2DCOR-GC: An Application of the Generalized Two-Dimensional

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Anal. Chem. 2004, 76, 6197-6206

2DCOR-GC: An Application of the Generalized Two-Dimensional Correlation Analysis as a Route To Optimization of Continuous Flow Supercritical Fluid Reactions Jason R. Hyde,*,† Richard A. Bourne,† Isao Noda,‡ Phil Stephenson,† and Martyn Poliakoff†

School of Chemistry, University of Nottingham, University Park, Nottingham, U.K., NG7 2RD, and Procter & Gamble Company, 8611 Beckett Road, West Chester, Ohio 45069

A new approach for optimization and monitoring of continuous reactions has been developed using 2D correlation methods for the analysis of GC data (2DCOR-GC). 2DCOR-GC maps are obtained following perturbation of the system that allow the effect of changing reaction parameters such as time, temperature, pressure, or concentration to be both monitored and sequenced with regard to changes in the raw GC data. In this paper, we describe the application of the 2DCOR-GC technique to monitoring the reverse water-gas shift reaction in scCO2. 2DCOR-GC is combined with FT-IR data to validate the methodology. We also report the application of 2DCORGC to probe the mechanism of the alkylation of m-cresol with isopropyl alcohol in scCO2 using Nafion SAC-13 as the catalyst. These results identify coeluting peaks that could easily be missed without exhaustive method development. Two-dimensional (2D) correlation spectroscopy was first introduced into NMR in the late 1960s and has since flourished as an analytical technique. The application of the correlation approach has led to the development of familiar experiments such as COSY, NOSY, and DOSY, which reveal substantially more information than 1D experiments.1,2 The concept of a generalized 2D correlation was first extended to IR spectroscopy in 1993 by Noda,3 where it provides further insight into spectral changes with respect to an external perturbation. The correlation function as described by Noda is based upon a mathematical background,4 where the only prerequisite is the subtraction of reference data from a set of spectral data collected sequentially under an external perturbation. Perturbation-based 2D IR studies have elucidated many systems where little informa* To whom correspondence should be addressed. Fax: +44 115 9513058; Tel: +44 115 9513386; E-mail: [email protected]. † University of Nottingham. ‡ Procter & Gamble Co. (1) Ernst, R.; Bodenhausen, G.; Wakaun, A. Principles of NMR in One and Two Dimensions; Oxford Univeristy Press: Oxford, U.K., 1987. (2) Bax, A. Two-Dimensional Nuclear Magnetic Resonance in Liquids, Delft University Press: Boston, 1982. (3) Noda, I. Appl. Spectrosc. 1993, 47, 1329-1336. (4) Ozaki, Y.; Sasic, S.; Tanaka, T.; Noda, I. Bull. Chem. Soc. Jpn. 2001, 74, 1-17. 10.1021/ac049360l CCC: $27.50 Published on Web 09/23/2004

