Article pubs.acs.org/EF
Real-Time Specific Surface Area Measurement via Laser-Induced Breakdown Spectroscopy Kathryn E. Washburn,*,† Justin E. Birdwell,§ and James E. Howard‡ †
Ingrain, Inc., 3733 Westheimer Road, Houston, Texas 77027, United States U.S. Geological Survey, Central Energy Resources Science Center, Denver, Colorado 80225, United States ‡ ConocoPhillips, 118 Geoscience Building, Bartlesville, Oklahoma 74004, United States §
ABSTRACT: From healthcare to cosmetics to environmental science, the specific surface area (SSA) of micro- and mesoporous materials or products can greatly affect their chemical and physical properties. SSA results are also widely used to examine source rocks in conventional and unconventional petroleum resource plays. Despite its importance, current methods to measure SSA are often cumbersome, time-consuming, or require cryogenic consumables (e.g., liquid nitrogen). These methods are not amenable to high-throughput environments, have stringent sample preparation requirements, and are not practical for use in the field. We present a new application of laser-induced breakdown spectroscopy for rapid measurement of SSA. This study evaluates geological samples, specifically organic-rich oil shales, but the approach is expected to be applicable to many other types of materials. The method uses optical emission spectroscopy to examine laser-generated plasma and quantify the amount of argon adsorbed to a sample during an inert gas purge. The technique can accommodate a wide range of sample sizes and geometries and has the potential for field use. These advantages for SSA measurement combined with the simultaneous acquisition of composition information make this a promising new approach for characterizing geologic samples and other materials. spectrum to obtain elemental information on a sample.13 A high-power laser ablates a small amount of material generating a high-temperature plasma. Light at characteristic elemental wavelengths is emitted as the plasma cools. Generally, LIBS is considered to be a nondestructive method as it consumes only nanograms to micrograms of material and is often used in industrial processes to provide quality control on sample composition.14 In this study, we have evaluated LIBS to determine if it can be applied to shales and other source rocks to provide a rapid measurement of surface area. During LIBS measurements, it is common to perform an atmospheric purge. The reason for this is twofold: (1) the purge helps aid in the quantification of hydrogen, carbon, oxygen, and nitrogen elements in the sample that are also present in the atmosphere; and (2) some gases, such as argon, serve to intensify the LIBS signal, improving signal-to-noise through reduction of self-absorption.15 During the atmospheric purge, argon becomes adsorbed to the material surface, as it is during BET measurements. As the surface is ablated, the adsorbed argon is transformed into plasma. The larger the surface area, the more argon will be detected. The signal contribution from argon in the gas phase is negligible, as the density in the purge gas is much lower than that of the adsorbed phase on the sample surface and LIBS is more efficient at volatizing solid and solid-bound material than gaseous material.16 While this method can potentially be performed with other gases, argon is an excellent probe molecule for this type of characterization. It is not an element commonly found in
