Microsc. Microanal. 20, 815–824, 2014 doi:10.1017/S1431927614000749
© MICROSCOPY SOCIETY OF AMERICA 2014
Analysis of Catalytic Gas Products Using Electron Energy-Loss Spectroscopy and Residual Gas Analysis for Operando Transmission Electron Microscopy Benjamin K. Miller and Peter A. Crozier* School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287-6106, USA
Abstract: Operando transmission electron microscopy (TEM) of catalytic reactions requires that the gas composition inside the TEM be known during the in situ reaction. Two techniques for measuring gas composition inside the environmental TEM are described and compared here. First, electron energy-loss spectroscopy, both in the low-loss and core-loss regions of the spectrum was utilized. The data were quantified using a linear combination of reference spectra from individual gasses to fit a mixture spectrum. Mass spectrometry using a residual gas analyzer was also used to quantify the gas inside the environmental cell. Both electron energy-loss spectroscopy and residual gas analysis were applied simultaneously to a known 50/50 mixture of CO and CO2, so the results from the two techniques could be compared and evaluated. An operando TEM experiment was performed using a Ru catalyst supported on silica spheres and loaded into the TEM on a specially developed porous pellet TEM sample. Both techniques were used to monitor the conversion of CO to CO2 over the catalyst, while simultaneous atomic resolution imaging of the catalyst was performed. Key words: operando, in situ TEM, environmental TEM (ETEM), mass spectrometry, EELS, catalyst
I NTRODUCTION Heterogeneous catalysis is playing an increasingly important role in chemical synthesis as well as many technologies related to sustainable energy such as fuel synthesis, solar fuels, and fuel cells (Bell et al., 2008; Benson et al., 2009; Kudo & Miseki, 2009; Gorte & Vohs, 2011). In addition to lowering the energy barriers associated with the rate-limiting step, an ideal catalyst should show long-term stability and be composed of earth abundant elements. A typical heterogeneous catalyst is a nanostructured material of high surface area to facilitate contact between the reactants. The catalyst must also contain suitable surface or subsurface structures (e.g. Behrens et al., 2012) that function to locally accelerate the reaction kinetics. An ideal catalyst structure and composition is dependent on the reaction of interest. Understanding the structure-reactivity relations for catalytic systems is a critical step in developing the next generation of improved catalysts. Determining the structure of the active form of a nanocatalyst is challenging because the functionally relevant nanostructure or surface motifs may only form under reaction conditions. The need to understand catalyst structure under reaction conditions has been a major motivation for the development of a wide range of in situ characterization techniques such as environmental transmission electron microscopy (ETEM) (Chenna & Crozier, 2012a; Jinschek & Helveg, 2012; Sharma, 2012; Wagner et al., 2012; Yokosawa et al., 2012). In a modern ETEM, the catalyst structure and Received September 29, 2013; accepted March 21, 2014 *Corresponding author. [email protected]
composition can be determined at atomic or near atomic resolution in the presence of reactive gases at elevated temperatures. Such instruments, which may be equipped with monochromators and aberration correctors, are either based on differential pumping systems or windowed cells and can allow the positions of atoms on the surface of nanoparticles to be precisely determined in the presence of reactive gases (Helveg et al., 2004; Sharma & Crozier, 2005; Creemer et al., 2008; Crozier, 2011; Yokosawa et al., 2012). Careful in situ measurement of structural evolution during catalyst activation can then be correlated with ex situ reactor data to construct structure-reactivity relations (Chenna et al., 2011). In situ ETEM allows the catalyst structure and composition to be determined in the presence of reactive gases, but it does not confirm that catalysis is actually taking place in the microscope. Indeed, the need for techniques, which simultaneously measure catalyst structure and activity, has been recognized for some time and was first developed for Raman spectroscopy where the term operando was introduced (Bañares & Wachs, 2002). The ability to determine structure and activity in the same experiment is a powerful approach to catalysis research and greatly facilitates the determination of structure-reactivity relations. It has been demonstrated that electron energy-loss spectroscopy (EELS) can be employed to measure the composition of the gas phase directly in the reaction cell of the ETEM (Crozier & Chenna, 2011). This approach was recently extended to show that catalytic products could also be unambiguously detected, thus opening the door to operando transmission electron microscopy (TEM) for catalysis research (Chenna & Crozier, 2012b).
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While the previous work (Chenna & Crozier, 2012b) demonstrated the possibility of using EELS for the detection of catalytic products there remain many questions about how best to implement spectroscopic approaches for operando TEM in catalysis research. A long-term goal is to make accurate determination of reaction kinetics directly inside the microscope. To achieve this goal more work is required to develop rapid quantitative methods to quantify gas compositions in the TEM using both low-loss and core-loss EELS. The composition of the gas can also be determined from residual gas analysis (RGA) using a mass spectrometer. However, the RGA is a high vacuum instrument and is typically located some distance away from the reaction cell. It is not clear how quantitative RGA can be for the measurement of gas composition in the reaction cell under typical high pressure conditions. How do EELS and RGA compare for gas determination in the microscope? In a differentially pumped ETEM it is necessary to employ a large volume of catalyst in an operando experiment to achieve reliable and quantifiable conversions in the microscope. This can lead to significant charging problems and the previous work did not demonstrate atomic resolution during catalysis. For successful operando TEM, two goals must be simultaneously accomplished; catalytic products must be detected and atomic resolution imaging achieved. To accomplish these goals, improved methods for TEM sample preparation are required. In the current work, methods to quantify EELS and RGA spectra are developed and explored for operando TEM with a primary focus on their application to gases of relevance for CO oxidation. CO oxidation has been a wellresearched reaction over the past decade, in part because of its application in fuel cells, where CO impurities in H2 fuel gas are a serious problem (Cheng et al., 2007). Several metals can catalyze this reaction, given in the following equation, but one of the most active is Ru (Over & Muhler, 2003). 2CO + O2 ¼ 2CO2 :
(1)
Despite the volume of research, there is still debate over exactly what surface structures are most active for CO oxidation. One group demonstrated that the highest activity is obtained from Ru metal with a chemisorbed oxygen layer (Goodman et al., 2007a, 2007b; Gao et al., 2009). Others suggested that RuO2 forms on the surface and this is the more active species for CO oxidation (Over et al., 2007, 2009; Flege et al., 2008). Work on large grain RuO2 powders identified an active suboxide as well as a more active metallic form of the material (Rosenthal et al., 2009). Operando TEM is ideally suited to address this issue because direct observation of the surface of Ru nanoparticles is possible while the catalytic conversion of CO is being simultaneously monitored. Moreover, the gases involved in this reaction are relatively simple to detect with EELS making it an ideal model system to further develop the operando approach. In this paper, approaches to determine the molar concentrations of gas mixtures using both low-loss and core-loss EELS are developed. In both approaches, quantification is achieved using least squares methods to fit linear combinations
of spectra from reference gas samples to a spectrum from the gas mixture. Robust analysis procedures are developed that can be easily integrated into the ETEM for rapid quantitative determination of the gas composition. The ETEM was modified to allow an RGA to be employed to continuously measure the gas composition in the reaction cell under high pressure conditions. Methods to quantify the RGA spectra were also developed and compared with those determined by low-loss and core-loss EELS. The sensitivity of both EELS and RGA to pressure variations was investigated and the two techniques compared for gas quantification. Finally, a novel approach for preparing a catalyst sample for operando TEM so that high conversions and atomic resolution imaging may be achieved is described. This approach was then employed along with simultaneous RGA and EELS to show a Ru catalyst activating and deactivating while changing the reaction conditions inside the ETEM. Simultaneous high-resolution electrom microscopy (HREM) images were obtained and correlated with different CO conversions demonstrating that atomic resolution operando TEM was accomplished.
MATERIALS
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METHODS
EELS To determine the gas composition in the reaction cell during an operando experiment, both core-loss and low-loss EELS were utilized. During spectral acquisition, the sample was shifted away from the electron beam, so that the beam passed only through the gas. During the operando experiment described, the total gas pressure was ∼2 torr, while during the comparison experiment the total pressure was ∼1.5 torr. Some spectra were acquired as single spectra, while others were acquired using a script for digital micrograph (Mitchell & Schaffer, 2005), which allows a series of spectra to be acquired automatically. This script additionally saves the entire CCD image of the spectrum before summation in the nondispersive direction. The spectral processing was performed using codes written in MATLAB. All the low-loss spectra were acquired using an extraction voltage of 3,500 V, and the largest spot size in TEM image mode. The spectrometer was set to a dispersion of 0.05 eV/ channel, using an entrance aperture of 2 mm, and an exposure time of 4 s. With such a long exposure time, the zero-loss peak must be shifted off the detector to prevent saturation. The collection angle is limited to 50 mrad by the lower differential pumping aperture of the environmental cell. The low-loss method used for measuring gas composition involved finding a linear combination of reference spectra from single gases, which best fits the experimental spectrum from a gas mixture. (The low-loss spectrum can be modeled as a simple linear combination in the case of gases, because the gas molecules have little electronic interaction with each other). Since reference spectra are used to quantify an experimental spectrum, it is essential that the experimental data be acquired under electron-optical conditions identical to those of the reference spectra.
