Watching Kinetic Studies as Chemical Maps Using Open-Source

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Watching kinetic studies as chemical maps using open-source software Marine Cotte, Tiphaine Fabris, Giovanni Agostini, Debora Motta Meira, Laurence De Viguerie, and Vicente Armando Sole Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b04819 • Publication Date (Web): 24 May 2016 Downloaded from http://pubs.acs.org on May 25, 2016

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Analytical Chemistry

Watching kinetic studies as chemical maps using open-source software Marine Cotte1,2, Tiphaine Fabris1, Giovanni Agostini1, Debora Motta Meira 1, Laurence De Viguerie2, Vicente Armando Solé1* 1 European Synchrotron Radiation Facility (ESRF), 71 avenue des martyrs 38000 Grenoble, France. 2 Sorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 8220, Laboratoire d’archéologie moléculaire et structurale (LAMS), 4 place Jussieu 75005 Paris, France * corresponding author: V. A. Solé, tel : 33 (0)4 76 88 25 84, fax : 33 (0)4 76 88 27 85, e-mail : [email protected] Abstract A non-proprietary software package, “PyMca”, primarily developed for X-ray fluorescence analysis offers an easy-to-use interface for calculating maps, by integrating intensity (of X-ray fluorescence, but as well of any spectral data) over Regions Of Interest (ROI), by performing per pixel operations or by applying multivariate analysis. Here we show that, while initially developed to analyze hyperspectral two (spatial) dimensional maps, this tool can be beneficial as well to anyone interested in measuring spectral variations, over one or two dimensions, these dimensions being time, temperature, etc. Different possibilities offered by the software (pre-processing, simultaneous analysis of replicas, of different conditions, ROI calculation, multivariate analysis, determination of reaction rate constant and of Arrhenius plot) are illustrated with two examples. The first example is the Fourier transform infrared spectroscopy (FTIR) follow-up of the saponification of oil by lead compounds. The disappearance of reagent (oil) and formation of products (lead carboxylates and glycerol) can be easily followed and quantified. The second example is a combined extended X-ray absorption fine structure (EXAFS), diffuse reflectance infrared Fourier transform spectroscopy (DRIFT) and mass spectroscopy (MS) analysis of RhAl2O3 catalyst under NO reduction by CO in the presence of O2. It is possible to appreciate in a single shot Rh particles’ structure and surface changes and gas release and adsorption in the reaction conditions. Keywords PyMca, free software, Regions of Interest, Kinetics, Fourier transform infrared spectroscopy (FTIR), saponification, X-ray absorption spectroscopy (XAS), catalysts, Batch analysis, multimodal analysis

Introduction Developing state-of-the-art instruments is a daily activity at synchrotron radiation (SR) facilities. In parallel to the improvements of hardware (faster and larger detectors, more efficient and more focusing optics, brighter sources, etc), efforts are also dedicated to develop software packages. SR facilities are no longer exclusive clubs where only experts can go, and go back to use the

