Anal. Chem. 1997, 69, 4586-4591
Evolved Gas Analysis of Inorganic Materials Using Thermochromatography: Model Inorganic Salts and Palagonite Martian Soil Simulants Mihkel Koel,*,† Mihkel Kaljurand,‡ and C. H. Lochmu 1 ller§
Institute of Chemistry, Akadeemia tee 15, Tallinn, EE0026, Estonia, Institute of Chemistry, Tallinn Technical University, Ehitajate tee 5, Tallinn, EE0026, Estonia, and Paul M. Gross Chemical Laboratory, Duke University, Durham, North Carolina 27708
Evolved gas analyses are presented of several model inorganic salts and Mars soil simulating palagonite minerals. The measurements are performed with a low-volume, thermal furnace, interfaced to a capillary gas chromatograph (GC) via a computer-controlled, pneumatic sample inlet device using pseudorandom injection and correlation analysis. The system, a thermochromatograph (ThGC), permits temperature-resolved, high-speed GC analysis of the evolving components in the pyrolyzing sample’s headspace. The “information content” performance of this inherently simpler system is comparable to that of TG/ GC/MS and TG/GC/IR systems when similar materials are analyzed. An ideal evolved gas analysis (EGA) instrument would provide a time- or temperature-based record of the appearance and disappearance of all evolved compounds together with their identification. Various approaches, some now of historical interest alone, have been proposed for the detection and identification of evolved compounds over the last several decades. Most recently, thermogravimetry/mass spectrometry (TG/MS) and TG/infrared (TG/IR) instruments have become available.1,2 Although the current instruments can continuously monitor the evolved products with scan speeds of fractions of a second, their complete capability may only be required when the evolved gas composition is a truly complex mixture. Because of the limited number of evolved compounds, the interpretation of the chromatogram is straightforward and follows from the general understanding of the retention mechanisms of these compounds. Weight loss curves can be readily reconstructed from EGA curves (taking care that all evolved components are detected and detector responses to all components are known). Even then, better resolution, in terms of noncollinear information content revealed, might be achieved by a TG/GC/MS/IR system.3 On the other hand, many EGA applications, especially those for inorganic samples, have relatively simple and predictable evolved gas composition, and the justification for spectrometric identification tools is not clear. In addition to high cost per sample, these seemingly more sophisticated approaches add several technical problems: (a) †
Institute of Chemistry. Tallinn Technical University. § Duke University. (1) Earnest, C. M. Anal. Chem. 1984, 56, 1471A. (2) Young, W. J.; Quan, X. X.; Zheng, J. T. Thermochim. Acta 1987, 111, 325. (3) McClennen, W. H.; Buchanan, R. M.; Arnold, N. S.; Dworzanski, J. P.; Meuzelaar, H. L. C. Anal. Chem., 1993, 65, 2819. ‡
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relatively large furnace volume, (b) potential sample component loss in interconnecting lines, (c) some compromise version of automatic injection, and (d) unwanted selective detection (e.g., not all evolved components are IR active.) Furnace volume can degrade system performance. A TG cell must be flushed with inert gas at high flow rates to minimize the influence of the thermobalance volume on the thermal resolution and temperature-scale validity of the EGA curve obtained as a final product. In TGA, this purge gas is vented to the atmosphere as a convenience. In EGA measurements, this same gas acts as a diluent increasing the detection limits for evolved gas components and, as such, is a “necessary evil”. The required purge gas flow rate can be estimated as follows: If the furnace volume is V and the gas mass flow rate F, then the furnace is flushed during the time V/F. The analytical consequence is that an infinitely narrow sample band evolving from the heated zone will have a width equal to ∆t ) V/F when leaving the furnace. If the sample is heated with the rate b, then the finite volume of the furnace contributes to the possible thermal peak extra width with the amount of ∆Te ) bV/F. If the thermal peak has a minimum, fundamental width ∆T, then total thermal peak width, ∆Tt, can be estimated by summing second moments:
