Mineralogical analysis of ceramic tiles by FTIR: A quantitative attempt

Applied Clay Science 115 (2015) 1–8

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Mineralogical analysis of ceramic tiles by FTIR: A quantitative attempt J.D. Jordá a, M.M. Jordán b,⁎, R. Ibanco-Cañete c, M.A. Montero b, J.A. Reyes-Labarta d, A. Sánchez c, M. Cerdán c a

Multidisciplinary Institute for Environmental Research “Ramon Margalef”, University of Alicante, Alicante, Spain Dept. of Agrochemistry and Environment, Miguel Hernández University of Elche, Elche, Alicante, Spain c Dept. of Agrochemistry and Biochemistry, University of Alicante, Alicante, Spain d Dept. of Chemical Engineering, University of Alicante, Alicante, Spain b

a r t i c l e

i n f o

Article history: Received 4 February 2015 Received in revised form 2 July 2015 Accepted 3 July 2015 Available online xxxx Keywords: Mineralogical analysis Ceramic tiles FTIR XRD Quantitative attempt

a b s t r a c t A method for quantitative mineralogical analysis by ATR-FTIR has been developed. The method relies on the use of the main band of calcite as a reference for the normalization of the IR spectrum of a mineral sample. In this way, the molar absorptivity coefficient in the Lambert–Beer law and the components of a mixture in mole percentage can be calculated. The GAMS equation modeling environment and the NLP solver CONOPT (©ARKI Consulting and Development) were used to correlate the experimental data in the samples considered. Mixtures of different minerals and gypsum were used in order to measure the minimum band intensity that must be considered for calculations and the detection limit. Accordingly, bands of intensity lower than 0.01 were discarded. The detection limit for gypsum was about 7% (mol/total mole). Good agreement was obtained when this FTIR method was applied to ceramic tiles previously analyzed by X-ray diffraction (XRD) or mineral mixtures prepared in the lab. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Mineralogical characterization of complex samples, such as clays, soils, or pottery, is a main topic in order to determine their physicochemical characteristics and functionality. The need for new, cheap, and fast analysis techniques has led to research in new technologies, particularly those based on spectroscopic methods (Viscarra Rossel et al., 2009). Traditionally, the main methods employed have been NIRS (near-infrared spectroscopy); however, the range between 400 and 4000 cm−1 of the mid-infrared bands is being increasingly used because of the specificity of the absorbance bands within this range (Linker, 2004). NIRS has been used to quantify the soil mineral composition and other soil properties such as color. Quantification was performed by comparing the decrease in the soil mineral band depth to the band depth of the pure mineral, and the percentage was calculated with respect to the total quantified minerals. Up to 0.3 wt.% of calcite or 0.4% of goethite was detected. However, one of the limitations of NIRS is its inability to detect quartz (Viscarra Rossel et al., 2009). ATR (attenuated total reflectance) is a spectroscopic method frequently used with IR spectrophotometers. It is very simple, since it consists in placing a powdered sample in intimate contact with a glass having a high refraction index. When infrared radiation comes into contact with the glass, total internal reflection takes place. The radiation interacts with the sample in contact and the radiation is attenuated at the

corresponding frequencies of the fundamental modes of vibration of the crystalline structure in the sample. The resultant spectrum shows bands at the same positions, but with relative intensities different from a usual IR spectrum. This technique has been widely applied in mineralogical studies (Matteson and Herron, 1993; Xu et al., 2001), including ancient pottery (De Benedetto et al., 2002; Sathya and Velraj, 2011; Centeno et al., 2012; Legnaiolia et al., 2013) and ceramics (Lavat et al., 2009; Mansur et al., 2011; Rajamannan et al., 2013). In soil sciences, methodologies based on infrared spectroscopy for the study of soil fertility that include processes, such as nitrification (Linker et al., 2006), are now being developed. Nevertheless, in media as complex as these, problems remain such as the extensive use of blanks in order to properly quantify samples. In aqueous media, calculating the molar absorptivity of the Lambert– Beer law is easy, and it can be used for component concentration calculations since an aqueous, constant concentration matrix exists. In solid media, such as soils, clays, or ceramics, this may not occur and the problem is more complicated, having to take into account that the main components are silicates that provide signals within the same wavenumber range (Downs, 2006; Rajamannan et al., 2013). Therefore, focusing on the problem from another point of view is necessary. Each point on the FTIR spectrum is the sum of the signals produced by each component in the sample: I ¼

⁎ Corresponding author at: Department of Agrochemistry and Environment, Miguel Hernández University of Elche, Avda. de la Universidad, s/n. 03202 Elche, Alicante, Spain. E-mail address: [email protected] (M.M. Jordán).

http://dx.doi.org/10.1016/j.clay.2015.07.005 0169-1317/© 2015 Elsevier B.V. All rights reserved.