ACS Paragon Plus Environment


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instruments. Now, new users are regularly welcome, from various fields (environmental sciences, geosciences, archaeometry, paleontology, medicine…). Thus, considering the concomitant increase of complexity of techniques and instruments and the increase in the range of users and scientific fields, offering easy-to-use software is a must. PyMca is a good example of such tools. It is a Python application initially developed in 2004 to offer the possibility to fit X-ray fluorescence (XRF) spectra, in interactive mode, and in batch fitting mode 1 . While it was initially devoted to SR-users, it soon became a widespread software package for XRF fitting in general, and is now regularly used to analyze XRF data obtained with laboratory or portable equipments, in particular in the field of cultural heritage. One important advance was the implementation of models for X-ray tubes as sources, as an alternative to the monochromatic SR beam, initially targeted. A famous example of application is the non-invasive study of the “sfumato technique” developed by Leonardo Da Vinci to paint his most famous portraits (Mona Lisa, Saint John the Baptist, Saint Anne, the Virgin and the Child, La Belle Ferronniere, Bacchus), exhibited in the Louvre museum 2,3. In parallel to the development of fitting tools, it appeared essential to offer an easy tool for the visualization and simple analysis of 2D µXRF mappings. This was the purpose of the tool “ROI imaging” (ROI for Regions Of Interest). Again, considering the success of this tool for µXRF maps, updates were added to offer users the possibility to visualize and analyze a much broader range of multi-spectral data: Fourier transform infrared (FTIR) maps, X-ray diffraction maps (XRD, providing that 1D diffraction patterns are integrated from 2D patterns), particle induced X-ray emission maps, Raman maps, X-ray absorption spectroscopy (XAS) maps. To achieve this, multiple data formats are supported (OMNICTM .MAP, JCAMP-DX 4, CSV, TIFF, HDF5 5, EDF, SPEC file, ...) This versatility is a strong advantage at beamlines such as ID21, ESRF, which offer on a single beamline four of these techniques (µXRF, µFTIR, µXAS and µXRD) 6. Once users are trained for one technique, they can easily handle data from other techniques, without the need to learn a new, specific tool. Here, we would like to underline the interest of applying PyMca/ROI imaging to multi-dimensional (non-spatial) and multi-modal maps. FTIR kinetic follow-up of oil saponification by lead oxide The first example considered is a set of FTIR kinetic studies acquired and published previously7. The context is the time-resolved follow-up of oil saponification by different lead compounds, under different conditions (with or without water, at different temperatures). This reaction was common in the synthesis of lead-based pastes, used for cosmetics and medical purposes, since the Antiquity until the beginning of the XXth Century. This reaction was also very common in the preparation of oil paint media, where lead driers are added to oil while heating to produce a drying oil of regular usage. In both cases, lead compounds (often litharge, PbO) provoke the oil saponification and the formation of lead carboxylates. The main differences between the two domains, medicine and paintings, is the type of oil: non-drying (typically olive oil) in pharmacy, versus drying oil (typically linseed or walnut oil) in paint and the proportion lead oxide/oil (around 1/2 in pharmacy versus around 1/3 down to 1/20 in painting, to be mixed with colours). FTIR is perfectly adapted to monitor such reactions since it probes simultaneously both reagents and products, without any sample preparation (analyses were performed in Attenuated Total Reflectance mode, FTIR spectra were saved here as .CSV). Fig. 1 shows spectra obtained in the case


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of the most representative recipe: olive oil mixed with PbO and water in the mass proportion 2-1-2, and heated at 100°C over 4.75h (conditions and experimental set-up similar to the ones published in 7 ). Spectra show a clear decrease of the ester C=O stretching band at 1743 cm-1, and the progressive appearance of the carboxylate C=O asymmetric and symmetric stretching bands at 1516 and 1401 cm-1, respectively.

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Figure 1: FTIR spectra acquired during the reaction of PbO on triolein, with water, at 100°°C. Spectra are offset for a better readability.

The saponification can be followed by measuring the integrated intensity of the different characteristic bands. During our initial analyses, this calculation was done using the proprietary OMNICTM software (Thermo ScientificTM), in a tedious way (spectrum per spectrum). Such calculations can be done with much less efforts using PyMca. All the following discussion is illustrated in a dedicated on-line tutorial (see kinetics tutorial at http://pymca.sourceforge.net/documentation.html). By opening the set of spectra as a 1D map, it is possible to apply standard processing and analysis tools, initially developed for the analysis of spectral 2D maps. Spectra can be processed (baseline correction, derivative…) prior to be analyzed. It is also possible to open and analyze, in parallel ROI windows, the same set of data, after different processing steps (for example raw spectra and derivative spectra). The simplest analysis consists in integrating the intensity over specific regions of interest (ROI tool). Both raw and net (intensity over a baseline) intensities can be calculated. Lists of ROI can be saved and re-loaded to perform identical treatments on several data sets. As an example, Fig. 2 shows the display of the ROI tool window, with i) top left panel: the color scale display of the original data (integration over the full spectral range), ii) top right panel: the color scale display of a selected ROI (integration over a specific ROI, here CO ester stretching, with baseline correction, showing the progressive reaction of ester groups), iii) bottom panel: the display of spectra, and of a list of ROIs.



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Figure2: a snapshot of the ROI Imaging window, showing the main panels (see text for details).

Several maps can be generated for different ROIs regions and listed under “RGB Correlator” window (RGB meaning Red Green Blue). In this panel, it is possible to superimpose several images, and see for example any time correlation or anti-correlation of formation and disappearance of different FTIR bands. As an example, Figure 3A shows the superimposition of integrated intensity of CO ester stretching band (red), CO carboxylate asymmetric stretching band (green), and OH stretching band (blue). It shows that esters disappear when carboxylates form (during the first 5 points, corresponding to 20 minutes), while hydroxyls form only much latter in the process (after 200 minutes). Color maps can be converted into profiles (Figure 3B) that can be exported as images or text files.

