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Extraction of compositional and hydration information of sulfates from laser-inducedplasma spectra recorded under Mars atmospheric conditions — Implications forChemCam investigations on Curiosity rover

Pablo Sobron a,⁎, Alian Wang a, Francisco Sobron b

a Department of Earth and Planetary Sciences and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130, USAb Unidad Asociada UVa-CSIC a traves del Centro de Astrobiología, Parque Tecnológico de Boecillo, Parcela 203, Boecillo (Valladolid), 47151, Spain

a b s t r a c ta r t i c l e i n f o

Article history:Received 13 July 2011Accepted 2 January 2012Available online 21 January 2012

Keywords:LIBSMartian sulfatesCompositionHydration stateCalibration curve

Given the volume of spectral data required for providing accurate compositional information and thereby in-sight in mineralogy and petrology from laser-induced breakdown spectroscopy (LIBS) measurements, fastdata processing tools are a must. This is particularly true during the tactical operations of rover-based plan-etary exploration missions such as the Mars Science Laboratory rover, Curiosity, which will carry a remoteLIBS spectrometer in its science payload. We have developed: an automated fast pre-processing sequenceof algorithms for converting a series of LIBS spectra (typically 125) recorded from a single target into a reli-able SNR-enhanced spectrum; a dedicated routine to quantify its spectral features; and a set of calibrationcurves using standard hydrous and multi-cation sulfates. These calibration curves allow deriving the elemen-tal compositions and the degrees of hydration of various hydrous sulfates, one of the two major types of sec-ondary minerals found on Mars. Our quantitative tools are built upon calibration-curve modeling, throughthe correlation of the elemental concentrations and the peak areas of the atomic emission lines observedin the LIBS spectra of standard samples. At present, we can derive the elemental concentrations of K, Na,Ca, Mg, Fe, Al, S, O, and H in sulfates, as well as the hydration degrees of Ca- and Mg-sulfates, from LIBS spec-tra obtained in both Earth atmosphere and Mars atmospheric conditions in a Planetary Environment andAnalysis Chamber (PEACh). In addition, structural information can be potentially obtained for various Fe-sulfates.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Laser-induced breakdown spectroscopy (LIBS) is recognized as apowerful tool for detailed geochemical investigations of rocks andregolith and for exploratory survey in future landedmissions on plan-etary bodies including Mars [1], the Moon [2], and Venus [3,4]. Astand-off LIBS spectrometer is part of the ChemCam instrument [5],included in the scientific payload of NASA's Mars Science Laboratory(MSL) rover (named Curiosity), that was launched in late 2011.ChemCam will determine the chemical composition of rocks and reg-olith on Mars at distances ranging from 1 to 7 m.

The LIBS technique is particularly well suited for planetary explo-ration, as it is sensitive to all rock-forming elements, as well as H, C, N,O, S, P, Fe, and Cl, relevant to the search for habitable environmentsand for traces of past and present signs of life on Mars. In LIBS, thewavelength and the intensity of atomic emission lines are used to

detect and to quantify the concentration of major, minor, and oftentrace elements present in a sampling target. The accuracy and preci-sion of the quantification of elemental concentrations based on LIBSmeasurements are influenced mainly by two factors: the matrix ef-fects within a specific sample and the fluctuations in experimentalconditions. A good explanation of the matrix effects in LIBS wasgiven in Cremers and Radziemski [6]. They grouped the matrix effectinto two kinds: physical and chemical matrix effects. On one hand,physical matrix effects, related to the physical properties of the target(e.g., grain size, surface roughness, absorptivity and thermal conduc-tivity). Physical matrix effects complicate quantitative analysis withLIBS by causing uncontrolled random fluctuations in the emissionfrom the plasma. On the other hand, chemical matrix effects, relatedto the elemental and molecular compositions of the sample. Theycan result in higher emission from easily ionized elements existingin the matrix, i.e., the same concentration of an element in differentmatrices will result in emission lines with different intensities, thusaffecting the accuracy of compositional quantification using LIBSdata. As for the fluctuations in experimental conditions, they includepulse-to-pulse variations in the properties of laser beam (pulse fre-quency, pulse width, and energy density), variations in laser-to-

Spectrochimica Acta Part B 68 (2012) 1–16

⁎ Corresponding author at: Space Science & Technology, Canadian Space Agency, 6767Rte. de l'Aéroport, St. Hubert, QC, Canada J3Y 8Y9. Tel.:+1 450 926 5847; fax:+1 450 9264766.

E-mail addresses: [email protected], [email protected] (P. Sobron).

0584-8547/$ – see front matter © 2012 Elsevier B.V. All rights reserved.doi:10.1016/j.sab.2012.01.002

Contents lists available at SciVerse ScienceDirect

Spectrochimica Acta Part B

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sample optical path (e.g., accidental scatters), and variations insample-to-collection optics optical path (including atmospheric ef-fects). The latter two can be considered physical matrix effects aswell, since they are mainly due to random effects in the laser ablationphase and contribute strongly to the standard deviation of LIBS mea-surement by modifying the collection angles.

All the factors mentioned above compromise the reproducibilityof LIBS measurements and the accuracy of quantitative calculationsbased on LIBS data. To compensate for matrix effects and variationsin experimental conditions, normalization procedures are commonlyemployed [1,6,7]. In most normalization approaches, the intensity of ameasured peak is normalized to that of another peak in the samespectrum that is known to reflect spectral changes due to matrix ef-fects [1,6]. However, in applications where such internal standarddoes not exist (e.g., most mineralogical analyses of geological sam-ples, such as ChemCam measurements on Mars), normalization bythe total emission intensity of the spectrum is preferred (e.g., [7]).

In the preceding paragraphs we have emphasized those factorsthat affect quantitative analyses of targets. By quantitative analyseswe mean a series of mathematical operations performed on a LIBSspectrum that aim to reconstruct the stoichiometry of a target inter-rogated by the laser, i.e., to determine the elemental abundances ina sample. Most common quantitative analysis methods use calibra-tion procedures to generate calibration curves for one or several ele-ments from the spectra of standard samples in a specific matrix, eachsample containing precisely known concentrations of the elements ofinterest [8]. Different calibration curves are often constructed for dif-ferent matrices. The concentrations of the elements in unknown sam-ples are determined from the calibration curves by a quantitativemeasure of certain spectral features in their spectra. An alternativeapproach to the quantitative analysis of LIBS spectra is multivariateanalysis (MVA), i.e., the simultaneous analysis of more than one sta-tistical variable. MVA is widely used and seems to be preferred forthe study of materials relevant to Mars exploration [9,10]. The useof artificial neural networks (ANN) has also been suggested as a pow-erful quantitative approach in LIBS [11–13]. Calibration-free (CF)techniques have been proposed for processing LIBS spectra withoutusing matrix-similar standards [14,15]. CF-LIBS essentially derivesthe composition of the sample from calculations of the temperature

and electron density of the plasma created by the laser pulse. In CF-LIBS, local thermodynamic equilibrium (LTE) is assumed. Note thatLTE and other conditions relevant to CF-LIBS have been verified [16].

In this paper we describe a methodology for general automatedpre-processing and quantitative analysis of LIBS spectral data and re-port its step-by-step application to construct a set of calibrationcurves for common elements in sulfate matrix under Mars andEarth atmospheric conditions. We also show the application of thecalibration curves to characterize two natural sulfate-bearing sam-ples. At the time this manuscript was prepared, matrix-specific cali-bration curves for additional matrices relevant to Mars (basalts,phyllosilicates, carbonates, etc.) are under construction.

2. Experimental

2.1. Sulfate standard samples for the construction of calibration curves

Sulfates were selected for the development of this first set of cali-bration curves because they are one of the twomajor types of second-ary minerals found on Mars that may provide potentially habitableenvironments due to their association with ancient aqueous environ-ments in which life might have thrived. In this work we used pure hy-drous sulfates with multiple cations synthesized in previous studiesby the authors [17,18], and several commercial-grade sulfates. Thesesulfates are used as standards to establish the calibration curvesbased on their LIBS spectra obtained under Mars atmospheric condi-tions. All of the samples, including their provenance and the atomicfractions of the elements relevant for this work calculated from X-ray fluorescence measurements (described below), are listed inTable 1. A manual pellet press was used to apply ~4 tons of pressureto the powdered samples, which were placed in compressible alumi-num cups (Chemplex Industries, Inc. PelletCups) to form solid bri-quettes. The top flat surfaces of the pellets were used for LIBSmeasurements.

Samples for this study were analyzed for major and minor ele-ments in the XRF laboratory at Washington University in St. Louis(under the direction of Rex Couture) using novel procedures (Cou-ture, 2011; personal communication). The elemental abundances

Table 1Samples used to construct the calibration curves.

# Name Empirical formula Origin XRF-calculated atomic fractions of relevant elements

K Fe Ca Mg Na Al H S O

1 Natrojarosite NaFe3+3(SO4)2(OH)6 Excalibur MineralCorporation

3.6E-03 0.18 9.7E-04 3.7E-04 0.05 8.9E-04 3.8E-03 0.12 0.65

2 Anhydrite CaSO4 Fisher Scientific 0.17 0.01 0.16 0.663 Ferric sulfate pentahydrate Fe3+2(SO4)3·5H2O Synthetic 0.06 0.36 0.08 0.504 Kieserite MgSO4·H2O Fisher Scientific 0.11 0.23 0.11 0.555 Thenardite Na2SO4 Fisher Scientific 0.28 1.1E-03 0.146 Potassium sulfate K2SO4 Fisher Scientific 0.23 0.12 0.12 0.537 Ferricopiapite Fe3+0.66Fe3

+2(SO4)6(OH)2·20H2O

Synthetic 0.05 0.40 0.07 0.48

8 Magnesiocopiapite MgFe3+4(SO4)6(OH)2·20H2O Synthetic 0.04 0.01 0.42 0.06 0.479 Aluminocopiapite Al3+0.66Fe3

+2(SO4)6(OH)2·20H2O

Synthetic 0.04 0.01 0.41 0.06 0.44

10 Gypsum CaSO4·2H2O Fisher Scientific 0.09 0.32 0.09 0.5111 Sodium bisulfate

monohydrateNaHSO4·H2O Fisher Scientific 0.10 0.32 0.09 0.49

12 Alum-(K) KAl(SO4)2·12(H2O) Marysville, UT 0.02 0.03 0.02 0.01 0.12 0.2913 Bassanite 2(Ca)2(SO4)·H2O Fisher Scientific 0.14 0.11 0.14 0.6114 Hexahydrite MgSO4·6H2O Synthetic 0.04 0.52 0.04 0.4115 Starkeyite MgSO4·4H2O Synthetic 0.06 0.44 0.06 0.4116 Kieserite MgSO4·H2O Fisher Scientific 0.11 0.23 0.11 0.5517 Magnesium sulfate anhydrate MgSO4 Fisher Scientific 0.16 0.01 0.16 0.6618 Rhomboclase HFe3+(SO4)2·4H2O Synthetic 0.04 0.47 0.05 0.4519 Kornelite Fe3+2(SO4)3·7H2O Synthetic 0.05 0.38 0.08 0.5020 Rozenite Fe2+(SO4)·4H2O Synthetic 0.04 0.47 0.05 0.45

2 P. Sobron et al. / Spectrochimica Acta Part B 68 (2012) 1–16


calculated on the basis of these XRF measurements are reported inTable 1.