Xa

i j¼1 j

ð1Þ

where I is the signal intensity at a wavenumber (λ); i, the signal of each component at that λ; and a, the number of components in the sample.

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Table 1 References used for the calculation of the minimum intensity detected. % mol

M1

M2

M3

M4

Quartz Sepiolite Calcite Gypsum

30.8 50.0 15.6 3.6

28.1 45.6 14.2 12.1

24.7 40.1 12.5 22.8

22.6 36.6 11.4 29.4

According to the Lambert–Beer law, the signal intensity of each component is proportional to its concentration: i ¼ lεc

ð2Þ

where l is the radiation path length; ε, the molar absorptivity; and c, the concentration. Eq. (1) may then be rewritten as: I ¼

Xa j¼1

l εj cj

ð3Þ

where εj is the molar absorptivity of component j; cj is the concentration of component j; and l, the radiation path length that is constant for all samples (Xu et al, 2001). The main problem for concentration calculation by Eq. (3) is obtaining the coefficient ε. The sensitivity of the different minerals to the radiation must be distinct. Taking into account that the IR technique measures bond vibrations as the number of covalent bonds (mainly O bonds) in a molecule increases, the sensitivity of the technique must be higher, too. For example, a mole of quartz (SiO2) that only contains one mole of Si must be less sensitive than a mole of mica KAl2(AlSi3O10)(F,OH)2 that contains three moles. Many other effects, such as crystal arrangement or the number of vibrational modes, affect the value of ε. The ε value gathers the IR absorption characteristics of each substance so that its measure is necessary if quantitative analyses are addressed. Nevertheless, in the routine infrared spectroscopy procedure, spectra are normalized assigning a value of 1 to the most intense band independently of the absorptivity, and the normalization changes the relative signals of the different components in a mineral sample. In a mixture of quartz and mica, the two bands of the same intensity do not mean the same concentration. Not only should the number of bonds influence the signal intensity, but also the elements involved, so differences due to S–O, Fe–O, or Si–O bonds or crystal arrangement are also expected. Therefore, in this paper, an approach for calculating the relative intensities of each mineral species in a mixture when they are measured by ATR–FITR is proposed, comparing the intensities of the major bands of different minerals with the main band of calcium carbonate, since the overlap between the main calcite band (1380 cm−1) and the main bands of other minerals is minimum. 2. Materials and methods

Fig. 2. Main peak intensity in the IR range of 600–4000 cm−1 with respect to the calcite main peak for several silicates as a function of the number of Si atoms in the formula.

scans. Spectra were also compared to the RRUFF IR database (Downs, 2006). 2.2. Samples Fifty different blanks from the mineralogical collection of the University of Alicante were used, including silicates, sulfates, carbonates, nitrates, and phosphates. Due to the range of λ, the majority of oxides are not detected by this method. Calcite and gypsum (Panreac™, RA) were used as references. The raw spectra were processed for the baseline and normalized as described in Xu et al. (2001) using Excel software (Microsoft®). The samples analyzed were pure minerals (crystalline samples and lab substances) or mineral mixtures used as standards in order to prove the quality of the analysis (MM1: quartz 17.7%, calcite 23.5%, vermiculite 40.2%, hematite 12.6%, gypsum 5.9%; MM2: quartz 37.4%, calcite 28.3%, dipotassium hydrogen phosphate 21.0%, hematite 6.5%, gypsum 6.7%; MM3: quartz 31.5%, calcite 16.2%, sepiolite 51.1%; all percentages in mole). Clays and ceramic tiles from San Vicente de TaguaTagua (SV) and Litueche (LIT) (Chile) were also analyzed. These clays and ceramic tiles were characterized by XRD (Meseguer et al., 2010; Pardo et al., 2011) using a Siemens D-5000 diffractometer, CuKα radiation, in both powder (bulk samples) and oriented aggregates (natural and treated with ethylene glycol and heated to 550 °C for 2 h) of the clay fraction obtained following the criteria expressed in Moore and Reynolds (1997). Semi-quantitative analysis was carried out following the Jordán et al. (1999) methodology.

