XANES-based quantification of carbon functional group concentrations

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XANES-based quantification of carbon functional group concentrations Corentin Le Guillou, Sylvain Bernard, Francisco De la pena, and Yann Le Brech Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b00689 • Publication Date (Web): 08 Jun 2018 Downloaded from http://pubs.acs.org on June 9, 2018

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Analytical Chemistry

XANESXANES-BASED QUANTIFICATION OF CARBON FUNCTIONAL GROUP CONCENTRATIONS Corentin Le Guillou 1*, Sylvain Bernard 2, Francisco De la Pena 1 and Yann Le Brech 3 1*

Unité Matériaux et Transformations, UMET, UMR-CNRS 8207, Université de Lille, France., Corresponding author : [email protected] 2 Muséum National d'Histoire Naturelle, Sorbonne Université, CNRS UMR 7590, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Paris, France 3

Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, UMR 7274, Nancy, France

ABSTRACT: X-ray absorption spectroscopy in the soft X-ray range is used in many research fields to identify the nature of functional groups in organic compounds and carbon materials. However, the concentrations of these functional groups has so far remained difficult to quantify. Using X-ray absorption near edge structure (XANES) spectra of reference materials (polymers and compounds of known molecular composition), we established a correlation between measured optical densities and functional groups concentration. This methodology relies on an alternative method for normalization to the total amount of carbon and for deconvolution of the spectra. It allows precisely quantifying the N/C atomic ratio (σ1 = 0.02 at.) as well as the concentration of [aromatic + olefinic] groups (σ1 = 3.7 at. %), [ketone + phenol + nitrile] groups (σ1 = 2.2 at. %), [aliphatic] groups (σ1 = 11.2 at. %) and [carboxylic] groups (σ1 = 7.4 at. %). We validated this quantification by comparing with nuclear magnetic resonance data obtained on pyrolized lignin samples. We also provide an easy-to-use python program automating XANES-based quantification of carbon functional group concentrations.

INTRODUCTION X-ray absorption spectroscopy is a widely used technique in numerous research fields to document, in particular, the carbon speciation from near edge X-ray absorption fine structure (NEXAFS) or X-ray absorption near edge structure (XANES) spectra. This includes the study of meteorites or cometary particles1-6, biogeochemistry7-14, petroleum geology15-17, paleontology18-28, cultural heritage29-32, soil sciences3339 , environmental sciences40-45, and polymer sciences46-60. A number of soft X-ray beamlines are available in an increasing number of synchrotrons around the world, providing increasing opportunities for expanding the number of analyses (beamlines at ALS, CLS, SLS, PLS, NSLS II, BESSY II, DESY, SOLEIL, Elettra, Diamond, Australian synchrotron). Beamlines include “bulk” instruments relying on large beam (100 µm to mm) as well as highly spatially resolved instruments such as scanning transmission X-ray microscopy, which allows hyperspectral imaging with a 20 nm pixel size61-71. It has now become possible to image frozen hydrated specimens at temperatures as low as 100 K using cryo-STXM as well as to perform 3D STXM experiments by combining STXM with angle-scan tomography72-73. At the carbon K-edge, XANES allows identifying the presence of functional groups (such as aromatic, aliphatic or carboxylic groups), and qualitatively comparing the relative abundances of given functional groups between different samples. For instance, XANES data allow documenting the aromatization of organic compounds during thermal matura-

tion12,27,74. However, in contrast to what can be achieved using nuclear magnetic resonance (NMR) spectroscopy on large amounts of materials36,75-80, truly quantitative estimations of functional group concentration from XANES data collected on sub-micrometric particles has up to now remained out of reach, notably because of the complexity of the physical absorption processes. Theoretical ab initio calculations have aimed at simulating absorption spectra, but while the main features could be identified, calculations fail reproducing experimental data, notably because of core-hole effects, secondary absorptions, multiple scattering, and convolution with the monochromator, lenses and detectors of the instrument81-85. It thus cannot be used for direct quantification purposes. We report a new analytical method to quantitatively assess the molecular composition of carbon materials, i.e. a method to estimate functional group concentrations. This method requires normalizing the data to the total amount of carbon probed by the X-ray beam and deconvolving them using Gaussian functions. We calibrated the method using reference compounds covering a large range of composition from highly aromatic-rich to highly aliphatic-rich materials containing variable amounts of Oxygen and/or Nitrogen. We validated the method using samples for which NMR data were available (complex macromolecular compounds). We also provide an easy-to-use python program automating these data processings to be easily used by all research groups working on various kind of materials.