2.2. Laboratory LIBS setup in Earth and Mars atmospheric conditions

Our laboratory LIBS setup allows LIBS to conduct measurementsunder dual atmospheric conditions: Earth and Mars [19]. The labora-tory features two optical configurations that share the same excita-tion laser (an actively Q-switched Nd:YAG Minilite-II laser fromContinuum: 7 ns pulse width, max 45 mJ/pulse, 1064 nm wave-length) and the same spectrograph (Andor Mechelle 5000) equippedwith an intensified CCD detector (Andor iStar 712). Two identical setsof front optics sit in the Earth and Mars sides of the laboratory, andare selectable by a flip mirror. The front optics for LIBS measurementsin Mars conditions was installed inside a recently developed Plane-tary Environment and Analysis Chamber (PEACh) [19,20]. The frontoptics includes a dichroic mirror to direct the 1064 nm laser pulseinto a 5× microscope objective lens assembly (OFR LMH-5X-1064)that focuses the laser pulse onto the sample surface for LIBS excita-tion, with a spot size of ~100 μm diameter. When operating in Marsconditions, the excitation pulse laser beam penetrates through aquartz fused silica viewport mounted on a lateral wall of the PEAChand the LIBS signal collecting optical fiber cable connects to the spec-trometer (outside the PEACh) via a feedthrough mounted on the topwall of the PEACh.

A bare UV-enhanced optical fiber (230 μm core diameter) posi-tioned at ~10 mm and 45° from the surface of the sample collectsthe light from the plasma emission. The other end of optical fiberfor LIBS signal collection is connected to the high-resolution broad-band spectrometer assembly. When using a 50 μm entrance slit, thespectrometer configuration allows covering the 200–950 nm spectralrange with a wavelength-dependent spectral resolution ranging from0.04 to 0.1 nm. For measurements in terrestrial atmosphere, the spec-tra were collected with a 700 ns delay, and the integration time wasset to 4 μs. For measurements in Mars atmosphere, the delay and in-tegration times were set to 100 ns and 4 μs, respectively. The spec-trometer was wavelength-calibrated using ten atomic emission linesfrom an NIST-traceable Hg/Ar lamp. In order to correct the overallspectral response of the whole system, especially the nonlinear effi-ciency of the dispersion grating in the Echelle spectrograph and thespectral response of detector, a NIST-traceable dual deuterium/quartztungsten halogen lamp was used as an intensity standard. For each ofthe standard samples used for constructing the calibration curves, anaverage of five spots across the surface of the sample pellets were an-alyzed; 25 single-shot spectra were recorded at each spot, yielding atotal of 125 spectra per calibration target. Dual measurements weremade in Earth laboratory atmospheric conditions (1050 mbar of air)and in Mars atmospheric conditions (pressure inside the PEACh firstreduced to 0.07 mbar, then raised to 7 mbar of CO2). The deliveredenergy at the sampling spot was set to 30 mJ for all of the analyzedsamples in either atmospheric condition.

3. Automated routines for data processing

3.1. Pre-processing of spectral data

Once the spectral acquisition is finished, the data must be pre-processed to minimize the influence of variable experimental condi-tions and physical matrix effects. In the following sections we willrefer to a series of spectra as a set of typically 125 spectra collectedat five spots across the surface of an individual sample. In our LIBSsetup, the acquisition software stores single-pulse spectra, henceallowing the manipulation of individual spectra within a series ofspectra. We have developed a pulse-to-pulse analysis for the pre-processing of the LIBS series of spectra that consists of five steps:

baseline removal, spectral normalization, outlier discarding, spectraaveraging, and resolution enhancement.

3.1.1. Baseline removalLIBS spectra feature background signals essentially caused by

bremsstrahlung and recombination processes [21]. Background con-tributes as a form of baseline, although LIBS spectra acquired with agated detector such as the one used in this work show relativelyweak background. In this work we have used the baseline removalroutines developed by Sobron et al. [22] to generate a baseline by lin-ear interpolation from a certain number of spectral points, which isthen subtracted point by point from the spectrum. Although original-ly developed for baseline removal purposes in Raman spectra, theseroutines generate excellent baselines for the LIBS spectra recordedin this work. However, the implementation of the baseline removalroutines resulted in no significant improvement in the subsequentquantitative calculations performed on the spectra. This issue hasbeen recently discussed at length by Tucker et al. [23]. Fig. 1 showsa single-shot LIBS spectrum of CaSO4·2H2O and the calculated base-line in the 420–430 nm region. The spectrum was collected in Earthatmosphere. The interpolated intensity value of the baseline at theposition 422.67 nm (Ca I emission line) is two orders of magnitudesmaller than the intensity of the Ca emission signal. After applyingthe LIBS data baseline removal routine to all the spectra we observedthat the ratio spectrum intensity-to-baseline intensity ranges from 10to 103 for the emission lines considered for quantification purposes(see Section 3.2.1). We therefore consider that the background is es-sentially inexistent, mostly thanks to the use of a gated CCD. Chem-Cam on Curiosity rover will use non-gated detectors, thereforehigher levels of background are expected and baseline removal willbe necessary.

3.1.2. Normalization of the spectraIn Section 1 we mentioned that most normalization approaches in

LIBS use internal standards to compensate for spectral changes due tomatrix effects and variation in experimental conditions. In our partic-ular case, finding such standard peaks is not possible as the concen-trations of all elements vary significantly across the set of sampleswe analyzed. A more accurate approach seems to be normalizingeach spectrum to the total emission integrated intensity, or the totalarea under the spectrum in the 275–850 nm spectral range. Sincethe total collected emission integrated intensity represents approxi-mately the total energy released by the plasma in every shot, this nor-malization helps, in principle, correcting for pulse-to-pulse variations

Fig. 1. Single-shot LIBS spectrum of CaSO4·2H2O in Earth atmosphere (blue) and thecalculated baseline (green).

3P. Sobron et al. / Spectrochimica Acta Part B 68 (2012) 1–16


in the laser energy, spot size, plasma geometry and brightness, collec-tion geometry, and physical matrix effects in our experiments.

3.1.3. Outlier discarding within a series of spectraAfter normalization, the differences in the spectral features among

spectra in the same series are reduced, yet they are noticeable. In ourview, these differences can be accounted for by variations in pulse-to-pulse plasma size, or perhaps by plasma initiation by dust particles.These phenomena, among others, result in so-called outlier within aseries of spectra. Most spectroscopic techniques, including LIBS, usespectrum arithmetic averaging to generate a single (averaged) spec-trum from all of the spectra collected on the same sample regardlessof differences between them. Although this approach is generallyvery effective in the case of, for instance, Raman and infrared spec-troscopy, signal variations are often large within LIBS spectral seriesand may affect quantitative analyses (see discussion above about fac-tors compromising LIBS repeatability). It has been demonstrated thatdiscarding outlier spectra in combination with other data manipula-tion techniques prior to spectral averaging improves the accuracy ofLIBS elemental analysis (e.g., [24]).

The pre-processing package we have developed includes a routineto remove outlier spectra based on the Chauvenet criterion [25]. Toapply this outlier removal criterion to our particular case, we takethe values of the intensity of the most intense peak in a (previouslybaseline-corrected and normalized) spectrum as the data points (dis-criminating variables) in the discrimination algorithm. The intensitymaximum is computed for each spectrum in a series of spectra withinthe 275–850 nm range (a wavelength region that features the ele-ment emission lines relevant for this study of hydrous sulfates). Themean (MMean) and standard deviation (MMean) of the intensitymaxima within the whole series (125 spectra) is then calculated. Aspectrum is considered outlier if the value of its intensity maximumis beyond an interval around MMean defined as: MMean±n×Mσ.The selection of n was made by empirical investigation of differentspectral series and its typical value is 0.75. When outliers are found,the corresponding spectra are discarded from the series, and themethod is iteratively applied to the remaining spectra in the seriesuntil: (i) no outliers are found, or (ii) the number of remaining spec-tra in the series is less than (typically) 60% of the total number oforiginal spectra.

Fig. 2(a) shows the 125 single-shot baseline-corrected and nor-malized LIBS spectra of CaSO4. The spectra were collected in simulat-ed Mars atmosphere. For visualization purposes only a small spectralregion (420–430 nm) is shown. Fig. 2(b) shows the series of spectraof CaSO4 after the first iteration of the outlier removal routine(n=0.75). Note that 50 spectra have been discarded in this first iter-ation, and the variability, Mσ (Msigma in the figure), has been re-duced by a factor of 2.3 (from 0.1881 to 0.0827). If a seconditeration were applied (Fig. 2(c)), Mσ would be further reduced, asshown in the plot's statistics, but only 33 spectra of the starting 125spectra would remain for this particular case (CaSO4). However, asdiscussed below (Section 3.1.4), the SNR of the final averaged spec-trum (generated from the remaining spectra after outlier discarding)is highly affected by the number of discarded spectra. Therefore, atrade-off needs to be considered between the need to remove outliersand the necessity to keep enough spectra in a series of spectra foraveraging. In practice, our rule is to always keep at least 60% of theoriginal spectra in a series during the outlier removal process.

The effect of the outlier discarding methodology on the precisionand repeatability of the LIBS measurements on the reference samplesused for constructing the calibration curves is summarized in Table 2.The relative standard deviation (RSD) of the intensity maxima in rawand pre-processed spectra has been calculated by groups of elementsto provide insight on the pulse-to-pulse variation in the emissionproperties of different cations in sulfate matrix, and under differentatmospheres. The discussion of the phenomenology associated to

Fig. 2. (a) 125 baseline-corrected and normalized series of LIBS spectra of CaSO4 inMars atmosphere. (b) Series of spectra after the first iteration of the outlier removalroutine. (c) Series of spectra of CaSO4 after the second iteration of the outlier removalroutine.

Table 2Comparison of pulse-to-pulse variance measured as the relative standard deviation(RSD=100×Mσ×Mmean) in the intensity maxima of the raw and pre-processedspectra in laboratory and Mars atmospheres. The number of outlier spectra is inbrackets given as a percentage to the total number of raw spectra in a series.

Samplescontaining

Raw spectra RSD RSD pre-processed spectra[outlier spectra]

Labatmosphere

Marsatmosphere

Labatmosphere

Marsatmosphere

K 58.7 26.8 18.8 [44.6] 24.77 [42.0]Fe 67.7 37.4 18.0 [43.1] 20.97 [44.7]Ca 45.0 29.8 12.4 [24.9] 12.94 [34.7]Mg 51.6 37.6 12.1 [35.0] 17.38 [40.5]Na 71.4 33.0 19.3 [47.5] 11.05 [74.8]Al 55.7 26.5 15.7 [49.4] 24.5 [37.6]



the differences observed is, however, beyond the scope of this paperand the reported values are used as a quality control parameter forfurther quantitative analyses only. The general conclusion is that theoutlier discarding process increments significantly the precision ofour LIBS measurements for all of the elements. The number ofremaining (good) spectra per series is in the order of 60% for all ele-ments in both atmospheres, excepting for Na in Mars atmosphere(25%).

3.1.4. Spectra averagingOnce the outlier spectra are removed from a spectral series the

remaining spectra are averaged. This step results in a single spectrumfor each sample that is signal-to-noise enhanced with respect to theshot-to-shot individual spectra within a series. Fig. 3 shows an over-lay of a single-shot spectrum of CaSO4 collected in Mars atmosphereand the average spectrum of the same sample in the 420–430 nm re-gion obtained after the pre-processing routines were applied. Pre-processing includes baseline subtraction, normalization and a singleiteration for the removal of 50 outlier spectra. The improvement inthe signal-to-noise ratio (SNR) upon spectral averaging is qualitative-ly noticeable. From a quantitative point of view, Table 3 shows a com-parison between precision and signal to noise ratio (SNR) values oftwo emission peaks for pre-processed LIBS series of spectra of CaSO4

as a function of the number of iterations for outlier discarding.Whereas using a high (≥2) number of iterations yields better RSDs,it implies discarding a great number of the original 125 spectra inthe series, which we have observed has a negative effect in laterquantitative analyses. In addition, the SNRs worsen as the numberof remaining spectra in a series decreases. This is not problematic inthe case of the Ca I emission peak at 422.67 nm and other peakswhere the SNRs are large enough to perform accurate quantitativecalculations even with rather noisy averaged spectra, but it becomesan issue for instance for the S emission peak at 545.38 nm shown inTable 3 and other low intensity peaks where SNRs are small

Special care has been taken throughout this work to produce aver-aged spectra from spectral series that contain the maximum numberof single-shot spectra while discarding those spectra that, due to thereasons explained above, deviate much from their companion spectrain a given recorded series. The semi-arbitrary 60% cutoff value for themaximum number of removed outliers allowed per series of spectrais derived from careful empirical inspection of the RSD and SNR statis-tical values obtained upon processing each of the spectra collectedthroughout the experiments.