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vibrations that arise from the fan, which cools the RGA electronics. The dedicated turbo pump was placed in an adjacent room to minimize its impact on the microscope resolution. This setup allowed the RGA to be operated close to a pressure of 10 − 4 torr, which is near the upper pressure limit for the instrument by adjusting the leak valve appropriately for each experiment, yielding high signal-to-noise in the data. All the techniques used to measure the gas composition inside the TEM should ideally yield identical results if correctly implemented and quantified. To determine whether this was the case, a simple comparison experiment was performed, in which a 50–50 mixture of CO and CO2 was flowed through the ETEM cell, and the composition measured over 70 min. For the first 20 min, the low-loss EELS method was used, and for the final 20 min core-loss spectra were taken. During the entire experiment, data from the RGA was collected simultaneously.
CO Oxidation and Operando TEM Figure 1. A schematic diagram showing how the residual gas analyzer (RGA) is interfaced to the reaction cell of the ETEM (which is located between the upper and lower polepieces of the objective lens). A leak valve and second small turbo pump work together to control the pressure at the RGA.
The core-loss spectra were acquired in TEM diffraction mode, with an extraction voltage of 4,100 V and the largest spot size. The spectrometer dispersion was usually 0.05 eV. The entrance aperture and acquisition time are identical to the low-loss case: 2 mm and 4 s, respectively. The convergence and collection semiangles were 2.4 and 2 mrad, respectively. The quantification of the core-loss data is similar to the low-loss technique. A linear combination of the reference spectra from the individual gases are fit to the mixture spectrum.
RGA A residual gas analyzer (RGA) was used to perform mass spectrometry on the gases present in the TEM vacuum system during the operando experiments. A Pfeiffer Prisma QMA 200 RGA was employed that uses an electron beam to ionize gas molecules passing through it, and then accelerates the resulting charged species through a set of quadrupoles. The ions are separated according to their mass-to-charge ratio (m/z) and the ion currents are measured using a Faraday cup. The RGA was set to measure 15 different massto-charge ratios approximately every 10 s. The vacuum setup for the RGA is shown in Figure 1. This required modification of the ETEM vacuum system, including the addition of a turbo pump dedicated to the RGA. To maintain the high vacuum necessary for its operation, the RGA was placed behind a leak valve, and was pumped by the dedicated turbo pump to a vacuum less than 10 − 4 torr. A bellows was placed between the microscope and the RGA to damp out
CO oxidation was performed over a Ru nanoparticle catalyst, supported on silica spheres prepared by the Stober method. Initial ex situ reactor studies have been published previously (Chenna & Crozier, 2012b) and show that the oxidation reaction to form CO2 occurs above a temperature of 150°C and conversions increase with increasing temperature. For the in situ operando experiments, CO and O2 were mixed in stoichiometric quantities, and then flowed through a carbon filter, to eliminate carbonyls, and into the microscope through a leak valve to achieve a pressure of around 2 torr in the ETEM reaction cell. The ETEM used was a differentially pumped FEI Tecnai F20 operated at 200 kV. The temperature was measured and controlled using a Gatan Ta hot stage, and was held at various temperatures from 150 up to 340°C. The microscope was equipped with a Gatan imaging filter allowing in situ EELS to be performed. To perform operando TEM, a relatively large amount of catalyst must be present inside the TEM sample holder, to provide adequate surface area for the reaction, and this requires the development of unique TEM sample preparation techniques. Standard ETEM samples for studying catalytic nanoparticles usually consist of a metallic grid onto which catalyst nanoparticles are dispersed. This grid has an insufficient surface area over which to disperse the silica-supported catalyst. In addition, small amounts of CO can be converted to CO2 on the sample holder thermocouple wires at temperatures over 300°C, so it is important to increase the CO conversion from the catalyst making the contribution of the holder negligible. To this end, a 3 mm diameter porous pellet (Fig. 2c) was created with a surface area nearly 50 times larger than a standard grid, allowing products generated by the now abundant catalyst to be monitored in the microscope. In the new sample preparation method, in addition to the traditional grid, a porous glass-wool pellet was secured inside the TEM holder, as shown in Figure 2c. Cu was chosen as the grid metal in this case, as it does not catalyze the CO oxidation reaction. The pellet was fabricated by firing
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Figure 2. Schematic diagram showing method of preparing catalyst for operando transmission electron microscopy (TEM). a: Supported metal catalyst consisting of Ru nanoparticles supported on silica spheres. b: Supported metal catalyst dispersed over silica fibers. c: Silica fibers are formed into a pellet of 3 mm in diameter. A Cu grid provides a conductive support for high-resolution electrom microscopy imaging of the silica sphere-supported catalyst.
crushed glass-wool packed inside a quartz tube, cutting sections, and then grinding it to a thickness between 0.5 and 0.8 mm. The pellet was then filled with the silica-supported catalyst (Fig. 2a) using a wet-impregnation technique. This dual-sample approach allows a high loading of catalyst into the TEM, while maintaining the ability to capture highresolution images of the catalyst. A more detailed description of this novel sample preparation technique will be given elsewhere.
RESULTS AND D ISCUSSION EELS A typical low-loss spectrum from a nominally 50–50 mixture of CO and CO2 is shown in Figure 3 along with the reference spectra for CO and CO2, which have been used to fit the spectrum from the gas mixture. The code, which automates the process of finding the ideal linear combination by a weighted least squares method, can perform this operation on a single spectrum, multiple spectra, or on the data cube generated by the multiple EELS acquisition code referenced in the experimental section (Mitchell & Schaffer, 2005). In addition to performing the least squares fit, the MATLAB code automatically shifts the energy scale and accounts for the background. To do this, the code first normalizes the reference spectra so their integrated intensities are all unity over the energy range of 4–44 eV. Then the first peak in both the reference spectra and the mixture spectrum are found and the energy of the measured peak set equal to the reference, in case the mixture spectrum has an incorrect energy calibration. Next, the linear combination of selected gases that best fits the spectrum from the gas mixture in the TEM is computed by a weighted least squares method, where the peaks in the mixture spectrum are given a higher weighting, by setting the weights equal to the square of the intensity. This least squares fit is repeated many times, while shifting the mixture spectrum slightly relative to the (precisely calibrated) reference spectra to get a precise, automatic energy calibration of the mixture spectrum, as shown in Figure 4, where an optimal fit is given in green and the poorest fits in red.
Figure 3. Illustration of the linear combination method for lowloss electron energy-loss spectroscopy of gases quantification for a 50/50 mixture of CO and CO2. The spectra from multiple individual gases (dotted lines) were suitably combined to yield a spectrum (blue) closely fitting the spectrum obtained from a mixture of those gases (green).
After the ideal fit is found (Fig. 5a), it is seen that the residuals often show a clear functional dependence, as seen in Figure 5b. This is owing to the zero-loss tails of the reference and mixture spectra being slightly different. No background removal is performed on either the reference or mixture spectra to attempt to completely remove the zeroloss tails; instead the backgrounds of the reference and mixture spectra were matched. This is done in practice by fitting the residuals using an inverse power law, and this fit is subtracted from the measured spectrum. This procedure, shown in Figure 5, is equivalent to performing background subtraction on both the mixture and reference spectra, to remove the zero-loss tails, but is more robust in this case, where only a small window before the peak onset is available, making a more traditional background subtraction difficult. After the residual fit is subtracted, the background-matched spectrum is taken through the steps of fitting and shifting again to determine a new optimized fit. This process of matching the background is repeated as necessary until no clear power law dependence of the residuals is found
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Figure 4. The energy calibration method used for the low-loss electron energy-loss spectroscopy (EELS) analysis automation. To automatically and precisely calibrate the energy-loss axis of spectra measured from gas mixtures, the mixture spectrum is systematically shifted through a range of a few electron volts, while the reference spectra used to compute the fit (blue) remain fixed. a: Examples of good (green) and poor (red) energy shifts. b: The root mean square error (RMSE) of the linear combination fit plotted as a function of energy shift showing a single minimum when the spectrum is correctly calibrated.
Figure 5. The background matching method used for the low-loss electron energy-loss spectroscopy (EELS) analysis automation. a: After fitting a linear combination of experimentally obtained spectra of individual gases (orange) to a spectrum from a gas mixture (blue), the background of the composite spectrum and mixture spectrum do not match. b: The residuals from the fit show a clear pattern. This pattern can be fit quite well with a power law function (black). c: Subtracting this fitted function yields a mixture spectrum with a background that more closely matches that of the individual reference spectra so that no clear functional dependence is seen in the residuals (d).
(Fig. 5d). No procedure for deconvolution of plural scattering is applied, as plural scattering is minimal at the gas pressures used where the electron mean free path is much larger than the pole piece gap. (The typical values of scattering parameter are in the range 0.01–0.02 for the current experiments). Once the linear combination coefficients have been determined, information about the reference spectra is used to compute the gas partial pressures, as detailed in Crozier and Chenna (2011). Application of this quantification method to the mixture spectrum in Figure 3 yields a result of 49.2% CO2 and 50.8% CO, with a standard deviation of 0.7% as discussed below.
