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Dual-Spectroscopy Platform for the Surveillance of Mars Mineralogy using a Decisions Fusion Architecture on Simultaneous LIBS-Raman Data



Javier Moros, Mohamed ElFaham, and José Javier Laserna Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/ acs.analchem.7b04124 • Publication Date (Web): 01 Jan 2018 Downloaded from http://pubs.acs.org on January 2, 2018



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Analytical Chemistry

Dual simultaneous laser-based spectroscopy to explore mineralogy on Mars ACS Paragon Plus Environment

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Dual-Spectroscopy Platform for the Surveillance of Mars Mineralogy using a Decisions Fusion Architecture on Simultaneous LIBS-Raman Data Javier Moros,† Mohamed Mostafa ElFaham,‡ J. Javier Laserna*,† †Universidad

de Málaga, Departamento de Química Analítica, UMALASERLAB, 29010 - Málaga, España

ABSTRACT: A single platform, integrated by a laser-induced breakdown spectroscopy detector and a Raman spectroscopy sensor, has been designed to remotely (5 m) and simultaneously register the elemental and molecular signatures of rocks under Martian surface conditions. From this information, new data fusion architecture at decisions level is proposed to the correct categorization of the rocks. The approach is based on a decisions making process from the sequential checking of the spectral features representing the cationic and anionic counterparts of the specimen. The scrutiny of the LIBS response by using a moving-window algorithm informs on the diversity of the elemental constituents. The output rate of emission lines allows projecting in a loop the elements as the cationic counterpart of the rock. In parallel, the Raman response of the unknown is compared with all the molecular counterparts of the hypothesized cation that are stored in a spectral library. The largest similarity rate unveils the final identity of the unknown. The identification capabilities of the architecture have been underscored through blind tests of ten natural rocks with different origins. The great majority of forecasts have matched with the real identities of the inspected targets. The strength of this platform to simultaneously acquire the multielemental and the molecular information from a specimen by using the same laser events greatly enhances the "onsurface" missions for the surveillance of mineralogy.

The planet Mars began gaining an immense public attention on July 4, 1997, when the rover Sojourner, delivered by the Pathfinder lander, successfully touched down on a rock-laden area known as Ares Vallis.1 This landing site was chosen because it offered the prospect of analyzing a variety of rock types from the ancient cratered highlands, to the reworked channel deposits, through the intermediate-age ridged plains.2 From landing until the final data transmission on September 27, 1997, Sojourner returned more than 15 chemical analyses using an Alpha proton Xray spectrometer (APSX) to measure the abundance of elements in rocks and soils.3,4 APXS was used again, together with additional series of analytical tools, on the Mars exploration car-sized robotic rovers Spirit (January 4, 2004) and Opportunity (January 25, 2004) to yield data on the distribution and composition of Martian minerals, rocks, and soils surrounding the different landing sites as the Columbia Hills in the Gusev crater and the Meridiani Planum region.5-8 It was not until the landing of the Mars Science Laboratory rover, nicknamed Curiosity (August 6, 2012), on Aeolis Palus in the Gale crater when laser-induced breakdown spectroscopy (LIBS), an effective technique used in Earth with huge potential to target the multielemental composition of geologic materials, was launched in a suite of remote sensing instruments named ChemCam.9-11 A very similar case is that of Raman spectroscopy, a practical

Earth exploration tool to confirm the molecular signature of mineral matrices.12-15 A Raman instrument called the Mars multibeam Raman spectrometer was also one of the instruments developed for use on Spirit and Opportunity rovers.16 Although at the time this spectrometer was passed over for other instruments, fortunately, Raman instruments are planned for future planetary missions. Close-up miniature Raman–LIBS system is under testing for the 2018 European Space Agency's (ESA) ExoMars rover mission at the time of this writing to be included together with a variety of instruments in the science package payload collectively called Pasteur suite.17-19 Furthermore, a remote-sensing instrument, nicknamed SuperCam, which uses remote optical measurements and laser spectroscopy to determine fine-scale mineralogy, chemistry, and atomic and molecular composition of samples encountered on the red planet, is envisaged to be part of science payload for the Mars 2020 mission.20 The instrumental complementarity between LIBS and Raman to examine samples with laser beams, in terms of a single wavelength high-intensity excitation source and spectrographs of comparable performance to disperse the signals, has been powerfully demonstrated in numerous investigations.21-24 However, there is a limitation inherent to applying of these analytical techniques with regard to the flux regime of the ongoing incident photons for observing the emission and the scattering phenomena. The

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switching from one operating technique to the other is the most common action to meet this requirement. Thus, spectral information is sequentially gathered either by inspection of an identical area using different laser energy levels or by pointing the same laser energy over differently sized areas. Unfortunately, this hierarchical performance does not guarantee that measurements refer to the same entity in the same state, especially if it is a very heterogeneous, complex and thermally unstable sample containing aggregates and volatile components. In this context, the energy density distribution into a focused Gaussian laser beam has proved counteracting effectively any hierarchy by simultaneously exciting both phenomena. Thus, exploiting moreover the asynchrony between the two spectroscopic outputs, the isolated but concurrent gathering of atomic and molecular information from the same sampling area using a single laser pulse is a fact.25 Consequently, since measurements are fully correlated to the same specimen, information of the two sensors can be associated for the benefit towards a more accurate knowledge of the inspected material.26-28 Much has been said so far about the gains resulting from the combined use of atomic and molecular information from the point of view of overcoming the constraints of LIBS and Raman operating alone. Although several attempts have been made,29-33 to date, there has not been published any research on the gathering of remote, instant, and simultaneous emission and scattering from single spots on the target as perfectly isolated signals to enhance the geochemical categorization scheme of rock constituents on the basis of cation-anion pairs in a remote hostile extraterrestrial environment. The effects of the critical parameters involved on the simultaneous spectral data acquisition, the possible conflicting issues affecting the performance of both measurement modes, as well as how such information can be associated and exploited to deal with the goal pursued will be detailed. These efforts are made in order to cope with the different science goals of the Mars exploration program to assist any future space mission that actively explores the Martian surface to search for and characterize rocks and soils holding more clues on the planet’s origin and its geological evolution.