∆Tt2 ) ∆T2 + ∆Te2/12 Assuming that thermal peak width extra increase, δT, is small compared to the natural width, the latter formulas can be developed as: ∆Tt2 - ∆T2 ) (∆Tt - ∆T)(∆Tt + ∆T) ≈ δT2∆T. It can be shown that the relative increase in thermal peak width, δT/∆T, is equal to
1 bV δT ) ∆T 24 F∆T
2
( )
Taking the following typical values for the narrow EGA peaks of inorganic salts δT/∆T ) 10%, b ) 10 °C/min, F ) 30 cm3/min, and ∆T ) 10 °C, then V < 50 cm3. On the other hand, if the furnace volume is 150 cm3, the relative peak width increase is δT/ ∆T ) 100%. Furnace volumes of contemporary TG analyzers are frequently larger than 100-200 cm3, and thus, the furnace volume influence on the fundamentally narrow EGA peak shapes is significant when thermogravimeters are used as counterparts of EGA devices. Special precautions must be taken to avoid the condensation of tars and other high-boiling products in the line connecting the S0003-2700(97)00554-4 CCC: $14.00
© 1997 American Chemical Society
TG to the GC (or IR or MS). This need has been overlooked in some EGA system designs but can be met using heated, quartz capillary tubing. Automatic injection of the evolved gas onto the GC column or into a spectrometer must be achieved at relatively high temperatures as well. Most commercially available, mechanical sampling valves exhibit accelerated mean time to failure when maintained at the higher temperatures needed to avoid condensation and to match common GC column inlet operating conditions. An alternative is to have sampling done with a pneumatic sampling valve with no moving parts but at the expense of some further sample dilution. Such samplers operate on the principle of carefully balancing of the pressures in the sample stream and the carrier gas flow and were first described by Deans.4 The possible construction of such a sampler for EGA was described later by Arnold et al.5 Despite its many attractive features, use of GC as an EGA detector introduces at least two more problems. First is the detection of the evolved compounds in the case of samples that release only a small, total amount of gas during their heating (relative to their total mass), as is the case for many inorganic samples. Second is that separation of the evolved components on a GC column requires time. This latter consequence complicates “continuous” monitoring of the evolving gas composition and especially if the sample releases its gas over a narrow temperature range. There are clearly several options for the presentation of the final thermochromatogram for visualization by the analyst and final report readers. In EGA by GC, the result is set of chromatograms of the gases released at the corresponding (sampling) temperatures; i.e., a particular sample temperature can be associated with each chromatogram. If the temperature interval between two chromatograms is small and a set of consists of very many recorded chromatograms, it is straightforward to form an EGA response surface with axes: sample temperature at injection time vs chromatographic run time. Image processing methods display such 2D objects as either contour or mesh plots. Although such plots do not add much to the final presentation of the EGA resultssthe evolution rates of the released gas components as a function of sample temperaturesthey provide a convenient “picture” for the analyst of the thermal events occurring in sample. Mesh plots of EGA chromatograms are also a visual representation of the input data structures for chemometric processing. By analogy to other hyphenated analytical techniques and due to the axis names, this method is thermochromatography (ThGC) and corresponding 2D plots are thermo (gas) chromatograms. If there are several independent reactions occurring in the sample during the entire course of heating, then the corresponding thermochromatogram is “a linear combination of the thermochromatograms of individual processes”. The separation of total thermochromatograms into components that correspond to individual, independent processes is of obvious interest in thermal analysis. This can be accomplished using a chemometrical technique known as factor analysis. Factor analysis considers a thermochromatogram as a mathematical objectsa matrix or data structure. The analysis first finds the rank of this matrix and then its abstract factors and factor loadings by principal component analysis (PCA). (4) Deans, D. R. J. Chromatogr. 1987, 289, 43. (5) Arnold, N. S.; McClennen, W. H.; Meuzelaar, H. L. Anal. Chem. 1991, 63, 299.
Figure 1. Schematic of a thermochromatographic experiment.