© 2004 American Chemical Society

tion was previously available. It has the advantage that, even when there is little understanding of a spectral region, changes can be correlated with features in better-understood regions of the spectrum, thus revealing more information about an overall system. Perturbations have included changes in concentration5 (which is particularly applicable in cases where the Beer-Lambert law is not obeyed, as a linear response to the perturbation is not required) and changes in temperature6,7 and pressure,8,9 which are commonly measured as a function of time. The application of 2D correlation analysis is not limited to different regions of the same spectral observation. Heterospectral correlation of differing measurements is relatively commonplace and has found applications in various combinations of techniques, such as IR-Raman and IR-NIR. This type of analysis holds the advantage that IR is well understood, and correlations with the Raman or NIR help explain and expand the understanding of these other spectral observations.10-12 In recent years, the 2D correlation analysis has been extended in its application to include nonspectroscopic analytical techniques. Two-dimensional correlation gel permeation chromatography (2D GPC) has recently gained much attention,13-17 where the 2D correlation analysis has been applied to help understand the changes in polymer structure during polymerization processes. For example, the 2D-GPC technique has yielded information about the growth of SiO2 during a sol-gel production.15 (5) Marrcott, C.; Noda, I.; Dowrey, A. E. Anal. Chim. Acta 1991, 1, 131-143. (6) Noda, I.; Liu, C. Y.; Ozaki, Y.; Czarnecki, M. A. J. Phys. Chem. 1995, 99, 3068. (7) Ozaki, Y.; Liu, C. Y.; Noda, I. Appl. Spectrosc. 1997, 51, 526. (8) Noda, I.; Story, G. M.; Marcott, C. Vib. Spectrosc. 1999, 19, 461. (9) Smeller, L.; Heremans, K. Vib. Spectrosc. 1999, 19, 375. (10) Noda, I.; Liu, C. Y.; Ozaki, Y. J. Phys. Chem. 1996, 100, 8674. (11) Noda, I. Chemtracts-Macromol. Chem. 1990, 1, 89. (12) Ozaki, Y.; Noda, I. Two-Dimensional Correlation Spectroscopy; American Institute of Physics: Melville, NY, 2000. (13) Izawa, K.; Ogasawara, T.; Masuda, H.; Okabayashi, H.; O’Connor, C. J.; Noda, I. PCCP Phys. Chem. Chem. Phys. 2002, 4, 1053-1061. (14) Izawa, K.; Ogasawara, T.; Masuda, H.; Okabayashi, H.; O’Connor, C. J.; Noda, I. Phys. Chem. Commun. 2002, 12-16. (15) Izawa, K.; Ogasawara, T.; Masuda, H.; Okabayashi, H.; Noda, I. Macromolecules 2002, 35, 92-96. (16) Izawa, K.; Ogasawara, T.; Masuda, H.; Okabayashi, H.; O’Connor, C. J.; Noda, I. J. Phys. Chem. B 2002, 106, 2867-2874. (17) Izawa, K.; Ogasawara, T.; Masuda, H.; Okabayashi, H.; Noda, I. Phys. Chem. Commun. 2001, article no. 12.

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The principal method for displaying 2D correlated chromatograms is to specify two orthogonal axes that define the correlation plane.13,16 In 2D correlation analysis (COR)-GC, the axes represent the chromatograms collected, where the variable is the retention or elution time, τ, of each of the individual compounds. The correlation intensity is plotted on a third axis, normal to this plane, and is displayed as contours. This diagram is known as a “correlation map” and simplifies a series of complex chromatograms revealing overlapping or coeluting peaks by the extension into the second dimension and also identifies peaks that are selectively coupled by the mechanism of a reaction. To achieve a change in intensity of a chromatographic peak, a perturbation has to be applied to the studied system. This perturbation can be any reasonable systematic physical or chemical change. The chromatograms are collected in the normal quantitative manner at intervals as the system is perturbed. The change of intensity of the chromatographic peaks, as a function of this perturbation, is known as the dynamic chromatogram. (The term dynamic chromatogram in the context of this paper refers to the changes in intensity of a chromatographic peak as a function of the perturbation and should not be confused with a “dynamic” system, such as the interconversion of stereoisomers in chromatography.) Thus, if a reaction were monitored by GC while the temperature of the system was increased, the dynamic chromatogram represents changes from low to high temperatures. The corresponding correlation maps also reflect this perturbation, and correlations between eluting compounds will be related or coupled by this increase in temperature. The variation of chromatographic signal intensity, y, can be thought of quite simply as y(τ,t), where t is the tth step of the perturbation. This encompasses the perturbation, t, measured between Tmin and Tmax at each chromatographic index, τ, e.g., retention or elution time. In a similar fashion, the dynamic chromatogram can be defined as ˜y(τ,t), but it is more formally defined as

˜y (τ,t) )

{

y(τ,t) - jy(τ) 0

for Tmin e t e Tmax otherwise

where jy(τ), the reference chromatogram, is usually taken to be the mean-averaged chromatogram collected between Tmin and Tmax, again defined more formally as

jy(τ) )