1. INTRODUCTION Specific surface area (SSA) affects various chemical and physical processes for a wide range of materials.1−3 Quantifying material surface area is important for evaluation, simulation, and quality assurance processes in many different fields. The rate of chemical reaction is often controlled by catalyst surface area.4 Surface area also influences nutrient fortification5 and sensory properties6 in foods and affects how quickly pharmaceuticals are released into the body.1 Surface area is also an important property of petroleum resource rocks, particularly shales and mudstones.7 The standard approach for measuring surface area is the Brunauer−Emmett−Teller (BET) gas adsorption method using cryogenic nitrogen.8 BET measurements are made by introducing a pure gas into a dried, evacuated sample chamber in a stepwise fashion and monitoring the amount of gas adsorbed to the material’s surface using pressure measurements to calculate the difference between the volume of gas introduced and the volume of gas remaining in the chamber dead space. Typically, nitrogen (N2) is used for these measurements,9 but other gases such as carbon dioxide (CO2) or argon (Ar) may also be used. While the method is generally considered to be quite accurate, BET is slow, taking on the order of 4 to 5 h to acquire adsorption and desorption isotherms. Gas adsorption methods like BET are also limited in that they cannot be applied to very small samples, on large sample subsections, or on very large samples. Other, less commonly used techniques to determine SSA include the solution absorption (p-nitrophenol),10 dynamic vapor sorption,11 and scanning electron microscopy12 methods. Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic method that utilizes optical emission within the ultraviolet to near-infrared ranges of the electromagnetic © XXXX American Chemical Society
Received: October 17, 2016 Revised: December 16, 2016 Published: December 19, 2016 A
DOI: 10.1021/acs.energyfuels.6b02698 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 1. Sample Information sample
processing
age
formation
source
country
state/province/city
Alum Anvil Points Mine (APM-1) APM-2 APM-3 APM-4 Blackstone Condor Garden Gulch Member (GGM-1) GGM-2 GGM-3 GGM-4 GGM-5 GGM-6 Ghareb Glen Davis Horse Draw Mine (HDM-1) HDM-2 HDM-3 Irati Joadja Julia Creek Kukersite Park Canyon Mahogany (PCM-1) PCM-2 PCM-3 PCM-4 Phosphoria Pumpherston Rundle Stuart Temi Timahdit Unocal Mine (UM-1) UM-2
None None ISSa (360 °C, 6 h) ISS (360 °C, 24 h) ISS (360 °C, 72 h) None None None ISS (360 °C, 24 h) ISS (360 °C, 72 h) ISS (360 °C, 120 h) ISS (360 °C, 268 h) HPb (360 °C, 72 h) None None None ISS (360 °C, 72 h) FAc (500 °C, 1 h) None None None None None ISS (360 °C, 72 h) HP (360 °C, 72 h) FA (500 °C, 1 h) None None None None None None None HP (360 °C, 72 h)
Cambrian Eocene Eocene Eocene Eocene Jurassic Cretaceous Eocene Eocene Eocene Eocene Eocene Eocene Late Cretaceous Permian Eocene Eocene Eocene Permian Permian Cretaceous Ordovician Eocene Eocene Eocene Eocene Permian Carboniferous Tertiary Tertiary Permian Late Cretaceous Eocene Eocene
Alum Shale Green River Green River Green River Green River Kimmeridge Hillsborough Basin Green River Green River Green River Green River Green River Green River Ghareb Sydney Basin Green River Green River Green River Irati Sydney Basin Toolebuc Baltic Oil Shale Basin Green River Green River Green River Green River Phosphoria Pumpherston Rundle Rundle Sydney Basin Timahdit Green River Green River
Surface mine Surface mine Surface mine Surface mine Surface mine Outcrop Outcrop Core cuttings Core cuttings Core cuttings Core cuttings Core cuttings Core cuttings Surface mine Underground mine Surface mine Surface mine Surface mine Surface mine Underground mine Outcrop Surface mine Outcrop Outcrop Outcrop Outcrop Outcrop Outcrop Outcrop Core Surface mine Surface mine Surface mine Surface mine
Sweden USA USA USA USA UK Australia USA USA USA USA USA USA Israel Australia USA USA USA Brazil Australia Australia Estonia USA USA USA USA USA UK Australia Australia Australia Morocco USA USA
Vastmanlands Colorado Colorado Colorado Colorado England Queensland Colorado Colorado Colorado Colorado Colorado Colorado Hadarom New South Wales Colorado Colorado Colorado Parana New South Wales Queensland Narva Utah Utah Utah Utah Montana Scotland Queensland Queensland New South Wales Centre-South Colorado Colorado
a
In situ simulator.18,19 bHydrous pyrolysis.19 cFischer assay.19