2.1. FTIR analysis

2.3. Approach to the calculation of molar absorptivity

For the FTIR analysis, a fine powder of the different mineral samples was placed with no further treatment on the glass window of the ATRFTIR instrument spectrometer (BRUKER IFS 66/S). Spectra were recorded between 600 and 4000 cm−1 with a resolution of 2 cm− 1 and 64

Obtaining ε values for each mineral is complicated, since in most cases the main bands overlap. To approach signal differences, and therefore mineral concentrations in samples, equimolar mixtures of different minerals and calcite (Panreac, purity 98%) were analyzed (Table 2). The

Fig. 1. FTIR spectra of equimolecular mixtures of quartz or muscovite and calcite.

J.D. Jordá et al. / Applied Clay Science 115 (2015) 1–8

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Fig. 3. FTIR spectra of quartz and opal.

main calcite band (῀ 1400 cm− 1) appears quite distant from silicate, phosphate, or sulfate bands (900–1000 cm− 1). Silicate, phosphate, or sulfate spectra overlap each other and therefore cannot be used as a reference. An organic or inorganic substance with a single IR spectrum and good signal absorption bands, which do not overlap with those of other minerals, is needed. Calcite is a cheap substance, easily available with a certified, high purity grade, with intense infrared bands that do not overlap with the silicate, phosphate, or sulfate bands.

The calcite signal was given the value 1 and the main intensities of the pure mineral samples were measured against it. 2.4. Mineral quantification in samples Calculation of the mineral content in each sample was conducted as described in Matteson and Herron (1993), and especially in Xu et al. (2001), by solving Eq. (1). The inclusion of the first and second derivative of the spectrum enhanced the resolution of the original spectrum

Table 2 Values of the main band intensity of several mineral samples respect to the calcite mean band (ε). Detection limits were estimated by comparing these values with εgypsum (see text). Minerals classified according to Klein and Hurlbut (1993). Group

Formula

Mineral name

ε

Detection limit (% mol)

Cyclosilicate Cyclosilicate Inosilicate Inosilicate Inosilicate Inosilicate Inosilicate Inosilicate Nesosilicate Nesosilicate Nesosilicate Nesosilicate Phyllosilicate Phyllosilicate Phyllosilicate Phyllosilicate Phyllosilicate Phyllosilicate Sorosilicate Sorosilicate Tectosilicate Tectosilicate Tectosilicate Tectosilicate Tectosilicate Oxide Oxide Sulfate Borate Phosphate Phosphate Nitrate Hydroxide Oxide Oxide Oxohydroxide Carbonate

NaFe3Al6(BO3)3Si6O18(OH)4 Be3Al2(SiO3)6 (Mn, Fe, Mg, Ca)SiO3 Ca0.9Na0.1Mg0.9Fe0.2Al0.4Ti0.1Si1.9O6 Si2O6AlNa CaMgSi2O6 CaSiO3 Ca2(Mg,Fe)5Si8O22(OH)2 Al2SiO5 (Mg,Fe)2SiO4 Ca5(SiO4)2CO3 (SiO4)3Al2Mg3 KAl2(AlSi3O10)(F,OH)2 K(Mg,Fe,Mn)3Si3AlO10(F,OH)2 K(Li,Al)3(Si3Al)O10(F,OH)2 (Mg,Fe,Al)3(Al,Si)4O10(OH)2·4H2O Mg3Si4O10(OH)2 Mg4Si6O15(OH)2·6H2O Ca5Si2O7(CO3)2 Ca2Al3(Si2O7)(SiO4)O(OH) Na0.5–0.3Ca0.5–0.7Al1.5–1.7Si2.5–2.3O8 Na4Si3Al3O12Cl Ca4Al6Si6O24CO3 KAlSi3O8 KAlSi3O8 SiO2 SiO2·nH2O CaSO4·2H2O H3BO3 K2HPO3 Ca5(PO4)3F Ca(NO3)2 Al(OH)3 MnO2 Fe2O3 FeOOH CaCO3