METHODS

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Selection and preparation of materials The reference compounds and polymers used for the calibration include polymers and organic compounds obtained from Aldrich © as well as published spectra of polymers51 and of amino-acids58. Altogether, it includes: alanine, chitin, ethylene propylene rubber, high density polyethylene, histidine, lysine, phenylalanine, poly-ethylene succinate, poly-lactic acid, poly-methyl metacrylate, poly-vinyl alcohol, poly-vinyl pyridine, poly-methyl styrene, polybutadiene, poly-butylene

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terephthalate, polycarbonate, poly-ether-ether ketone, polyethylene naphtalate, poly-ethylene terephthalate, polyisobutylene, poly-isoprene, polypropylene, polystyrene, polyvinyl pyrrolidone, tyrosine, lignin, tryptophane, nebulotron, type I and type III kerogens, and a synthetic epoxide resin. The lignin was pyrolized in an inert atmosphere for several hours, at temperature up to 350°C80. Polymers were ultramicrotomed at a thickness of 70 and 90 nm and sections were deposited on TEM grids.

Fig. 1: C-XANES spectra of the reference compounds and polymers used to calibrate the method. Left: Spectra collected at CLS for the present study. Right: Original figure using data from Dhez et al.51 available at: http://www.physics.ncsu.edu/stxm/polymerNEXAFS.html.

Organic compounds were finely powdered and deposited on silicon nitride membranes. The pyrolized samples were embedded, pressed in a KBr matrix and ultra-microtomed. XANES spectroscopy Synchrotron-based scanning transmission X-ray microscopy (STXM) allows imaging at the 20 nm scale and collecting X-ray absorption near edge structure (XANES) spectra with a spectral resolution of 0.1 eV. STXM-XANES analyses were performed using the 10ID-1 SM beamline65 at the Canadian Light Source (CLS). It uses soft X-rays (130-2500 eV) generated with an elliptically polarized undulator inserted in the 2.9 GeV synchrotron storage ring (250-150 mA). The microscope chamber is evacuated to 100 mTorr after sample insertion and filled with He. A 100 nm thick titanium filter is used to remove second order light when working at the C and N Kedges. Measurements are performed using the low energy

grating and a circularly polarized light. Energy calibration is accomplished using the well-resolved 3p Rydberg peak at 294.96 eV of gaseous CO2. XANES spectra were obtained by collecting image stacks, i.e., by rastering the beam. Stacks were collected with a dwell time of one millisecond per pixel to avoid irradiation damage. In some cases, the beam was defocused to further reduce potential beam damage. Extraction of the XANES spectra have been done using the aXis2000 software86. For speciation measurements, spectra were collected with a spectral resolution of 0.1 eV over the carbon XANES region (283-293 eV) and of 1 eV up to 390 eV (Fig. 1). For N/C measurements, spectra were collected with a spectral resolution of 1 eV over the entire absorption range (i.e. from 250 and 450 eV).

NORMALIZATION, DECONVOLUTION, DECONVOLUTION, CALIBRATION CALIBRATION Normalization


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Quantifying functional group concentrations from XANES data requires to normalize the spectra to the total amount of carbon probed by the beam, which depends on the thickness and density of the sample. This is generally performed by dividing spectra by the value of the optical density at a given energy in the post-edge region15,47 or by dividing the spectrum by the value of the linear function used to fit a given portion the post-edge region (typically from 320 to 340 eV) extrapolated at the ionization threshold of carbon (typically 291.5 eV)21,87. Yet, the appropriate physical way is to fit the carbon pre-edge (270-280eV) and post-edge (370-390 eV) regions with the f2 component of the carbon scattering factor58. This requires to collect spectra up to an energy at which the EXAFS oscillations are no longer present, i.e. up to 390 eV for carbon, which is time consuming and potentially dam-