∫

1 Tmax - Tmin

Tmax

Tmin

y(τ,t) dt

The reference chromatogram can in fact be any single chromatogram in the series, for example, the first or last, or it can even be set to zero, in which case the dynamic chromatograms are the same as the raw chromatographic data. Once a series of dynamic chromatograms have been collected, the generalized cross-correlation function can be applied. This function takes the form

Φ(τ1,τ2) + iΨ(τ1,τ2) )

1 π(Tmax - Tmin )

∫ Y˜ (ω)Y ∞

0

1

* 2

(ω) dω

Φ(τ1, τ2) and Ψ(τ1, τ2), the real and imaginary components of the 2D correlation function, are known respectively as the 6198

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synchronous and asynchronous 2D correlation intensities. Y˜ 1(ω) and Y˜ 2*(ω) are the forward Fourier transform and the conjugate of the Fourier transform of the chromatographic intensity variations ˜y(τ,t) observed at retention times τ1 and τ2, which are calculated with respect to the variable t. The Fourier angle, ω, is also traced along the external variable, t. Fortunately, such complex calculations are not necessary in most cases, provided that the chromatograms are collected at m equally spaced intervals, because then the correlation intensities can be directly calculated by

Φ(τ1,τ2) )

Ψ(τ1,τ2) )

m

1 m-1

1 m-1

∑˜y (τ )y˜ (τ )

m

∑

1

j

j

2

j)1

m

∑N ˜y (τ )

˜y j(τ1)

j)1

jk k

2

k)1

where Njk is the Hilbert-Noda transform matrix.

Njk )

{

0 1/π(k - j)

if j ) k otherwise

These direct calculations can be solved by a number of mathematical packages such as Mathematica, Maple, or MatLab. In such cases, the correlation planes are plotted as a series of contour lines of equal correlation intensity. Synchronous correlation maps are an indication of the degree of similarity or coherence between two chromatographic signals τ1 and τ2, and represent the simultaneous changes of these signals. Synchronous correlation maps are necessarily symmetric about the diagonal axis, τ1 ) τ1. Since τ1 will always change simultaneously with respect to itself, any change in the dynamic chromatogram will exhibit a signal at coordinate Φ(τ1, τ1). Such signals are termed “autopeaks” and are always positive. Autopeaks show the correlation intensity of an individual signal with respect to the particular external perturbation. Thus, if chromatographic peak intensity changes strongly over the course of the perturbation, it will exhibit a strong autopeak. Peaks that do not undergo strong changes of intensity display weaker autopeaks and, in some cases, may not exhibit any autopeak at all. This is an advantage in correlation chromatography, since peaks that do not change in intensity, never appear on the correlation map. Thus, the inclusion of an internal standard will not interfere with the correlation analysis in any way, and it can be used to align data prior to the calculation of the dynamic chromatograms. Cross-peaks occur at coordinates Φ(τ1, τ2) and are due to simultaneous or coincidental changes at two different retention or elution times. These signals can be either positive or negative and represent changes in the same or opposite directions of intensity, respectively. Thus, if Φ(τ1, τ2) is positive, both τ1 and τ2 are increasing or decreasing at the same time; if Φ(τ1, τ2) is negative, one of the peaks is increasing while the other is decreasing simultaneously. Zero correlation between τ1 and τ2 indicates that the two signals are independent, changing randomly, or that one of the signals does not vary. Asynchronous correlation maps are antisymmetric about the τ1 ) τ1 diagonal and show the degree of dissimilarity or out-of-