Figure 3: A) snapshot of the RGB panel: 3 images can be superimposed in Red, Green and Blue, allowing an easy check of time-correlation, anti-correlation and non-correlation of events. B) Profile view of the same maps. For readability the CO ester (red) and carboxylate (green) bands are plotted under the left Y axis and the OH stretching band is plotted over the right Y axis.

The tool Σ allows the calculation of maps from these ROI maps. For instance, it is possible to normalize maps by a reference map. Here, we use the calibration method described in our previous study7 to convert spectral intensities of CO ester stretching and CO carboxylate asymmetric stretching bands into percentage of saponification (Fig. S1A and on-line tutorial). Fig. S1B is the corresponding profile view.


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To go a step further into data analysis, the multivariate capabilities offered by PyMca were used. Principal Component Analysis (PCA) was first used to estimate the number of components needed to account for most of the data variance. In the case mentioned above (Fig. 1), two components represent 96% of the variance. The third component represents 2.25% of the variance. Non-negative Matrix Approximation (NNMA) was then performed, on the assumption of three components. This approach is also known as positive matrix factorization (PMF) or Nonnegative Matrix Factorization (NMF) in the literature. Here the term “approximation” is used to highlight the fact the decomposition is not unique. As highlighted in Fig 4A, the two first vectors (NNMA Image 00 and 01) are representative of pure lead carboxylates (here reference spectrum of lead stearate) and pure oil. The corresponding images and profiles (Fig. 4B) show a distribution equivalent to the ones obtained using ROI tool (Fig. 3B). However, the evolution of vector 01 shows a decrease after the 17th point (110 min). This decrease is explained by the formation of other compounds, represented by vector 02. This vector gives a very good matching with the glycerol spectrum (Fig. 4A). Glycerol is a sideproduct of the saponification reaction.

Figure 4: Results of NNMA analysis, based on 3 components. A) the three vectors (solid lines) compared to reference spectra (oil, lead stearate and glycerol references). Spectra are offset for a better readability. B) Snapshot of the Scan Window, showing the profile distribution of the three NNMA components.

Since PyMca/ROI Imaging is essentially developed for processing 2D maps, the exploitation of a second dimension is straightforward. As an example, if each p point of the kinetics has been analyzed with r replica, the full set of data can be opened and processed as a unique matrix of p rows × r columns. As an example, Fig. S2 shows the ester ROI map for the same kinetics, with 3 replicas. Fig. S2B shows the sum profile, over the 3 replicas. In addition to the simultaneous management of different times (1D) and different replicas (1D), the 2D capabilities of PyMca/ROI imaging can be also exploited to visualize, to process and to analyze simultaneously different sets of data, acquired under different conditions (different concentrations



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of reagents, different temperatures, etc). As an example, Fig. 5 shows the results obtained from the saponification of trioleine by PbO (without water), in the mass proportion 2-1 while mixed at different temperatures. The horizontal axis represents the 11 time-points (0 to 80 min.). The vertical axis represents the 5 temperatures (100, 120, 140, 160 and 180°C). All the FTIR spectra are analyzed as a unique set of data, in a unique step and the resulting maps highlight, in 2D images, the full variance of the chemical reactions (Fig. 5A). Different profiles can then be extracted from these images: the temporal follow-up of saponification, at one temperature (Fig. 5B), as well as the rate of saponification after a certain time, for the various temperatures (Fig. 5C). The NNMA results of these data are shown in Fig. S3. PyMca can be further exploited to analyze such multi-temperatures kinetic profiles, for example to derive Arrhenius plots (cf. Fig. S4).

Figure 5: 2D visualization of time-resolved (over 80 min.) temperature-resolved (from 100 to 180°°C) FTIR follow-up of saponification of triolein by PbO (soap amount calculated from ROI of CO ester stretching and carboxylate asymmetric stretching bands). A) 2D color scale representation, B) and C) snapshot of two 1D profiles as represented in A): B) 1D profile of the kinetics at 180°°C, and C) 1D profile of soap amount after 35 min., for the different temperatures.