aging for the samples. We propose an alternative method, sometimes used for electron energy loss spectroscopy88. We used nine different samples of known N/C values to validate this method. To make sure that no beam damage occurred, we first adapted the method described in Alleon et al.58 and fitted the spectra with the sum of two power laws and the f2 components of the scattering factors over the pre-edge and post-edge regions (270-282 eV; 355-395 eV; 430-450 eV; Fig. 2a). The obtained N/C values are very close to the expected one (Fig. 2d,e). The alternative method consist of integrating the spectra (after subtraction of a power law background) from the preedge regions up to the mean ionization energies (e.g. 282291.5 eV for Carbon and 395-406.5 eV for Nitrogen - Fig. 2b,c). N/C values can then be obtained by

Figure 2: Estimations of N/C values using two methods of normalization. a) Example of an absorption cross section fitting: following Alleon et al.58, we fitted power laws together with the f2 components of Carbon and Nitrogen. The figure is original. b and c) Example of an areas integration. d and e) Estimated N/C values vs. true N/C values. Both methods offer a precision of 0.02 at. %.

weighting the integrated areas by the absorption factors of carbon and nitrogen (i.e. ~3.67 for C and ~3.81 for N, i.e. N/C = (AN/AC)*(3.67/3.81) with AC and AN the integrated areas). Plotted against the reference N/C, these values define a line with a slope close to 1 (R2 = 0.993 - Fig. 2d,e). We thus propose to normalize all spectra by simply dividing the spectra by the measured C or N abundance, after subtraction of a power law fitting the pre-edge region. Since spectra only need to be collected up to 291.5 eV at the C K-edge (or up to 406.5 eV at the N K-edge), this normalization method reduces the acquisition time by a factor of 2, thereby limiting potential beam damage. Altogether, this normalization method will allow robust comparison of spectra and quantification of functional group concentrations. Deconvolution We established a deconvolution method at the C-K edge which can be applied to carbonaceous materials with a large range of molecular composition. Different functional group

absorb at different energies47,51,60,89, the intensity of the absorption at these energies being directly related to their concentration (Beer-Lambert’s law). However, it is not straightforward to directly relate absorption intensity and concentration since the exact values of the oscillator strengths are not known83,90, especially for complex macromolecules such as kerogens or soils39,74,91. Because many absorption bands may overlap, reproducible deconvolution of spectra must be performed. Literature examples of deconvolution have been designed for specific cases and can only be applied to a given range of molecular compositions15,16,21,33,40,41,60,92,93,94. We referred to the literature to set 21 Gaussian functions at fixed energies between 284.1 and 291.5 eV. In order to limit the degrees of liberty for the fit, we chose to fix a half-width at half maximum (HWHM) of 0.2 eV for all Gaussians so that even the narrowest peaks could be properly fitted. Above 291.5 eV, the absorption is no longer due to relatively sharp 1s→π* electronic transitions, but rather to highly delocalized excited states, sometimes referred to as 1s→σ* virtual state transi-



tions, and for the overlapping contribution of Feshbach resonances95. It is not established down to which energy these effects are contributing. In order to take them into account, some authors used an arctangent function associated with Gaussians15,16,74. We used Gaussians with increasing widths up to 305 eV rather than an arctangent function, because the tails of these Gaussians extending down to lower energies improve the regression coefficient of the calibration curves. The main “large Gaussian” contributing below 291.5 eV is placed at 292.1 eV and has a HWHM of 1.3 eV. After background subtraction and normalization to the total abundance of carbon, we run a least square fitting of the sum of all the Gaussians between 282 and 305 eV (Fig. 3).

Fig. 3: Example of a spectrum deconvolution of phenylalanine displaying a sharp aromatic peak distinct from the aliphatic region. All Gaussians share the same band positions and width, only their heights are fitted.