3.1.5. Resolution enhancementTo correct for peak distortion effects due to the finite resolution of

the spectrometer we treat all of our pre-processed LIBS spectra througha series of routines based on Fourier self-deconvolution (FSD), originallydeveloped by Kaupinnen et al. [26,27]. By way of an example, Fig. 4shows the LIBS spectrum of natrojarosite [NaFe3+3(OH)6(SO4)2] inMars atmosphere in the 775–780 nm region before and after FSD. Thetwo intense peaks correspond to oxygen emission lines. The resolutionof the spectrum is notably improved upon FSD (the HWHM of the777.19 nm O I peak is reduced from 0.08 to 0.05 nm), allowingthe detection of one additional peak located at 777.08 nm withinthe broad emission of O I peak at 777.19 nm and two additionalpeaks within the broad emission peak of O at 777.42 nm, located at777.55 and 777.72 nm. These three additional self-resolved peaks areassigned by NIST's Atomic Spectra Database [28] to Fe I (777.08 nm),O I (777.55 nm), and Xe III (ambiguous, 777.72 nm) emissions. The777.72 nm peak is probably an artifact introduced in the spectrum bythe FSD procedure (over-deconvolution), given the extremely lowprobability of detecting Xe at such a high ionization state with ourexperimental setup.

Further than improving the resolution of the LIBS spectra, self-resolution methods provide a way to compare spectra collectedwith different instrumental set-ups by minimizing the influence ofthe experimental conditions in the broadening and shifting of theemission peaks. In combination with our Mars atmosphere simula-tion capabilities, this might be useful for providing support for Chem-Cam data analyses of Martian materials by Curiosity, assuming that

Fig. 3. Single-shot spectrum of CaSO4 in Mars atmosphere (blue) and the average spec-trum obtained upon pre-processing (red, from the spectral subset shown in Fig. 2(b)).

Table 3Comparison of relative standard deviation (RSD) and signal to noise ratio (SNR) valuesfor the series of LIBS spectra of CaSO4 after pre-processing. RSD is calculated as de-scribed in Table 2. SNR is calculated in the averaged spectra after each iteration (upto four) as the ratio of the intensity of the emission peaks and the root mean square(RMS) of the spectral response over a wavelength region which is essentially free ofemission lines, hence representative of noise alone.

Iteration number Remaining spectraa RSD SNR Ca422.67 nm

SNR S545.38 nm

1 75 10.7 344 6.92 33 4.9 328 6.13 13 2.0 306 5.54 7 0.5 259 6.00b 1b – 150 4.4

a The number of spectra in the raw series is 125.b SNR calculated for the single-shot spectrum plotted in Fig. 3.

Fig. 4. LIBS spectrum of natrojarosite in Mars atmosphere before (blue) and after Fou-rier self-deconvolution (green) showing the detection of additional peaks (O, Fe, Xe)after spectral resolution enhancement.



ChemCam's data preprocessing tools allow generating comparableLIBS spectra. It is worth noting that a precise independent character-ization of the instrumental broadening function is required forobtaining an adequate performance of the FSD-based methods.

The flowchart in Fig. 5 lists the five pre-processing operations de-scribed above and summarizes their main features and highlights.

3.2. Quantification of LIBS spectral features

The tools we have developed for the quantification of LIBS spectralfeatures include two steps. The first is an automated set of routines toidentify the peaks in the pre-processed spectra and to relate them torelevant elements in a targeted sample. The second is to use peak-fitting routines to quantify a set of parameters (center wavelength,intensity, width, and Gauss–Lorentz factor) for all of the identified el-ement emission peaks for further correlation with the elemental con-centrations of the targets. These routines are described in thefollowing two subsections.

3.2.1. Line selectionThe pre-processed LIBS spectra are compared to a database of

emission lines built upon the NIST Atomic Spectra Database [28,29]and our own spectral libraries to identify the elements in the interro-gated sample. Our routines operate on the search–match principle asexplained in a previous paper by the authors for Raman spectroscopy[22]. To avoid misidentifying elements insofar possible, our databaseincludes multiple emission lines for a given element, each showingminimal or no overlap with the emission lines of other elements. Inpractice, the later is very difficult to achieve as most geological sam-ples have complex compositions that give raise to LIBS spectra popu-lated with many peaks, most of which overlap with each other. As ageneral rule to avoid this inconvenience we have discarded emission

lines from an element that show major overlap with emission linesfrom a different element (e.g., the 308.22/308.37 nm Al/Fe peaks) inour LIBS spectra. Another constrain we have imposed to our databaseis to include only emission lines within the spectral range of Chem-Cam (224–327, 384–473, and 494–933 nm) [30], so that our methodscould eventually provide support to tactical operations of the upcom-ing MSL mission based on ChemCam LIBS data. Table 4 shows the el-ement emission spectral database relevant to the samples studied inthis work. An example of the automated peak identification is given inFig. 6, which shows the pre-processed LIBS spectrum of aluminocopia-pite [Al0.66Fe43+(SO4)6(OH)2·20(H2O)] in the 275–850 nm region. Thepeaks that match emission lines within our database have been anno-tated with their corresponding element identification.

3.2.2. Peak area calculationThe proportional relationship between the intensities of the emis-

sion peaks of an element in a LIBS spectrum and the concentration ofthis element in a sample is the basis for quantitative analysis usingLIBS data. Unfortunately, an emission line observed in a LIBS spec-trum extends over a range of wavelengths rather than to a singlewavelength. In addition, under certain circumstances, its center maybe shifted from its nominal central wavelength. Distortions such asline broadening and line shift are caused by local effects during theformation and evolution of the plasma plume, such as thermal andpressure broadening as well as by non-local effects such as opacitybroadening (absorption of electromagnetic radiation as it travelsthrough space), self-absorption and macroscopic Doppler broadeningand shift [31]. The FSD approach mentioned in Section 3.1.5 aims tocorrect for these line broadening effects, yet the spectral peaksshow non-zero linewidth after applying the self-deconvolution, dueto the intrinsic limitations of the method and to the lack of knowledgeof a function that accounts for the physical and instrumental

Fig. 5. Flowchart of the five pre-processing operations and summary of their main features.



broadenings altogether. It is our opinion that the intensity of a peakmeasured at a single wavelength may not express the true intensityof the emission line associated to it, and that the best correlation be-tween the spectral peaks associated to an element and the concentra-tion of this element in a target (i.e., the most accurate calibrationcurve) is obtained in terms of the integrated intensity over a rangeof wavelengths around the central peak position (i.e., peak area) rath-er than the intensity at that central peak position.

In our approach to the calculation of the integrated intensity of apeak or peak area, the spectral peaks related to elements identifiedupon comparison to the emission lines database are fitted to Voigtfunctions by another type of spectral deconvolution: non-linear iter-ative curve fitting based on the Marquardt–Levenberg method [32].A typical peak-fitting for the spectra used in this work for the quanti-fication of a single Fe emission line is displayed in Fig. 7.

Each of the emission lines listed in Table 4 has been carefully ex-amined and constraints related to the number of bands that composea peak envelope and their parameters have been determined andimplemented in the automated peak-deconvolution routines. This ef-fort benefits in turn the overall effectiveness of the LIBS data proces-sing package by reducing time and computer memory consumption.

In summary, the result of peak-fitting as applied in this work isconvolution spectral peaks that are composed of a number of peakswhose parameters (center wavelength, peak intensity, FWHM, andGLF) are accurately characterized and optimized to fit the originalspectral emission peaks with minimum deviation. The derived peakareas of the fitted peaks associated to the elements identified in theLIBS spectra of the calibration standards listed in Table 1 are the ana-lytical signals used to construct our calibration curves. For unknownsamples, the peak areas of the fitted peaks associated to the elementsidentified in their LIBS spectra are used to determine the elemental

composition upon comparison with our calibration curves (sulfate-bearing samples based on this study).

4. Development of the calibration curves

Plots of the peak area versus the XRF-calculated elemental atomicfraction have been constructed for selected emission lines of K, Fe, Na,Ca, Al, Mg, H, O and S in sulfate matrix. The relative standard devia-tion of the peak fitting and peak area calculation processes is below5% for all of the peaks used to generate the calibration curves, except-ing for the S peak. Other factors affecting the overall deviations in thevalues of the peak areas are roughly accounted for by the RSDs de-scribed in Sections 3.1.4 and 3.2.2. We have estimated the RSDs ofthe data points in the calibration curves for all elements to be with-in the 5–10% range. The vertical error bars in the calibration curvescorrespond to 10% of the peak area value except where indicatedotherwise. In the following subsections, we report the calibrationcurves for the aforementioned elements and discuss their expectedaccuracy for predicting elemental concentrations in geological sam-ples relevant for Mars exploration. The influence of matrix effectsand self-absorption in the calibration curves is also discussed.Here we pay attention to the calibration curves in Martian atmo-sphere only, although the equivalent curves constructed for Earthatmospheric conditions are also available for the same elements,excepting S.

4.1. Ca

Gypsum (CaSO4·2H2O) has been detected on Mars from orbit innorthern latitudes [33,34] and in layered deposits near the equator[35–37]. The occurrence of anhydrite (CaSO4) and bassanite[2(Ca)2(SO4)·(H2O)] at typical Martian cold temperatures hasbeen suggested based on dehydration/rehydration experiments onnatural gypsum [38] and from Thermal and Evolved Gas Analyzer(TEGA) measurements on board the Phoenix lander [39]. MawrthVallis (23°N), one of the final candidate landing sites for the MarsScience Laboratory mission, provides access to both phyllosilicatesand sulfates [40], including bassanite [41]. Syngenite [K2Ca(SO4)2·(H2O)]is a diagenetic component of marine salt deposits formed after Na–K–Mg–Ca–Fe–Cl–SO4–H2O-rich brines from which certain Martian mineralassemblages are presumed to have originated [42,43]. However, syngen-ite has not been identified on Mars surface to date. Fig. 8 shows the cal-ibration curve for Ca developed using three Ca-sulfates with different

Table 4Spectral lines of the elements used in this work displayed in wavelength order. Transi-tion type in brackets.

Element Emission lines (nm)

Al (I) 308.2 (I) 309.3 (I) 394.4 (I) 396.2Ca (II) 315.9 (II) 317.9 (II) 370.6 (II) 373.7 (I) 422.7 (I) 534.9Fe (II) 278 (I) 404.6 (I) 438.4 (I) 561.6H (I) 434 (I) 656.3K (I) 404.7 (I) 693.6 (I) 766.5 (I) 769.9Mg (I) 285.2 (II) 292.9 (I) 293.7 (I) 517.3Na (I) 589 (I) 589.6O (I) 777.2 (I) 777.4S (II) 414.5 (II) 545.4 (II) 547.4 (II) 551 (II) 556.5 (II) 560.6

Fig. 6. Automated peak identification in a pre-processed LIBS spectrum of aluminoco-piapite recorded under Mars atmospheric conditions.