Combined multi-technique kinetic follow-up of NO reduction with rhodium catalysts


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The second example covers a multi-technique experiment where dispersive X-ray absorption fine structure (DXAFS), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and mass spectroscopy (MS) were performed simultaneously. NO-CO reaction (NO+CO ½ N2+CO2), for automotive purposes, has been widely studied especially for rhodium catalysts8. However, the real automotive condition is under oxidative atmosphere on which the catalyst properties are not well established. DXAFS, MS and DRIFTS are perfectly suited to be performed together to monitor under reaction conditions the catalyst structural and electronic properties, the gas released or adsorbed on the catalysts surface, respectively9. In the following example, a RhAl2O3 catalyst was in-situ reduced (5%H2/He, 250 0C, 10 0C/min, 30 min). After that, the temperature was increased (275 0C) and the NO reduction by CO in the presence of O2 was performed. A switching valve was used to alternate between two streams: the first one a NO+O2 mixture (5%NO/He and 10%O2/He) and the second one 5%CO/He. Each stream was kept for 20s. The sample was measured in the XAS/DRIFT/MS cell developed at ESRF. DXAFS measurements were carried out at Rh K-edge on ID24 at ESRF with time resolution of 100ms, while DRIFT spectra was collected by FTIR instrument Varian 680, with time resolution was 0.9s per spectra. The infrared background was considered as the first spectra under CO to evaluate it evolution on the particle surface. The gases outlet was measured by Hidden Omnistar MS (intensity measured for 10 masses, corresponding to different gas, in particular CO2) with a resolution time of 300ms. PyMca/ROI imaging allows the correlation between data collected at the same time, in a multitechnique experiment and their simultaneous analysis. In the present data, X-Ray absorption, infrared, and mass spectroscopy data were acquired simultaneously, and can be analyzed with a single tool, using PyMca/ROI Imaging, giving a full picture of both catalyst state as well as gas released or adsorbed. In order to have data with the same frequency, DXAFS and MS data were reduced to fit with the low time resolution used for DRIFTS: DAXFS and MS spectra were averaged over periods of 0.9s (ending into 23 points). The full data sets can anyway be analyzed separately. Python macros were used to convert the three reduced data set (DXAFS spectra, MS data, DRIFTS spectra) into three separate .HDF5 files. First, ROI imaging was launched to load XAS data (Fig. 6A). Spectra were normalized and Extended XRay Absorption Fine Structure (EXAFS) signal extracted using the XAS plug-in recently implemented in PyMca. This tool is very flexible and powerful, having the control of all parameters used in the data reduction: pre- and post-edge interval in data normalization as well as the energy of the edge position, the K-range, K-weight and number and position of knots for the extraction of EXAFS oscillations. For the Fourier Transform (FT), the range interval and type of apodization window can be selected. The possibility to tune all these parameters, depending to the signal to noise ratio, is very important because it allows the extraction of EXAFS data even with poor data quality spectra. All parameters can be saved and applied to a series of spectra treating in batch thousand of spectra at the same time using the same constrains. This capability is very significant, especially for DXAFS techniques, where millisecond time resolution can be reached and a huge amount of data can be collected in a short time. Fig. 6A shows the modulus of the FT (not phase correct) in R space during a CO pulse after an NO+O2 feeding. The spectra show two different contributions corresponding to Rh-O and Rh-Rh contributions, at 1.6 Å and 2.5 Å respectively. The Rh-O contribution is related to an interaction between the Rh particles with the alumina support and/or with the gases. The peaks’ intensity