Core-loss quantification is similar to the low-loss method, and uses a linear combination of reference spectra to fit the spectrum from a gas mixture. For all the core-loss spectra, including the reference spectra, the backgrounds were fit with the usual inverse power law using a pre-edge window of ∼25 eV. This background was extrapolated 25 eV past the edge onset and subtracted before beginning the analysis. (This is in contrast to the low-loss method, where the backgrounds of the reference and mixture spectra are matched rather than subtracted). After background subtraction, a simple least squares approach was used to find the linear combination of reference spectra from CO and CO2
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Figure 6. Illustration of the linear combination method for coreloss electron energy-loss spectroscopy of gases quantification for a 50/50 mixture of CO and CO2. The spectra from multiple individual gases (dotted lines) were suitably combined to yield a spectrum (blue) closely fitting the spectrum obtained from a mixture of those gases (green).
Figure 7. The composition determined from quantification of electron energy-loss spectroscopy (EELS) spectra series from both the low-loss (blue) and core-loss (orange) regions are compared with the signal from the residual gas analyzer (RGA, gray). Energy-loss spectra were taken every 20 s in two 20 min blocks. Peaks in the mass spectrum were monitored every 10 s. The time between the two blocks of EELS spectra was used to adjust the spectral acquisition parameters.
that best fit the mixture spectrum. This is shown in Figure 6 for a nominally 50/50 mixture of CO and CO2. The spectra were fit over the entire acquisition range of about 50 eV, and the result obtained was a composition of 51.0% CO2 and 49.0% CO, with a standard deviation of 0.4%. This is quite close to the nominal composition, and in good agreement with the low-loss EELS result. Using the data from the comparison experiment (Fig. 7), the precision of each EELS technique can be evaluated independently of its accuracy because EELS spectra were taken automatically every 20 s using a script for digital micrograph (Mitchell & Schaffer, 2005). Sixty spectra were taken in the low-loss region of the spectrum, giving a mean value of 49.6% CO2 and 60 from the core-loss region, giving a mean of 50.7% CO2. It is clear that the variance for both quantification methods is small. The standard deviation of the results shown in Figure 7 for the low-loss and core-loss
Figure 8. Raw analog data from the residual gas analyzer (RGA) for a 50/50 mixture of CO/CO2 (orange), as well as a background spectrum acquired just before beginning the gas flow (blue). Peaks for CO/N2, CO2, as well as 13 others are monitored by the RGA software over time to yield the usual RGA data.
techniques are 0.7 and 0.4%, respectively, and the standard errors of the means are 0.10 and 0.05%. The difference between the mean value obtained using the low-loss and core-loss techniques is 1.1%, with the mean of the low loss just below the nominal composition of 50%, and that of the core loss just above. The pressures in the microscope during the EELS reference spectra acquisition were controlled to within 2% of 1 torr for both CO and CO2. Even this small error is much larger than the variation in gas composition computed from the 60 individual core-loss or low-loss spectra shown in Figure 7, so the greatest uncertainty in the calculation of gas partial pressures using either region of the spectrum comes from the uncertainty in the reference gas pressures, rather than the quantification procedures, which are quite precise. The low-loss and core-loss EELS techniques used to quantify gas mixtures have both advantages and disadvantages. The low-loss spectra are more difficult to quantify by a linear combination than the core-loss spectra for the CO–CO2 mixture. In the low-loss energy regime all the peaks are overlapping, and other gases present in the system in small quantities can contribute to the signal, whereas in the core-loss case, only carbon-containing compounds contribute to the edge and the main peaks are well separated. However, the low-loss technique is quite versatile, as any mixture of gases can be analyzed over the same energy range, while the core-loss technique would be much more difficult for combinations of gases with edges far apart. For mixtures containing hydrogen gas, the low-loss region must be used as that is the only region of the spectrum than contains the H2 edge.
RGA Raw analog data from the RGA is given in Figure 8, where peaks for CO, CO2, and several other gases present in the TEM vacuum can be seen. The background gas pressure in the ETEM cell is easily detectable in the RGA and this background varies from day to day, depending on the most recent experiments. The signal from the background and from a nominally 50/50 mixture of CO and CO2 is shown in
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Figure 8. As described in the experimental section, the RGA software automates the monitoring of 15 different mass-tocharge ratios as a function of time, so that it is not necessary to acquire or analyze raw mass spectra. The ratio of CO2 to the total CO + CO2 as a function of time for the comparison experiment is shown in Figure 7. This result was obtained by first subtracting a background, the RGA signal before gas is admitted to the reaction cell, and then simply taking the ratio of the currents from CO and CO2, divided by known standard ionization cross-sections (O’Hanlon, 2003). This yields a value that ranges from 48.5% CO2 to 50.3% CO2. This simple ratio is assumed to adequately represent the ratio of the partial pressures of the two gases in the vacuum system. This apparently satisfactory result may have been somewhat fortuitous because the mass spectrometry technique using the RGA is one that introduces significant complications to quantification. Though it is extremely sensitive to small quantities of gas molecules, if a molecule of oxygen were to be doubly ionized, or split into two oxygen ion fragments, with one charge each, the resulting species would be seen at a mass of 16 amu. This is the same mass as a methane molecule, and this example underscores one of the primary deficiencies of this technique, namely that the signals from different gas species sometimes overlap completely. Most notably for our experiments, CO and N2 have an identical mass of 28 (Fig. 8), and are thus difficult to distinguish, leading to an overestimate of the CO concentration. A simple method employed here is to subtract constant background values, obtained when no gases are admitted to the ETEM, from each of the experimental peaks. A more thorough treatment of this peak overlap issue would require calibration spectra to be taken for all the gases present in the system, so that a linear combination fit to the experimental data could be obtained. The RGA data shown in Figure 7 exhibits a sharp dip and broad peak around 30 min into the experiment; 28 min after beginning the gas flow, the flow was suddenly slowed, so that the pressure in the cell dropped about 20%. This pressure change does not significantly affect the molar concentration ratio in the reaction cell but it caused the RGA peak ratio to drop by about 5%, and thus changed the computed CO to CO2 composition. When the pressure was restored to the original value the signal ratio did not return to the exact same value. Indeed, the steady drop with time that can be seen in Figure 7 is primarily because of a gradual pressure drop in the mixing tank leading to a corresponding pressure drop in the cell. This demonstrates that the signal from the RGA as a function of pressure is not linear, making it difficult to directly relate a measured current in the RGA to a partial pressure of gas in the vacuum system. Furthermore, the current placement of the RGA (near the turbo pump that provides the first level of differential pumping as shown in Fig. 1) far from the catalyst sample along with the RGA’s differential pumping system, may result in the gas composition in the RGA being different from that in the reaction cell due to differential diffusion in the vacuum system. The initial rise in the RGA
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data in Figure 7 demonstrates that gas reaches the RGA within about 1 min of its introduction to the ETEM, but about 5 min are required for the composition to reach equilibrium. These limitations combine to make the RGA data quantification uncertain, though as the results given in Figure 7 show, simple data quantification still seems to be reasonably accurate in this case. Even with the limitations of the RGA previously described, mass spectrometry is a useful technique for monitoring the gas composition within the ETEM. The main benefit of the technique is its independence from the operation of the microscope. The RGA can collect data during imaging, EELS acquisition, or even with the electron beam blanked. Thus, data can be acquired continuously over the course of an entire experiment. Furthermore, the data can be easily visualized live while the user is operating the microscope, allowing any changes in gas composition to be identified immediately. The EELS and RGA techniques have advantages and disadvantages. The EELS techniques are quantitative and probe the gas in the environmental cell directly. In either EELS technique, a disadvantage for operando studies is that the beam cannot be used for high-resolution imaging while it is employed to detect the gases via EELS. This is in contrast to the mass spectrometry technique using the RGA, which can gather data continuously during the course of the experiment. The RGA is, however, a less reliable technique for accurate quantification of gas compositions, and probes the gas in the vacuum system far from the ETEM reaction cell. The RGA and EELS techniques are thus complimentary, and ideally suited to simultaneous use.
Operando Some of the core-loss data acquired during the operando experiment are given in Figure 9. It is clear from a simple inspection of the data that significant conversion of CO to CO2 took place as the temperature was increased from
Figure 9. The carbon K-edge recorded as a function of temperature from the CO oxidation operando experiment with the glass fiber pellet. The spectra were quantified using the peak integration method and the calculated conversions are shown in the inset.
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Figure 10. High-resolution image of a Ru particle during CO oxidation. Lattice fringes are clearly visible in the image, and analysis of the fast Fourier transform shown in the inset confirms that the particle is in the metallic state, aligned along the [001] zone axis. When the image was acquired, the CO conversion inside the environmental transmission electron microscopy cell was 55% as determined by the electron energy-loss spectroscopy core-loss quantification method.