scope –73 cm in length and 23 cm in diameter. A concave 'primary' mirror reflected the gathered light to a focus on the surface of a 'secondary' flat mirror, which guided the light at right angle towards the tip of a bifurcated optical fiber (600 μm in diameter). Thus, to spectrally resolve the emission and the scatter domains, the light was split into two spectrometers. Emission signals were spectroscopically resolved using 300 grooves mm-1 diffraction grating blazed at 500 nm. This arrangement provided a spectral window of roughly 300 nm –spanning the spectral range from 360 nm up to 620 nm– with a resolution of 0.06 nm. LIBS signals were acquired using a 5 µs gate width after a delay time of 50 ns. To scrutinize the scattered light a holographic imaging spectrograph (85 mm focal length, f/1.8i, 50 µm slit) coupled to an intensified CCD detector (2048 × 512 pixels array, 13.5 µm2 effective pixel size, intensifier tube of 18 mm in diameter) was used. This configuration provided a spectral window containing frequencies between 50 cm-1 and 2500 cm-1 –≈ 80 nm– with a resolution of 2.4 cm-1. Raman scattering was collected using 8 ns gate width after a delay of 38 ns. In parallel, to examine light emitted from laser-induced plasmas, a Czerny-Turner spectrograph (303 mm focal length, f/4, 10 µm slit) coupled to an intensified CCD detector (2048 × 512 pixels array, 20.25 µm2 effective pixel size, intensifier tube of 25 mm in diameter) was used. It must be noteworthy that the temperature for the two ICCDs was set at -12°C. Despite Raman spectra were measured in the extended spectral range from 50 to 2500 cm-1, in this study no useful information was observed in the low-frequency region from 50 cm-1 to 500 cm-1. It should be noted that the integrated spectroscopies strategy used here leaves the energy density potential partially unexploited for both phenomena, since light scattering progresses in parallel with the emerging plasma formed from the same laser event. Consequently, spectral signals gathered may often depart from the optimum spectroscopic outcome captured when using a single probe. Particularly for Raman, only the strong bands out of all the expected Raman active modes are detected, whereas weak Raman lines usually go undetected. In the present experiments, Raman spectra were governed by the internal fundamental modes of vibration of ions appearing in the high-frequency region from 600 cm-1 to 1800 cm-1. In contrast, the rotational and translational lattice modes appearing in the low-frequency region from 50 cm-1 to 500 cm-1 were not detected. While the scattered light from 50 laser events was accumulated for building up the Raman response, the average of the light emitted from the 50 plasmas occurring simultaneously was used to establish the LIBS response. Although continuous excitation at the same location may lead to deep craters that may distort emitted light intensity, sampling site was not refreshed during the 50 laser events. Nevertheless, to dispose of representative information, a collection of five binary spectral responses were registered for each material. During experimentation, the relative humidity in the analysis chamber was maintained between 50%–55%, meanwhile its temperature was maintained at 25 °C. Data were exported in text format and analyzed

■ EXPERIMENTAL SECTION Dual Spectroscopy Laser-Based Sensor. A Q-Switched Nd:YAG laser (20 Hz, 1064 nm, 350 mJ per pulse, 8 ns pulse width) was used as excitation source. Laser pulses of 124 ± 5 mJ each were delivered normal to the sample surface at a repetition rate of 1Hz. By using a 5× output beam expander, visible green light (532 nm) was first expanded and then focused on surface of geologic targets located 5 meters apart inside a Martian atmosphere chamber (≈ 1000 cm3 volume). Under this configuration, an ellipsoidal (≈ 4000 μm × 1000 µm) spot size on the target, with a hottest inner core of ca. 2600 µm × 600 µm, was produced; entailing therefore an irradiance gradient from 0.48 GW cm-2 to 1.22 GW cm-2. The radiated light from targets was collinearly collected using a Newtonian reflecting tele2

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intensity Raman spectrum arises.41 Notwithstanding, the optimum efficiency of Raman scattering drops when molecules are ever more weakly irradiated as the intensity of the incident radiation decreases. Something similar holds true for emitted photons (LIBS), when such decreasing of laser energy results in irradiance below plasma formation threshold. Thus, LIBS and Raman responses are not expected for very low excitation energy, whatever the dimensions of the irradiated area. In this context, the tradeoff between the intensity of incident photons and the beam focal conditions can be attained by exciting the molecules with a Gaussian laser beam whose energy intensity distribution profile confers an inhomogeneous irradiance over the material.42 The central part of the beam profile is endowed with a large irradiance, thereby making possible the plasma formation at the inner part of the illuminated area. Simultaneously, the lower, yet at a sufficient level, irradiance of the outer region of the beam profile allows observation of the molecular scattering. Any modification in this irradiance distribution of the beam profile will lead one phenomenon to dominate over the other, and will transform the spectral outputs. Thus, for the incoming radiation set high enough, a smaller laser spot size, even providing photon scattering, will benefit the LIBS signal. Contrarily, a larger laser spot size will highlight the Raman response. Needless to say that matter plays a crucial role for the boundaries in the fully harmonized energetic gradient which allows supplying simultaneously the best signal-tonoise ratio (SNR) for LIBS and Raman spectra. To put this in context, consider the case of nitrates depicted in Figure 2. As shown, the defined excitation conditions (Experimental Section) matched the radiative parameters to simultaneously gather good SNR in the LIBS and Raman spectra of the calcium and mercury nitrates. In contrast, that irradiance gradient would be excessive in the cases of Pb(NO3)2 and Sr(NO3)2.