phase changes between τ1 and τ2. Since an individual signal cannot change in either of these two manners, asynchronous correlation maps cannot contain autopeaks. Instead, the asynchronous maps only show off diagonal peaks, which must be connected via the τ1 ) τ1 diagonal to construct correlation squares between τ1 and τ2. Again, these peaks may be positive or negative, but in this case, a positive peak is obtained if an intensity change in τ1 occurs before an intensity change in τ2; negative peaks are delayed responses, and thus an intensity change in τ1 occurs after an intensity change in τ2. This rule, however, is reversed when the synchronous correlation intensity at the corresponding coordinate is negative; i.e., Φ(τ1, τ2) < 0. Collectively, these relations are known as Noda’s rules.3 In the context of 2DCOR-GC, coeluting compounds exhibit τ1 ≈ τ2 and thus may give rise to a single chromatographic peak. However, provided that the two compounds exhibit different dynamic behavior, an asynchronous relationship can still arise, even if the two signals are moving in the same direction. This point is of great importance to cross-correlation chromatography, since rarely do two separate compounds chromatographically behave identically, and thus identification of coelution can be simplified enormously, as demonstrated in this paper. In this paper, we describe and validate the application of the 2D correlation analysis as directed toward gas chromatography, with the aim both to optimize and to understand chemical reactions performed in supercritical fluids (SCFs). An SCF is defined as any substance that is above its critical temperature and pressure (but below the pressure required to form a solid) and close to its critical density.18 SCFs exhibit a unique combination of gaslike and liquidlike properties.19 This combination of unique properties enables an SCF to dissolve both liquids and solids, where the solubility is generally pressure dependent. This phenomenon has been exploited to reduce mass transport limitations present during hydrogenation reactions.20,21 More recently it was used to tune reaction conversion and selectivity in a wide range of synthetically valuable transformations.22,23 However, the interrelation of SCF reaction parameters, conversion, and selectivity are not always simple, and much research is needed to understand the role of phase behavior and catalyst activity.24 Some of these research activities involve continuous fixed-bed catalytic reactors interfaced to on-line GC analysis.25 Such apparatus can yield so much data that information about the reaction can only be extracted with difficulty. It is our intent to tackle this superabundance of data that leads to the development of 2DCORGC. The primary purpose of this paper is to demonstrate the validity of our technique and to show that it really can provide chemically (18) A. D. M. IUPAC Recommendations; Blackwell Science Ltd.: Oxford, U.K., 1997. (19) Hyde, J. R.; Leitner, W.; Poliakoff, M. In High-Pressure Chemistry; Eldik, R. V., Klarner, F., Eds.; Wiley VCH: Weinheim, Germany, 2002; p 371. (20) Hitzler, M. G.; Poliakoff, M. Chem. Commun. 1997, 1667-1668. (21) Hitzler, M. G.; Smail, F. R.; Ross, S. K.; Poliakoff, M. Org. Proc. Res. Dev. 1998, 2, 137-146. (22) Hitzler, M. G.; Smail, F. R.; Ross, S. K.; Poliakoff, M. Chem. Commun. 1998, 359-360. (23) Gray, W. K.; Smail, F. R.; Hitzler, M. G.; Ross, S. K.; Poliakoff, M. J. Am. Chem. Soc. 1999, 121, 10711-10718. (24) Grunwaldt, J. D.; Wandeler, R.; Baiker, A. Catal. Rev.-Sci. Eng. 2003, 45, 1-96. (25) Walsh, B.; Hyde, J. R.; Poliakoff, M. Green Chemistry, submitted.

useful information about a real reaction under SCF conditions. Therefore, we first validate 2DCOR-GC by analyzing the relatively simple supercritical reverse water-gas shift reaction (RWGSR), shown by eq 1, and then apply the same technique to the