The rank of the thermochromatogram matrix is equal to the number of independent thermal processes in the sample. A caveat is in order. The first action, PCA, is a straightforward mathematical procedure. The determination of the rank of the ThGC matrix relies completely on the insight of the analyst. This follows from the fact that in the case of independent thermal processes the data structure can be considered as a bilinear object.6 Once this analysis is complete, and using several chemically appropriate assumptions, the abstract factors and factor loadings can be transformed to “real” EGA curves for individual components evolved during the heating of sample. In this paper, we demonstrate that many of the common goals of EGA can be met by total computer control of the experiment using a small-volume reactor and a GC as a detector.7 In a modern sense, the GC in its entirety is the detector in this experiment providing both separation in space of the evolved components and some information about abundance on one information axis of the reulting matrix. The integral detector of the GC alone and the separation column of the experiment alone do not provide that information in isolation. Computerization of the sampling procedure permits the standard, equi-interval sampling to be replaced by more sophisticated sampling sequences. Carefully selected, these more efficient methods result both in the lowering of the detection limits by 1 order of magnitude and in continuous monitoring of the evolving gas composition. EXPERIMENTAL SECTION Furnace, Sampler, and Chromatograph. A schematic of the overall system is presented in Figure 1. The quartz tube furnace, which replaces the injection port of the chromatograph, has a volume of 4 mL and is where the sample is located. The furnace/reactor temperature is controlled by a separate, stand(6) Booksh, K. S., Kowalski, B. R. Anal. Chem. 1994, 66, 782A-791A. (7) Doi, Y.; Koda, T.; Adachi, M.; Wakamatsu, N.; Goto, T.; Kamemizu, H.; Moriwaki, Y.; Suwa, Y. J. Biomed. Mater. Res. 1995, 29, 1451.
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alone temperature programmer with operating range of 70-600 °C at heating rates of 1-25 deg/min. Aliquots of sample are introduced into a capillary column using a Deans-type, pneumatic sampling valve. A complete description of the sampler is given in earlier work.8 The chromatograph is a Carlo Erba 4200 GC with thermoconductivity detector and a porous layer column 0.53 mm i.d and 25 m in length [NSW-PLOT (HNU Nordion Ltd., Oy, Finland)]. Sampling Control and Data Acquisition. The sampling is controlled by an Apple II computer and locally made interface card. The detector signal is converted to digital form by a 23-bit analog to digital converter (Ecta Ltd.) received by the Apple II control computer and transferred to another PC for final data processing. The control software is capable of generating either of two modes of sampling sequence. In the first, sampling events occur at intervals equal to the separation time of the evolved components. This sampling mode is the equivalent of the common EGAGC experiment where chromatograms of evolved gases are found at certain, predetermined temperatures. The result of any given sampling event is delayed by some longer interval (total chromatogram runtime). Alternatively, sampling can be done according pseudorandom selected time intervals. This is accomplished by using a feedback shift-register algorithm random number generator.9 The mean interval between neighboring injections is short compared to total chromatogram run time. The detector signal resembles a random function, but it is transformed to a set of chromatograms by correlating the input sequence with the detector signal. This approach creates reduced detection limits while providing for near-continuous monitoring of the chromatographically separated, evolved gas components. Because the final data are obtained via the correlation procedure, the EGA approach using this method strongly resembles the Fourier and Hadamard transform techniques that are widely used in IR and NMR spectroscopy. The theory and applications of the pseudorandom sample introduction for continuous monitoring of time-varying concentration flows (known also as correlation chromatography) ar described elsewhere.10 The authors wrote most of the software used. Equipment control (sampling and data recording) software for the Apple II computer was written in compiled Basic and 6502 assembly language. Raw data are transmitted to a 486-type PC over an RS232 interface for further processing. Chromatogram preprocessing (baseline correction, spike removal, digital filtering, etc.) software were written in C. The decorrelation subroutines and thermochromatogram 2D representation subroutines are written in MATLAB (MathWorks, Natick, MA). PCA of thermochromatograms was done using MATLAB function (“svd”) which computes singular values of a matrix. The number of nonzero singular elements was taken as a matrix rank. Samples. These consisted of inorganic salts (Aldrich), palagonites from Hawaii volcanoes (provided by J. Orenberg from San Francisco State University), commercial silica xerogel for column liquid chromatography, and a commercial 5 Å pore size molecular sieve powder. All common laboratory practice rules for the handling and transfer of samples for EGA were followed. No extraordinary precautions were taken, and the samples were kept (8) Koel, M.; Urov, K. Oil Shale 1993, 10, 261-269. (9) Kaljurand, M.; Kullik, E. Computerised Multiple Input Chromatography; Ellis Horwood Ltd Chichester, 1989. (10) Koel, M.; Kaljurand, M. Crit. Rev. Anal. Chem., 1996, 26, 149.