using Matlab (The Mathworks Inc., South Natick, MA, U.S.A.). Samples. To evaluate the strengths and weaknesses on the accurate identification of the mineral composition of rocks from the simultaneous LIBS-Raman measurements, different pure inorganic salts were considered. Samples included barium – BaSO4 –, cadmium – CdSO4·8/3H2O –, calcium – CaSO4·2H2O –, manganese – MnSO4·H2O –, mercury – HgSO4 –, and potassium – K2SO4 – sulfates. In parallel, several carbonates, such as CaCO3, K2CO3, Na2CO3, and NaKCO3, were also considered. Furthermore, different hydration states were covered through nine nitrates– Al(NO3)3·9H2O, Ca(NO3)2·4H2O, Co(NO3)2·6H2O, Cr(NO3)3·9H2O, Hg2(NO3)2·2H2O, KNO3, Pb(NO3)2, Sr(NO3)2, and Zn(NO3)2·6H2O. These salts were decided since they are products from chemical weathering at soils or sediments and, moreover, have been found in large quantities and widely spread along the Mars surface. Sulfate-rich sedimentary deposits have been discovered in equatorial and north polar regions of Mars.34-36 In parallel, carbonates have been also detected in Mars meteorites, outcrops, the deep crust, and the Martian dust.37,38 In addition, sulfates and carbonates are key potential products of aqueous processes and may hold essential clues about history of ancient water on the surface of Mars. Furthermore, scarcely a year ago, NASA's Curiosity Rover discovered the first signs of nitrates, biochemically accessible forms of nitrogen considered as a key 'life-ingredient' on the surface of the Red Planet.39 All those synthetic minerals were primed and arranged for the analysis in pellet form. Powdered salts were first ground and then compacted using a manual hydraulic press by applying 7.0 T cm-2 for five minutes to result in cylindrical pellets of ca. 200 mm2 in area and 6 mm in thickness. Complementing those salts, some natural rocks were also analyzed.

■ RESULTS AND DISCUSSION Rationale for Simultaneous Sensing of Plasma Emissions and Raman Scattering from Minerals. The simultaneous observation of plasma emissions and inelastic scattering produced from the same light-matter interaction event is not a trivial matter since each phenomenon demands specific excitation conditions. The combined effect of the flux of incident photons and the laser beam focal condition on the performance of the retrieved photons can be discussed from the diagram depicted in Figure 1. As known, when a tightly focused large energy laser pulse hits the sample surface, the most favorable conditions for plasma formation are attained.40 Consequently, the exclusive recording of plasma emitted is anticipated, and therefore only the LIBS output is expected. If the size of the laser print area is enlarged while keeping the pulse energy, the irradiance level goes down towards the plasma formation threshold. Then, the first scattered photons may start to emerge; the dual spectral gathering became feasible. From here, the larger the illuminated area -up to a certain maximum-, the higher the number of irradiated molecules, and incoming photons undergo more scattering to the detriment of their emission. Therefore, a greater

Figure 1. Phenomena diagram for the retrieved photons from laser-matter interaction as a function of the intensity of incident photons and the size of the irradiated area.


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Figure 2. Normalized simultaneous LIBS-Raman spectral outputs of a series of nitrates acquired under the same irradiative conditions (more information detailed in the Experimental Section). The thermal map in the center displays the coordinates for the most adequate parameters to yield a good signal-to-noise ratio in the simultaneous gathering of LIBS and Raman data of the different materials. Lower flux of the incident photons should be considered to prevent the undesirable blooming effect that broadens the spectral features and degrades the resolution in their Raman responses. In parallel, to counteracting the "impoverishment" of LIBS signal intensity that causes the decline of the photon flux, the irradiated area should be reduced to preserve the irradiance high enough. The opposite occurs in the case of KNO3. To ensure good SNR for both simultaneous spectral responses a more efficient energetic gradient is demanded: the incoming radiation must be increased while downsizing the laser irradiated area. At this juncture, a more aggressive approach must be applied to gain LIBS signals for Al and Zn nitrates due to the higher plasma ignition threshold for these hydrated salts. In contrast, a completely opposite maneuver is needed to gather the two spectroscopic signals for Co(NO3)2·6H2O and Cr(NO3)3·9H2O, which feature useful atomic signals but without Raman scattering under the operating conditions; the irradiated area should be enlarged while the laser incoming radiation should be concurrently increased. It should be pointed out that unraveling any possible connection between a disparate dual spectroscopy and the com-

plicated structures of these hydrated and solvated materials has not been attempted. Certainly, dual spectroscopy from a single laser event has one common reason: conditions that favor Raman scattering tend to reduce LIBS intensities and vice versa. Nevertheless, the physicochemical properties of materials are key factors that guide the specific mechanisms governing the processes of absorption and scattering which, in short, define the spectral signals. Hence, identifying those harmonized conditions that lead to the best quality for their LIBS and Raman responses is not a trivial issue. Photo-sensing with LIBS and Raman probes, either single, successive or simultaneous, requires a careful optimization of experimental variables leading to the best sensor response, since there is no a general irradiation condition that allows gathering the optimum spectral information of all the samples. Beyond offering an advantage, the proposed simultaneous LIBS-Raman approach provides a guarantee: the atomic and molecular information gathered corresponds exactly to the same entity and to the actual conditions of the surface being inspected. This association is not secured when the sequential monitoring of the surface is performed.



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of the atmosphere causes any physical-chemical change to the mineral lattice.