significant quantities within the porous materials themselves such that there are no concerns in determining how much signal comes from the adsorbed gas and how much signal comes from the matrix. A challenge in using LIBS is that this method suffers from matrix effects;17 the signal intensities of elemental peaks are dependent, not only on the quantity of the element present, but also on the other elements present in the sample. This can make basic linear analysis of LIBS data challenging and frequently necessitates the use of multivariate analysis to overcome matrix effects.18
Total organic carbon (TOC) content of the samples was measured using a C744 Series analyzer (LECO Corp., St. Joseph, MI) following the manufacturer’s instructions after removal of carbonate minerals via acid digestion with 6 M hydrochloric acid. Mineralogy was determined using X-ray diffraction (XRD). An internal standard (20 wt % corundum) was mixed with the sample and a PANalytical X’Pert Pro MPD X-ray diffractometer (Westborough, Massachusetts) equipped with a Cu X-ray tube and a θ/θ goniometer, and an X’celerator solid-state “strip” detector was used to collect diffractograms. The diffractograms were interpreted to obtain mineral weight percentages by the Rietveld method using the Jade Software package (Materials Data Inc., Livermore, California). Low-pressure gas adsorption measurements were acquired with N2 as the adsorbate using an ASAP 2020 instrument (Micromeritics Instruments, Norcross, GA). The samples were crushed and sieved to a particle size of 30−90 μm and dried in a vacuum oven at 100 °C for at least 12 h. The sample de-gas protocol involved raising the temperature in several steps to 70 °C and holding for 6 h under a vacuum of 0.5 mm Hg (6.6 × 10−4 atm). Several samples continued to release an oily film on the sample tube walls after multiple de-gas steps; this was resolved by further drying in the vacuum oven prior to measurement. A conventional N2 adsorption and desorption
2. MATERIALS AND METHODS The application of LIBS for SSA measurement was evaluated using nanoporous geological materials. Testing was performed on 34 oil shale and source rock samples from numerous locations around the world; these samples were obtained from the collection housed in the U.S. Geological Survey’s Energy Geochemistry Laboratory in Denver, Colorado. The sample compositions vary widely, and information on their respective origin, organic richness, and mineralogy is found in Tables 1 and 2. Some samples have been subjected to pyrolysis methods, including anhydrous batch pyrolysis,19,20 hydrous pyrolysis,20 and Fischer assay.20 B
DOI: 10.1021/acs.energyfuels.6b02698 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 2. Sample Mineralogy (wt %, Organic-Free Basis) and Total Organic Carbon (TOC, wt %) Content
a
sample
total carbonate
total clay
quartz + feldspar
iron sulfides
phosphates
amorphous
TOC
Alum Anvil Points Mine (APM-1) APM-2 APM-3 APM-4 Blackstone Condor Garden Gulch Member (GGM-1) GGM-2 GGM-3 GGM-4 GGM-5 GGM-6 Ghareb Glen Davis Horse Draw Mine (HDM-1) HDM-2 HDM-3 Irati Joadja Julia Creek Kukersite Park Canyon Mahoganyn (PCM-1) PCM-2 PCM-3 PCM-4 Phosphoria Pumpherston Rundle Stuart Temi Timahdit Unocal Mine (UM-1) UM-2
0 29 27 27 26 4 2 15 NDa 17 ND ND 7 58 0 7 1 1 2 0 61 22 22 25 26 34 0 4 10 4 0 14 ND ND
35 4 8 8 8 24 31 30 ND 26 ND ND 26 4 3 2 1 2 31 29 12 6 3 8 21 7 25 57 54 47 28 56 ND ND
40 28 34 32 35 7 35 24 ND 27 ND ND 31 3 29 59 76 81 43 3 10 10 19 18 20 29 18 17 15 15 8 9 ND ND
10 0 0 1 1 2 1 2 ND 1 ND ND 2 0 0 2 0 2 7 0 1 0 0 1 1 1 0 2 1 1 0 2 ND ND
0 0 0 0 0 0 0 0 ND 0 ND ND 0 7 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 ND ND
15 39 30 31 29 64 31 28 ND 30 ND ND 33 27 68 30 22 14 17 68 15 61 56 48 32 29 32 21 20 32 65 19 ND ND
12.7 19.3 13.1 13.0 11.0 43.7 20.3 10.0 6.6 7.1 5.4 5.3 4.9 15.5 58.7 9.9 5.4 3.5 10.6 62.2 14.2 41.3 29.7 20.5 16.0 10.3 20.9 16.6 11.2 19.4 53.2 9.6 14.1 4.9
Not determined.