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Figure 2. Chromatogram of thermal decomposition of calcium oxalate Sample temperature, 415 °C. (1) CO; (2) CO2; (3) ) H2O.
isolated from environments contaminated with organic and inorganic vapors, e.g., synthetic chemistry laboratories. RESULTS AND DISCUSSION The goal of the work reported was to test ThGC and to demonstrate its performance in the case where sudden and sharp evolution of gas-phase materials occurs. Previous work, already cited, dealt with polymerssorganic polymers and geopolymersswhose pyrolysis/thermolysis occurs for the most part over wider temperature ranges than is the case for evolving gas analysis of pure inorganic “standards”. As the work progressed, the opportunity to add palagonite samples was seized on because these are intermediate in their behavior to pure inorganic salts vs organic polymers or shale oil-bearing geopolymers. In the end, we studied pure salts of known thermal behavior, slightly more complex systems, and, finally, palagonite, montmorillonite, silica xerogel, and synthetic aluminosilicate “zeolite” molecular sieves. The hope was that the known thermal chemistry of some standards would aid in interpretation, but in fact, discoveries were made as well. Factor analysis methods were applied to confirm that these “spectra”/data structures were amenable to factor analysis techniques, as was the case for previous samples studied, and to determine the rank of each total ThGC overall in terms of independent evolution processes. Despite previous successes in using evolving factor analysis in complex samples like oil shale, the simplicity and well-known behavior of the simple salts would add to the credibility of the use of factor analytical methods in the analysis of ThGC experiments. Inorganic Salts. Calcium oxalate has been perhaps the most widely studied substance in thermal analysis because of its clearly separated stages of degradation with different gases evolving at distinct temperatures. The common scheme for describing the TGA-observed processes is
CaC2O4‚H2O w CaC2O4 + H2Ov, at temperatures 145-240 °C CaC2O4 w CaCO3 + COv, at temperatures 430-500 °C [and perhaps 2CO w CO2v + C] Of more than minor significance in terms of the difference between ThGC results and ordinary TGA is in the second step of degradation, where chromatography of the evolved products shows coevolution of CO2 with CO (Figure 2) in what appears to
Figure 4. Thermal decomposition of magnesium carbonate hydroxide hydrate. differential evolution curve.
Figure 3. Thermal decomposition of ammonium metavanadate: (a) differential evolution curve; (b) 2D ThGC surface.
be a disproportionation of CO into CO2 and C. Qualitative evidence that this may be the case is in the black color of the sample after the heating, suggesting carbon deposition. If this is true, then there are two reactions at the second temperature range: CaC2O4 w CaCO3 + COv and 2CO w C + CO2v. Similar reactions were described in the case of barium oxalate hemihydrate decomposition in a N2 atmosphere.11 In the case reported here, the fraction of CO participating in this secondary reaction is 30% of the total produced. Clearly, given the abundance of ammonium salts, ammonia can be a product of degradation in the thermolysis of such salts or salts that can lose ammonia as a chemical degradation product during heating. In the case of true ammonium salts, ammonia is evolved along with water as part of the total weight loss at the same temperature. If each evolution process is to be distinguished, there are distinct advantages in obtaining separate evolution curves. In this case, ThGC offers a simpler solution to the detection of simultaneous evolving gases than, for example, the approach used by Paulik et al., thermogas titrimetry (TGT).12 A good example is the discovery and quantitation of ammonia in the thermolysis of ammonium metavanadate. It could be expected that thermal degradation of ammonium metavanadate takes place according to the formula 2NH4VO3 w 2NH3v + H2Ov + V2O5. If so, the processes should give two peaks in the chromatograms over one temperature region. Interestingly, this assumption is not confirmed by experiment. The ThGC total evolving curves (Figure 3) show that there are three maxima to be followed. The first temperature region is 165-240 °C when water and ammonia (11) Paulik, J.; Paulik, F.; Erdey, L. Anal. Chim. Acta 1969, 44, 153. (12) Paulik, J.; Paulik, F.; Erdey, L. Microchim. Acta 1966, 97, 886-893.