Tourmaline (schorl) Beryl Rhodonite Augite Jadeite Diopside Wollastonite Actinolite Kyanite Olivine Spurrite Almandine Muscovite Biotite Lepidolite Vermiculite Talc Sepiolite Tilleyite Clinozoisite Labradorite Sodalite Meionite Orthoclase Sanidine Quartz Opal Gypsum Boric acid Dipotassium hydrogen phosphate Fluorapatite Calcium nitrate Aluminum hydroxide Manganese oxide Hematite Goethite Calcite

2.3 2.3 0.7 0.3 1 0.5 0.5 3.6 0.2 0.4 1.1 0.6 1.1 0.4 1.1 1.3 1.9 2.1 0.7 0.3 2 3 8.4 1.9 0.8 0.4 0.1 0.9 0.7 1.7 2 1.8 0.2 0.2 0.3 0.1 1

3 3 10 23 7 14 14 2 35 18 6 12 6 18 6 5 4 3 10 23 4 2 1 4 8 18 70 8 10 4 2 4 35 35 23 70 7

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Fig. 4. FTIR spectra of several clay/gypsum mixtures (% weight). Total spectrum (a), overlapped bands silicate–sulfate (b).

while eliminating background problems (Khoshmanesh et al., 2012). Accordingly, since each spectrum is a sum of spectra, the derivative is also the sum of derivatives. First and second derivative spectra were multiplied by 25 in order for their magnitude to be of the same order as the bands in the original spectrum. The GAMS equation modeling environment (Brooke et al., 2012) and the NLP solver CONOPT (©ARKI Consulting and Development) were used to correlate the experimental data in the samples considered. The correlation procedure determined the parameters of Eq. (1), providing the minimum of a selected objective function (O.F.): 2

O:F: ¼

3 Ndata X X  Ωk;i; exp: −Ωk;i;calc:  wk

ð4Þ

k¼1 i¼1

where Ndata is the number of experimental points (1469), Ωk,i,exp represents the experimental absorption data as obtained by the IR apparatus or its first or second derivative with respect to the frequency or time depending on the value of k (1, 2, or 3, respectively). Ωk,i,cal is the calculated value of the corresponding proposed Eq. (1). wk represents the weight of the magnitude Ωk (IR data, first or second derivative) in the objective function (Eq. (2)).

Fig. 5. Relationship between igypsum in Eq. (1) and the gypsum concentration.

The correlation process consisted of two consecutive steps. First, the experimental data were correlated using the whole set of NB standards. After that, a new correlation was performed using in this case only the standards that in the first loop satisfied the following constraint: Am; jðstep1Þ N

τ εB; j

ð5Þ

where ε B,j is the extinction coefficient of standard j and B is the tolerance value used in the present work, equal to 0.01. The execution time needed for the whole correlation process is around 3 s for each sample studied. 2.5. Minimum intensity and detection limit In order to determine the ability of the technique to discriminate between overlapping bands, i.e., minimum ij, Eq. (1), decreasing amounts of gypsum were added to a silicate sample (C1). Gypsum is a common mineral whose IR bands overlap with silicate bands, and available as a laboratory reagent of known composition. XRD analysis of the silicate showed quartz and albite as main components and lower quantities of anorthite and hematite. These minerals are usually found in ceramic samples. Mixtures were obtained between 0–100% (gypsum wgt/wgt). Mixtures of known minerals and gypsum were also prepared in the lab (Table 1). The main gypsum band appeared around 1100 cm−1 and

Fig. 6. Relationship between the intensity of the gypsum signal and its concentration.

J.D. Jordá et al. / Applied Clay Science 115 (2015) 1–8 Table 3 Actual and estimated composition of mineral standards. % mol

Quartz Gypsum Calcite Hematite Vermiculite Dipotassium hydrogen phosphate Sepiolite

MM1

MM2

MM3

Actual

Estimated

Actual

Estimated

Actual

Estimated

17.7 5.9 23.5 12.7 40.2 0.0

b18% b8 24.0 0.0 42.2 0.0

37.4 6.7 28.3 6.5 0.0 21.0

44.7 b8 26.0 0.0 0.0 17.7

32.0 0.0 16.2 0.0 0.0 0.0

30.2 0.0 11.4 0.0 0.0 0.0

0.0

0.0

0.0

0.0

51.9

54.6

clearly overlapped the main silica band (900–1000 cm−1). The samples were analyzed by FTIR in the same way as described in the previous sections.