Simultaneous Dual Spectroscopic Data under Distinct Planetary Atmospheres. Figure 3 shows the simultaneous LIBS-Raman spectra of several sulfates interrogated at atmospheric pressure in air and in a simulated Mars ambient (7 mbar CO2). Atmosphere effects on the remote monitoring of the dual information of mineral phases are discussed in terms of frequencies deviation and shape of spectral features. Since information was only intended to label the identity of the mineral phase ‒no quantification was attempted, data were normalized.27 Prior to this rescaling, laser wavelength spectral region ‒from 529 nm to 535 nm‒ was discarded in the LIBS signals. Meanwhile, the sloped baseline in Raman spectra was removed by fitting to a polynomial curve. Then, to measure the differences within atomic and molecular information when gathered under the distinct atmospheric conditions, the correlation coefficient value (denoted by r) and the root mean square error (RMSE) were calculated.28 The statistical meaningful of these scores is as follows: the closer are the value of r to 1 and the value of RMSE to 0, the lesser the differences between the spectra under consideration and, therefore, fewer side effects of the buffer atmosphere on acquired spectra. As can be check in Figure 3, perceptible spectral differences emerge between LIBS signals, as denoted by the r values below 0.9 ‒ exemption given for potassium sulfate due to the scarcity of meaning features. Differences consist mainly in a broadening of emission lines by 1.8 to 2.3 times (from the full width at half maximum of several peaks) and a high continuum emission background in LIBS data from plasmas in air. This is consistent with differences associated with plasma dynamics due to the pressure of surrounding gas.43,44 The lower pressure at Mars-like conditions – 7.5 mbar – as compared with that for the terrestrial environment –1013.25 mbar – allows plasmas to expand more and to be less densely populated. Thus, in these lowpressure cold plasmas, fewer inelastic collisions of the emitters with neighboring particles and atomic interactions occur.45 The resulting higher spectral resolution benefits a finer qualitative labeling of the spectral features and the use of small-sized spectrographs of high performance to disperse the signals, thereby being more advantageous to the limited scientific payload of a rover. By contrast, composition of surrounding gases has shown not to seed the LIBS signals with different spectral features along the wavelengths region scanned. This circumstance may be explained by parallel difficulties to ionize the gases constituent molecules due to the similarity between their ionization potential values: 15.59 eV for N2 and 12.07 for O2 in the case of air, and 13.77 eV for CO2.46 Regarding Raman outputs, no remarkable differences were found between information gathered under both the Earth- and Mars-like environments. As displayed, r values stand at nearly 1 ‒ exemption given for the mercury sulfate because of its lower SNR ‒ whereas RMSE scores were kept well below 0.02, that is, an order of magnitude below as compared than those reached for LIBS. Results are consistent with light scattering phenomenon. The divergence in Raman spectra is not expected to occur unless the action

Figure 3. Simultaneous LIBS-Raman spectral data gathered for several sulfates under typical atmospheric compositions and pressures on Earth (blue) and Mars (red). The corresponding similarity rates, computed as correlation coefficient (r) and root mean square error (RMSE), are also indicated.

n summary, this information about changes on spectral outcomes caused by the atmosphere may facilitate the design of direct transfer functions that allow using the robust categorization algorithms under distinct environments. Decision-Level Fusion Architecture for the Mineralogical Labeling of Martian Rocks. In some cases, the scrutiny of single spectral information may not to be sufficiently reliable and effective to label the identity of the material being examined. Figure 4 illustrates a pair of examples of some LIBS and Raman episodes. Figure 4A shows LIBS signals for calcite, nitrocalcite and gypsum. As seen, differences between emission spectra from plasmas of these three materials are barely detectable as all of them are dominated by emission lines of calcium. The remaining low-intensity signals, ascribed to impurities rather than to constituent elements like C, N, H, O and S, are no valid to any categorization approach. Figure 4B depicts Raman scattering response of Ca(NO3)2·4H2O and Na2CO3. As can be seen, the intense peaks attributed to symmetric stretch-


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ing mode of the nitrate and carbonate groups are featured at a frequency value of ≈ 1080 cm-1. The frequency for the peak of the Raman active mode associated to an anionic group may be influenced not only by the mass and ionic radius of its substituting cation but also by the degree of hydration. Thus, here, simply the combination of such circumstances leads to a matching between the peaks for these cation-anion pairs completely different. Consequently, differentiation between them is a difficult task, even using advanced chemometrics approaches.

format of the output data in the simultaneous acquisition of combined signals is a key factor for their further utilization. The simple use of a single detection system leads to the gathering of atomic emission and molecular scattering in a single spectrum. For this architecture of plasma emission mixed with scattered light in the same output signal, any overlapping between emission peaks and vibration modes may occur. Furthermore, the distinct time scales of both phenomena as well as the higher intensity of ionic and atomic emissions relative to Raman scattering suggest the separate detection as a more suitable choice to show highly resolved and identifiable peaks of both LIBS and Raman signals. Once data are acquired, to cope with the mineral phase categorization, available information can be combined in architectures at different fusion levels – data, features and decisions. Authors propose here a sequential decisionmaking strategy from the LIBS and Raman data as the most feasible option. This decision is justified by the potential heterogeneity of expected host rocks. The presence of impurities and veins at sub-millimeter and millimeter scales within the interrogated rock may adulterate the spectral information, mainly LIBS data. Based on this situation, more uncertainty would be added to any combined attribute built on the basis of fusion architectures at data and feature level. The identification algorithm does not preclude the combined evaluation of the Raman and LIBS data in the case they have been collected in the context of successive laser irradiation episodes. However, the correspondence between atomic and molecular data remains evidently questionable. Figure 5 depicts the flow chart for the categorization process of an unknown rock from its simultaneous LIBSRaman spectral data. The first step involves a scrutiny of the LIBS response. By using a moving-window algorithm, emission signals detected according to the 3s-criterion in the LIBS spectrum are element-assigned by a crosschecking with a spectral database. This analysis reports on the multielemental diversity of the rock. A ranking of elements, according to the manifestation percentage of their emission lines across the complete spectrum, is drawn. As a systematic starting point, the first-ranked element is hypothesized as the cationic component of the mineral phase. Subsequently, the algorithm evaluates the Raman spectrum of the unknown sample against the Raman spectra from all the oxy-anions sharing this cation that are stored in the database. The similarity rate between spectra is computed through the correlation coefficient (r). As a threshold, an r value above 0.7 dictates the identity of the mineral phase. By contrast, if no cross-checked Raman candidates meet this criterion, the algorithm starts a feedback loop. At this iterative step, the cationic counterpart is successively updated to the next-ranked elements, if applicable. In parallel, the Raman spectrum from the unknown is cross-checked with its new-related Raman spectra in the database. When the single-element option as cationic component is exhausted, the algorithm is programmed to consider double and triple combinations of elements. Finally,

A)

B)

Figure 4. A) Normalized LIBS spectra of several calcium salts, CaCO3 (blue solid line), Ca(NO3)2·4H2O (red dashed line), and CaSO4 (green dash-point line). B) Normalized Raman spectra of Ca(NO3)2·4H2O (blue solid line) and Na2CO3 (red dashed line). Spectra were simultaneously acquired with their corresponding spectroscopic counterpart ‒not shown‒ using acquisition parameters detailed in the body of the text.

