spectroscopy for the quantitative analysis of metals in sediments using natural zeolite matrix Heat-treated natural zeolite is a suitable matrix for the quantitative LIBS analyses of sediments
Use of an alternative and inexpensive binder that act as diluent of high purity metal powders in preparation for the calibration curves
Detection and quantification of Cu, Cr, Pb in river sediment samples using a potentially matrix-matched standard
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