5

(Fig. 3) and the greatest absorption of quartz at ~770 cm−1. However, the absorptivity of opal was four times less (Table 2). Minerals can contain impurities or ionic substitutions that modify their absorptivity, but from all the spectra obtained, it does not seem that the variations in ε can be important, and within the mineralogical series, there will always be greater similarity with one of the members of the series. Despite their different crystalline structure and the change of Mg for Ca, there were no differences in absorptivity between wollastonite and diopside (Table 2), for example, while in other cases, such as orthoclase and sanidine, the change in crystalline structure provoked important variations in ε (Table 2). This is a common problem in any mineralogical analysis. The values for other minerals, such as sulfates or phosphates, are also shown in Table 2. Phosphates were easily detected by FTIR even though they were present in low concentrations because their ε coefficients were much higher than the others. In contrast, oxides required high concentrations to be detected. 3.2. Minimum intensity and detection limit

3. Results and discussion 3.1. Molar absorptivity estimation The FTIR spectrum of the equimolar mixtures of quartz (SiO2), one Si atom, and mica (KAl2 (AlSi3O10) (F, OH)2), 3 Si atoms, with calcite (CaCO3), respectively, is shown in Fig. 1. Calcite was taken as a reference by assigning the value 1 to the band at ~1400 cm−1. The quartz intensity (band at 1083 cm−1) was much lower than the calcite intensity although the quantity of both minerals is the same (mole). Muscovite, with three Si atoms (band at 979 cm−1), had a main band approximately three times more intense. Thus, these values can be used as approximations of the coefficient ε, from Eq. (2). The main band intensities of several silicates with respect to calcite are a function of the number of Si atoms in the formula (Fig. 2). Different behavior was observed for tectosilicates and the other silicates, showing that other factors, such as atom arrangement, may also contribute to the final signal, as could be expected. The former produced much higher intensities than the non-tectosilicates for the same number of atoms, and even almost double the calcite intensity. Each Si atom contributes with 1.3 intensity units to the overall intensity of the tectosilicate band and 0.4 for the other silicates. This difference from the effect of the Si, depending upon the crystalline structure of the silicate, highlights the multitude of factors that influence the ε value that modify the vibrational modes possible and that cause the intensity of 1 Si atom to decrease as much as 3 times depending upon the crystallography. Consequently, silicates, such as pyroxenes or amphiboles, will be more difficult to identify by infrared in combinations with feldspars, for example. Opal (amorphous) was well distinguished from quartz by FTIR due to the presence of water, which presented a clear signal at ~ 1500 cm− 1

FTIR spectra of several gypsum/clay mixtures were made to calculate the minimum intensity and the detection limit (Fig. 4). For higher sulfate contents, the main band of the mixture became that of the gypsum and not of the clay. Therefore, there was a strong discontinuity in the intensities i (Eq. (2) of gypsum (Fig. 5) and the concentration when the reference band was sulfate instead of clay. Saturation was reached above 60% (wgt/wgt) of gypsum, while for concentrations under 2%, no signal was detected for this mineral. The gypsum intensity value for 2% gypsum in the sample was 0.01. Therefore, band intensities under 0.01 and concentrations under 2% were neglected. Similar results were obtained for the M1–M4 mixtures (0–16% gypsum (weight); 0–30 mole (%)) (Table 1). In addition to the low intensity, a lack of linearity at low concentrations was observed (Fig. 6). The detection limit is the minimum concentration at which the Lambert-Beer law is fulfilled. Although the mineral is near the detection limit, there is great uncertainty in the concentration, since the intensity of the IR signal hardly varies with the concentration (Fig. 6). Accordingly, although above 2% gypsum was detected, concentrations below 8% should not be taken into account. For the remaining minerals, the detection limit was estimated proportionally from their ε values, i.e., for a mineral M, as:   gypsum limit εgypsum =εM :

ð6Þ

3.3. Analysis of the ceramic samples and mineral mixtures FTIR analysis of three mineral mixtures prepared in the lab and several ceramic samples analyzed by XRD are shown in Tables 3 and 4. In

Fig. 7. Left, FTIR spectra for MM3 and calculated as linear combination of spectra of pure minerals. Right, relationship between the experimental and calculated intensities.