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decrease during the CO feeding stating changes on the coordination numbers and/or on the Debye Waller factor. The signal integration over the full spectra range is on the left upper panel. On the left bottom panel are the first and last spectra (black and red, respectively) and on its right, the ROI placed around the peak at higher distance, related to Rh-Rh contribution (noted Rh-Rh_CN). As described before, the top right panel displays the color scale for the selected ROI. A first slave ROI Imaging window was open to display the DRIFTS data (Fig. 6B). In the bottom left part of Fig. 6B, the first and the last spectra are plotted (black and red, respectively). In the first spectrum, no bands were observed since it was considered as background and in the last one, bands assigned to CO adsorbed linear on Rh (Rh-COlinear, 2060 cm-1), CO2 (2355 cm-1) and an isocyanate specie (2252 cm-1) adsorbed on the support were identified10. A ROI corresponding to Rh-COlinear band was selected (top right part of Fig. 6B). From the same master PyMca/ROI Imaging, a second slave ROI Imaging window can be open to load MS data (Fig. 6C). Data were recorded as intensity at a few discontinued masses (and not as continuous spectra as for the other techniques). In particular, the peak at 4 comes from He, in which all gases are diluted. The upper left image corresponds to the integrated intensity over all measured masses. The ROI tool was used to follow intensity of a specific mass (here m=44 corresponding to CO2 or N2O). The upper right image shows the evolution of CO2 and/or N2O released during experiment. In the master ROI Imaging window, ROI images from the three data set can be grouped, and superimposed in a unique RGB panel (displayed in Fig.S3) in order to evaluate the respective speed (simultaneous or consecutive steps) of the different chemical reactions or structural modifications. In this example, the blue color corresponds to the Rh-Rh contribution (from DXAFS), the red one is the intensity of Rh-COlinear band (from DRIFT) and the green one the evolution of reaction products (from MS). In Fig. 6D, the 1D profile of these three curves is plotted together versus spectrum number (curves are normalized for easier comparison; same color code as in Fig. S5). Three different steps in the reaction can be defined after switch of the gas feed from NO+O2 to CO: 1) after an induction time, structural changes of Rh nanoparticles were observed (Rh-Rh contribution decreased by EXAFS data); 2) CO2 and/or N2O production (m=44 detected by MS) 3) adsorption of CO on the surface of Rh nanoparticles (COlinear band measured by DRIFT). As illustrated here, thanks to PyMca, data acquired simultaneously with three techniques can be analyzed with a unique tool. Here, it was possible to observe that due to the CO-NO reaction, structural changes are promoted on the particle, and once the NO is consumed, the CO is able to adsorb on Rh surface.


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Figure 6: snapshot of the ROI imaging windows showing multi-modal time-resolved in-situ reduction of CO+ NO by RhAl2O3 catalyst: A) the master window with the Fourier Transform of the EXAFS data, B) a slave window with the DRIFTS data and C) a slave window with the MS data. For these three windows, the black spectrum is the first point and the red spectrum the last point; a ROI is selected to highlight a specific species (see text for details). D) 1D profiles from the RGB panel (cf. Fig. S3) showing ROI intensity of DAXFS Rh-Rh contribution (blue), of DRIFT Rh-COlinear band (red) and of mass signal for CO2 (green).

Conclusions In conclusion, we would like to emphasize the possibility to use of PyMca/ROI imaging to nonimaging data, or said differently to extend the notion of images to any 1D or 2D data, where the dimensions are not necessarily spatial. PyMca is a free software, with a graphical interface, easily usable without further knowledge in programming, and can be of general interest for any hyperspectral data. The first example is focused in time-resolved FTIR analysis, however this approach can be extended to any 1D or 2D-resolved (time, temperature, concentration, etc) spectral data. The second example reports the recent development of analytical tools for EXAFS showing the potential to normalize and extract data simultaneously on several spectra. It also highlights the possibility to handle data from a multi-technique experiment offering a straightforward comparison between results from different techniques. More generally, PyMca can also be used to assess rate laws, since data can be easily manipulated, for example to calculate, plot and fit the evolution of concentration of reactives, its log or its inverse vs. time, to assess 0, 1st or 2nd order respectively. Supporting Information Available This material is available free of charge via the Internet at http://pubs.acs.org. References 1. Solé, V. A.; Papillon, E.; Cotte, M.; Walter, P.; Susini, J., Spectrochim. Acta B 2007, 62, 63-68. 2. de Viguerie, L.; Walter, P.; Laval, E.; Mottin, B.; Solé, V. A., Angew. Chem. Int. Edit. 2010, 49, 61256128;



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3. 4. 5. 6. 7.

de Viguerie, L.; Solé, V. A.; Walter, P., Anal. Bioanal. Chem. 2009, 395, 2015-2020. McDonald, R. S.; Wilks, P. A., Appl. Spectrosc. 1988, 42, 151-162. Group, T. H. Hierarchical Data Format, version 5, 1997-2015. Pouyet, E.; Cotte, M., Acta Artis Academica 2014, 99-110. Cotte, M.; Checroun, E.; Susini, J.; Dumas, P.; Tchoreloff, P.; Besnard, M.; Walter, P., Talanta 2006, 70, 1136-1142. 8. Srinivasan, A., Depcik, C., Catal. Rev. 2010, 52, 462-493. 9. Bordiga, S., Groppo, E., Agostini, G., Bokhoven, J. A Van, Lamberti, C., Chem. Rev. 2013, 17361850. 10.Hiromichi, A., Tominaga, H., J. Catal. 1976, 131-142.

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Watching Kinetic Studies as Chemical Maps Using Open-Source

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