Calibration We established correlations between the height of some of the Gaussians functions and the concentration of four sets of functional groups: aromatic-olefinic, ketone-phenol-nitrile, aliphatic and carboxylic-ester-acetal groups (Fig. 3, 4). The concentration of aromatic-olefinic groups can be estimated with a good precision (determined here as the least square standard deviation of the regression line, σ1 = 3.7 at. %) from the sum of the heights of the five Gaussians centered at 284.1, 284.4, 284.7, 285 and 285.4 eV (Fig. 4; reported values are divided by the number of Gaussians). The slope of the regres-

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sion is 0.0015 and the intercept is close to zero (0.0052). The maximum of absorption of the 1sπ* resonance depends on the environment of the C=C bond itself. It is found here between 284.9 and 285.4 eV. In the 286-287 eV energy range, the absorption signal corresponds to the superimposition of the contributions of ketones (R-C=O), phenols (Aro-OH) and nitriles (R-C≡N). The sum of the height of the Gaussians centered at 286.2, 286.5 and 286.8 eV constitutes the best prox y for their concentration (σ1 = 2.2 at. % - Fig. 4). The slope of the regression is 0.0034 and the intercept is 0.01 is also close to zero. The 1sσ*electronic transition of the aliphatic groups (-CH-, -CH2- and -CH3) are responsible for an absorption in the 287.3-288 eV energy range. Here, the best proxy of the aliphatic concentration is the sum of the height of the Gaussians centered at 287.5 and 287.8 eV. The averaged standard deviation (σ1) is 11.2 at. % but it is actually higher for aliphatic content below 50 at. % C and lower for higher contents (Fig. 4). The regression slope is 0.0013 and the intercept (0.036) is not zero. There is always an absorption in this energy range, even in the absence of aliphatic groups, but it apparently does not dependent on the molecular composition. This absorption could be due to secondary absorptions of unsaturated carbons which can induce multiple bond states93 or interlayer states96. It has also been suggested that the absorption intensity could be a function of the number of H atoms attached to each carbon, i.e., that -CH3 groups should induce a larger absorption than -CH2 and -CH groups (e.g., Cody et al.2). We tested correlations using aliphatic concentration weighted by the number of H atoms, but the quality of the regressions were lower (R2 = 0.83 instead of 0.87 using the 287.5 and 287.8 eV). The influence of the number of H atoms attached to each carbon appears to be minor with respect to the accuracy of the method. The height of the Gaussian centered at 288.55 eV appears to constitute a good proxy of the concentration of carboxylic-ester-acetal groups (O=C-O) (σ1 = 7.5 at. % - Fig. 4). The slope of the regression is 0.011. As for aliphatics, the intercept is positive (0.09). The linearity of the relationships confirms previous measurements and assumptions97,98 and allows the quantification of these functional groups to be performed. The linear correlations ensure that the optical densities probed by the different sets of Gaussians functions are mostly a function of the concentration and vary little with the surrounding environment of the carbon atoms. It also enables to tackle the issue of different groups absorbing at similar energies. If a bulk sample has a N/C ratio of 0.1 for instance, it implies that it cannot contain more than 10% nitrile and therefore allows to narrow down the concentration of ketones and phenols accordingly. Of note, the procedure of quantification proposed here cannot be applied to anisotropic materials, for which the absorption signal will also contain structural (orientation) information as is the case for instance for polycyclic aromatic compounds, graphitic materials and carbonates99. Graphitic materials display an exciton at 291.5 eV which reflects the electron delocalization over several aromatic units95. This exciton appears for crystallite size larger than 5-10 nm74 which is the threshold above which this quantification could be biased.

VALIDATION AND APPLICATIONS APPLICATIONS

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Validation for macromolecular carbons: comparison with NMR data Kerogens in sedimentary rocks91 or organics found in meteorites are highly complex molecules100 which XANES spectra show a rather continuous absorption in contrast to the sharp peaks measured in polymers3,4,6,16. To validate the quantification procedure proposed here and its applicability to complex materials such as those found in natural settings, we compared our quantification results with NMR results obtained on lignin samples (e.g., Le Brech et al.80).

Fig 4: Correlations between the Gaussian heights and the concentrations of the functional groups in the reference materials. The standard deviations of these correlations correspond to the precision achievable for the quantification.