Fig. 7. LIBS spectrum of aluminocopiapite recorded under Mars atmospheric conditions(blue circles) with fitted peaks (cyan), convolution spectrum (dark blue), and residuals(gray). The central peak is contributed by Fe emission lines alone.



hydration states. The natrojarosite data point was used to mark trace Caconcentration. The highly non-linear behavior of the data points at highconcentrations of Ca (Ca atomic fraction>0.08) is due to self-absorption, which can be explained as follows: Laser ablation producespersistent amounts of aerosol above the interrogated sample that sup-plies material into the hot plasma in subsequent pulses. When the con-centration of certain species in the plasma exceeds a threshold, i.e., invery high plasma density conditions, the plasma absorbs its own emis-sion.Wisbrun et al. [44] demonstrated that laser repetition rate have sub-stantial influence in the intensity of emission lines in LIBS spectra dueessentially to the mentioned self-absorption effect. In addition toinfluencing the intensity of the observed emission lines in a LIBS spec-trum, self-absorption causes line-broadening and intensity dips at thecenter of the emission lines, as shown in Fig. 9 (pulses #10 and #20).The four single-shot spectra of a CaSO4 sample plotted in the figurewere collected at the same spot in Earth atmosphere using 35 mJ/pulseand 1 Hz repetition rate. The emission peak of Ca I at 422.67 nm deterio-rates quickly after a few pulses. Spectra collected after 10 shots show anintense dip at exactly 422.67 nm that can lead the observer, or automat-ed software, to believe that there are two peaks instead of a single fairlysymmetrical peak typically associated to the Ca I emission at that wave-length. Self-absorption of this resonant Ca emission line also occurs inMars atmosphere, although there is relatively low spectral distortioncompared to Earth atmosphere. In both cases we mitigated it insofar

possible by moving the sample a few microns between laser pulsesusing a high resolution motorized rotation stage installed inside thePEACh and high resolution linear positioning stages outside, for Marsand Earth configurations, respectively [19,20].

Since the Ca curve is essentially flat in the 0.09–0.16 Ca atomicfraction region, examining the peak area of the Ca I emission line at422.67 nm alone is not enough for distinguishing between CaSO4,2(Ca)2(SO4)·H2O and CaSO4·2H2O. In fact, none of the emissionlines considered here for Ca-identification purposes offers peak areavariations within these three compounds beyond ±10% uncertainty;therefore, H and O emission lines should be examined to determinethe hydration state of these Ca-sulfates. The ideal Ca atomic fractionof syngenite (~0.08) is, however, within the quasi-linear region ofour calibration curve (Fig. 8).

4.2. Na

Na-sulfate minerals have been observed on Mars surface fromorbit using VNIR reflectance spectroscopy data provided by theObservatoire pour la Mineralogie, l'Eau, les Glaces et l'Activité(OMEGA) instrument on board Mars Express (e.g., [45]). West CandorChasma hosts one of the largest sulfate deposits observed on Mars, in-cluding Na-rich sulfate mineralogy [46]. Thenardite (Na2SO4) seemsto be the most likely Na-sulfate to occur on Mars as a result of evap-oration events [47], although mirabilite (Na2SO4·10H2O) and blödite(Na2SO4·MgSO4·4H2O) often occur in association with thenardite interrestrial evaporite deposits (e.g., [48,49]). Fig. 10 shows the calibra-tion curve for Na developed using thenardite, sodium bisulfate mono-hydrate and natrojarosite. The area of the emission line increasesmonotonously as the atomic fraction of Na in the standard samplesincreases. Self-absorption is apparent in the Na emission lines inNa2SO4. Sallé et al. [50] did not observe self-absorption in the589.59 nm Na I emission line under Mars atmosphere, but they ana-lyzed this emission line in non-sulfate-rich natural rocks up to anNa atomic fraction of ~0.031 (trachyte). The Na atomic fractions inthe samples used in our calibration curve are much higher (0.05–0.30). Whether a sulfate matrix favors self-absorption of certain Naemission lines in Mars atmosphere, specifically in the Na atomic frac-tion range studied here, is unclear given that our calibration curve isconstructed with three data points only. Additional Na-rich standardsulfates are needed to provide insight in this issue. Assuming self-absorption of the Na emission lines considered in this work occurs,only the ideal atomic fractions of Na in mirabilite and blödite (0.054and 0.080, respectively) are within the linear region of our Na

Fig. 8. Calibration curve for Ca from the LIBS spectra of Ca-sulfates recorded under Marsatmospheric conditions.

Fig. 9. Single-shot LIBS spectra of CaSO4 in Earth atmosphere collected at the same spotafter different pulses count showing an intensity dip at the central wavelength and linebroadening as pulses count increases.

Fig. 10. Calibration curve for Na from the LIBS spectra of Na-sulfates recorded underMars atmospheric conditions.



calibration curve. Nevertheless, mirabilite is not stable even undernormal terrestrial laboratory conditions, not to mention those ofMars.

4.3. K

The presence of alunite [KAl3(SO4)2(OH)6] on Mars has been sug-gested form orbital measurements with the Compact ReconnaissanceImaging Spectrometer for Mars (CRISM) on the Mars ReconnaissanceOrbiter (MRO) [51]. Although not detected on Mars through January2012, alum-(K) [KAl(SO4)2·12H2O] often forms from oxidation of sulfu-rous gasses in geothermal environments (e.g. [52]). Such environmentsare widespread on Mars and include volcanic and hydrothermal sys-tems, which are considered promising sites for finding evidence ofpast life on the Martian surface [53]. Jarosite [KFe3+3(OH)6(SO4)2]was identified by Mössbauer spectrometer on board Opportunityrover and it accounted for 10 wt.% of a Meridiani outcrop on Mars[54]. Fig. 11 shows the calibration curve for K developed using alum-(K), natrojarosite, and K2SO4. The sum of the area of the emissionlines increasesmonotonously as the atomic fraction of K in the standardsamples increases. There is no evidence of self-absorption of the 766.45and 769.90 nm K I emission lines. Similar to Na, Salle et al. [50] did notobserve self-absorption for K under Martian conditions, although thehighest K atomic fraction used in their work is ~0.027 for trachyte,much smaller that the atomic fraction of K2SO4 (0.23) used in our cali-bration curve. It must be noted, however, that reduced pressure inMars atmosphere facilitates plasma expansion [55–57]. Rapid plasmaexpansion reduces the probability of re-absorption of the emitted pho-tonswhen they reach the edges of the plasma plume; this phenomenoncontributes strongly to the self-absorption mechanism [21]. This couldbe the case in our K calibration curve; although this claim is tentativedue to the lack of reliable reference samples featuringK atomic fractionsin the 0.04–0.3 range. Equally tentative is fitting the data points to a lin-ear calibration curve. One possible explanation for the relatively poor fitis the low intensity of the K emission peaks in the LIBS spectra of alum-(K) and natrojarosite, which increases the uncertainty in the quantifica-tion of the peak areas. Additional reliable standards should be used tooptimize this regression. The extended concentration range shown inthe curve plot embraces all of the possible K concentrations (majorand minor) in sulfate matrix.

It has been demonstrated that alum-(K) dehydrates under lowpressure conditions [58]. Although there is no evidence, to our knowl-edge, that dehydrated phases of alum-(K) exist on Mars surface, theirK atomic fractions (0.02 to 0.083) would be within the boundaries ofour calibration curve. The ideal K atomic fraction of syngenite and

alunite/jarosite is 0.15 and 0.038, respectively, well within the spanof our calibration curve.

4.4. Al

Alunite, alunogen [Al2(SO4)3·17(H2O)], and halotrichite [Fe2+

Al2(SO4)4·22(H2O)], among other metal-sulfate salts rich in alumi-num, are known to form through hydrothermal alteration[52,59,60] and pyrite oxidation [61–63]. Such Al-bearing sulfatesmay also have formed on Mars through similar processes. Gendrinet al. [36] have suggested the presence of halotrichite on Marsfrom OMEGA observations, whereas Lane et al. [64] speculate thathalotrichite may contribute to some spectroscopic data from thesalty soils at Gusev crater obtained with the Mössbauer instrumenton board the Spirit rover (Mars Exploration Rovers). As mentionedabove, alunite has been detected from orbit. Fig. 12 shows the cali-bration curve for Al developed using aluminocopiapite, alum-(K), andnatrojarosite. Although the natrojarosite data point features traceatomic fraction of Al, the three data points can be fitted to a straightline with minimal error. The ideal Al atomic fractions of the Mars-relevant alunite, alunogen and halotrichite are 0.115, 0.029 and 0.022,respectively. These Al atomic fractions are beyond the span of our cal-ibration curve. However, we can take advantage of the linear behaviorof the curve and extend the calibration to higher Al atomic fractions.

4.5. Mg

Kieserite (MgSO4·H2O) has been identified by OMEGA in many lo-cations on Mars [36]. Experimental work [65–69] suggests that highlyhydrated Mg-sulfates could have formed and persisted under moder-ate relative humidity and warmer temperature conditions expectedfor Mars during periods of high obliquity [70]. Wang and Freeman[71] have recently demonstrated through laboratory experiments thatkieserite, starkeyite (MgSO4·4H2O), a low-temperature MgSO4·7H2Ophase, andmeridinaniite (MgSO4·11H2O) are stable/metastable phasesof Mg-sulfate under current T-RH conditions at the Mars surface andsubsurface. Fig. 13 shows the calibration curve for Mg developedusing magnesiocopiapite, hexahydrite, starkeyite, kieserite and anhy-drate MgSO4. The effect of self-absorption previously discussed for theCa calibration curve can be observed here for high Mg concentrations(Mg atomic fraction>0.04). The samples weremoved before the acqui-sition of each single-shot spectrum tominimize the spectral distortionsdue to self-absorption. Although the Mg atomic fractions of all the pos-sible hydrated phases of Mg-sulfate are within the span of our calibra-tion curve, self-absorption of the Mg I 285.21 nm emission lineprevents extraction of the hydration state of the Mg-salts based on

Fig. 11. Calibration curve for K from the LIBS spectra of K-sulfates recorded under Marsatmospheric conditions.

Fig. 12. Calibration curve for Al from the LIBS spectra of Al-sulfates recorded underMars atmospheric conditions.



this line alone. Similar to Ca-sulfates, H andO emission linesmust be ex-amined for this purpose. Meridianiite is an exception since its ideal Mgatomic fraction is most likely within the quasi-linear region of our cali-bration curve.

4.6. Fe

Iron sulfates, including szomolnokite (FeSO4·H2O) and other hy-droxylated ferric sulfates (possibly copiapite and jarosite) have beendetected from orbit in localized areas on Mars surface [37,72]. Jarositeand nonspecific ferric sulfates have been identified in Meridiani Pla-num and Gusev Crater by the Mars Exploration Rovers [54,73]. Theoccurrence of ferricopiapite [Fe2+0.66Fe43+(SO4)6(OH)2·20(H2O)], hy-dronium jarosite [(H3O)Fe3+3(SO4)2(OH)6], fibroferrite [Fe3+(SO4)(OH)·5(H2O)], rhomboclase [HFe3+(SO4)2·4(H2O)] and paracoquim-bite [Fe3+2(SO4)3·9(H2O)] has been suggested based on PanCamspectra analysis of salty soils excavated by Spirit rover at the foot ofHusband Hill in Gusev crater [74]. These Fe-rich sulfates are just fewof the minerals that can occur on Mars through oxidative weatheringof iron sulfides [75], although other alternatives for Fe-rich mineralo-gy formation have been proposed [76–78].