CO2 + H2 h CO + H2O

(1)

continuous alkylation of m-cresol over a solid acid catalyst in supercritical carbon dioxide (scCO2). The RWGSR is a well-known reversible reaction in equilibrium among CO, H2O, CO2, and H2. It is particularly relevant to reactions in SCFs, because it is a potential side reaction in the hydrogenation of organic compounds in scCO2 where it generates CO, a toxic gas, which can poison the catalyst. The advantage of choosing a reaction like the RWGSR from the point of view of validating 2DCOR-GC is that there are relatively few simple analytical instruments that can detect all four components simultaneously. However, there are many that can detect two or three of the components. In this paper, we have used three instruments, FT-IR and two separate micro-GCs fitted with different analytical columns (M5A and HSA, respectively), each of which can quantify only three of the components. However, by 2D correlation of the outputs from these instruments, we can obtain a full analytical picture of the equilibrium. Furthermore, the simplicity of the equilibrium means that it is easy to check that the 2D correlation is producing meaningful results. The RWGSR actually has a more immediate application in our own research interest area, since it is a key process in the generation of H2 + CO2 mixtures by the catalytic decomposition of HCO2H, which we are using as a route to miniaturizing supercritical hydrogenation “without gases”.26 The second part of this paper involves applying 2DCOR-GC to continuous Friedel-Crafts alkylation in scCO2. These reactions still have serious mechanistic and analytical questions,27 which can be addressed by 2DCOR-GC. Furthermore, the nature of these experiments makes it very easy to apply appropriate perturbations that are needed for successful 2DCOR-GC studies. The Friedel-Crafts alkylation of aromatics is a valuable synthetic tool that is widely employed in industry. Usually the aim is to make the monoalkylated product often with the alkylation occurring at a specific location on the aromatic ring. Traditionally, this is achieved by using an excess of a Lewis or Brønsted acid catalyst.28 The chemistry, however, often ends up being environmentally unfriendly, generating an excess of acidic waste that requires a large amount of cleanup. The reaction in scCO2 offers a promising route to cleaner alkylation by using solid acid catalysts, particularly in a continuous process.23,29,30 Performing this reaction in a continuous reactor gives several advantages including ease of catalyst and solvent separation, shorter reaction times, control of reactant concentrations and continuous production of product from a small reactor volume.22 Thymol (T, Chart 1) is a commercial product that can be produced by the Friedel(26) Hyde, J. R.; Poliakoff, M. Chem. Commun. 2004, 13, 1482-1484. (27) Albright, L. F. Ind. Eng. Chem. Res. 1998, 37, 296-297. (28) Olah, G. A. Friedel-Crafts and Related Reactions; Wiley Interscience: London, 1963. (29) Chateauneuf, J. E.; Nie, K. Abstr. Pap. Am. Chem. Soc. 2000, 219, 446ORGN. (30) Funamoto, G.; Tamura, S.; Segawa, K.; Wan, K. T.; Davis, M. E. Res. Chem. Intermed. 1998, 24, 449-459.