curve was collected at −196 °C. The protocol for N2 adsorption measurements included additional very low pressure steps (P/P0 < 0.005) intended to capture details of the smaller pores in the samples. Surface area was calculated with the multipoint BET model that uses the slope of the adsorption curve between P/P0 values of 0.05 and 0.30. Measured SSA values for the samples are listed in Table 3. LIBS measurements were performed using a ChemReveal Analyzer from TSI (Minneapolis, MN). The pulverized material was pelletized into 13 mm discs prior to analysis. Samples were not dried further before measurement. A single laser pulse of 36 mJ and 8 ns duration was applied to six different locations on the pellet surface with a laser spot-size of 400 μm. These six spectra were averaged together to produce a single spectrum for each sample. Measurements were taken under an argon purge flowing at 10 L per minute at standard temperature and pressure. While the use of low temperature or elevated pressure may improve the sensitivity of the technique through increased adsorption,21 surface adsorption of argon under normal atmospheric conditions appears to be sufficient for this application. The total measurement time was approximately 30 s. The data were analyzed by two different approaches in the R programming language (Free Software Foundation Inc., Boston, MA) with a partial least-squares
(PLS) regression22 using the “pls” R package.23 First, the entire LIBS spectrum was used for analysis. Second, prediction was performed using only the argon peaks in the 810 to 812 nm region of the LIBS spectrum. The log10 of the SSA values was used for analysis due to the measured surface areas spanning several orders of magnitude. Cross-validation was performed with the “Leave-One-Out” method to avoid overfitting. The number of principal components used for prediction for the full-spectrum and Ar-only models were 10 and 7, respectively. Use of additional components neither improved nor diminished the predicted results appreciably.
3. RESULTS AND DISCUSSION The cross-validated results show that this approach, combining LIBS with Ar purging modeled by PLS regression, provides good predictive capability for SSA (Figure 1). Values generated by the Ar-only and full-spectrum models are shown in Table 3. Correlation between measured and predicted values using the full-spectrum model yielded a coefficient of determination (R2) value of 0.84. The Ar-only model shows a modest improvement in prediction based on a slightly better correlation (R2 = 0.88). This improvement from focusing on argon alone is not surprising, as the presence of extraneous data during multivariate analysis can lead to overfitting through incidental C
DOI: 10.1021/acs.energyfuels.6b02698 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 3. Measured Specific Surface Area (SSA, m2/g) Values Determined Using the Brunauer-Emmett-Teller (BET) Method and Predicted SSA Values from Laser-Induced Breakdown Spectroscopy (LIBS) Measurements (Includes Ar-Only and Full-Spectrum Models) sample Alum Anvil Points Mine (APM-1) APM-2 APM-3 APM-4 Blackstone Condor Garden Gulch Member (GGM-1) GGM-2 GGM-3 GGM-4 GGM-5 GGM-6 Ghareb Glen Davis Horse Draw Mine (HDM-1) HDM-2 HDM-3 Irati Joadja Julia Creek Kukersite Park Canyon Mahogany (PCM-1) PCM-2 PCM-3 PCM-4 Phosphoria Pumpherston Rundle Stuart Temi Timahdit Unocal Mine (UM-1) UM-2
SSA-BET measured
SSA-LIBS Ar-only
SSA-LIBS fullspectrum
26.154 2.755
34.220 2.478
10.086 4.607
0.684 0.907 1.217 10.937 22.122 5.222
0.887 0.863 1.231 11.684 14.440 5.198
1.100 1.205 0.970 10.124 23.434 8.590
2.17 3.814 4.391 6.621 12.543 10.229 7.194 3.608
2.573 2.776 6.913 4.113 16.092 11.534 8.375 7.084
4.541 3.136 4.649 6.103 6.855 16.199 5.595 3.435
1.616 3.612 12.075 5.595 6.805 4.692 3.414
2.005 3.422 15.560 2.821 4.505 4.371 2.804
2.186 4.889 15.255 3.786 2.546 4.428 1.706
0.461 1.164 2.267 7.765 14.792 20.585 16.249 17.086 22.756 3.39 7.681
1.210 1.235 2.778 12.005 13.112 24.785 12.886 8.546 22.807 3.089 4.016
0.420 1.294 1.361 12.812 14.180 20.320 17.420 10.334 14.592 3.690 5.491