150 to 340°C. These data were analyzed using the linear combination method, and the results of this quantification are also given in Figure 9. This maximum conversion observed in the operando experiment was surprisingly high considering that the reactor geometry of the ETEM cell is complex, with a large volume of gas, and a small TEM sample, so that at any given moment only a small amount of the gas is in contact with the catalyst, and some gas molecules move through the system without ever interacting with the sample. The purpose of the unique sample preparation used in this experiment was to increase the catalyst surface area inside the ETEM, so that significant conversion would take place. This was clearly effective. In addition to the high conversions observed and quantified using EELS, high-resolution TEM images were also successfully obtained from the catalyst during the reaction. These images can be directly linked to the gas composition present inside the ETEM cell at the moment they were acquired. Figure 10 shows an atomic resolution image acquired from a Ru particle while catalysis was taking place in the microscope. In this case the temperature was 233°C and the CO conversion measured by EELS was ∼50%. This particular Ru particle was on a silica sphere that was in direct contact with the Cu grid. The glass-wool fibers that make up the pellet are insulating, and charge under the electron beam, degrading imaging, so that atomic resolution is difficult if not impossible to obtain from catalyst particles in contact with these fibers. The charging effects are significantly reduced when gas is present in the reaction cell, presumably because the gas can also facilitate the discharging process. Nanometer resolution imaging can be achieved in the presence of gas, but so far atomic resolution has not been obtained from fiber-supported particles. The Ru/silica particles that are in direct contact with the metallic grid are much more stable under the electron beam and high-resolution images can be easily obtained. The metallic and glass-wool parts of the TEM sample should have
very similar environments since they are in close proximity within the heating holder and the gas in the reaction cell is well mixed. This similar environment means that observations of particles made on the grid-supported silica spheres should be representative of the entire population of catalyst particles inside the ETEM. Figures 9 and 10 demonstrate that both high conversions and atomic resolution have now been achieved and thus this novel sample preparation technique is ideal for performing atomic resolution operando TEM. Insights into the structure-reactivity relations can be obtained by looking at changes in the structure of catalysts during activation and deactivation cycles (Chenna et al., 2011; Chenna & Crozier, 2012b). Catalysts may undergo changes when significant activation of reactants takes place because the ambient gas environment may change as products are generated and the distribution and composition of adsorbates on the surface may also change. The easiest way to dynamically change the activation is by varying the sample temperature while flowing reactants through the environmental cell. As the temperature increases, the reactants activate on the surface of the catalysts causing the reaction rate to increase resulting in the generation of a large concentration of products. Figure 11 shows the CO, CO2, and O2 signals from the RGA during an activation/deactivation cycle. During the ramp-up, the temperature was held constant at 130, 170, and 230°C corresponding to distinct plateaus in the CO2 profile. The RGA provides a powerful approach for visualization of the relative changes in the rate of product generation inside the ETEM as the temperature is ramped up and down. The drop in the CO and O2 signal correlates exactly with an increase in the CO2 signal showing that CO oxidation is taking place. Energy-loss spectra were recorded from the gas at these three plateau points to quantify the actual conversions taking place within the cell. The derived conversions were 0, 12, and 55% at temperatures of 130, 170, and 230°C, respectively. HREM images were also recorded from individual particles at approximately the same temperature/conversion. These images show the presence of both Ru and RuO2 phases even within the same particles while active catalysis is being performed. The sample was then cooled and the RGA profile immediately showed the drop in the CO2 signal as the conversion falls and the reaction effectively turned off at temperatures below 150°C. A detailed discussion of structure-reactivity relations will not be presented here, but Figure 11 clearly demonstrates the power of the combination of RGA and EELS to monitor and quantify the evolution of catalytic products generated within the ETEM. The ability to observe the atomic structure of individual nanoparticles during activation/deactivation cycles provides a powerful tool to develop a deeper understanding of the functioning of active heterogeneous catalysts.
CONCLUSIONS It is clear from the operando experiment that it is possible to image a catalyst with atomic resolution under reaction
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Figure 11. The simultaneous acquisition of data from the RGA, TEM, and EELS used in the operando experiment. a: The RGA collects qualitative data about the gas composition continuously. The activation of the catalyst is clearly seen when the temperature is raised to 170ºC. b: Electron energy-loss spectrum from the 230ºC plateau, showing a CO conversion of about 55%. c: A high-resolution image shows both RuO2 and Ru metal fringes. RGA, residual gas analysis; TEM, transmission electron microscopy; EELS, electron energy-loss spectroscopy.
conditions, while simultaneously monitoring those conditions and calculating catalytic conversions using EELS and RGA. EELS provides a quantitative method for monitoring the gas composition inside the ETEM reactor. Both low-loss and core-loss regions of the spectrum can be used, depending on their convenience for the gases to be monitored. Least square fitting methods were developed to quantify the spectra and accuracies can be better than 1% for both low-loss and core-loss analysis of CO/CO2 mixtures. RGA has also been successfully employed to monitor the gas composition, and though it is not fully quantitative, the data is continuous, providing an ideal complement to the EELS techniques. These three techniques are in reasonable agreement for a mixture of CO and CO2, as demonstrated by the simple comparison experiment. Ensuring that enough catalyst is present inside the microscope is essential to successfully perform operando TEM, and the fibrous pellet samples sandwiched between two copper grids work well for this purpose. The preparation method yielded conversions of up to 80% and atomic resolution images. Operando TEM has great potential for elucidating structure-reactivity relationships for catalysts at the atomic scale, and the future use of aberration-corrected ETEMs will surely increase the power of operando TEM.
ACKNOWLEDGMENTS This work was supported by the National Science Foundation grant NSF-CBET 1134464. The use of ETEM at the John M. Cowley Center for HR Microscopy at Arizona State is gratefully acknowledged. The authors also gratefully acknowledge Trevor Barker who prepared the operando porous pellets.
REFERENCES BAÑARES, M.A. & WACHS, I.E. (2002). Molecular structures of supported metal oxide catalysts under different environments. J Raman Spectrosc 33, 359–380. BEHRENS, M., STUDT, F., KASATKIN, I., KUHL, S., HAVECKER, M., ABILD-PEDERSEN, F., ZANDER, S., GIRGSDIES, F., KURR, P., KNIEP, B.-L., TOVAR, M., FISCHER, R.W., NORSKOV, J.K. & SCHLOGL, R. (2012). The active site of methanol synthesis over Cu/ZnO/Al2O3 industrial catalysts. Science 336, 893–897. BELL, A.T., GATES, B.C., RAY, D. & THOMPSON, M.R. (2008). Basic research needs: Catalysis for energy, US Department of Energy Basic Energy Sciences Workshop. http://science.energy.gov/ ~/media/bes/pdf/reports/files/cat_rpt.pdf BENSON, E.E., KUBIAK, C.P., SATHRUM, A.J. & SMIEJA, J.M. (2009). Electrocatalytic and homogeneous approaches to conversion of CO2 to liquid fuels. Chem Soc Rev 38, 89–99.
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CHENG, X., SHI, Z., GLASS, N., ZHANG, L., ZHANG, J., SONG, D., LIU, Z.-S., WANG, H. & SHEN, J. (2007). A review of PEM hydrogen fuel cell contamination: Impacts, mechanisms, and mitigation. J Power Sources 165, 739–756. CHENNA, S., BANERJEE, R. & CROZIER, P.A. (2011). Atomic-scale observation of the Ni activation process for partial oxidation of methane using in situ environmental TEM. Chem Cat Chem 3, 1051–1059. CHENNA, S. & CROZIER, P.A. (2012a). In situ environmental transmission electron microscopy to determine transformation pathways in supported Ni nanoparticles. Micron 43, 1188–1194. CHENNA, S. & CROZIER, P.A. (2012b). Operando transmission electron microscopy: A technique for detection of catalysis using electron energy-loss spectroscopy in the transmission electron microscope. ACS Catal 2, 2395–2402. CREEMER, J.F., HELVEG, S., HOVELING, G.H., ULLMANN, S., MOLENBROEK, A. M., SARRO, P.M. & ZANDBERGEN, H.W. (2008). Atomic-scale electron microscopy at ambient pressure. Ultramicroscopy 108, 993–998. CROZIER, P.A. (2011). Nanocharacterization of heterogeneous catalysts by ex situ and in situ STEM. In Scanning Transmission Electron Microscopy, Pennycook, S.J. & Nellist, P.D. (Eds.), pp. 537–582. New York, NY: Springer New York. CROZIER, P.A. & CHENNA, S. (2011). In situ analysis of gas composition by electron energy-loss spectroscopy for environmental transmission electron microscopy. Ultramicroscopy 111, 177–185. FLEGE, J., HRBEK, J. & SUTTER, P. (2008). Structural imaging of surface oxidation and oxidation catalysis on Ru(0001). Phys Rev B 78, 165407-1–165407-5. GAO, F., WANG, Y., CAI, Y. & GOODMAN, D.W. (2009). CO oxidation over Ru(0001) at near-atmospheric pressures: From chemisorbed oxygen to RuO2. Surf Sci 603, 1126–1134. GOODMAN, D.W., PEDEN, C.H.F. & CHEN, M.S. (2007a). CO oxidation on ruthenium: The nature of the active catalytic surface. Surf Sci 601, L124–L126. GOODMAN, D.W., PEDEN, C.H.F. & CHEN, M.S. (2007b). Reply to comment on “CO oxidation on ruthenium: The nature of the active catalytic surface” by H. Over, M. Muhler, A.P. Seitsonen. Surf Sci 601, 5663–5665. GORTE, R.J. & VOHS, J.M. (2011). Catalysis in solid oxide fuel cells. Annu Rev Chem Biomol Eng 2, 9–30.