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both cases, a good approximation to the mineralogical composition was obtained. The data from Table 3 were obtained by the calculation of the best linear combination of infrared spectra, which were adjusted to the experimental spectrum. As example, the spectrum for the MM3 mixture along with the calculated spectrum is shown in Fig. 7. Good correlation exists between both. The spectrum in Fig. 7 was normalized to the highest peak in order to make the calculations. The linear combination obtained was:

where εq and εs are the molar absorptivity of the quartz and sepiolite, respectively (column ε of Table 4) with Xq and Xs their molar fractions. When normalizing the spectrum, all the intensities are divided by iM, i.e. by iq + is, including that of the calcite, ic, which just the same as those previous, defined as:

MM3 ¼ 0:1 quartz þ 0:09calcite þ 0:9sepiolite

  iNq ¼ iq =iM ¼ εq Xq = εq Xq þ εs Xs :

ð7Þ

where MM3 is the FTIR spectrum of the mixture MM3, quartz is the FTIR spectrum of the quartz, calcite the FTIR spectrum of the calcite, and sepiolite the FTIR spectrum of the sepiolite. These coefficients appear in the iN column of Table 4. The most intense signal, iM (around 1000 cm−1), in the spectrum of Fig. 7 is the sum of the quartz signals (iq) and those of the sepiolite (is), which for each the radiation path length is proportional to its molar absorptivity (ε) and to its concentration, which by being a solid is represented best by the molar fraction (X). As the radiation path length is the same for all, it can be considered that it is included in ε, such as: iq ¼ εq Xq

ð8Þ

is ¼ εs Xs

ð9Þ

ic ¼ εc Xc

ð10Þ

where εc is the molar absorptivity of calcite and Xc is its molar fraction. Therefore, these normalized intensities would be calculated by: ð11Þ

For the quartz (Eq. (11)), and in the same manner for the sepiolite (Eq. (12)) and the calcite (Eq. (13)):   iNs ¼ is =iM ¼ εs Xs = εq Xq þ εs Xs

ð12Þ

  iNc ¼ ic =iM ¼ εc Xc = εq Xq þ εs Xs :

ð13Þ

That is, the normalized intensities (iN, Table 4) do not depend only upon their concentration and absorptivity, but rather the concentration and absorptivity of the minerals that produce the most intense signal. Notwithstanding, to reduce this effect, they can be divided by the greatest N N intensity, in this case that of sepiolite, iN s . As such, the values of the i /is column of Table 4 are obtained.

Table 4 Calculation of the concentration of the MM3 mixture. a

Quartz Calcite Sepiolite

ε

iN

iN/iN s

mol/mols

X

0.4 1 2.1

0.1 0.09 0.9

0.11 0.10 1.00

0.58 0.21 1.00

0.30 0.11 0.55

b. Comparison of data obtained from two ceramic samples analyzed by FTIR and DXR Ceramic sample (SV)

FTIR results

Mineral (% mol)

Formula

1160 °C

1080 °C

975 °C

830 °C

Amorphous Quartz Orthoclase Labradorite Albite Phosphate Mica Kaolinite Ceramic sample (SV) Mineral Cristobalite Quartz Microcline Albite Anorthite Enstatite–Wollastonite Hematite Ceramic sample (LIT) Mineral (% mol) Amorphous Quartz Sanidine Microcline Orthoclase Phosphate Ceramic sample (LIT) Mineral Quartz Microcline Mullite

SiO2 SiO2 KAlSi3O8 Na0.5–0.3 Ca0.5–0.7 Al1.5–1.7Si2.5–2.3O8 NaAlSi3O8 Al3(PO4)2(OH, F)3·5H2O KAl2(AlSi3O10)(F,OH)2 Al2Si2O5(OH)4

19.6 41.0 b7 − − − b7 − XRD results 1160 °C ++ + − − − + ++ FTIR results 1160 °C b18 60.4 b7 b7 − − XRD results 1160 °C ++ + (+)

24.9 39.8 9.5 − b7 3.5 − −

27.1 42.4 11.9 − b7 3.7 − −

18.6 34.8 7.4 b5 b7 4.5 − b10

1080 °C ++ ++ + − − + ++

975 °C (+) +++ ++ ++ + − +

830 °C (+) ++++ +++ ++++ + − +

1080 °C 28.1 54.8 −

830 °C 34.6 44.4 −

− −

975 °C 48.3 39.7 b7 b7 − 2.8

1080°°C +++ + −

975 °C +++ ++ −

830 °C ++++ ++ −

Formula SiO2 SiO2 KAlSi3O8 NaAlSi3O8 CaAl2Si2O8 MgSiO3–CaSiO3

Formula SiO2 SiO2 KAlSi3O8 KAlSi3O8 KAlSi3O8 Ca5(PO4)3(OH) Formula SiO2 KAlSi3O8 Al6Si2O13

++++ (N20%); +++ (N15%); ++ (N10%); + (N5%); (+) present (5–2%); − (not present).

b7 3.2

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Fig. 8. FTIR spectra for San Vicente de Tagua-Tagua ceramic samples and the calculated spectra (a,b). Feldspars spectra (c).