Given the abundance of Fe-rich sulfates relevant to Mars and theirimportance we have analyzed eight standard iron sulfates to con-struct the Fe calibration curves shown in Fig. 14. The values of thesum of the peak areas of the three Fe emission lines selected seemto group into two calibration curves (Fig. 14(b) and (c)). We proposean explanation in terms of the chemical matrix effect associatedto different crystalline structures in the Fe-sulfates analyzed. TheFe-calibration curve labeled as number 1 (Fe cal. cur. 1, Fig. 16(b))was constructed using the data points obtained for ferricopiapite, alu-minocopiapite, magnesiocopiapite [MgFe3+4(SO4)6(OH)2·20(H2O)]and HFe3+(SO4)·4H2O. In copiapite-group minerals, chains of[Fe(OH)(H2O)O3] octahedra link through additional [SO4] tetrahedrato form infinite chains [79,80]. These chains are linked togetherthrough interstitial water and unconnected [AH2O] octahedra(A=Fe3+, Al3+, Mg2+) that contribute to the complex arrangementof hydrogen bonds present in the structure. (There are subtle struc-tural differences among the three copiapites associated to the occupa-tion of the A site [81]; however, for the purpose of our discussion theyare considered isostructural). HFe3+(SO4)2·4H2O has an equallycomplex hydrogen bonding scheme involving interstitial (H5O2

+)dimmers that link finite clusters of (SO4) tetrahedral and (FeO6) octa-hedra [80].

The structures featuring infinite chains (copiapites) and finiteclusters [HFe3+(SO4)2·4H2O] are very different than the structure of

(natro)jarosite and the rest of Fe-sulfates whose data points wereused to construct the Fe calibration curve number 2 (Fe cal. cur. 2,Fig. 14(c)). At high atomic fractions (>0.06) the computed sum ofthe peak area deviates substantially from this linear behavior, mostlikely due to self-absorption of the three Fe emission lines in thenatrojarosite standard. The structure of jarosite-group minerals is

Fig. 13. Calibration curve for Mg from the LIBS spectra of Mg-sulfates recorded underMars atmospheric conditions.

Fig. 14. Calibration curves for Fe from the LIBS spectra of Fe-sulfates recorded underMars atmospheric conditions.



based on infinite [Fe(OH)6(SO4)2] sheets, some of which are held to-gether by interstitial cations and hydrogen bonds [80]. The structureof Fe2(SO4)3·7H2O is based on infinite sheets of (SO4) tetrahedraand (Fe3+O6) octahedra. Similar to jarosite, these sheets are linkedby hydrogen bonds through (H2O) groups in the interlayer [80,82].Slabs featuring the same polyhedral arrangement are found in thestructure of Fe2(SO4)3·5H2O, although there are no (H2O) groups inthe interlayer, and the Fe octahedra are connected solely via (SO4)tetrahedra [83]. The structure of FeSO4·4H2O is based on finite clus-ters of (SO4) tetrahedra and (Fe2+O6) octahedral linked solely by hy-drogen bonds within and between adjacent clusters [80].

Considering the differences in the complex Fe-rich crystallinestructures of the standards used in this work, it seems appropriateto construct two Fe calibration curves to correlate the sum of thepeak areas of the Fe II 259.83, Fe I 259.88 and Fe I 259.93 nm emissionlines with the atomic fraction of Fe in the samples. Aside from thistriplet peak, no other Fe emission lines recorded in this work generat-ed simple or informative calibration curves. The dual Fe calibrationcurve we have obtained analyzing these particular Fe emission linesis probably the best way to account for the chemical matrix effects as-sociated to structural arrangements at the molecular level. We sug-gest that molecular (structural) information can be obtained fromthe interrogated sample along with its elemental composition in thecontext of standard calibration curve-based quantitative LIBS ana-lyses. It must be noted that it is not the aim of this work to providea detailed correlation between the fine structure of Fe-sulfates andtheir spectral features. This is a somewhat unexplored territory, as itis often assumed that the plasma generation/breakdown process pre-cludes the characterization of the structural information from thesample.

4.7. S

Fig. 15 shows the calibration curve for S, which includes all of thestandard samples used in this work. We have tentatively outlined twopossible calibration curves that show very small steepness (~1:500).For comparison, the steepness of the calibration curves of Ca, Na, K,Al, Mg, and Fe (Fe cal. cur. 1) is ~1:10, 1:2, 1:1.7, 1:1, 1:2, and 1:0.5.

This was a-priori expected, as the detection of sulfur in LIBS spectraof sulfur-bearing materials is often a very challenging task. Thereare three main reasons for this: (1) the strongest S lines in LIBS emis-sion are found in the vacuum UV (b200 nm) and the NIR (>900 mm)ranges [84,85], while our LIBS setup, as well as ChemCam on Curiosityrover, is designed to analyze the range between 200 nm and 900 nm.Only low intensity S lines are observed in the visible region(400–600 nm); (2) S lines in this spectral range overlap with Fe emis-sion lines (where present) which are notably stronger; and (3) elec-tronically excited S in the plasma of S-bearing samples can reactwith oxygen in laboratory atmosphere [85] and, to a lesser extent,with oxygen produced by molecular breakdown of CO2 in Mars atmo-sphere. This phenomenon results in a very poor signal-to-noise ratioof the S lines in the mentioned spectral range, as we showed in a pre-vious paper [19]. We can conclude that relatively accurate detectionof S in sulfate-bearing samples can be achieved in Mars atmosphere,but not in Earth atmosphere. Nevertheless, quantification of S,where possible, is subject to large uncertainty.

4.8. H

The characterization of the hydration state of sulfate minerals andsalt assemblages is important to reconstruct ancient Martian environ-ments, which has broad implications for habitability studies. The LIBStechnique is uniquely suited to this purpose, since it is in principlesensitive to hydrogen. Fig. 16 shows the calibration curve for H,which includes all of the standard samples used in this work. The fig-ure shows also a plot of a linear fitted function that represents, how-ever, a poor fit. This is due to chemical matrix effects caused by thecomplexity of hydrogen bonding in the crystal structures of thesesamples, and to a relatively low sensitivity of our system to H. Notethat the steepness of the linear curve is ~1:20. Close inspection ofFig. 16 allows observing that certain minerals tend to group in consis-tent trends where the peak area of the broad H I 656.3 nm line in-creases monotonously as a function of H atomic fraction. For clarity,Fig. 17 shows close-up views of the individual H calibration curveswe have constructed for Ca-, Mg-, and Fe-rich sulfates. The peakareas of the analyzed H emission lines increase monotonously as H

Fig. 15. Calibration curves for S based on the LIBS spectra of all the standard sulfate samples recorded under Mars atmospheric conditions. Vertical error bars are plotted on a ±15%basis.



concentration increases in the Ca and Mg-sulfate samples, enablingthe extraction of the hydration state of the hydrous CaSO4.nH2O andMgSO4.nH2O series from their LIBS spectra. This capability can becompromised when encountering a mineral mixture in a samplingspot on Mars. However, during the ChemCam investigations byCuriosity, multiple sampling at one location can help mitigate theproblem.

In the Fe-sulfates, the H calibration curve displayed in Fig. 17(c)does not show a trend consistent with all of the plotted values. Thestructural differences among the various Fe-sulfates discussed inSection 4.6 account for most of the chemical matrix effects in thesamples thought to be responsible for the likely dual calibrationcurve shown in Fig. 14. They are also likely to be responsible for thefar-from-linear behavior of the H calibration curve for iron sulfates.Natrojarosite, featuring very low amounts of hydrogen, can be readilyidentified from the H calibration curve in Fig. 17(c). Copiapite-groupminerals can also be identified from this plot. However, the specifictype of copiapite (Al-, Mg- or Fe-copiapite) cannot be determined asthey feature almost identical concentration of hydrogen. For that pur-pose, it is necessary to extract the concentration of Al, Mg and Fe fromthe corresponding calibration curves. The same is true for the remind-ing hydrous Fe-sulfates.

4.9. O

Fig. 18 shows the calibration curve for O, which includes all of thestandard samples used in this work. There is no correlation betweenthe peak area of the O I 777.19/777.42 nm doublet and oxygen con-centration. This is because atmospheric oxygen (from the breakdownof CO2 molecules) contributes strongly to the LIBS signal from theoxygen-containing sulfates. This complicates further quantitative cal-culations. This phenomenon has been investigated by Knight et al.[57]. They determined that at typical Mars CO2 pressures, certain Olines in LIBS spectra of soil samples double their intensity comparedto those obtained for the same sample in Ar atmosphere. Differentia-tion of atmospheric oxygen from oxygen in samples is a very difficulttask which has to be carried on a sample-specific basis, as plasma cou-pling with the gaseous matrix is most likely determined by the chem-ical composition and structure of the sample under investigation.While oxygen detection is possible, further investigation is requiredto accurately remove the contribution of atmospheric oxygen to the

O emission lines in the LIBS spectra of geological samples, thus en-abling quantitative measurements. This applies to both Earth andMars atmospheres. Finally, it is worth noting that the detection of car-bon in geological samples in Martian atmosphere is also known to behighly compromised by the coupling of atmospheric carbon (fromCO2 breakdown) and carbon in the samples [86]. More work needsto be done in this direction to enable the unambiguous detection oforganic biomarkers using ChemCam on the MSL rover.

5. Application of the standard calibration curves to thecharacterization of natural samples

5.1. Natural sulfate from a playa deposit

We have analyzed a sample collected during field work in north-western Qaidam Basin on Qinghai–Tibet Plateau, China [48,87]. Qing-hai paleolake evaporated completely in the Late Pleistocene at DaLangtan area creating a playa surface. The evaporative salts fromlakes and playas in this region represent the nearly final stage ofevaporation sequence of K, Na, Ca, Mg, Fe, C, B, S, and Cl-bearingbrines. They may serve as an analog site for studying the precipitationsequence and subsequent dehydration/degeneration of Martian salts.The analyzed sample was a whitish granular aggregate potentiallycontaining several mineral phases. It was pressed into a pellet as de-scribed in Section 2.1. The LIBS spectra were recorded in Mars atmo-sphere using the experimental conditions detailed in Section 2.2.Fig. 19 displays the LIBS spectrum of the sample labeled DL0216.The statistics of the spectral pre-processing and the results of thequantitative analysis using the calibration curves are listed inTable 5. The quantitative analysis revealed that the sample containsrelatively large amounts of Al, relatively low amounts of Na and K,trace amounts of Mg, and below limit of detection amounts of Feand Ca. Cu and Si were also identified although their abundanceswere not quantified. The atomic fractions of three potential phasesthat we suggest could be present in the analyzed sample are alsolisted in Table 5: natroalunite [NaAl3(SO4)2(OH)6], alum-(K) andnatrochalcite [NaCu2(SO4)2(OH)·H2O]. These three minerals are con-sistent with evaporated hyper-saline lakes and lacustrine environ-ments [88], oxidation of sulfides in shales and slates [89], andoxidation of copper deposits in hyper-arid environments [90], respec-tively. Note that some of the minerals identified by the authors using

Fig. 16. Calibration curve for H based on the LIBS spectra of all the standard sulfate samples recorded under Mars atmospheric conditions.



Raman spectroscopy in the playa deposits in Qaidam Basin includecarbonate–gypsum-bearing surface soils, salt–clay-bearing deposits,dehydrated Na-sulfates, hydrous multication-sulfates, carbonates,and chlorites [48]. The presence of phyllosilicates and hydrated sul-fate mixtures, known to happen in the field site where the samplewas collected [91], explains the Si spectral lines present in the LIBSspectrum. Cu–S deposits are frequent in the northern rim of the

Qaidam Basin [92]. Cu-enriched sulfate salts form as a byproduct ofthe oxidation of such metal-sulfide mineral deposits. Although thesesalts are generally very soluble in water, the extreme aridity of theregion favors its preservation. It is likely that trace amounts of Cu-sulfates are mixed with the major mineral phases within our samplethereby giving rise to Cu lines in its LIBS spectrum.