HELVEG, S., LÓPEZ-CARTES, C., SEHESTED, J., HANSEN, P.L., CLAUSEN, B.S., ROSTRUP-NIELSEN, J.R., ABILD-PEDERSEN, F. & NØRSKOV, J.K. (2004). Atomic-scale imaging of carbon nanofibre growth. Nature 427, 426–429. JINSCHEK, J.R. & HELVEG, S. (2012). Image resolution and sensitivity in an environmental transmission electron microscope. Micron 43, 1156–1168. KUDO, A. & MISEKI, Y. (2009). Heterogeneous photocatalyst materials for water splitting. Chem Soc Rev 38, 253–278. MITCHELL, D.R.G. & SCHAFFER, B. (2005). Scripting-customised microscopy tools for Digital MicrographTM. Ultramicroscopy 103, 319–332. O’HANLON, J. (2003). A user’s guide to vacuum technology. 3rd ed. Hoboken NJ: Wiley-Interscience. OVER, H., BALMES, O. & LUNDGREN, E. (2009). Direct comparison of the reactivity of the non-oxidic phase of Ru(0001) and the RuO2 phase in the CO oxidation reaction. Surf Sci 603, 298–303. OVER, H. & MUHLER, M. (2003). Catalytic CO oxidation over ruthenium—bridging the pressure gap. Prog Surf Sci 72, 3–17. OVER, H., MUHLER, M. & SEITSONEN, A.P. (2007). Comment on “CO oxidation on ruthenium: The nature of the active catalytic surface” by D.W. Goodman, C.H.F. Peden, M.S. Chen. Surf Sci 601, 5659–5662. ROSENTHAL, D., GIRGSDIES, F., TIMPE, O., BLUME, R., WEINBERG, G., TESCHNER, D. & SCHLÖGL, R. (2009). On the CO-oxidation over oxygenated Ruthenium. Z Phys Chem 223, 183–208. SHARMA, R. (2012). Experimental set up for in situ transmission electron microscopy observations of chemical processes. Micron 43, 1147–1155. SHARMA, R. & CROZIER, P.A. (2005). Environmental transmission electron microscopy in nanotechnology. In Handbook of Microscopy for Nanotechnology, Yao, N. & Wang, Z.L. (Eds.), pp. 531–565. Boston, MA: Kluwer Academic Publishers. WAGNER, J.B., CAVALCA, F., DAMSGAARD, C.D., DUCHSTEIN, L.D.L. & HANSEN, T.W. (2012). Exploring the environmental transmission electron microscope. Micron 43, 1169–1175. YOKOSAWA, T., ALAN, T., PANDRAUD, G., DAM, B. & ZANDBERGEN, H. (2012). In-situ TEM on (de)hydrogenation of Pd at 0.5–4.5 bar hydrogen pressure and 20–400°C. Ultramicroscopy 112, 47–52.
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Analysis of Catalytic Gas Products Using Electron Energy-Loss Spectroscopy and Residual Gas Analysis for Operando Transmission Electron Microscopy Benjamin K. Miller and Peter A. Crozier* School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287-6106, USA
Abstract: Operando transmission electron microscopy (TEM) of catalytic reactions requires that the gas composition inside the TEM be known during the in situ reaction. Two techniques for measuring gas composition inside the environmental TEM are described and compared here. First, electron energy-loss spectroscopy, both in the low-loss and core-loss regions of the spectrum was utilized. The data were quantified using a linear combination of reference spectra from individual gasses to fit a mixture spectrum. Mass spectrometry using a residual gas analyzer was also used to quantify the gas inside the environmental cell. Both electron energy-loss spectroscopy and residual gas analysis were applied simultaneously to a known 50/50 mixture of CO and CO2, so the results from the two techniques could be compared and evaluated. An operando TEM experiment was performed using a Ru catalyst supported on silica spheres and loaded into the TEM on a specially developed porous pellet TEM sample. Both techniques were used to monitor the conversion of CO to CO2 over the catalyst, while simultaneous atomic resolution imaging of the catalyst was performed. Key words: operando, in situ TEM, environmental TEM (ETEM), mass spectrometry, EELS, catalyst
I NTRODUCTION Heterogeneous catalysis is playing an increasingly important role in chemical synthesis as well as many technologies related to sustainable energy such as fuel synthesis, solar fuels, and fuel cells (Bell et al., 2008; Benson et al., 2009; Kudo & Miseki, 2009; Gorte & Vohs, 2011). In addition to lowering the energy barriers associated with the rate-limiting step, an ideal catalyst should show long-term stability and be composed of earth abundant elements. A typical heterogeneous catalyst is a nanostructured material of high surface area to facilitate contact between the reactants. The catalyst must also contain suitable surface or subsurface structures (e.g. Behrens et al., 2012) that function to locally accelerate the reaction kinetics. An ideal catalyst structure and composition is dependent on the reaction of interest. Understanding the structure-reactivity relations for catalytic systems is a critical step in developing the next generation of improved catalysts. Determining the structure of the active form of a nanocatalyst is challenging because the functionally relevant nanostructure or surface motifs may only form under reaction conditions. The need to understand catalyst structure under reaction conditions has been a major motivation for the development of a wide range of in situ characterization techniques such as environmental transmission electron microscopy (ETEM) (Chenna & Crozier, 2012a; Jinschek & Helveg, 2012; Sharma, 2012; Wagner et al., 2012; Yokosawa et al., 2012). In a modern ETEM, the catalyst structure and Received September 29, 2013; accepted March 21, 2014 *Corresponding author. [email protected]
composition can be determined at atomic or near atomic resolution in the presence of reactive gases at elevated temperatures. Such instruments, which may be equipped with monochromators and aberration correctors, are either based on differential pumping systems or windowed cells and can allow the positions of atoms on the surface of nanoparticles to be precisely determined in the presence of reactive gases (Helveg et al., 2004; Sharma & Crozier, 2005; Creemer et al., 2008; Crozier, 2011; Yokosawa et al., 2012). Careful in situ measurement of structural evolution during catalyst activation can then be correlated with ex situ reactor data to construct structure-reactivity relations (Chenna et al., 2011). In situ ETEM allows the catalyst structure and composition to be determined in the presence of reactive gases, but it does not confirm that catalysis is actually taking place in the microscope. Indeed, the need for techniques, which simultaneously measure catalyst structure and activity, has been recognized for some time and was first developed for Raman spectroscopy where the term operando was introduced (Bañares & Wachs, 2002). The ability to determine structure and activity in the same experiment is a powerful approach to catalysis research and greatly facilitates the determination of structure-reactivity relations. It has been demonstrated that electron energy-loss spectroscopy (EELS) can be employed to measure the composition of the gas phase directly in the reaction cell of the ETEM (Crozier & Chenna, 2011). This approach was recently extended to show that catalytic products could also be unambiguously detected, thus opening the door to operando transmission electron microscopy (TEM) for catalysis research (Chenna & Crozier, 2012b).