correlations that are not generally relevant, reducing predicative capability.24 The LIBS method to derive SSA appears to be quite robust given the variability in sample origin and composition. The root-mean-square errors of prediction (RMSEP) for the Ar-only and full-spectrum models are 1.4 m2/g and 1.5 m2/g, respectively. While this error is greater than the value of some BET SSA measurements, it is common that with a larger sample set, the error of prediction will converge to that of the reference method. 25 Furthermore, a more homogeneous sample set (e.g., more similar composition, narrower SSA range of interest) is also anticipated to reduce the error of measurement. A drawback of this particular methodology compared to BET is that it is unable to provide information on the pore sizes in the material. As expected, LIBS was unable to predict the pore-size distributions obtained from BET, as these do not appear to have an effect on the LIBS spectra. To better understand how sample constituents relate to SSA, the regression coefficients of the full-spectrum PLS model were examined. The regression coefficients show which portions of the spectra are positively correlated, inversely correlated, or unrelated to a property of interest. These coefficients are shown in Figure 2. In addition to the 810 and 812 nm argon peaks used in the argon-only model, positive correlation to SSA was observed with argon peaks occurring at 796, 800, 801, and 920 nm in the full-spectrum model. Weak positive correlations were associated with aluminum peaks (308.2 nm, 396 nm) and silicon (288 nm). This was expected as aluminosilicate minerals, such as clays, tend to have high surface area.26 There was also positive SSA correlation with hydrogen (656 nm) and oxygen (777 nm), which again suggests a correlation with clay hydroxyl groups. Interestingly, the numerous lines in the 250 to 350 nm range associated with the presence of iron were also positively correlated with SSA. We attribute this to the high concentration of argon in the plasma, limiting iron self-absorption and leading to stronger intensities for its elemental peaks. No trend in the samples themselves is observed between SSA and iron content. An inverse correlation is observed for the strong peaks of sodium (589 nm, 820 nm), magnesium (389 nm, 512 nm), and lithium (610 nm, 670 nm), as well as with a number of their weaker peaks. These elements tend not to show trends with SSA, suggesting their use in analysis is related to compensating
Figure 1. Laser-induced breakdown spectroscopy prediction of specific surface area (SSA) compared to Brunauer−Emmett−Teller (BET) measurements using the (a) Full-spectrum model (R2 = 0.84), and (b) Ar-only model (R2 = 0.88). Solid line represents 1:1 correspondence. D
DOI: 10.1021/acs.energyfuels.6b02698 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels
Figure 2. Regression coefficients for the full-spectrum partial least-squares regression model for prediction of specific surface area to laser-induced breakdown spectroscopy measurements (blue peaks represent positive and red peaks represent negative coefficients). Selected elemental peaks are labeled.
names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
for matrix effects. Elements such as calcium (370.3 nm, 393.4 nm) appear to have no significant effect.
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4. CONCLUSIONS Future applications of this method are potentially wide ranging. Statistical analysis on samples may be improved by the ability to run orders of magnitude larger sample sets in a given period of time. LIBS equipment can be integrated with conveyer belts for online quality control of surface area, something not possible with other methods. Another advantage of LIBS is portability. Hand-held LIBS equipment with argon-purge capability has recently become commercially available.27 This would enable surface-area measurements in the field and on samples that are not practical to analyze in the lab.28 While we used pulverized material to be consistent with the BET measurements, crushing of the sample is not necessary for LIBS. This combined with the small spot-size can facilitate precision measurement on multiconstituent samples where an individual component may not be easily separated for BET measurement, such as in finely laminated mudstone samples. In summary, the minimal sample preparation, speed, and flexibility of LIBS make it a promising tool for surface-area quantification and related quality control efforts.