both are evolving. This could be degradation according to the expected formula. In textbooks, the melting point of ammonium vanadate is given at 200 °C. Melting, apparently, is accompanied by degradation. The existence of two more, different temperature regions where gases evolve suggests that complex reactions are taking place in the melting salt. Only a fraction of the ammonium vanadate decomposes according to the straightforward equation presented above. This is supported by total weight loss during the experiment. Predicted loss was 22.2%, but the observed was 27%. In the second temperature region (280-350 °C), when some water and continued to evolve, a third substance began to evolve. The third temperature region (370-430 °C) is where only water and this third substance evolved. This unknown substance was identified chromatographically as oxygen by the use of standards and corrected retention time. This surprising result may be analogous to the decomposition of manganese dioxides which shows (a) water loss, (b) dehydroxylation, and finally (c) oxygen release.13 The next compound studied belongs to group of aquo-oxides with a complicated crystal structure and they commonly exhibit multi-stage decomposition.14 The curve (Figure 4) of total evolution for ThGC of magnesium carbonate hydroxide hydrate (4MgCO3‚Mg(OH)2‚5H2O) had three distinct steps: (1) a range 220-335 °C when mainly water and some CO2 are evolved; (2) a range 350-450 °C where CO2 and small part of the H2O are evolved; (3) a range 450-550 °C when only CO2 evolves. The thermal decomposition might be expected to consist of these steps: (1) dehydration; (2) dehydroxylation; (3) degradation of carbonate; all these processes proceeding consecutively with some overlap. ThGC permits one to follow the evolution of gaseous products independently; it was, therefore, possible to measure amounts of a given gas at distinct temperature intervals. This way it is found that 5 parts of water evolved at the first step and 1 part of water at the second step. This corresponds to the water of hydration (five molecules) and hydroxide (one group). The amounts of evolved CO2 in different stages suggested that there were distinct intermediates undergoing decomposition in each range. The intermediate formed has been demonstrated to be an amorphous one that finally crystallizes at 500 °C.13 The major phase of CO2 evolution occurs during steps 2 and 3. Apparently equal parts of (13) Giovanoli, R. Thermochim. Acta 1994, 234, 303. (14) Paulik, F. Special Trends in Thermal Analysis John Wiley & Sons: New York, 1995.
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Figure 5. (a) Thermochromatogram of evolved gases of palagonite calculated from detector signal by correlation methods.; (b) thermochromatogram of a palagonite sample reconstructed from the set of equi-interval single-injection chromatogram.
Figure 6. Evolution of water from four different samples: P, palogonite; M, Fe- montmorillonite; S, silica xerogel; Z, commercial zeolite (5 Å molecular sieve).
carbonate degrade at these steps. These data were interpreted by the following sequenced reactions in the sample:
4MgCO3‚Mg(OH)2‚5H2O w 4MgCO3‚Mg(OH)2 + 5H2Ov (220-335 °C) 4MgCO3‚Mg(OH)2 w 2MgCO3‚3MgO + H2Ov + 2CO2v (350-450 °C) 2MgCO3‚3MgO w 5MgO + 2CO2v
(450-550 °C)
Palagonites. Given the surprises found in the decomposition of what are essentially simple inorganic salts in terms of evolution of unsuspected species, the motivation to study materials that are principally inorganic but complex substances was driven further by the possibility of distinguishing related minerals and the opportunity provided by the gift of a well-characterized set of Mars soil simulants. One such simulant is to be found in the palagonite minerals and, specifically, the volcanic minerals of Hawaii. As the amount of volatile during the heating of the sample is known to be small (2-3 wt %), problems were anticipated with detection limits for ordinary thermal conductivity detection. 4590
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An example of the effect of pseudorandom sampling on reduction of detection limits and the possibility of using it to obtain continuous monitoring of the output is given by the comparison of the EGA results obtained first by sequence of equi-interval sampling and, second, by pseudorandom sampling of the same palagonite sample. In Figure 5a, the detected signal obtained from 1022 injections spaced pseudorandomly with mean interval 0.25 s is shown. Random function-like output with two evolution maxima was clearly evident. After decorrelation, the detector output transformed to a 2D surface with two ridges showing evolution for CO2 and H2O. For comparison, the same sample analyzed by equi-interval sampling was to be found to be that shown in Figure 5b. The evolving curves were estimated, and different samples were compared from thermochromatograms obtained by PRBS injection of the sample. The evolving curves are given in Figure 6 for four samples. The evolved gas analysis provides additional information over that which might be apparent by solid-state analysis alone. In work by Pan and co-workers,15 several nontronite and palagonite samples were analyzed by differential scanning calorimetry and simultaneous thermogravimetric analysis and dif-