For the quartz, iNq =iNs ¼ εq Xq =εs Xs ¼ εq molq =εs mols

ð14Þ

where molq are the moles of quartz within the mixture and mols those of sepiolite. Equally so for the calcite: iNc =iNs ¼ εc Xc =εs Xs ¼ εc molc =εs  mols

ð15Þ

Therefore:     molq =mols ¼ εs= εq  iNq =iNs

ð16Þ

and     molc =mols ¼ εs= εc  iNc =iNs

ð17Þ

such as microcline or orthoclase, which have good absorption in the infrared as compared to quartz. One of the characteristic signals of these minerals appeared at 660 cm−1 and it was clearly observed how it disappeared with the temperature. This observation coincides with the data from the XRD diffractograms (Figs. 9 and 10). The XRD and FTIR comparison between ceramic samples showed the similarity between the two procedures (Table 4). Quartz, feldspars, and pyroxenes were the main mineralogical species present in different concentrations depending on the heating temperature, as was expected. The presence of quartz was detected best by XRD rather than FTIR because of its relatively low ε coefficient (Table 2). On the other hand, the low ε coefficient is compensated by the high abundance of quartz in nature compared to other silicates and, in this way, easy detection of all of them is possible, as well as an approximation of the mineral content in the sample. Both techniques were similar in the detection of the same minerals. However, the detection limits and the sensitivity for each mineral are quite different. XRD patterns are dominated by quartz, unlike the FTIR

for the calcite. The molar fraction calculation is therefore very simple, starting from the molar intensities and absorptivities, for example for the quartz: Xq ¼

 molq molq molc mols : = þ þ mols mols mols mols

ð18Þ

The calcite and sepiolite molar fraction calculation is done in a similar manner. This calculation appears in the last column of Table 4 and multiplying the percentages from Table 3 by 100. As was expected from the data in Table 3, oxides were not well detected in the IR wave length range used in this paper. The experimental and calculated infrared spectra for ceramic wings from San Vicente de Tagua-Tagua, cooked at 830 °C and 1165 °C, respectively, are shown in Fig. 8. The spectra are dominated by the feldspars,

Fig. 9. XRD of the San Vicente de Tagua-Tagua fired samples (830–975–1080–1160 °C). Legend: Qtz: quartz; Ort: orthoclase; I: illite; Ens: enstatite; Au: augite.

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References

Fig. 10. XRD of Litueche (830–975–1080 °C). Legend: Qtz: quartz; Mi: microcline.

spectra, where this mineral was more difficult to detect. In FTIR, compounds with similar spectra like sanidine and microcline can be difficult to solve, (Fig. 8c). On these occasions, XRD remains a key support technique because of its ability to discriminate between different crystal structures. For FTIR, the sensitivity was corrected by calculating the values of ε, but not for XRD, which explains the different ratio calculated for each mineral.

4. Conclusions The method described in this paper, and the use of the main carbonate band as a reference for the calculation of the coefficient of extinction results in a useful, easy, fast and non-destructive technique for semiquantitative analysis of mineral samples like ceramics, soils, tiles, pottery, and other natural or industrial origin samples. Although in some cases, due to the overlap and similarity of the bands, some samples may be more difficult to resolve, the combination with other techniques, such as XRD, allows solving many problems of analysis of solid samples. Differences in light absorption due to the molecular composition are not usually taken into account. One of the goals of this work was to develop a method for calculating such differences that can be applied to other spectroscopic techniques that use solid samples because it allows the use of spectra libraries and linear regression algorithms for quantification purposes. However, direct measurements of (ε) for all minerals and research on matrix effects are needed to improve the analysis.