In an effort to overcome the limitations of singlespectroscopy uses to mineral identification and to exploit the synergistic effect of complementary atomic and molecular information, several configurations of LIBS-Raman analytical techniques have been efficiently integrated into single instruments proposed for mineralogical and geochemical analyses already.29,30,32,33,47,48 Nevertheless, the



if none of these options succeeds the rock will remain unidentified.

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#7– and the three polymorphs of calcium carbonate – #2, #3, and #8. To confirm the applicability and validity of the designed decisions-based architecture, the algorithm was implemented to the identification of the concerned rocks. Results for these blind tests are reported in Table 1. Therein, the total of emission signals detected in the LIBS spectrum of the unknown sample and their elemental correspondence that establishes the cationic ranking are listed. The candidates from the database whose Raman signals have been cross-checked with Raman spectrum of the unknown, together with the similarity rate, are reported. Finally, the identity predicted by the algorithm and the disclosed true identity are also briefed. As inferred from Table 1, six – #2, #3, #5, #7, #8, and #10 – of the ten unknown rocks scheduled in the test were correctly identified applying our decisions-based algorithm. Identity discrepancies for samples #5 and #10 are resolved when considered this decisions sequence as compared when LIBS and Raman individual spectral data acts as single identifying element. Furthermore, the contribution of Raman cross-checking clarifies any uncertainty on identity arising from the single use of LIBS information. These results show the improvement of the algorithm developed here for identifying unknown rocks. The mislabeling of samples #1, #4, #6, and #9 cannot be connected directly to the running of the algorithm. The false positive in the identity prediction of NaAl(SO4)2·12H2O is justified by the frequency matching because of harmonized effects of cations and hydration.49 The Raman band assigned to the symmetric stretching mode of ‘free’ molecule SO42− centered at 983 cm−1 for the K2SO4 overlaps to the non-resolved doublet with wave numbers 974 cm−1 and 989 cm-1 for the SO42− groups in the soda alum ‒spectra not shown. A similar argument explains the non identification of #9, aware that the mineral dolomite was not included in the database. The position of Raman band assigned to the CO32− symmetric stretching mode is a function of the crystal structure of the carbonate mineral.50 Calcite shows a single band at 1088 cm−1. In contrast, the cation substitution of Ca for Mg in the dolomite mineral leads the principal wavenumber of the strongly active mode towards 1092 cm-1, as a result of the contribution of two independent discrete normal modes of vibration of the crystal structure having values of 1088 and 1098 cm-1. Finally, the poor matching for rocks #4 (r = 0.4957) and #6 (r = 0.4009) is attributable to the low SNR of their Raman spectra ‒ not shown. A broad background due to the grained quartz-enriched sandstone matrix of protoquartzite dominates the Raman spectrum and masks the small peak yielded from the CO32- group of calcite fragments. Despite these identity uncertainties, the proposed hierarchy of single decisions from the simultaneous LIBS and Raman spectroscopic information exploits the convergence of data to determine the identity of the rock.

Figure 5. Flowchart of strategic decision making to unravel the identity of an unknown rock from its LIBS-Raman spectral responses simultaneously acquired.

To contrast the bidirectional benefit of the fusion architecture of simultaneous LIBS-Raman data, Table S1 in supporting information summarizes statistics to the identification of ten natural rocks from different sources on the basis of single spectroscopic information. It only reports on three candidates from the database showing, in descending order, the largest similarity to the unknown. As can be checked, LIBS itself cannot provide exclusive information on the identity of materials. The cationic component of the mineral monopolizes the identification process Clear example is the only consideration of calcium salts as potential candidates. Furthermore, any decision-making towards an accurate identity by only considering optical emission spectroscopy was highly compromised due to the parallelism between the similarity rates. By contrast, the single use of Raman spectroscopy may dispel doubts about categorization due to its larger selectivity (conferred by the functional groups) as compared to LIBS. Notwithstanding, some minerals may become hardly differentiable each other solely from their Raman spectroscopy, since the frequencies for the most relevant vibration modes of their anions may incidentally coincide. On the basis of the outputs, in several cases ‒#2, #3, #4, #7, #8, and #9‒ both spectroscopies agreed to the same candidate as identity for the unknown. However, only satisfactory identities ‒r values higher than 0.7‒ were considered for gypsum and –


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Table 1. Statistics on identification of natural rocks using the architecture based on fusion of decisions made from their simultaneous LIBS-Raman spectral responses. from LIBS spectrum of the unknown Identified Ranking elements Ca (9) 56.25 % Mg (3) 18.75 % Al (2) 12.50 % Na (1) 6.25 % K (1) 6.25 %

upon Raman spectrum of the unknown Cross-checked Similarity compound rate (r) CaCO3 0.0169 CaSO4·2H2O 0.1305 Ca(NO3)2·4H2O 0.0017 0.0026 Al(NO3)3·9H2O 0.0023 Na2CO3 0.8224 K2SO4 0.0251 K2CO3 0.0125 KNO3

#1

Total emission lines 16

#2

28

Ca (23) Mg (4) Na (1)

84.1 % 14.3 % 3.6 %

CaCO3 CaSO4·2H2O Ca(NO3)2·4H2O

0.7466 0.0078 0.1123

CaCO3

CaCO3 (aragonite)

#3

18

Ca (18)

100 %


0.9902 0.0020 0.1181

CaCO3

CaCO3 (calcite)

#4

37

Ca (24) Si (5) Mg (4) Ba (2) Sr (1) Na (1)

64.9 % 13.5 % 10.8 % 5.4 % 2.7 % 2.7 %

CaCO3 CaSO4·2H2O Ca(NO3)2·4H2O BaSO4 Sr(NO3)2 Na2CO3

0.4957 0.0857 0.1354 0.0269 0.0012 0.1296

ǂ

Quartz Feldspar CaCO3

Unknown

Predicted identity K2SO4

True identity NaAl(SO4)2·12H2O (soda alum)

#5

35

Ca (21) Si (5) Fe (5) Mg (4)

60.0 % 14.3 % 14.3 % 11.4 %


0.9701 0.0082 0.1234

CaCO3

#6

34

Ca (23) Si (4) Fe (4) Mg (3)