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Crafts alkylation of m-cresol (M-C) with isopropyl alcohol or propene. Unless optimized, the reaction yields several products that have similar physical properties and are often difficult to separate fully by conventional GC. In addition, there are several possible routes from M-C to a particular product, and it is not necessarily clear which route predominates under a given set of experimental conditions. We demonstrate that solving or elucidating such problems is particularly suitable for tackling with 2DCORGC. EXPERIMENTAL SECTION The continuous flow scCO2 reactors at Nottingham have been described previously21 and were used with only minor modifications for this work. In particular, all reactions were performed with a Jasco 1580-81 back pressure regulator rather than manual valves, to simplify product collection and to facilitate expansion of the SCF in a single stage. The different experiments were carried out as follows: Water-Gas Shift Temperature-Based Perturbation. Bottled CO2 and H2 (1:1 molar ratio) were delivered into a 10-mL catalyst bed containing a commercially available catalyst (2% Pd supported on SiO2/Al2O3) heated to reaction temperature (160 f 420 °C, in 20 °C steps). The system pressure was maintained by an electronic back pressure regulator set to 100 bar. Effluent gases were passed into a high-pressure infrared flow cell24 (path length 1 mm, CaF2) and IR spectra were measured (Nicolet Impact 410, 4-cm-1 resolution, 64 scans). The low-pressure gases were then passed via 1/16-in. SS-316 tubing into a glass tee piece with an internal volume of 2.5 mL. A vent was placed at the third entrance to the tee piece to avoid overpressurizing the sample line that led to the micro-GC. Gases were collected into a Varian 4900 micro-GC with an internal pump that drew gaseous samples into the injection port. Analysis was performed on two capillary columns (M5A 20 m with back flush (8 s) 120 °C/20.0 psi isobaric; HSA 0.4 m; 80 °C/20.0 psi isobaric. Both channels used a 50-ms injection time), and results were converted into the AIA format (a standard chromatographic file type). Water-Gas Shift Concentration-Based Perturbation. Formic acid (99.9% anhydrous, Aldrich UK) was passed by HPLC pump over a 10-mL catalyst bed (JM catalyst, 2% Pd/SiO2) heated to 450 °C. The pressure of the gases generated was maintained by an electronic back pressure regulator (Jasco 1580-81) and held constant at 100 bar. Water was then mixed with HCO2H via HPLC pumps with a gradient flow such that the total flow rate was maintained at 1 mL/min over a period of 3 h. The solution was passed over the catalyst bed. GC analyses of the gases exiting from the infrared cell were collected in a manner identical to that described previously. Alkylation of m-cresol by isopropyl alcohol (IPA): The design and use of a supercritical CO2 reactor has been described previously22 and was used without further modification to the design. The reactor used was a 12-mm tube ∼16 cm in length packed with Nafion SAC-13 catalyst. scCO2 and reactants were delivered as described earlier; m-cresol and IPA (Aldrich, 99%; 3:1 molar ratio) were pumped via HPLC pump into the static mixer where it was mixed with the scCO2. The catalyst temperature was monitored by a thermocouple (K-type) from within the catalyst bed. The flow rates of the organic mixture and scCO2 were 6200 Analytical Chemistry, Vol. 76, No. 21, November 1, 2004

Table 1. Detection Table for the RWGSR Reactants and Products

µGC HSAa µGC M5Ab,c FT-IR

CO2

H2

CO

H2O

yes no yes

coelute yes no

coelute yes yes

yes no yes

a HSA. porous polymer. b M5A, molecular sieves. c Using He as the carrier gas, the response to H2 is nonlinear, but this nonlinearity is not important in the experiment reported here.

increased such that the total concentration remained constant. Organic samples were collected directly from the BPR. Each sample was analyzed by standard quantitative GC methods (100 µL org/5 mL acetone, 0.1-µL injection volume, Shimadzu 2010, RTX-5 30 m, 40 °C iso 2 min, 10 °C/min 200 °C, iso 5 min). 2D-Correlation Analysis. 2D correlation analysis of both the GC and FT-IR was performed by converting the AIA (a standard chromatographic file type) format files into ACSII XY. These files were imported into MATLAB, and 2D contour maps were generated by a program based on code similar to that made available by Ozaki et al. (Matlab toolbox available for download at http://science.kwansei.ac.jp/∼ozaki/twod.zip) The reference for each correlation analysis was taken as the mean, jy(τ), in each case. Chromatograms were baseline normalized by accumulative summation and differentiation. As explained above, the 2D correlation analysis produces two different types of 2D plots, synchronous and asynchronous. The data from these two plots yield different information, provided that the process under study is not in an equilibrium state without further changes, as in the unperturbed RWGSR. The analysis can be performed on two types of data; a “homo” correlation, where the two sets of data come from the same analytical instrument (e.g. FT-IR-FT-IR, or GC1-GC1) and “hetero” correlation, where the data come from different instruments (e.g., FT-IR-GC, GC1-GC2). We exploit the full combinations of synchronous, asynchronous, and homo- and heterocorrelation methods. RESULTS AND DISCUSSIONS 2D Correlation of the Water-Gas Shift by FT-IR and GC. We have perturbed the RWGSR in two simple ways: first by changing the reaction temperature, which will alter the equilibrium constant, or second, by an isothermal change in reactant concentrations, which will change the equilibrium position. The experiment involves a 1:1 molar ratio of CO2 and H2 passed over a heated metal catalyst, which promotes the RWGSR. Both GC and FT-IR probes are then used to analyze the gases produced. This experiment provides us with three sets of data, one from FT-IR and one each from the two GC systems. These data can be used for traditional IR-IR 2D correlation, which validates the experiment, and IR-GC and GC-GC correlations, which demonstrate the principle of our new method. The detection capabilities of the three techniques are shown in Table 1. The chromatograms were collected using (i) a M5A molecular sieve column, which is capable of light gas separation with an elution order of H2, Ar, Ne, O2, N2, CH4, and CO, and (ii) an HSA porous polymer column, which separates gases heavier than CO and hydrocarbons. The H2, N2, and O2 elute as a single peak on the HSA column but are separated on the M5A; see Figure 1.