Brooke, T., Kendrick, D., Meerhaus, T., Raman, R., 2012. General algebraic modeling system (GAMS). Language Guide. Gams Development Corporation, Washington DC. Centeno, S.A., Williams, V.I., Little, N.C., Speakman, R.J., 2012. Characterization of surface decorations in Prehispanic archaeological ceramics by Raman spectroscopy, FTIR, XRD and XRF. Vib. Spectrosc. 58, 119–124. De Benedetto, G.E., Laviano, R., Sabbatini, L., Zambonin, P.G., 2002. Infrared spectroscopy in the mineralogical characterization of ancient pottery. J. Cult. Herit. 3, 177–186. Downs, R.T., 2006. The RRUFF Project: an integrated study of the chemistry, crystallography, Raman and infrared spectroscopy of minerals. Program and Abstracts of the 19th General Meeting of the International Mineralogical Association in Kobe, Japan. Or., pp. 3–13 Jordán, M.M., Boix, A., Sanfeliu, T., de la Fuente, C., 1999. Firing transformations of cretaceous clays used in the manufacturing of ceramic tiles. Appl. Clay Sci. 14, 225–234. Khoshmanesh, A., Perran, L., Cook, M., Wood, B.R., 2012. Quantitative determination of polyphosphate in sediments using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy and partial least squares regression. Analyst 137, 3704–3709. Klein, C., Hurlbut, C.S., 1993. Manual of Mineralogy: After James D. Dana. John Wiley & Sons. Lavat, A.E., Trezza, M.A., Poggi, M., 2009. Characterization of ceramic roof tile wastes as pozzolanic admixture. Waste Manag. 29 (5), 1666–1674. Legnaiolia, S., Anabitarte Garcia, F., Andreotti, A., Bramanti, E., Díaz Pace, D., Formola, S., Lorenzetti, G., Martini, M., Pardini, L., Ribechini, E., Sibilia, E., Spiniello, R., Palleschi, V., 2013. Multi-technique study of a ceramic archaeological artifact and its content. Spectrochim. Acta A Mol. Biomol. Spectrosc. 100, 144–148. Linker, R., 2004. Wavebands selection for determination of nitrate in soil using mid-IR/ ATR spectroscopy. Appl. Spectrosc. 58 (11), 1277–1281. Linker, R., Weiner, M., Shmulevich, I., Shaviv, A., 2006. Nitrate determination in soil pastes using FTIR-ATR mid-infrared spectroscopy: improved accuracy via soil identification. Biosyst. Eng. 94, 111–118. Mansur, A.A.P., do Nascimento, O.L., Oréfice, R.L., Mansur, H.S., 2011. Porcelain tile surface modification with isocyanate coupling agent: interactions between EVA modified mortar and silane improving adherence. Surf. Interface Anal. 43 (3), 738–743. Matteson, A., Herron, M.M., 1993. Quantitative Mineral Analysis by Fourier Transform Infrared Spectroscopy SCA Conference Paper Number 9308, pp. 1–15. Meseguer, S., Pardo, F., Jordán, M.M., Sanfeliu, T., González, I., 2010. Ceramic behaviour of five Chilean clays which can be used in the manufacture of ceramic tile bodies. Appl. Clay Sci. 47, 372–377. Moore, D.M., Reynolds, R.C., 1997. X-Ray Diffraction and the Identification and Analysis of Clay Minerals. Oxford University Press, Inc., Oxford. Pardo, F., Meseguer, S., Jordán, M.M., Sanfeliu, T., González, I., 2011. Firing transformations of Chilean clays for the manufacture of ceramic tile bodies Appl. Clay Sci. 51, 147–150. Rajamannan, B., Kalyana Sundaram, C., Viruthagiri, G., Shanmugam, N., 2013. Instrumental characterization of flyash added ceramic tiles by XRF, XRD and FTIR. Indian J. Appl. Res. 3 (3), 101–103. Sathya, P., Velraj, G., 2011. FTIR spectroscopic and X-ray diffraction analysis of archaeological grey potteries excavated in Alagankulam, Tamilnadu, India. J. Exp. Sci. 2 (5), 4–6. Viscarra Rossel, R.A., Cattle, S.R., Ortega, A., Fouad, Y., 2009. In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma 150, 253–266. Xu, Z., Cornilsen, B.C., Popko, D.C., Pennington, W.D., Wood, J.R., Hwang, J., 2001. Quantitative mineral analysis by FTIR spectroscopy. Internet J. Vib. Spectrosc. 5, 1–4 (www. ijvs.com).