67.6 % 11.8 % 11.8 % 8.8 %


0.4009 0.0053 0.0794

ǂ

(Protoquartzite: grained quartz-enriched sandstone containing calcite fragments) CaCO3 (calcarenite) Limestone composed predominantly by more than 50% of carbonate grains

Quartz Feldspar CaCO3 (Protoquartzite: grained quartz-enriched sandstone containing calcite fragments) CaSO4·2H2O (gypsum)

#7

56

Ca (30) Ti (12) Ba (3) Mn (3) Al (2) Cu (1) Na (1) ? (4)

53.6 % 21.4 % 5.4 % 5.4 % 3.6 % 1.8 % 1.8 % 7.0 %


0.0114 0.9816 0.0131

CaSO4·2H2O

#8

29

Ca (28) Na (1)

96.5 % 3.5 %


0.9807 0.0042 0.1085

CaCO3

CaCO3 (vaterite)

#9

35

Ca (29) Mg (4) Cs (1) Na (1)

82.9 % 11.4 % 2.85 % 2.85 %

CaCO3 CaSO4·2H2O Ca(NO3)2·4H2O Na2CO3

0.3183 0.0313 0.1255 0.1215

ǂ

CaMg(CO3)2 (dolomite)

#10

28

Ca (24) Fe (3) Na (1)

85.7 % 10.7 % 3.6 %


0.9805 0.0039 0.1206

CaCO3

CaCO3 (travertine)

ǂ None r score calculated by the algorithm satisfies the threshold (> 0.7) to reliably assign an identity to the unknown

? Emission lines for which the responsible emitter has not been identified



Page 10 of 12

minerals and carried out the experiments. J. -M. analyzed the data and wrote the paper, with the input from J.J. -L. All authors gave approval to the final version of the manuscript.

■

CONCLUSIONS In the quest to improve the operativity of laser-based devices for mineralogical exploration in future planetary missions, a fused LIBS-Raman sensor has been tested. The photon density gradient within a single laser-matter interaction event at the sample surface enables the simultaneous generation of both the optical emission and the Raman scattering from the same entity ‒ an equally-sized interrogated area. Doing so ensures a compositional relation to the resulting multielemental and molecular information. In this way, by following a hierarchy and cyclic framework of combined decisions from these spectral data a more accurate identification of the material is attained as compared to the performance from its single LIBS and Raman fingerprints. The uncertainty on identity of an unknown that arise from the multielemental spectral data may be clarified by its vibrational counterpart, and vice versa. The declaration on the identity of unknown rocks have been readily accomplished via the fused decisions and the cross-checking of spectral data with a short database containing spectra of the minerals of interest constructed and established in advance. The bidirectional benefit from this advanced combination of LIBS and Raman contributes to resolve conflicting situations on the identification and to enlarge its confidence. Further studies on the robustness and ruggedness of this dual spectroscopic sensor and on the consistency of the algorithm to deal with highly complex rocks are in progress. Research on the effects of the physical properties of rocks – morphology, grain size, compactness ... – on the spectral data gathered and on the performance and the effectiveness of the strategy in the identification of minerals is also being conducted.

ACKNOWLEDGMENTS The present research has been supported by Project CTQ2014-56058-P of the Ministerio de Economía y Competitividad, Secretaría de Estado de Investigación, Desarrollo e Innovación of Spain.

REFERENCES (1) Golombek, M. P. J. Geol. Res. 1997, 102, 3953–3965. (2) Golombek, M. P.; Cook, R. A.; Moore, H. J.; Parker, T. J. J. Geol. Res. 1997, 102, 3967–3988. (3) Rieder, R.; Wänke, H.; Economou, T.; Turkevich, A. J. Geol. Res. 1997, 102, 4027–4044. (4) Foley, C. N.; Economou, T.; Clayton, R. N. J. Geophys. Res. 2003, 108, 8096–8116. (5) Rieder, R.; Gellert, R.; Brückner, J.; Klingelhöfer, G.; Dreibus, G.; Yen, A.; Squyres, S. W. J. Geophys. Res. 2003, 108, 8066– 8078. (6) Brückner, J.; Dreibus, G.; Gellert, R.; Squyres, S. W.; Wänke, H.; Yen, A.; Zipfel, J. Mars Exploration Rovers: Chemical Composition by the APXS. In The Martian Surface Composition, Mineralogy and Physical Properties; Bell, J. Ed.; Cambridge University Press: Cambridge, 2008, 58–102. (7) Rieder, R.; Gellert, R.; Anderson, R. C.; Brückner, J.; Clark, B. C.; Dreibus, G.; Economou, T.; Klingelhöfer, G.; Lugmair, G. W.; Ming, D. W.; Squyres, S. W.; d'Uston, C.; Wänke, H.; Yen, A.; Zipfel, J. Science 2004, 306, 1746–1749. (8) Gellert, R.; Rieder, R.; Brückner, J.; Clark, B. C.; Dreibus, G.; Klingelhöfer, G.; Lugmair, G.; Ming, D. W.; Wänke, H.; Yen, A.; Zipfel, J.; Squyres, S. W. J. Geophys. Res. 2006, 111, E02S05. (9) Wiens, R. C.; Maurice, S.; Barraclough, B.; Saccoccio, M.; Barkley, W. C.; Bell III, J. F.; Bender, S.; Bernardin, J.; Blaney, D.; Blank, J.; Bouyé, M.; Bridges, N.; Bultman, N.; Caïs, P.; Clanton, R. C.; Clark, B.; Clegg, S.; Cousin, A.; Cremers, D.; Cros, A.; DeFlores, L.; Delapp, D.; Dingler, R.; D'Uston, C.; Darby Dyar, M.; Elliott, T.; Enemark, D.; Fabre, C.; Flores, M.; Forni, O.; Gasnault, O.; Hale, T.; Hays, C.; Herkenhoff, K.; Kan, E.; Kirkland, L.; Kouach, D.; Landis, D.; Langevin, Y.; Lanza, N.; LaRocca, F.; Lasue, J.; Latino, J.; Limonadi, D.; Lindensmith, C.; Little, C.; Mangold, N.; Manhes, G.; Mauchien, P.; McKay, C.; Miller, E.; Mooney, J.; Morris, R. V.; Morrison, L.; Nelson, T.; Newsom, H.; Ollila, A.; Ott, M.; Pares, L.; Perez, R.; Poitrasson, F.; Provost, C.; Reiter, J. W.; Roberts, T.; Romero, F.; Sautter, V.; Salazar, S.; Simmonds, J. J.; Stiglich, R.; Storms, S.; Striebig, N.; Thocaven, J. J.; Trujillo, T.; Ulibarri, M.; Vaniman, D.; Warner, N.; Waterbury, R.; Whitaker, R.; Witt, J.; Wong-Swanson, B. Space Sci. Rev. 2012, 170, 167–227. (10) Arvidson, R. E.; Bellutta, P.; Calef, F.; Fraeman, A. A.; Garvin, J. B.; Gasnault, O.; Grant, J. A.; Grotzinger, J. P.; Hamilton, V. E.; Heverly, M.; Iagnemma, K. A.; Johnson, J. R.; Lanza, N.; Le Mouélic, S.; Mangold, N.; Ming, D. W.; Mehta, M.; Morris, R. V.; Newsom, H. E.; Rennó, N.; Rubin, D.; Schieber, J.; Sletten, R.; Stein, N. T.; Thuillier, F.; Vasavada, A. R.; Vizcaino, J.; Wiens, R. C. J. Geol. Res. 2014, 119, 1322–1344. (11) Maurice, S.; Clegg, S. M.; Wiens, R. C.; Gasnault, O.; Rapin, W.; Forni, O.; Cousin, A.; Sautter, V.; Mangold, N.; Le Deit, L.; Nachon, M.; Anderson, R. B.; Lanza, N. L.; Fabre, C.; Payré, V.; Lasue, J.; Meslin, P. Y.; Léveillé, R. J.; Barraclough, B. L.; Beck, P.; Bender, S. C.; Berger, G.; Bridges, J. C.; Bridges, N. T.; Dromart, G.; Dyar, M. D.; Francis, R.; Frydenvang, J.; Gondet, B.; Ehlmann, B. L.; Herkenhoff, K. E.; Johnson, J. R.; Langevin, Y.; Madsen, M. B.; Melikechi, N.; Lacour, J. L.; Le Mouélic, S.; Lewin, E.; Newsom, H. E.; Ollila, A. M.; Pinet, P.; Schröder, S.; Sirven, J. B.; Tokar, R. L.; Toplis, M. J.; d'U-