5.2. Natural sulfate from an acid-mine related environment

The second sample we have studied was collected in Rio Tinto,Spain, in the surroundings of Peña del Hierro. Weathered shales asso-ciated with mine drainage from oxidized sulfides are common in thearea [93]. Rio Tinto, and Peña del Hierro in particular, is a sound ex-ample of acid mine drainage (AMD) environments, formed afterlarge amounts of sulfides are exposed to water, air, and often mi-crobes that catalyze the oxidation. Given the association of microor-ganisms such as Acidithiobacillus ferrooxidans with the aqueousoxidation of sulfides in Rio Tinto [94,95], the site is accepted bysome as a geo/bio/mineralogical setting on our planet which offerscomparable scenarios to those on Mars [96], and where technologiesfor Martian exploration can be defined and tested [97–99]. Our natu-ral sulfate sample is a coated slate displaying whitish, yellowish andorange colors. The coating was mechanically separated from theslate, powdered and pelletized as described in Section 2.1. The LIBS

Fig. 17. Mineral group-specific calibration curves for H based on the LIBS spectra of se-lected sulfates recorded under Mars atmospheric conditions. (a) CaSO4·nH2O hydratedseries. (b) MgSO4·nH2O hydrated series. (c) Fe-sulfates.

Fig. 18. Calibration curve for O based on the LIBS spectra of all the standard sulfatesamples used in this study recorded under Mars atmospheric conditions.

Fig. 19. LIBS spectrum of a natural sulfate-bearing sample (DL0216) from the QaidamBasin and automated element identification (labeled with red triangles) by our proces-sing software.



spectra were recorded in Mars atmosphere using the experimentalconditions detailed in Section 2. The LIBS spectrum of the sample la-beled loco6D is plotted in Fig. 20. The statistics of the spectral pre-processing and the results of the quantitative analysis using the cali-bration curves are listed in Table 6. While Mg, Na and Fe are the majorcations, there is significant abundance of Al and Ca. K and Cu werealso identified although their abundance was not quantified. Giventhe derived atomic fraction we suggest that the sample is potentiallycomposed of a mixture of magnesiocopiapite [MgFe3+4(SO4)6(OH)2·20(H2O)], aluminocopiapite, and natrojarosite. The ideal atomic frac-tions of these three minerals are also listed in Table 6. The presence oftrace amounts of other Cu-, K-, and Ca-rich sulfates is also suggested

to account for the observed emission lines related to these elements.Cuprocopiapite [CuFe3+4(SO4)6(OH)2·20(H2O)], jarosite and gypsum,found in the area by the authors in independent studies [100,101], arepossible candidates.

6. Summary and concluding remarks

We have developed a set of routines for LIBS spectral data proces-sing aimed to determine the elemental composition of sulfate sam-ples. Elements are identified by their fingerprint spectra and theiratomic fraction is calculated by using calibration functions con-structed upon analysis of reference sulfate materials under relevantatmospheric conditions (Earth and Mars). The calibration curves wehave developed for Fe are probably related to the crystalline structureof the Fe-sulfates we have analyzed in this work. The potential forLIBS for providing structural information is a promising possibilitythat warrants further investigation. In addition, the calibration curveswe have constructed allow determining the degree of hydration inhydrous Mg-, Ca-, and, to a certain extent, Fe-sulfates.

The routines and the calibration curves have been used to analyzethe LIBS spectra of two natural sulfate samples from potential Marsanalog sites. Both DL0216 and loco6D samples are good examples ofcomplex mixtures of several mineral phases. We have used our dataprocessing tools to identify most of the elements present in the sam-ples and we have extracted relatively accurate values for the abun-dances of several of these elements. The characterization of themineral phases present in the samples is however ambiguous assuch mixtures add great complexity to the LIBS spectra. This inconve-nience has been minimized by making use of the contextual geologicaland mineralogical information available for the field sites via publishedliterature and in-situ field investigations. In cases where such informa-tion is not available, techniques for mineral identification like laserRaman spectroscopy and X-ray diffraction should be used to constrainthe mineralogy of the samples under investigation.

Table 5Statistics and quantitative analysis of the series of LIBS spectra from playa deposit sam-ple DL0216.

Pre-processing

No. of spectra in raw series: 125No. of iterations for outliers removal: 2No. of spectra in final series: 93RSD final series: 36.7

Quantitative analysis

Elementa Area Atomicfractionb

Natroalunite Alum-K

Natrochalcitec

Mg 0.0013±5.E-05 b0.001d

H 0.0090±4.E-04 0.35–0.45±4.E-02

0.23 0.50 0.17

O 0.0459±2.E-03 NAe 0.54 0.42 0.56Fe 0.0000±0.E+00 b0.025d

S 0.0003±1.E-05 NAe 0.08 0.04 0.11Ca 0.0000±0.E+00 b0.001d

Na 0.0227±9.E-04 0.053–0.054±5.E-03

0.04 0.06

Al 0.0145±6.E-04 0.0168–0.0185±2.E-03

0.12 0.02

K 0.0177±7.E-04 0.0533–0.0565±6.E-03

0.02

a Additional elements identified: Cu, Si.b Given as an interval where the minimum and maximum values are calculated from

the calibration curve using the derived peak areas±5%.c Ideal Cu atomic fraction is 0.1111.d Atomic fraction is below the limit of detection or beyond the linear regime of the

calibration curve.e Not quantifiable at present (see calibration curves discussion, Sections 4.7–4.9).

Fig. 20. LIBS spectrum of a natural sulfate sample (loco6D) from Rio Tinto and automat-ed element identification (labeled with red triangles) by our processing software.

Table 6Statistics and quantitative analysis of the series of LIBS spectra from acid-mine depositsample loco6D.

Pre-processing

No. of spectra in raw series: 125No. of iterations for outliers removal: 2No. of spectra in final series: 84RSD final series: 25.1

Quantitative analysis

Elementa Area Atomicfractionb

Mg-copiapite

Al-copiapite

Na-jarosite

Mg 0.0101±4.E-04 0.0135–0.0145

0.01

H 0.0106±4.E-04 0.4–0.5±5.E-02

0.42 0.43 0.23

O 0.0353±1.E-03 NAc 0.46 0.47 0.54Fe 0.0441±2.E-03 0.0292–

0.0312±3.E-03

0.04 0.04 0.12

S 0.0004±2.E-05 NAc 0.06 0.06 0.08Ca 0.0007±3.E-05 b0.001d

Na 0.0017±7.E-05 0.0421–0.0447±4.E-03

0.04

Al 0.0020±8.E-05 0.0028–0.0030±3.E-04

0.01

K 0.0000±0.E+00 b0.02d

a Additional elements identified: Cu.b Given as an interval where the minimum and maximum values are calculated from

the calibration curve using the derived peak areas ±5%.c Not quantifiable at present (see calibration curves discussion, Sections 4.7–4.9).d Atomic fraction is below the limit of detection or beyond the linear regime of the

calibration curve.



The software we have developed can process 125 LIBS spectra col-lected at the same spot of a target to yield quantitative compositionalinformation in less than 30 s when using an Intel® Core™2 DuoE6550 CPU running Microsoft Windows XP. The whole package in-cludes, so far: (1) pre-processing of LIBS spectral data: baseline cor-rection, spectra normalization, outlier discarding, spectra averaging,and resolution enhancement; (2) quantitative spectral analysis algo-rithms for element identification against a database of 100+ atomicspectral lines, computation of peak areas and central wavelengthsupon spectral peak fitting; (3) tools for the derivation of the atomicfractions of up to eight elements in the interrogated spot of a givengeological sample (sulfate-bearing samples only based on the calibra-tion curves presented in this study).

Acknowledgments

This work was supported by NASA grant #1295053 under the MoOprogram for ExoMars mission; by NASA grants NNX07AQ34G andNNX10AM89G under the MFRP program for studying hydrous sulfatesrelevant to Mars; by NASA contract #09-030 under the MIDP programfor planetary instrument development. This work was also partiallysupported by a special fund from the McDonnell Center for the SpaceSciences at Washington University in St. Louis for developing the LIBSand Raman sensing technology in a Planetary Environment and Analy-sis Chamber (PEACh). We thank Roger Wiens and Sam Clegg of MSL-ChemCam team for providing advices in building the LIBS facility atWUSTL. The authors thank Rex Couture for conducting the XRF analysis.

References

[1] J.R. Thompson, R.C. Wiens, J.E. Barefield, D.T. Vaniman, H.E. Newsom, S.M. Clegg,Remote laser-induced breakdown spectroscopy analyses of Dar al Gani 476 andZagami Martian meteorites, J. Geophys. Res. Planets 111 (2006) E05006.

[2] R. Shu, H. Qi, G. Lu, D. Ma, Z. He, Y. Xue, Laser-induced breakdown spectroscopybased detection of lunar soil simulants for moon exploration, Chin. Opt. Lett. 5(2007) 58–59.

[3] S.M. Clegg, J.E. Barefield, S. Humphries, R.C. Wiens, D. Vaniman, M.D. Dyar, J.M.Tucker, S.K. Sharma, A.K. Misra, Remote Laser Induced Breakdown Spectroscopy(LIBS) Geochemical Investigation under Venus Atmospheric Conditions, AGUFall Meeting Abstracts, 31, 2009, 07.

[4] J.L. Lambert, J. Morookian, T. Roberts, J. Polk, S. Smrekar, S.M. Clegg, R.C. Weins,M.D. Dyar, A. Treiman, Standoff LIBS and Raman Spectroscopy Under Venus Con-ditions, Lunar and Planetary Institute Science Conference Abstracts, 41, 2010,abstract# 2608.

[5] R.C. Wiens, S. Clegg, S. Bender, N. Lanza, B. Barraclough, R. Perez, S. Maurice, M.D.Dyar, H. Newsom, C. Team, Initial Calibration of the ChemCam LIBS Instrumentfor the Mars Science Laboratory (MSL) Rover, Lunar and Planetary Institute Sci-ence Conference Abstracts, 40, 2009, abstract# 1461.

[6] D.A. Cremers, L.J. Radziemski, History and fundamentals of LIBS Laser InducedBreakdown Spectroscopy, in: Andrzej W. Miziolek, Palleschi Vincenzo, I. Schechter(Eds.), Laser Induced Breakdown Spectroscopy, Fundamentals and Applications,Cambridge University Press, 2006, pp. 1–39.

[7] R.S. Harmon, F.C. De Lucia, A.W. Miziolek, K.L. McNesby, R.A. Walters, P.D.French, Laser-induced breakdown spectroscopy (LIBS) — an emerging field-portable sensor technology for real-time, in-situ geochemical and environmen-tal analysis, Geochem. Explor. Environ. Anal. 5 (2005) 21–28.

[8] C.B. Stipe, B.D. Hensley, J.L. Boersema, S.G. Buckley, Laser-induced breakdownspectroscopy of steel: a comparison of univariate and multivariate calibrationmethods, Appl. Spectrosc. 64 (2010) 154–160.

[9] S. Rosenwasser, G. Asimellis, B. Bromley, R. Hazlett, J. Martin, T. Pearce, A. Zigler,Development of a method for automated quantitative analysis of ores usingLIBS, Spectrochim. Acta Part B 56 (2001) 707–714.

[10] J.B. Sirven, B. Salle, P. Mauchien, J.L. Lacour, S. Maurice, G. Manhes, Feasibilitystudy of rock identification at the surface of Mars by remote laser-inducedbreakdown spectroscopy and three chemometric methods, J. Anal. At. Spectrom.22 (2007) 1471–1480.

[11] J.B. Sirven, B. Bousquet, L. Canioni, L. Sarger, S. Tellier, M. Potin-Gautier, I. LeHecho, Qualitative and quantitative investigation of chromium-polluted soilsby laser-induced breakdown spectroscopy combined with neural networksanalysis, Anal. Bioanal. Chem. 385 (2006) 256–262.