Table 1. Determination of the Number of Independent Thermal Reactions in Inorganic Samples by PCA subjective determination of rank sample
no. of singular values (95% variation)
rank
% of variation
calcium oxalate ammonium metavanadate magnesium carbonate hydroxy hydrate palagonite 1
2 6 5 4
2 5 4 3
97 94 94 93
ferential thermal analysis, combined with mass spectrometric detection of evolved gases. The authors demonstrate that nontronite was distinguishable from palagonite by their respective responses to combined thermal and evolved gas analysis. Figure 6, a ThGC study of same type of samples, confirmed their results for evolved gas in the temperature region up to 600 °C very well. It is interesting to speculate to what extent the silica vs aluminosilicate character of the palagonite mineral dominated the EGA results. We compared palagonite, montmorillonite (a natural zeolite), a silica xerogel, and a synthetic aluminosilicate zeolite 5 Å molecular sieve in terms of water evolution after equilibration with ambient moisture for days. The results are shown in Figure 6. It can be seen that the palagonite and the natural zeolite responded remarkably alike. All the plots in the figure have been weight percent normalized. All samples showed the early loss of water to ∼200 °C that is well-known for silica xerogels and other siliceous materials. The synthetic zeolite had a large, broad, midrange water loss band and lost more water than the others. The natural zeolite lost water in that region but to a much smaller extent. Palagonite loses water but at a delayed/higher temperature. The synthetic zeolite likely had a much larger surface area for water sorption that the natural clay zeolite since it was synthesized to be a very porous material. Principal Component Analysis of Thermochromatograms. A well-known result in chemometrics is that experimental data matrices always have “full rank”; i.e., a matrix suggests as many processes as there are columns in the matrix. This arises from the fact that experimental data always contain random noise. A common way to proceed is to investigate the distribution or spectrum of matrix singular values and accept as many nonzero singular values as the analyst finds relevant. The singular value spectrum “amplitude” falls monotonically with the number of singular values determined. In favorable cases, the singular value falls suddenly at some number. The rest of the singular values (15) Heidbrink, J. L.; Li, J.; Pan, W.-P.; Gooding, J.; Aubuchon, S.; Foreman, J.; Lundgren, C. J. Thermochim. Acta 1996, 284, 241-251. (16) Malinowski, E. R.; Howery, D. G. Factor Analysis in Chemistry John Wiley & Sons: New York, 1980.
then become a very small, approximately constant, value. The singular value number at which the drop-off occurs can be taken as the matrix rank. When such a sudden drop is not evident, then more sophisticated methods of rank determination can be implemented.16 Another straightforward way is to take into account as many singular values as needed to account for, for example, 95% of the total sum of all singular values. These singular values are expected to contain or account for 95% of the total variation of the data. The result of the PCA of the inorganic sample data is presented in Table 1. The rank was determined by both a formal 95% sum criterion and a subjective examination of the singular value spectrum. Thus, it follows from the present work that the ThGC method does preserve the narrow band shapes of rapid evolution events and reveals unsuspected processes and the results to date are amenable to factor analysis. This would suggest that the previous successes with the use of evolving factor analysis in the pyrolysis of rubbers and geopolymers may also be applied to inorganic EGA results where the possibility of components with overlapping elution times might be present. With proper standard calibration methods, most of the common qualitative and quantitative EGA aims can be realized and the goals can be broadened as well. Speciation is possible with much simpler equipment than other approaches, and the cost per sample could be reduced by 1 order of magnitude for the same information content in the resulting data. ACKNOWLEDGMENT M.K. and C.H.L. acknowledge partial support of this work in the form of a NATO linkage grant via the Environmental Security Programme Grant ENVIR. CRG961291. Prof. J. Orenberg of San Francisco State University is acknowledged with thanks for providing the palagonite samples. Received for review May 30, 1997. Accepted September 3, 1997.X AC970554T X
Abstract published in Advance ACS Abstracts, October 15, 1997.
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