Figure 1. Chromatogram showing the separation of H2, CO, CO2, and adventitious air by (a) µGC HSA and (b) µGC M5A during the RWGSR (see Experimental Section.) Note that under the conditions used here the chromatograph is working in the region of nonlinear detection of H2, so that the peak, H2, shows only a small variation in height/area with changing concentration. Nevertheless, even this modest nonlinear response is quite sufficient to demonstrate the power of our 2DCOR-GC approach. Also note in (a) the peak due to H2/air which despite its very low intensity gives a signal in the 2D correlation.

Perturbation by Temperature. The RWGSR was run at constant pressure (10 MPa), and the temperature of the catalyst bed was varied between 160 and 420 °C, while the composition of the gas emerging from the reactor was analyzed. Figure 2b shows the correlation of the temperature-dependent IR data. The CO2 combination band at 3200 cm-1 and the corresponding ν3 fundamental bands at ∼2300 cm-1 can be seen to exhibit a positive correlation showing that the bands decrease or increase together as the catalyst bed temperature is changed. By contrast, the CO and H2O vapor bands, at 2100 and 1500 cm-1, respectively, show a negative correlation with the strong CO2 ≈ 2300-cm-1 band, indicating that as the CO2 concentration decreases, the CO and H2O concentrations both increase. This is the behavior expected in this system and shows that the RWGSR is indeed occurring. The apparent absence of autopeaks for the CO and H2O vapor indicates that under these conditions the absolute changes in intensity for these bands are small compared to that of CO2. Figure 2a presents the correlation of the M5A GC data. It can immediately been seen that the GC data have yielded a meaningful map. In more detail, the CO response shows a strong autopeak, which is due to the large change in intensity of the CO peak during the course of the perturbation; see Figure 1b. On the other hand, H2 has a much smaller change in intensity due to the nonlinear response, and the autopeak is too weak to register on this plot. However, despite the weakness of the H2 autopeak, it is absolutely clear from the off-diagonal peaks in Figure 2a that the concentration of H2 decreases synchronously as the concentration of CO increases. The two other correlation signals are due to O2 and N2 impurities, which arise because a small volume of air is drawn with the reacting gases from the gas injection device into the GC (see Experimental Section). Surprisingly, these impurities also