[12] J.B. Sirven, B. Bousquet, L. Canioni, L. Sarger, Laser-induced breakdown spectros-copy of composite samples: comparison of advanced chemometrics methods,Anal. Chem. 78 (2006) 1462–1469.

[13] A. Koujelev, M. Sabsabi, V. Motto-Ros, S. Laville, S.L. Lui, Laser-induced break-down spectroscopy with artificial neural network processing for material iden-tification, Planet. Space Sci. 58 (2010) 682–690.

[14] A. Ciucci, M. Corsi, V. Palleschi, S. Rastelli, A. Salvetti, E. Tognoni, New procedurefor quantitative elemental analysis by laser-induced plasma spectroscopy, Appl.Spectrosc. 53 (1999) 960–964.

[15] P. Yaroshchyk, D. Body, R.J.S. Morrison, B.L. Chadwick, A semi-quantitativestandard-less analysis method for laser-induced breakdown spectroscopy, Spec-trochim. Acta Part B 61 (2006) 200–209.

[16] M. Corsi, V. Palleschi, A. Salvetti, E. Tognoni, Making LIBS quantitative — a criticalreview of the current approaches to the problem, Res. Adv. Appl. Spectrosc. 1(2000) 41–46.

[17] W.G. Kong, A. Wang, J.J. Freeman, P. Sobron, A comprehensive spectroscopicstudy of synthetic Fe2+, Fe3+, Mg2+ and Al3+ copiapite by Raman, XRD, LIBS,MIR and vis–NIR, J. Raman Spectrosc. 42 (2011) 1120–1129.

[18] Z.C. Ling, A. Wang, A systematic spectroscopic study of eight hydrous ferric sul-fates relevant to Mars, Icarus 209 (2010) 422–433.

[19] P. Sobron, A. Wang, A planetary environment and analysis chamber (PEACh) forcoordinated Raman-LIBS-IR measurements under planetary surfaces environ-mental conditions, J. Raman Spectrosc. (2011), doi:10.1002/jrs.3017.

[20] P. Sobron, A. Wang, A Planetary Environment and Analysis Chamber Equippedwith Multiple Spectroscopic Sensors, Lunar and Planetary Institute Science Con-ference Abstracts, 41, 2010, abstract# 1994.

[21] D.A. Cremers, L.J. Radziemski, Handbook of Laser-Induced Breakdown Spectros-copy, John Wiley & Sons, Chichester, West Sussex, England; Hoboken, NJ, 2006.

[22] P. Sobron, F. Sobron, A. Sanz, F. Rull, Raman signal processing software for auto-mated identification of mineral phases and biosignatures on Mars, Appl. Spec-trosc. 62 (2008) 364–370.

[23] J.M. Tucker, M.D. Dyar, M.W. Schaefer, S.M. Clegg, R.C. Wiens, Optimization oflaser-induced breakdown spectroscopy for rapid geochemical analysis, Chem.Geol. 277 (2010) 137–148.

[24] D. Body, B.L. Chadwick, Optimization of the spectral data processing in a LIBS simul-taneous elemental analysis system, Spectrochim. Acta Part B 56 (2001) 725–736.

[25] W. Chauvenet, A manual of Spherical and Practical Astronomy, 5th ed. DoverPublications, New York, 1960.

[26] J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron, Fourier self-deconvolution — a method for resolving intrinsically overlapped bands, Appl.Spectrosc. 35 (1981) 271–276.

[27] J.K. Kauppinen, D.J. Moffatt, H.H. Mantsch, D.G. Cameron, Fourier-transforms inthe computation of self-deconvoluted and 1st-order derivative spectra of over-lapped band contours, Anal. Chem. 53 (1981) 1454–1457.

[28] Y. Ralchenko, A.E. Kramida, J. Reader, N.A. Team, NIST Atomic Spectra Database(ver. 4.0.1), National Institute of Standards and Technology, Gaithersburg, MD,2010 [Online].

[29] D.E. Kelleher, W.C. Martin, W.L. Wiese, J. Sugar, J.R. Fuhr, K. Olsen, A. Musgrove,P.J. Mohr, J. Reader, G.R. Dalton, The new NIST atomic spectra database, Phys.Scr. T83 (1999) 158–161.

[30] N.L. Lanza, R.C. Wiens, S.M. Clegg, A.M. Ollila, S.D. Humphries, H.E. Newsom, J.E.Barefield, C. Team, Calibrating the ChemCam laser-induced breakdown spectrosco-py instrument for carbonateminerals onMars, Appl. Optics 49 (2010) C211–C217.

[31] H.R. Griem, Spectral Line Broadening by Plasmas, Academic Press, New York, 1974.[32] D.W. Marquardt, An algorithm for least-squares estimation of non-linear param-

eters, J. Soc. Ind. Appl. Math. 11 (1963) 431–441.[33] Y. Langevin, F. Poulet, J.P. Bibring, B. Gondet, Sulfates in the north polar region of

Mars detected by OMEGA/Mars express, Science 307 (2005) 1584–1586.[34] K.E. Fishbaugh, F. Poulet, V. Chevrier, Y. Langevin, J.P. Bibring, On the origin of gyp-

sum in the Mars north polar region, J. Geophys. Res. Planets 112 (2007) E07002.[35] J.J. Wray, S.L. Murchie, S.W. Squyres, F.P. Seelos, L.L. Tornabene, Diverse aqueous

environments on ancient Mars revealed in the southern highlands, Geology 37(2009) 1043–1046.

[36] A. Gendrin, N. Mangold, J.P. Bibring, Y. Langevin, B. Gondet, F. Poulet, G. Bonello,C. Quantin, J. Mustard, R. Arvidson, S. LeMouelic, Sulfates in Martian layered ter-rains: the OMEGA/Mars Express view, Science 307 (2005) 1587–1591.

[37] J.L. Bishop, M. Parente, C.M. Weitz, E.Z.N. Dobrea, L.H. Roach, S.L. Murchie, P.C.McGuire, N.K. McKeown, C.M. Rossi, A.J. Brown, W.M. Calvin, R. Milliken, J.F.Mustard, Mineralogy of Juventae Chasma: sulfates in the light-toned mounds,mafic minerals in the bedrock, and hydrated silica and hydroxylated ferric sul-fate on the plateau, J. Geophys. Res. Planets 114 (2009) E00D09.

[38] D.T. Vaniman, D.L. Bish, S.J. Chipera, Bassanite on Mars, Lunar and Planetary In-stitute Science Conference Abstracts, 40, 2009, abstract# 1654.

[39] D.C. Golden, D.W. Ming, B. Sutter, B.C. Clark, R.V. Morris, W.V. Boynton, M.H.Hecht, S.P. Kounaves, Sulfur Mineralogy at the Mars Phoenix Landing Site,Lunar and Planetary Institute Science Conference Abstracts, 40, 2009, abstract#2319.

[40] J. Grotzinger, Beyond water on Mars, Nat. Geosci. 2 (2009) 231–233.[41] J.J.Wray, S.W. Squyres, L.H. Roach, J.L. Bishop, J.F. Mustard, E.Z.N. Dobrea, Identifica-

tion of the Ca-sulfate bassanite inMawrth Vallis, Mars, Icarus 209 (2010) 416–421.[42] A.G. Fairen, A.F. Davila, L.G. Duport, E.R. Uceda, D.S. Lim, R. Amils, C.P. McKay, A

Cold and Wet Mars, Lunar and Planetary Institute Science Conference Abstracts,39, 2008, abstract# 1453.

[43] P.L. King, D.T. Lescinsky, H.W. Nesbitt, The composition and evolution of primor-dial solutions on Mars, with application to other planetary bodies, Geochim.Cosmochim. Acta 68 (2004) 4993–5008.

[44] R. Wisbrun, I. Schechter, R. Niessner, H. Schroder, K.L. Kompa, Detector for traceelemental analysis of solid environmental-samples by laser-plasma spectrosco-py, Anal. Chem. 66 (1994) 2964–2975.

[45] M. Zhu, H. Xie, G. Guan, R.K. Smith, E. Stansbery, Mineral and Lithologic Mappingof Martian Low Albedo Regions Using OMEGA Data, Lunar and Planetary ScienceConference Abstracts, 37, 2006, abstract# 2173.



[46] N. Mangold, A. Gendrin, B. Gondet, S. LeMouelic, C. Quantin, V. Ansan, J.P. Bibring,Y. Langevin, P. Masson, G. Neukum, Spectral and geological study of the sulfate-rich region of West Candor Chasma, Mars, Icarus 194 (2008) 519–543.

[47] J.M. Moore, M.A. Bullock, H. Newsom, M. Nelson, Laboratory simulations of Marsevaporite geochemistry, J. Geophys. Res. Planets 115 (2010) E06009.

[48] A. Wang, M. Zheng, F. Kong, P. Sobron, D.P. Mayer, Saline Playas on Qinghai-Tibet Plateau as Mars Analog for the Formation-Preservation of Hydrous Saltsand Biosignatures, AGU Fall Meeting Abstracts, 12, 2010, 02.

[49] L.A. Hardie, The origin of the recent non-marine evaporite deposit of Saline Val-ley, Inyo County, California, Geochim. Cosmochim. Acta 32 (1968) 1279–1301.

[50] B. Salle, J.L. Lacour, P. Mauchien, P. Fichet, S. Maurice, G. Manhes, Comparativestudy of different methodologies for quantitative rock analysis by Laser-Induced Breakdown Spectroscopy in a simulated Martian atmosphere, Spectro-chim. Acta Part B 61 (2006) 301–313.

[51] G.A. Swayze, B.L. Ehlmann, R.E. Milliken, F. Poulet, J.J. Wray, R.O. Rye, R.N. Clark,G.A. Desborough, J.K. Crowley, B. Gondet, J.F. Mustard, K.D. Seelos, S.L. Murchie,Discovery of the acid-sulfate mineral alunite in Terra Sirenum, Mars, using MROCRISM: possible evidence for acid-saline lacustrine deposits? AGU Fall MeetingAbstracts, 44, 2008, 04.

[52] R. Martin, K.A. Rodgers, P.R.L. Browne, The nature and significance of sulphate-rich, aluminous efflorescences from the Te Kopia geothermal field, Taupo Volca-nic Zone, New Zealand, Mineral. Mag. 63 (1999) 413–419.

[53] J.J. Walker, J.R. Spear, N.R. Pace, Geobiology of a microbial endolithic commu-nity in the Yellowstone geothermal environment, Nature 434 (2005)1011–1014.

[54] G. Klingelhofer, R.V. Morris, B. Bernhardt, C. Schroder, D.S. Rodionov, P.A. deSouza, A. Yen, R. Gellert, E.N. Evlanov, B. Zubkov, J. Foh, U. Bonnes, E. Kankeleit,P. Gutlich, D.W. Ming, F. Renz, T. Wdowiak, S.W. Squyres, R.E. Arvidson, Jarositeand hematite at Meridiani Planum from Opportunity's Mossbauer spectrometer,Science 306 (2004) 1740–1745.

[55] A.J. Effenberger, J.R. Scott, Effect of atmospheric conditions on LIBS spectra, Sens.Basel 10 (2010) 4907–4925.

[56] Y. Iida, Effects of atmosphere on laser vaporization and excitation processes ofsolid samples, Spectrochim. Acta Part B 45 (1990) 1353–1367.

[57] A.K. Knight, N.L. Scherbarth, D.A. Cremers, M.J. Ferris, Characterization of laser-induced breakdown spectroscopy (LIBS) for application to space exploration,Appl. Spectrosc. 54 (2000) 331–340.

[58] A.K. Galwey, G.G.T. Guarini, A kinetic-study of the dehydrations of the alumsKCr(SO4)2.12H2O and KAl(SO4)2.12H2O, Philos. R. Soc. Lond. A Math. 441(1993) 313–329.