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While the previous work (Chenna & Crozier, 2012b) demonstrated the possibility of using EELS for the detection of catalytic products there remain many questions about how best to implement spectroscopic approaches for operando TEM in catalysis research. A long-term goal is to make accurate determination of reaction kinetics directly inside the microscope. To achieve this goal more work is required to develop rapid quantitative methods to quantify gas compositions in the TEM using both low-loss and core-loss EELS. The composition of the gas can also be determined from residual gas analysis (RGA) using a mass spectrometer. However, the RGA is a high vacuum instrument and is typically located some distance away from the reaction cell. It is not clear how quantitative RGA can be for the measurement of gas composition in the reaction cell under typical high pressure conditions. How do EELS and RGA compare for gas determination in the microscope? In a differentially pumped ETEM it is necessary to employ a large volume of catalyst in an operando experiment to achieve reliable and quantifiable conversions in the microscope. This can lead to significant charging problems and the previous work did not demonstrate atomic resolution during catalysis. For successful operando TEM, two goals must be simultaneously accomplished; catalytic products must be detected and atomic resolution imaging achieved. To accomplish these goals, improved methods for TEM sample preparation are required. In the current work, methods to quantify EELS and RGA spectra are developed and explored for operando TEM with a primary focus on their application to gases of relevance for CO oxidation. CO oxidation has been a wellresearched reaction over the past decade, in part because of its application in fuel cells, where CO impurities in H2 fuel gas are a serious problem (Cheng et al., 2007). Several metals can catalyze this reaction, given in the following equation, but one of the most active is Ru (Over & Muhler, 2003). 2CO + O2 ¼ 2CO2 :
(1)
Despite the volume of research, there is still debate over exactly what surface structures are most active for CO oxidation. One group demonstrated that the highest activity is obtained from Ru metal with a chemisorbed oxygen layer (Goodman et al., 2007a, 2007b; Gao et al., 2009). Others suggested that RuO2 forms on the surface and this is the more active species for CO oxidation (Over et al., 2007, 2009; Flege et al., 2008). Work on large grain RuO2 powders identified an active suboxide as well as a more active metallic form of the material (Rosenthal et al., 2009). Operando TEM is ideally suited to address this issue because direct observation of the surface of Ru nanoparticles is possible while the catalytic conversion of CO is being simultaneously monitored. Moreover, the gases involved in this reaction are relatively simple to detect with EELS making it an ideal model system to further develop the operando approach. In this paper, approaches to determine the molar concentrations of gas mixtures using both low-loss and core-loss EELS are developed. In both approaches, quantification is achieved using least squares methods to fit linear combinations
of spectra from reference gas samples to a spectrum from the gas mixture. Robust analysis procedures are developed that can be easily integrated into the ETEM for rapid quantitative determination of the gas composition. The ETEM was modified to allow an RGA to be employed to continuously measure the gas composition in the reaction cell under high pressure conditions. Methods to quantify the RGA spectra were also developed and compared with those determined by low-loss and core-loss EELS. The sensitivity of both EELS and RGA to pressure variations was investigated and the two techniques compared for gas quantification. Finally, a novel approach for preparing a catalyst sample for operando TEM so that high conversions and atomic resolution imaging may be achieved is described. This approach was then employed along with simultaneous RGA and EELS to show a Ru catalyst activating and deactivating while changing the reaction conditions inside the ETEM. Simultaneous high-resolution electrom microscopy (HREM) images were obtained and correlated with different CO conversions demonstrating that atomic resolution operando TEM was accomplished.
MATERIALS
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METHODS
EELS To determine the gas composition in the reaction cell during an operando experiment, both core-loss and low-loss EELS were utilized. During spectral acquisition, the sample was shifted away from the electron beam, so that the beam passed only through the gas. During the operando experiment described, the total gas pressure was ∼2 torr, while during the comparison experiment the total pressure was ∼1.5 torr. Some spectra were acquired as single spectra, while others were acquired using a script for digital micrograph (Mitchell & Schaffer, 2005), which allows a series of spectra to be acquired automatically. This script additionally saves the entire CCD image of the spectrum before summation in the nondispersive direction. The spectral processing was performed using codes written in MATLAB. All the low-loss spectra were acquired using an extraction voltage of 3,500 V, and the largest spot size in TEM image mode. The spectrometer was set to a dispersion of 0.05 eV/ channel, using an entrance aperture of 2 mm, and an exposure time of 4 s. With such a long exposure time, the zero-loss peak must be shifted off the detector to prevent saturation. The collection angle is limited to 50 mrad by the lower differential pumping aperture of the environmental cell. The low-loss method used for measuring gas composition involved finding a linear combination of reference spectra from single gases, which best fits the experimental spectrum from a gas mixture. (The low-loss spectrum can be modeled as a simple linear combination in the case of gases, because the gas molecules have little electronic interaction with each other). Since reference spectra are used to quantify an experimental spectrum, it is essential that the experimental data be acquired under electron-optical conditions identical to those of the reference spectra.
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vibrations that arise from the fan, which cools the RGA electronics. The dedicated turbo pump was placed in an adjacent room to minimize its impact on the microscope resolution. This setup allowed the RGA to be operated close to a pressure of 10 − 4 torr, which is near the upper pressure limit for the instrument by adjusting the leak valve appropriately for each experiment, yielding high signal-to-noise in the data. All the techniques used to measure the gas composition inside the TEM should ideally yield identical results if correctly implemented and quantified. To determine whether this was the case, a simple comparison experiment was performed, in which a 50–50 mixture of CO and CO2 was flowed through the ETEM cell, and the composition measured over 70 min. For the first 20 min, the low-loss EELS method was used, and for the final 20 min core-loss spectra were taken. During the entire experiment, data from the RGA was collected simultaneously.
CO Oxidation and Operando TEM Figure 1. A schematic diagram showing how the residual gas analyzer (RGA) is interfaced to the reaction cell of the ETEM (which is located between the upper and lower polepieces of the objective lens). A leak valve and second small turbo pump work together to control the pressure at the RGA.
The core-loss spectra were acquired in TEM diffraction mode, with an extraction voltage of 4,100 V and the largest spot size. The spectrometer dispersion was usually 0.05 eV. The entrance aperture and acquisition time are identical to the low-loss case: 2 mm and 4 s, respectively. The convergence and collection semiangles were 2.4 and 2 mrad, respectively. The quantification of the core-loss data is similar to the low-loss technique. A linear combination of the reference spectra from the individual gases are fit to the mixture spectrum.
RGA A residual gas analyzer (RGA) was used to perform mass spectrometry on the gases present in the TEM vacuum system during the operando experiments. A Pfeiffer Prisma QMA 200 RGA was employed that uses an electron beam to ionize gas molecules passing through it, and then accelerates the resulting charged species through a set of quadrupoles. The ions are separated according to their mass-to-charge ratio (m/z) and the ion currents are measured using a Faraday cup. The RGA was set to measure 15 different massto-charge ratios approximately every 10 s. The vacuum setup for the RGA is shown in Figure 1. This required modification of the ETEM vacuum system, including the addition of a turbo pump dedicated to the RGA. To maintain the high vacuum necessary for its operation, the RGA was placed behind a leak valve, and was pumped by the dedicated turbo pump to a vacuum less than 10 − 4 torr. A bellows was placed between the microscope and the RGA to damp out
CO oxidation was performed over a Ru nanoparticle catalyst, supported on silica spheres prepared by the Stober method. Initial ex situ reactor studies have been published previously (Chenna & Crozier, 2012b) and show that the oxidation reaction to form CO2 occurs above a temperature of 150°C and conversions increase with increasing temperature. For the in situ operando experiments, CO and O2 were mixed in stoichiometric quantities, and then flowed through a carbon filter, to eliminate carbonyls, and into the microscope through a leak valve to achieve a pressure of around 2 torr in the ETEM reaction cell. The ETEM used was a differentially pumped FEI Tecnai F20 operated at 200 kV. The temperature was measured and controlled using a Gatan Ta hot stage, and was held at various temperatures from 150 up to 340°C. The microscope was equipped with a Gatan imaging filter allowing in situ EELS to be performed. To perform operando TEM, a relatively large amount of catalyst must be present inside the TEM sample holder, to provide adequate surface area for the reaction, and this requires the development of unique TEM sample preparation techniques. Standard ETEM samples for studying catalytic nanoparticles usually consist of a metallic grid onto which catalyst nanoparticles are dispersed. This grid has an insufficient surface area over which to disperse the silica-supported catalyst. In addition, small amounts of CO can be converted to CO2 on the sample holder thermocouple wires at temperatures over 300°C, so it is important to increase the CO conversion from the catalyst making the contribution of the holder negligible. To this end, a 3 mm diameter porous pellet (Fig. 2c) was created with a surface area nearly 50 times larger than a standard grid, allowing products generated by the now abundant catalyst to be monitored in the microscope. In the new sample preparation method, in addition to the traditional grid, a porous glass-wool pellet was secured inside the TEM holder, as shown in Figure 2c. Cu was chosen as the grid metal in this case, as it does not catalyze the CO oxidation reaction. The pellet was fabricated by firing
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Figure 2. Schematic diagram showing method of preparing catalyst for operando transmission electron microscopy (TEM). a: Supported metal catalyst consisting of Ru nanoparticles supported on silica spheres. b: Supported metal catalyst dispersed over silica fibers. c: Silica fibers are formed into a pellet of 3 mm in diameter. A Cu grid provides a conductive support for high-resolution electrom microscopy imaging of the silica sphere-supported catalyst.
crushed glass-wool packed inside a quartz tube, cutting sections, and then grinding it to a thickness between 0.5 and 0.8 mm. The pellet was then filled with the silica-supported catalyst (Fig. 2a) using a wet-impregnation technique. This dual-sample approach allows a high loading of catalyst into the TEM, while maintaining the ability to capture highresolution images of the catalyst. A more detailed description of this novel sample preparation technique will be given elsewhere.