ASSOCIATED CONTENT Supporting Information LIBS-Raman simultaneous spectra of several sulfates interrogated under typical atmospheric compositions and pressures on Earth and Mars, examples of single LIBS and Raman spectral information that is not sufficiently reliable and effective to label the identity of the laser-examined material, and statistics to the identification of ten natural rocks from different sources on the basis of single either LIBS or Raman spectroscopic information.

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected] (J. Javier Laserna) Tel.: +34 951 95 3007; fax: +34 952 13 2000.

Present Addresses † On leave from Benha University, Faculty of Engineering, Basic Science Department, El-Shaheed Farid Nada, 13511, Egypt.

Author Contributions J.J. –L. planned the research strategy. J. -M. designed and built the experimental setup, with the input from J.J. -L. All authors designed the experiments. M.M.-E. prepared the synthetic


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ston, C.; Vaniman, D. T.; Vasavada, A. R. J. Anal. At. Spectrom. 2016, 31, 863–889. (12) Griffith, W. P. Nature 1969, 224, 264–266. (13) McMillan, P. F. Annu. Rev. Earth Planet Sci. 1989, 17, 255– 279. (14) Smith, D. C.; Carabatos-Nédelec, C. Raman Spectroscopy Applied to Crystals: Phenomena and Principles, Concepts and Conventions. In Handbook of Raman Spectroscopy. Practical Spectroscopy; Lewis, I. R., Edwards, H. G. M., Eds.; Marcel Dekker, Inc.: New York, 2001, 349–422. (15) Nasdala, L.; Smith, D. C.; Kaindl, R.; Ziemann, M. A. Raman Spectroscopy: Analytical Perspectives in Mineralogical Research. In EMU Notes in Mineralogy: Spectroscopic Methods in Mineralogy; Beran, A., Libowitzky, E., Eds.; European Mineralogical Union: Jena, 2004, 281–343. (16) Wang, A.; Haskin, L. A.; Lane, A. L.; Wdowiak, T. J.; Squyres, S. W.; Wilson, R. J.; Hovland, L. E.; Manatt, K. S.; Raouf, N.; Smith, C. D. J. Geophys. Res. 2003, 108, E15005. (17) Courrèges-Lacoste, G. B.; Ahlers, B.; Rull Pérez, F. Spectrochim. Acta, Part A 2007, 68, 1023–1028. (18) Escudero-Sanz, I.; Ahlers, B.; Courrèges-Lacoste, G. B. Opt. Eng. 2008, 47, 033001. (19) Edwards, H. G. M.; Hutchinson, I. B.; Ingley, R.; Parnell, J.; Vítek, P.; Jehlička, J. Astrobiology 2013, 13, 543–549. (20) Wiens, R. C.; Maurice, S.; McCabe, K.; Cais, P.; Anderson, R. B.; Beyssac, O.; Bonal, L.; Clegg, S.; Deflores, L.; Dromart, G.; Fischer, W. W.; Forni, O.; Gasnault, O.; Grotzinger, J. P.; Johnson, J. R.; Martínez-Frías, J.; Mangold, N.; McLennan, S.; Montmessin, F.; Rull, F.; Sharma, S. K.; Sautter, V.; Lewin, E.; Cloutis, E.; Poulet, F.; Bernard, S.; McConnochie, T.; Lanza, N.; Newsom, H.; Ollila, A.; Leveille, R.; Le Mouelic, S.; Lasue, J.; Melikechi, N.; Meslin, P. Y.; Misra, A.; Grasset, O.; Angel, S. M.; Fouchet, T.; Beck, P.; Bridges, N.; Bousquet, B.; Fabre, C.; Pinet, P.; Benzerara, K.; Montagnac, G. 47th Lunar and Planetary Science Conference, 2016, 1322. (21) Wiens, R. C.; Sharma, S. K.; Thompson, J.; Misra, A.; Lucey, P. G. Spectrochim. Acta, Part A 2005, 61, 2324–2334. (22) Giakoumaki, A.; Osticioli, I.; Anglos, D. Appl. Phys. A 2006, 83, 537–541. (23) Bruder, R.; Detalle, V.; Coupry, C. J. Raman Spectrosc. 2007, 38, 909–915. (24) Clegg, S. M.; Wiens, R.; Misra, A. K.; Sharma, S. K.; Lambert, J.; Bender, S.; Newell, R.; Nowak-Lovato, K.; Smrekar, S.; Dyar, M. D.; Maurice, S. Appl. Spectrosc. 2014, 68, 925–936. (25) Moros, J.; Lorenzo, J. A.; Lucena, P.; Miguel Tobaria, L.; Laserna, J. J. Anal. Chem. 2010, 82, 1389–1400. (26) Moros, J.; Lorenzo, J. A.; Laserna, J. J. Anal. Bioanal. Chem. 2011, 400, 3353–3365. (27) Moros, J.; Laserna, J. J. Anal. Chem. 2011, 83, 6275–6285. (28) Moros, J.; Laserna, J. J. Talanta 2015, 134, 627–639. (29) Sharma, S. K.; Misra, A. K.; Lucey, P. G.; Wiens, R. C.; Clegg, S. M. Spectrochim. Acta, Part A 2007, 68, 1036–1045. (30) Sharma, S. K.; Misra, A. K.; Lucey, P. G.; Lentz, R. C. F. Spectrochim. Acta, Part A 2009, 73, 468–476. (31) Lin, Q.; Niu, G.; Wang, Q.; Yu, Q.; Duan, Y. Appl. Spectrosc. Rev. 2013, 48, 487–508. (32) Matroodi, F.; Tavassoli, S. H. Appl. Phys. B 2014, 117, 1081–1089. (33) Matroodi, F.; Tavassoli, S. H. Appl. Optics 2015, 54, 400– 407. (34) Gendrin, A.; Mangold, N.; Bibring, J. P.; Langevin, Y.; Gondet, B.; Poulet, F.; Bonello, G.; Quantin, C.; Mustard, J.; Arvidson, R.; LeMouélic, S. Science 2005, 11, 1584–1586. (35) Catling, D. C.; Wood, S. E.; Leovy, C.; Montgomery, D. R.; Greenberg, H. M.; Glein, C. R.; Moore, J. M. Icarus 2006, 181, 26– 51. (36) Niles, P. B.; Michalski, J. Nat. Geosci. 2009, 2, 215–220.