[59] T. Minakawa, S. Noto, Botryogen from the Okuki mine, Ehime Prefecture, Japan,Chigaku Kenkyu 43 (1994) 175–179.

[60] R.L. Frost, M. Weier, J. Martinez-Frias, F. Rull, B.J. Reddy, Sulphate efflorescentminerals from El Jaroso Ravine, Sierra Almagrera — an SEM and Raman spectro-scopic study, Spectrochim. Acta Part A 66 (2007) 177–183.

[61] G. Garcia Garcia, The Rio Tinto mines, Huelva, Spain, Mineral. Rec. 27 (1996)275–285.

[62] D.K. Nordstrom, C.N. Alpers, Negative pH, efflorescent mineralogy, and conse-quences for environmental restoration at the Iron Mountain Superfund site, Cal-ifornia, Proc. Natl. Acad. Sci. U. S. A. 96 (1999) 3455–3462.

[63] T. Buckby, S. Black, M.L. Coleman, M.E. Hodson, Fe-sulphate-rich evaporativemineral precipitates from the Rio Tinto, southwest Spain, Mineral. Mag. 67(2003) 263–278.

[64] M.D. Lane, J.L. Bishop, M.D. Dyar, P.L. King, M. Parente, B.C. Hyde, Mineralogy ofthe Paso Robles soils on Mars, Am. Mineral. 93 (2008) 728–739.

[65] D.L. Bish, J. William Carey, D.T. Vaniman, S.J. Chipera, Stability of hydrous min-erals on the martian surface, Icarus 164 (2003) 96–103.

[66] S.J. Chipera, D.T. Vaniman, D.L. Bish, J.W. Carey, W.C. Feldman, E. Stansbery, Ex-perimental Stability and Transformation Kinetics of Magnesium Sulfate Hy-drates that may be Present on Mars, Lunar and Planetary Science ConferenceAbstracts, 36, 2005, abstract# 1497.

[67] I.M. Chou, R.R. Seal II, Determination of epsomite–hexahydrite equilibria by thehumidity-buffer technique at 0.1 MPa with implications for phase equilibria inthe system MgSO4–H2O, Astrobiology 3 (2003) 619–630.

[68] H.H. Emons, H. Jahn, G. Wolf, Investigations of salt-mixed solvent systems .38.Crystallization enthalpy of magnesium-chloride in mixed-solvent systems,J. Solut. Chem. 19 (1990) 447–455.

[69] D.T. Vaniman, D.L. Bish, S.J. Chipera, C.I. Fialips, J. William Carey, W.C. Feldman,Magnesium sulphate salts and the history of water on Mars, Nature 431(2004) 663–665.

[70] M.I. Richardson, M.A. Mischna, Long-term evolution of transient liquid water onMars, J. Geophys. Res. 110 (2005) E03003.

[71] A. Wang, J.J. Freeman, Pathways and rates of Mg-sulfate dehydration and rehy-dration on Mars, Conference on Micro-Raman Spectroscopy and LuminescenceStudies in the Earth and Planetary Sciences, 1473, 2009, abstract# 89–90.

[72] K.A. Lichtenberg, R.E. Arvidson, R.V. Morris, S.L. Murchie, J.L. Bishop, D.F. Remolar,T.D. Glotch, E.N. Dobrea, J.F. Mustard, J. Andrews-Hanna, L.H. Roach, Stratigraphyof hydrated sulfates in the sedimentary deposits of Aram Chaos, Mars,J. Geophys. Res. Planets 115 (2010) E00D17.

[73] R. Gellert, R. Rieder, J. Bruckner, B.C. Clark, G. Dreibus, G. Klingelhofer, G. Lugmair,D.W.Ming, H.Wanke, A. Yen, J. Zipfel, S.W. Squyres, Alpha particle X-ray spectrom-eter (APXS): results from Gusev crater and calibration report, J. Geophys. Res.Planets 111 (2006) E02S05.

[74] J.R. Johnson, J.F. Bell, E. Cloutis, M. Staid, W.H. Farrand, T. Mccoy, M. Rice, A.Wang, A. Yen, Mineralogic constraints on sulfur-rich soils from Pancam spectraat Gusev crater, Mars, Geophys. Res. Lett. 34 (2007) L13202.

[75] R.G. Burns, Ferric sulfates onMars, J. Geophys. Res. Solid Earth 92 (1987) E570–E574.[76] N.J. Tosca, S.M. McLennan, D.H. Lindsley, M.A.A. Schoonen, Acid-sulfate weather-

ing of synthetic Martian basalt: the acid fog model revisited, J. Geophys. Res.Planets 109 (2004) E05003.

[77] H. Nekvasil, F.M. McCubbin, N.J. Tosca, A. Smirnov, Hydrothermal activity onMars: can magmatic waters play a viable role? Lunar and Planetary Institute Sci-ence Conference Abstracts, 39, 2008, abstract# 1828.

[78] F.M. McCubbin, A. Smirnov, H. Nekvasil, J. Wang, E. Hauri, D.H. Lindsley, Hydrousmagmatism onMars: a source of water for the surface and subsurface during theAmazonian, Earth Planet. Sci. Lett. 292 (2010) 132–138.

[79] L. Fanfani, A. Nunzi, P.F. Zanazzi, A.R. Zanzari, Copiapite problem— crystal-structureof a ferrian copiapite, Am. Mineral. 58 (1973) 314–322.

[80] F.C. Hawthorne, S.V. Krivovichev, P.C. Burns, The crystal chemistry of sulfateminerals, Rev. Mineral. Geochem. 40 (2000) 1–112.

[81] J. Majzlan, R. Michallik, The crystal structures, solid solutions and infrared spec-tra of copiapite-group minerals, Mineral. Mag. 71 (2007) 553–569.

[82] P.D. Robinson, J.H. Fang, Crystal structures and mineral chemistry of hydratedferric sulphates .2. Crystal structure of paracoquimbite, Am. Mineral. 56(1971) 535–539.

[83] J. Majzlan, C. Botez, P.W. Stephens, The crystal structures of synthetics Fe2(SO4)3(H2-

O)5 and the type specimen of lausenite, Am. Mineral. 90 (2005) 411–416.[84] B. Salle, J.L. Lacour, E. Vors, P. Fichet, S. Maurice, D.A. Cremers, R.C. Wiens, Laser-

induced breakdown spectroscopy for Mars surface analysis: capabilities atstand-off distances and detection of chlorine and sulfur elements, Spectrochim.Acta Part B 59 (2004) 1413–1422.

[85] L. Dudragne, P. Adam, J. Amouroux, Time-resolved laser-induced break-down spectroscopy: application for qualitative and quantitative detectionof fluorine, chlorine, sulfur, and carbon in air, Appl. Spectrosc. 52 (1998)1321–1327.

[86] A.M. Ollila, J.G. Blank, R.C. Wiens, J. Lasue, H.E. Newsom, S.M. Clegg, A. Cousin, S.Maurice, Preliminary results on the capabilities of the ChemCam laser-inducedbreakdown spectroscopy (LIBS) instrument to detect carbon on Mars, Lunarand Planetary Science Conference Abstracts 42 (2011), abstract# 2395.

[87] P. Sobron, J.J. Freeman, A. Wang, Field Test of the Water-Wheel IR (WIR) Spec-trometer on Evaporative Salt Deposits at Tibetan Plateau, Lunar and PlanetaryInstitute Science Conference Abstracts, 40, 2009, abstract# 2372.

[88] C.N. Alpers, R.O. Rye, D.K. Nordstrom, L.D. White, B.-S. King, Chemical, crystallo-graphic and stable isotopic properties of alunite and jarosite from acid-hypersa-line Australian lakes, Chem. Geol. 96 (1992) 203–226.

[89] J.L. Jambor, D.K. Nordstrom, C.N. Alpers, Metal-sulfate salts from sulfide mineraloxidation, Rev. Mineral. Geochem. 40 (2000) 303–350.

[90] O.W. Jarrell, Oxidation at Chuquicamata, Chile, Econ. Geol. 39 (1944) 251–286.[91] D.P. Mayer, R.E. Arvidson, A. Wang, P. Sobron, M.P. Zheng, Mapping Minerals at a

Potential Mars Analog Site on the Tibetan Plateau, Lunar and Planetary InstituteScience Conference Abstracts, 40, 2009, abstract# 1877.

[92] D.Q. Zhang, X.Y. Dang, D.X. Li, C.Y. Feng, H.Q. She, J.W. Li, Two types of massivesulfide deposits along northern margin of Qaidam basin, Qinghai Province: II.Qinglongtan-style VHMS Cu–S deposits (in Chinese with English abstract),Miner. Deposita 24 (2005) 575–583.

[93] D.C. Fernández-Remolar, O. Prieto-Ballesteros, D. Gómez-Ortíz, M. Fernández-Sampedro, P. Sarrazin, M. Gailhanou, R. Amils, Río Tinto sedimentary mineral as-semblages: a terrestrial perspective that suggests some formation pathways ofphyllosilicates on Mars, Icarus 211 (2011) 114–138.

[94] A.I. Lopez-Archilla, I. Marin, R. Amils, Microbial community composition andecology of an acidic aquatic environment: the Tinto River, Spain, Microbial.Ecol. 41 (2001) 20–35.

[95] R. Amils, E. González-Toril, D. Fernández-Remolar, F. Gómez, N. Rodríguez, C.Durán, Interaction of the sulfur and iron cycles in the Tinto River ecosystem,Rev. Environ. Sci. Biotechnol. 1 (2002) 299–309.

[96] D.C. Fernandez-Remolar, R.V. Morris, J.E. Gruener, R. Amils, A.H. Knoll, The RioTinto basin, Spain: mineralogy, sedimentary geobiology, and implications for in-terpretation of outcrop rocks at Meridiani Planum, Mars, Earth Planet. Sci. Lett.240 (2005) 149–167.

[97] C.R. Stoker, L.G. Lemke, H. Cannon, B.J. Glass, S. Dunagan, J. Zavaleta, D. Miller, J.Gómez-Elvira, E. Stansbery, Field Simulation of a Drilling Mission to Mars toSearch for Subsurface Life, 36th Annual Lunar and Planetary Science Conference,36, 2005, abstract# 1537.

[98] F. Rull, G. Klingelhöfer, A. Sansano, I. Fleitcher, P. Sobrón, M. Blumers, A.Lafuente, D. Schmanke, J. Maul, In-situ micro-Raman and Moessbauer spectro-scopic study of evaporate minerals in Rio Tinto (Spain): applications for plane-tary exploration, Conference on Micro-Raman Spectroscopy and LuminescenceStudies in the Earth and Planetary Sciences, 1473, 2009, abstract# 68–69.

[99] V. Parro, L.A. Rivas, J.A. Rodríguez-Manfredi, Y. Blanco, G. de Diego-Castilla, P.Cruz-Gil, M. Moreno-Paz, M. García-Villadangos, C. Compostizo, P.L. Herrero, TheSOLID (Signs Of LIfe Detector) Instrument Concept: an Antibody Microarray-Based Biosensor for Life Detection in Astrobiology, EGU General Assembly 2009,11, 2009, abstract# 8614.

[100] P. Sobron, A. Sansano, A. Sanz, T. Acosta, B. Lafuente, F. Rull, F. Sobron, J. Medina,Raman spectroscopy, a powerful tool for the characterization of hydrated sul-fates and acidic water in Rio Tinto (Spain), Conference on Micro-Raman Spec-troscopy and Luminescence Studies in the Earth and Planetary Sciences, 1473,2009, abstract# 77–78.

[101] P. Sobron, A. Sanz, T. Acosta, F. Rull, A Raman spectral study of stream watersand efflorescent salts in Rio Tinto, Spain, Spectrochim. Acta Part A 71 (2009)1678–1682.


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