RESULTS AND D ISCUSSION EELS A typical low-loss spectrum from a nominally 50–50 mixture of CO and CO2 is shown in Figure 3 along with the reference spectra for CO and CO2, which have been used to fit the spectrum from the gas mixture. The code, which automates the process of finding the ideal linear combination by a weighted least squares method, can perform this operation on a single spectrum, multiple spectra, or on the data cube generated by the multiple EELS acquisition code referenced in the experimental section (Mitchell & Schaffer, 2005). In addition to performing the least squares fit, the MATLAB code automatically shifts the energy scale and accounts for the background. To do this, the code first normalizes the reference spectra so their integrated intensities are all unity over the energy range of 4–44 eV. Then the first peak in both the reference spectra and the mixture spectrum are found and the energy of the measured peak set equal to the reference, in case the mixture spectrum has an incorrect energy calibration. Next, the linear combination of selected gases that best fits the spectrum from the gas mixture in the TEM is computed by a weighted least squares method, where the peaks in the mixture spectrum are given a higher weighting, by setting the weights equal to the square of the intensity. This least squares fit is repeated many times, while shifting the mixture spectrum slightly relative to the (precisely calibrated) reference spectra to get a precise, automatic energy calibration of the mixture spectrum, as shown in Figure 4, where an optimal fit is given in green and the poorest fits in red.
Figure 3. Illustration of the linear combination method for lowloss electron energy-loss spectroscopy of gases quantification for a 50/50 mixture of CO and CO2. The spectra from multiple individual gases (dotted lines) were suitably combined to yield a spectrum (blue) closely fitting the spectrum obtained from a mixture of those gases (green).
After the ideal fit is found (Fig. 5a), it is seen that the residuals often show a clear functional dependence, as seen in Figure 5b. This is owing to the zero-loss tails of the reference and mixture spectra being slightly different. No background removal is performed on either the reference or mixture spectra to attempt to completely remove the zeroloss tails; instead the backgrounds of the reference and mixture spectra were matched. This is done in practice by fitting the residuals using an inverse power law, and this fit is subtracted from the measured spectrum. This procedure, shown in Figure 5, is equivalent to performing background subtraction on both the mixture and reference spectra, to remove the zero-loss tails, but is more robust in this case, where only a small window before the peak onset is available, making a more traditional background subtraction difficult. After the residual fit is subtracted, the background-matched spectrum is taken through the steps of fitting and shifting again to determine a new optimized fit. This process of matching the background is repeated as necessary until no clear power law dependence of the residuals is found
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Figure 4. The energy calibration method used for the low-loss electron energy-loss spectroscopy (EELS) analysis automation. To automatically and precisely calibrate the energy-loss axis of spectra measured from gas mixtures, the mixture spectrum is systematically shifted through a range of a few electron volts, while the reference spectra used to compute the fit (blue) remain fixed. a: Examples of good (green) and poor (red) energy shifts. b: The root mean square error (RMSE) of the linear combination fit plotted as a function of energy shift showing a single minimum when the spectrum is correctly calibrated.
Figure 5. The background matching method used for the low-loss electron energy-loss spectroscopy (EELS) analysis automation. a: After fitting a linear combination of experimentally obtained spectra of individual gases (orange) to a spectrum from a gas mixture (blue), the background of the composite spectrum and mixture spectrum do not match. b: The residuals from the fit show a clear pattern. This pattern can be fit quite well with a power law function (black). c: Subtracting this fitted function yields a mixture spectrum with a background that more closely matches that of the individual reference spectra so that no clear functional dependence is seen in the residuals (d).
(Fig. 5d). No procedure for deconvolution of plural scattering is applied, as plural scattering is minimal at the gas pressures used where the electron mean free path is much larger than the pole piece gap. (The typical values of scattering parameter are in the range 0.01–0.02 for the current experiments). Once the linear combination coefficients have been determined, information about the reference spectra is used to compute the gas partial pressures, as detailed in Crozier and Chenna (2011). Application of this quantification method to the mixture spectrum in Figure 3 yields a result of 49.2% CO2 and 50.8% CO, with a standard deviation of 0.7% as discussed below.
Core-loss quantification is similar to the low-loss method, and uses a linear combination of reference spectra to fit the spectrum from a gas mixture. For all the core-loss spectra, including the reference spectra, the backgrounds were fit with the usual inverse power law using a pre-edge window of ∼25 eV. This background was extrapolated 25 eV past the edge onset and subtracted before beginning the analysis. (This is in contrast to the low-loss method, where the backgrounds of the reference and mixture spectra are matched rather than subtracted). After background subtraction, a simple least squares approach was used to find the linear combination of reference spectra from CO and CO2
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Figure 6. Illustration of the linear combination method for coreloss electron energy-loss spectroscopy of gases quantification for a 50/50 mixture of CO and CO2. The spectra from multiple individual gases (dotted lines) were suitably combined to yield a spectrum (blue) closely fitting the spectrum obtained from a mixture of those gases (green).
Figure 7. The composition determined from quantification of electron energy-loss spectroscopy (EELS) spectra series from both the low-loss (blue) and core-loss (orange) regions are compared with the signal from the residual gas analyzer (RGA, gray). Energy-loss spectra were taken every 20 s in two 20 min blocks. Peaks in the mass spectrum were monitored every 10 s. The time between the two blocks of EELS spectra was used to adjust the spectral acquisition parameters.
that best fit the mixture spectrum. This is shown in Figure 6 for a nominally 50/50 mixture of CO and CO2. The spectra were fit over the entire acquisition range of about 50 eV, and the result obtained was a composition of 51.0% CO2 and 49.0% CO, with a standard deviation of 0.4%. This is quite close to the nominal composition, and in good agreement with the low-loss EELS result. Using the data from the comparison experiment (Fig. 7), the precision of each EELS technique can be evaluated independently of its accuracy because EELS spectra were taken automatically every 20 s using a script for digital micrograph (Mitchell & Schaffer, 2005). Sixty spectra were taken in the low-loss region of the spectrum, giving a mean value of 49.6% CO2 and 60 from the core-loss region, giving a mean of 50.7% CO2. It is clear that the variance for both quantification methods is small. The standard deviation of the results shown in Figure 7 for the low-loss and core-loss
Figure 8. Raw analog data from the residual gas analyzer (RGA) for a 50/50 mixture of CO/CO2 (orange), as well as a background spectrum acquired just before beginning the gas flow (blue). Peaks for CO/N2, CO2, as well as 13 others are monitored by the RGA software over time to yield the usual RGA data.
techniques are 0.7 and 0.4%, respectively, and the standard errors of the means are 0.10 and 0.05%. The difference between the mean value obtained using the low-loss and core-loss techniques is 1.1%, with the mean of the low loss just below the nominal composition of 50%, and that of the core loss just above. The pressures in the microscope during the EELS reference spectra acquisition were controlled to within 2% of 1 torr for both CO and CO2. Even this small error is much larger than the variation in gas composition computed from the 60 individual core-loss or low-loss spectra shown in Figure 7, so the greatest uncertainty in the calculation of gas partial pressures using either region of the spectrum comes from the uncertainty in the reference gas pressures, rather than the quantification procedures, which are quite precise. The low-loss and core-loss EELS techniques used to quantify gas mixtures have both advantages and disadvantages. The low-loss spectra are more difficult to quantify by a linear combination than the core-loss spectra for the CO–CO2 mixture. In the low-loss energy regime all the peaks are overlapping, and other gases present in the system in small quantities can contribute to the signal, whereas in the core-loss case, only carbon-containing compounds contribute to the edge and the main peaks are well separated. However, the low-loss technique is quite versatile, as any mixture of gases can be analyzed over the same energy range, while the core-loss technique would be much more difficult for combinations of gases with edges far apart. For mixtures containing hydrogen gas, the low-loss region must be used as that is the only region of the spectrum than contains the H2 edge.