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REFERENCES
(1) Vallet-Regi, M.; Balas, F.; Arcos, D. Angew. Chem., Int. Ed. 2007, 46, 7548. (2) Mihranyan, A.; Ferraz, N.; Strømme, M. Prog. Mater. Sci. 2012, 57, 875. (3) Chiou, C. T.; Lee, J.-F.; Boyd, S. A. Environ. Sci. Technol. 1990, 24, 1164. (4) Froment, G. F.; Bischoff, K. B. Chemical Reactor Analysis and Design; John Wiley & Sons: New York, 1979. (5) Dickmann, R. S.; Strasburg, G. M.; Romsos, D. R.; Wilson, L. A.; Lai, G. H.; Huang, H. Nutrients 2016, 8, 129. (6) Veith, S. R.; Perren, M.; Pratsinis, S. E. J. Colloid Interface Sci. 2005, 283, 495. (7) Kuila, U.; Prasad, M. Geophys. Prospect. 2013, 61, 341−362. (8) Brunauer, S.; Emmett, P. H.; Teller, E. J. J. Am. Chem. Soc. 1938, 60, 309. (9) Yukselen, Y.; Kaya, A. J. Geotech. Geoenviron. Eng. 2006, 132, 931. (10) Giles, C. H.; MacEwan, T. H.; Nakhwa, S. N.; Smith, D. J. Chem. Soc. 1960, 3973. (11) Arthur, E.; Tuller, M.; Møldrup, P.; de Jonge, L. W. Vadose Zone J. 2014, 13, 1. (12) Domine, F.; Cabanes, A.; Taillandier, A.; Legagneux, L. Environ. Sci. Technol. 2001, 35, 771. (13) Cremers, D. A.; Knight, A. K. Laser-Induced Breakdown Spectroscopy, in Encyclopedia of Analytical Chemistry; John Wiley & Sons: New York, 2006. (14) Barrette, L.; Turmel, S. Spectrochim. Acta, Part B 2001, 56, 715. (15) Kuzuya, M.; Mikami, O. Jpn. J. Appl. Phys. 1990, 29, 1568. (16) Hahn, D. W.; Omenetto, N. Appl. Spectrosc. 2012, 66, 347. (17) Eppler, A. S.; Cremers, D. A.; Hickmott, D. D.; Ferris, M. J.; Koskelo, A. C. Appl. Spectrosc. 1996, 50, 1175. (18) Laville, S.; Sabsabi, M.; Doucet, F. R. Spectrochim. Acta, Part B 2007, 62, 1557. (19) Birdwell, J. E.; Ruble, T. E.; Laughrey, C. D.; Roper, D. R.; Walker, G. Differentiating organic carbon residues in spent oil shale, in Proceedings of the 30th Oil Shale Symposium; Colorado School of Mines, Golden, CO, 2011. (20) Jin, J. M.; Kim, S.; Birdwell, J. E. Energy Fuels 2012, 26, 1054. (21) Talu, O.; Myers, A. L. Colloids Surf., A 2001, 187−188, 83. (22) Martens, H.; Naes, T. Multivariate Calibration; John Wiley & Sons: Chichester, 1992. (23) Mevik, B. H.; Wehrens, R. The pls Package: Principal component and partial least squares regression in R. J. Stat. Soft. 2007, 18, 1−24. (24) Anderssen, E.; Dyrstad, K.; Westad, F.; Martens, H. Chemom. Intell. Lab. Syst. 2006, 84, 69−74.
AUTHOR INFORMATION
Corresponding Author
*E-mail: Kathryn.washburn@nofima.no. Tel.: +47 77 62 90 32. ORCID
Kathryn E. Washburn: 0000-0002-2934-1437 Justin E. Birdwell: 0000-0001-8263-1452 Present Address #
Norwegian Institute of Food, Fisheries and Aquaculture (NOFIMA), P.O. Box 6122, N-9291 Tromsø, Norway Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank Elizabeth Krukowski for assistance with BET measurements and William Betterton for performing the XRD analyses. Endre Anderssen, Steve Buckley, and Robert Kleinberg provided helpful discussion on early drafts of this manuscript. Adam Boehlke, Julie Herrick, Dave Ferderer, and Janet Slate provided useful comments and edits as part of the USGS internal review process. Any use of trade, product or firm E
DOI: 10.1021/acs.energyfuels.6b02698 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels (25) Martens, H. A.; Dardenne, P. Chemom. Intell. Lab. Syst. 1998, 44, 99. (26) Saidian, M.; Godinez, L. J.; Prasad, M. Effect of Clay and Organic Matter on Nitrogen Adsorption Specific Surface Area and Cation Exchange Capacity in Shales (Mudrocks), in SPWLA 56th Annual Logging Symposium; Society of Petrophysicists and Well-Log Analysts, 2015. (27) Day, D.; Connors, B.; Jennings, M.; Egan, J.; Derman, K.; Soucy, P.; Moller, S.; Sackett, D. A full featured handheld LIBS analyzer with early results for defense and security, in Next-Generation Spectroscopic Technologies VIII; Proceedings of the SPIE Volume 9482, 2015. (28) Harmon, R. S.; DeLucia, F. C.; McManus, C. E.; McMillan, N. J.; Jenkins, T. F.; Walsh, M. E.; Miziolek, A. Appl. Geochem. 2006, 21, 730.
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DOI: 10.1021/acs.energyfuels.6b02698 Energy Fuels XXXX, XXX, XXX−XXX