(37) Boynton, W. V.; Ming, D. W.; Kounaves, S. P.; Young, S. M. M.; Arvidson, R. E.; Hecht, M. H.; Hoffman, J.; Niles, P. B.; Hamara, D. K.; Quinn, R. C.; Smith, P. H.; Sutter, B.; Catling, D. C.; Morris, R. V. Science 2009, 325, 61–64. (38) Sutter, B.; Boynton, W. V.; Ming, D. W.; Niles, P. B.; Morris, R. V.; Golden, D. C.; Lauer, H. V.; Fellows, C.; Hamara, D. K.; Mertzman, S. A. Icarus 2012, 218, 290–296. (39) Stern, J. C.; Sutter, B.; Freissinet, C.; Navarro-González, R.; McKay, C. P.; Archer, Jr., P. D.; Buch, A.; Brunner, A. E.; Coll, P.; Eigenbrode, J. L.; Fairen, A. G.; Franz, H. B.; Glavin, D. P.; Kashyap, S.; McAdam, A. C.; Ming, D. W.; Steele, A.; Szopa, C.; Wray, J. J.; Martín-Torres, F. J.; Zorzano, M. P.; Conrad, P. G.; Mahaffy, P. R. the MSL Science Team, P. Natl. Acad. Sci. USA 2015, 112, 4245–4250. (40) Cremers, D. A.; Radziemski, L. J. Handbook of LaserInduced Breakdown Spectroscopy, 2nd ed.; John Wiley & Sons, Ltd.: Chichester, 2013. (41) Ferraro, J. R.; Nakamoto, K.; Brown, C. W. Introductory Raman Spectroscopy, 2nd ed.; Elsevier Science, Academic Press: California, 2003. (42) Siegman, A. E. Lasers, University Science Books: Sausalito, 1986. (43) Scott, J. R.; Effenberger Jr., A. J.; Hatch, J. J. Chapter 4. Influence of Atmospheric Pressure and Composition on LIBS. In LaserInduced Breakdown Spectroscopy; Musazzi, S., Perini, U., Eds.; Springer-Verlag: Berlin, 2014, 91–116. (44) Cousin, A.; Forni, A.; Maurice, S.; Gasnault, O.; Fabre, C.; Sautter, V.; Wiens, R. C.; Mazoyer, J. Spectrochim. Acta, Part B 2011, 66, 805–814. (45) Knight, A. K.; Scherbarth, N. L.; Cremers, D. A.; Ferris, M. J. Appl. Spectrosc. 2000, 54, 331–340. (46) Dikshit, V.; Yueh, F.-Y.; Singh, J. P.; McIntyre, D. L.; Jain, J. C.; Melikechi, N. Spectrochim. Acta, Part B 2012, 68, 65–70. (47) Choi, S. J.; Choi, J. J.; Yoh, J. J. Spectrochim. Acta, Part B 2016, 123, 1–5. (48) Clegg, S. M.; Wiens, R.; Misra, A. K.; Sharma, S. K.; Lambert, J.; Bender, S.; Newell, R.; Nowak-Lovato, K.; Smrekar, S.; Dyar, M. D.; Maurice, S. Appl. Spectrosc. 2014, 68, 925–936. (49) Mabrouk, K. B.; Kauffmann, T. H.; Aroui, H.; Fontana, M. D. J. Raman Spectrosc. 2013, 44, 1603–1608. (50) Sun, J.; Wu, Z.; Cheng, H.; Zhang, Z.; Frost, R. L. Spectrochim. Acta, Part A 2014, 117, 158–162.




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