RGA Raw analog data from the RGA is given in Figure 8, where peaks for CO, CO2, and several other gases present in the TEM vacuum can be seen. The background gas pressure in the ETEM cell is easily detectable in the RGA and this background varies from day to day, depending on the most recent experiments. The signal from the background and from a nominally 50/50 mixture of CO and CO2 is shown in
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Figure 8. As described in the experimental section, the RGA software automates the monitoring of 15 different mass-tocharge ratios as a function of time, so that it is not necessary to acquire or analyze raw mass spectra. The ratio of CO2 to the total CO + CO2 as a function of time for the comparison experiment is shown in Figure 7. This result was obtained by first subtracting a background, the RGA signal before gas is admitted to the reaction cell, and then simply taking the ratio of the currents from CO and CO2, divided by known standard ionization cross-sections (O’Hanlon, 2003). This yields a value that ranges from 48.5% CO2 to 50.3% CO2. This simple ratio is assumed to adequately represent the ratio of the partial pressures of the two gases in the vacuum system. This apparently satisfactory result may have been somewhat fortuitous because the mass spectrometry technique using the RGA is one that introduces significant complications to quantification. Though it is extremely sensitive to small quantities of gas molecules, if a molecule of oxygen were to be doubly ionized, or split into two oxygen ion fragments, with one charge each, the resulting species would be seen at a mass of 16 amu. This is the same mass as a methane molecule, and this example underscores one of the primary deficiencies of this technique, namely that the signals from different gas species sometimes overlap completely. Most notably for our experiments, CO and N2 have an identical mass of 28 (Fig. 8), and are thus difficult to distinguish, leading to an overestimate of the CO concentration. A simple method employed here is to subtract constant background values, obtained when no gases are admitted to the ETEM, from each of the experimental peaks. A more thorough treatment of this peak overlap issue would require calibration spectra to be taken for all the gases present in the system, so that a linear combination fit to the experimental data could be obtained. The RGA data shown in Figure 7 exhibits a sharp dip and broad peak around 30 min into the experiment; 28 min after beginning the gas flow, the flow was suddenly slowed, so that the pressure in the cell dropped about 20%. This pressure change does not significantly affect the molar concentration ratio in the reaction cell but it caused the RGA peak ratio to drop by about 5%, and thus changed the computed CO to CO2 composition. When the pressure was restored to the original value the signal ratio did not return to the exact same value. Indeed, the steady drop with time that can be seen in Figure 7 is primarily because of a gradual pressure drop in the mixing tank leading to a corresponding pressure drop in the cell. This demonstrates that the signal from the RGA as a function of pressure is not linear, making it difficult to directly relate a measured current in the RGA to a partial pressure of gas in the vacuum system. Furthermore, the current placement of the RGA (near the turbo pump that provides the first level of differential pumping as shown in Fig. 1) far from the catalyst sample along with the RGA’s differential pumping system, may result in the gas composition in the RGA being different from that in the reaction cell due to differential diffusion in the vacuum system. The initial rise in the RGA
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data in Figure 7 demonstrates that gas reaches the RGA within about 1 min of its introduction to the ETEM, but about 5 min are required for the composition to reach equilibrium. These limitations combine to make the RGA data quantification uncertain, though as the results given in Figure 7 show, simple data quantification still seems to be reasonably accurate in this case. Even with the limitations of the RGA previously described, mass spectrometry is a useful technique for monitoring the gas composition within the ETEM. The main benefit of the technique is its independence from the operation of the microscope. The RGA can collect data during imaging, EELS acquisition, or even with the electron beam blanked. Thus, data can be acquired continuously over the course of an entire experiment. Furthermore, the data can be easily visualized live while the user is operating the microscope, allowing any changes in gas composition to be identified immediately. The EELS and RGA techniques have advantages and disadvantages. The EELS techniques are quantitative and probe the gas in the environmental cell directly. In either EELS technique, a disadvantage for operando studies is that the beam cannot be used for high-resolution imaging while it is employed to detect the gases via EELS. This is in contrast to the mass spectrometry technique using the RGA, which can gather data continuously during the course of the experiment. The RGA is, however, a less reliable technique for accurate quantification of gas compositions, and probes the gas in the vacuum system far from the ETEM reaction cell. The RGA and EELS techniques are thus complimentary, and ideally suited to simultaneous use.
Operando Some of the core-loss data acquired during the operando experiment are given in Figure 9. It is clear from a simple inspection of the data that significant conversion of CO to CO2 took place as the temperature was increased from
Figure 9. The carbon K-edge recorded as a function of temperature from the CO oxidation operando experiment with the glass fiber pellet. The spectra were quantified using the peak integration method and the calculated conversions are shown in the inset.
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Figure 10. High-resolution image of a Ru particle during CO oxidation. Lattice fringes are clearly visible in the image, and analysis of the fast Fourier transform shown in the inset confirms that the particle is in the metallic state, aligned along the [001] zone axis. When the image was acquired, the CO conversion inside the environmental transmission electron microscopy cell was 55% as determined by the electron energy-loss spectroscopy core-loss quantification method.
150 to 340°C. These data were analyzed using the linear combination method, and the results of this quantification are also given in Figure 9. This maximum conversion observed in the operando experiment was surprisingly high considering that the reactor geometry of the ETEM cell is complex, with a large volume of gas, and a small TEM sample, so that at any given moment only a small amount of the gas is in contact with the catalyst, and some gas molecules move through the system without ever interacting with the sample. The purpose of the unique sample preparation used in this experiment was to increase the catalyst surface area inside the ETEM, so that significant conversion would take place. This was clearly effective. In addition to the high conversions observed and quantified using EELS, high-resolution TEM images were also successfully obtained from the catalyst during the reaction. These images can be directly linked to the gas composition present inside the ETEM cell at the moment they were acquired. Figure 10 shows an atomic resolution image acquired from a Ru particle while catalysis was taking place in the microscope. In this case the temperature was 233°C and the CO conversion measured by EELS was ∼50%. This particular Ru particle was on a silica sphere that was in direct contact with the Cu grid. The glass-wool fibers that make up the pellet are insulating, and charge under the electron beam, degrading imaging, so that atomic resolution is difficult if not impossible to obtain from catalyst particles in contact with these fibers. The charging effects are significantly reduced when gas is present in the reaction cell, presumably because the gas can also facilitate the discharging process. Nanometer resolution imaging can be achieved in the presence of gas, but so far atomic resolution has not been obtained from fiber-supported particles. The Ru/silica particles that are in direct contact with the metallic grid are much more stable under the electron beam and high-resolution images can be easily obtained. The metallic and glass-wool parts of the TEM sample should have
very similar environments since they are in close proximity within the heating holder and the gas in the reaction cell is well mixed. This similar environment means that observations of particles made on the grid-supported silica spheres should be representative of the entire population of catalyst particles inside the ETEM. Figures 9 and 10 demonstrate that both high conversions and atomic resolution have now been achieved and thus this novel sample preparation technique is ideal for performing atomic resolution operando TEM. Insights into the structure-reactivity relations can be obtained by looking at changes in the structure of catalysts during activation and deactivation cycles (Chenna et al., 2011; Chenna & Crozier, 2012b). Catalysts may undergo changes when significant activation of reactants takes place because the ambient gas environment may change as products are generated and the distribution and composition of adsorbates on the surface may also change. The easiest way to dynamically change the activation is by varying the sample temperature while flowing reactants through the environmental cell. As the temperature increases, the reactants activate on the surface of the catalysts causing the reaction rate to increase resulting in the generation of a large concentration of products. Figure 11 shows the CO, CO2, and O2 signals from the RGA during an activation/deactivation cycle. During the ramp-up, the temperature was held constant at 130, 170, and 230°C corresponding to distinct plateaus in the CO2 profile. The RGA provides a powerful approach for visualization of the relative changes in the rate of product generation inside the ETEM as the temperature is ramped up and down. The drop in the CO and O2 signal correlates exactly with an increase in the CO2 signal showing that CO oxidation is taking place. Energy-loss spectra were recorded from the gas at these three plateau points to quantify the actual conversions taking place within the cell. The derived conversions were 0, 12, and 55% at temperatures of 130, 170, and 230°C, respectively. HREM images were also recorded from individual particles at approximately the same temperature/conversion. These images show the presence of both Ru and RuO2 phases even within the same particles while active catalysis is being performed. The sample was then cooled and the RGA profile immediately showed the drop in the CO2 signal as the conversion falls and the reaction effectively turned off at temperatures below 150°C. A detailed discussion of structure-reactivity relations will not be presented here, but Figure 11 clearly demonstrates the power of the combination of RGA and EELS to monitor and quantify the evolution of catalytic products generated within the ETEM. The ability to observe the atomic structure of individual nanoparticles during activation/deactivation cycles provides a powerful tool to develop a deeper understanding of the functioning of active heterogeneous catalysts.
CONCLUSIONS It is clear from the operando experiment that it is possible to image a catalyst with atomic resolution under reaction
Two Techniques for Operando TEM
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Figure 11. The simultaneous acquisition of data from the RGA, TEM, and EELS used in the operando experiment. a: The RGA collects qualitative data about the gas composition continuously. The activation of the catalyst is clearly seen when the temperature is raised to 170ºC. b: Electron energy-loss spectrum from the 230ºC plateau, showing a CO conversion of about 55%. c: A high-resolution image shows both RuO2 and Ru metal fringes. RGA, residual gas analysis; TEM, transmission electron microscopy; EELS, electron energy-loss spectroscopy.
conditions, while simultaneously monitoring those conditions and calculating catalytic conversions using EELS and RGA. EELS provides a quantitative method for monitoring the gas composition inside the ETEM reactor. Both low-loss and core-loss regions of the spectrum can be used, depending on their convenience for the gases to be monitored. Least square fitting methods were developed to quantify the spectra and accuracies can be better than 1% for both low-loss and core-loss analysis of CO/CO2 mixtures. RGA has also been successfully employed to monitor the gas composition, and though it is not fully quantitative, the data is continuous, providing an ideal complement to the EELS techniques. These three techniques are in reasonable agreement for a mixture of CO and CO2, as demonstrated by the simple comparison experiment. Ensuring that enough catalyst is present inside the microscope is essential to successfully perform operando TEM, and the fibrous pellet samples sandwiched between two copper grids work well for this purpose. The preparation method yielded conversions of up to 80% and atomic resolution images. Operando TEM has great potential for elucidating structure-reactivity relationships for catalysts at the atomic scale, and the future use of aberration-corrected ETEMs will surely increase the power of operando TEM.
ACKNOWLEDGMENTS This work was supported by the National Science Foundation grant NSF-CBET 1134464. The use of ETEM at the John M. Cowley Center for HR Microscopy at Arizona State is gratefully acknowledged. The authors also gratefully acknowledge Trevor Barker who prepared the operando porous pellets.
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