They quantified the chemical composition of lignin extracted from wood and pyrolyzed at temperatures up to 350 °C. Here, we used STXM to collect XANES spectra of the same samples of fresh lignin and lignin pyrolized at 300 and 350 °C. The estimations of functional group concentrations based on XANES data are consistent with NMR results (Fig. 5). The concentration of aliphatics appears slightly overestimated by XANES - but remains within 2σ error. This demonstrates that the calibration defined on polymers can be applied to macromolecular carbons as well.

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carbons6. The soil organic matter is poor in aromatic-olefinic (10 at. %) and ketone-phenol-nitrile groups (5 at. %) but rich in aliphatic and carboxylic-ester-acetal groups (34 and 11 at. %). Richer in aromatic-olefinic groups (28 at. %), the organic matter composing the ancient microfossil also displays a higher aliphatic group concentration (45 at. %). The chondritic macromolecular carbon appears much richer in aromaticolefinic (45 at. %) and ketone-phenol-nitrile groups (18 at. %) while it exhibits lower concentrations of aliphatic and carboxylic-ester-acetal groups (28 and 8 at. %), in agreement with NMR data101,102. These examples illustrate the type of studies that will benefit from the quantification approach as it will allow to reconstruct the absolute molecular concentration of unknown material for a wide range of compositions and origins.

Fig. 6: Normalized spectra of organic compounds from a soil, an ancient rock and a chondrite6,27,39 (original figure replotted from published spectra). The calibration proposed here allows quantifying their functional group concentrations.

A SOFTWARE FOR AUTOMATED DATA PROCESSING

Figure 5: XANES spectra of pyrolized lignin samples previously analyzed by 13C CP/MAS NMR80. XANES and NMR methods yield consistent results.

Potential applications: soils, ancient rocks and chondrites Recent studies relied on STXM-XANES data to investigate the chemical evolution of natural organic compounds during processes such as hydrothermal alteration, diagenesis or biodegradation. Yet, the interpretation of XANES data remained qualitative. We applied our method to three kinds of samples (Fig. 6). The soil sample is part of a long term bare fallow experiment and contains organic matter that has persisted over almost 100 years39. The ancient rock is a Gunflint chert containing 1.88 Ga organic microfossils27 and the chondrite is a 4.6 Ga meteorite containing abiotic macromolecular organic

In order to provide a tool that would allow different research groups to compare their results in a consistent manner and because the described procedure for data process can be timeconsuming for large datasets, we created an easy-to-use, open source python program with a graphical user interface to automatically: 1) normalize all spectra at once, 2) determine the N/C values, 3) deconvolve the normalized spectra, 4) quantify the concentration of the functional groups, 5) provide fitting details and errors (minimum least square), 6) provide plots and .txt files of normalized of C- and N-XANES , 7) Generate an Excel file containing all outputs, for an easy access to the data. Measurements must be made across a sufficiently large range of energy and with enough spectral resolution in the near-edge regions to ensure reproducibility. At the carbon K-edge, the spectra must be acquired from 270 eV up to 305 eV (from 370 to 406.5 eV for N-XANES spectra). For accurate deconvolution, the spectral resolution must be at least 0.1 eV between 282.5 and 292 eV for the C-K edge and between 399 and 407 eV for the N-K edge. To perform the cross section fitting (fits of the f2), spectra must be acquired up to 395 eV (450 eV for Nitrogen). This program with documentation is available here: https://pypi.org/project/quantorxs/.


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ACKNOWLEDGMENTS: STXM-based XAS data were acquired at beamline10ID-1 at the CLS, which is supported by the NSERC, the CIHR, the NRC, and the University of Saskatchewan. Special thanks go to Jian Wang and Yingshen Lu for their expert support of the STXM at the CLS. Antoine Lucas is warmly thanked for initiating the python programming part of this project. Anne-Marie Blanchenet is gratefully acknowledge for preparing the ultra-microtome sections of the polymers. This Research was funded through a grant of the Programme National de Planetologie (CNRS - INSU) to Corentin Le Guillou.

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