show an apparent correlation (negative) with CO because, as the CO concentration increases, there is a correspondingly smaller volume available for air to be drawn through the sample injection line. The heterocorrelation of the M5A and FT-IR data are shown in Figure 2c, clearly illustrating the relationship between the two techniques. As before, the CO and CO2 responses dominate the GC and FT-IR, respectively, and therefore, these signals show the strongest correlation intensities. The CO GC peak at just above 120 s in the M5A chromatogram strongly correlates with the entire FT-IR spectra, showing a negative correlation with both the fundamental and overtone IR bands of CO2 but a positive correlation with the CO and H2O IR bands. In addition, this series of correlation peaks can distinguish between the ν(O-H) bands of H2O and the overtones of CO2, which occur in a similar region of the spectrum, because the bands show opposite correlation with the CO peak. H2, O2, and N2 are all IR inactive due to their symmetry; nevertheless, they still correlate positively to the CO2 2300-cm-1 bands in the FT-IR spectra, because their concentrations are changing in the same direction. In summary, the correlations in this system can be seen to behave as expected both in the homo- and heterocorrelations, showing that the combination of these two measurements is a valid analytical technique. Moreover, there is little argument over the products of this reaction, its mechanism, and equilibrium position; the GC-GC, GC-FT-IR correlations indeed show the expected behavior. 2D Correlation GC of the Water-Gas Shift Perturbed by H2O Concentration. It is possible to perturb the RWGSR equilibrium by changing the concentration of one of the reactants isothermally. To this end, a mixture of CO, H2O, CO2, and H2 was generated by the catalytic decomposition of HCO2H over a 2% Pd catalyst. It is known that HCO2H can decompose both catalytically and thermally to produce a mixture of these gases, the composition of which depends entirely on the pressure, temperature, and particular catalyst. It is also known that high temperatures favor the pathway that leads to H2O and CO, but the presence of H2O is likely to shift the RWGSR equilibrium toward the left-hand side of eq 1 and to favor CO2 and H2 as the final products. We have previously employed the decomposition of HCO2H and H2O to generate high-pressure CO2 and H2 mixtures capable of hydrogenating organic compounds under SCF conditions. This approach to chemistry in an SCF holds the practical advantage that high-pressure bottled gases are eliminated from the laboratory,26 an aspect that is currently under commercialization by HEL Labs, UK. We now investigate the role of H2O by studying the effect of diluting pure HCO2H with H2O prior to the catalytic decomposition of the acid (see Experimental Section). The results are displayed in Figure 3, which shows the homocorrelations of the two GC chromatograms (plots a and b) and their hetrocorrelation (plot c). The M5A homocorrelation, Figure 3a, is similar to that in Figure 2a, although the nature of the perturbation was quite different in the two cases. The fact that these two plots are so similar increases confidence in the 2DCOR-GC approach. Figure 3b shows the corresponding homocorrelation of the HSA GC chromatograms. In this case, due the enhanced leveling of contours, it is possible to observe four peaks, one due to CO2, Analytical Chemistry, Vol. 76, No. 21, November 1, 2004

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Figure 2. (a) (Top left) Homo M5A and (b) (bottom right) homo-FT-IR produced during the temperature-perturbed RWGSR. (c) (Top right) The heterocorrelation of the M5A and FT-IR. Note the correlation of H2, O2, and N2, which are IR inactive, and also the small positive correlation of H2O close to the CO2 overtones ∼3600 cm-1. In all of the 2DCOR-GC plots, red is taken as a positive correlation and blue as a negative correlation.

one due to CO, and the final two very weak signals corresponding to H2, N2, and O2, and a small amount of CH4. It can be seen that the H2/air peak correlates negatively with CO and positively with CO2, while the CO2 and CO are negatively correlated. The heterocorrelation (Figure 3c) corroborates the identity of the major peaks. In detail, Figure 3 confirms that when the concentration of CO decreases (in the presence of H2O), the concentration of CO2 and H2 increase as expected. Alkylation of m-Cresol with Isopropyl Alcohol in scCO2 over a Nafion SAC-13 Catalyst. As a more realistic validation of the 2D correlation of GC data, we have tested the technique on a classical chemical reaction, the alkylation of m-cresol, performed under continuous flow scCO2. The key scientific question is whether, under these conditions, the reaction proceeds via initial alkylation of the OH group followed by rearrangement 6202 Analytical Chemistry, Vol. 76, No. 21, November 1, 2004

or via direct attack on the aromatic ring. The technical question in the context of 2DCOR-GC is whether any useful information can be derived from a reaction mixture that is far more complicated than that in the RWGSR. In this alkylation reaction, there are in fact at least eight possible species, which can be separated relatively easily by GC as shown in Figure 4, where for the purposes of this analysis we are ignoring any minor products. The synchronous and asynchronous maps for the alkylation reaction are shown in their entirety in Figure 5 and in detail in Figure 6. The perturbation was flow rate, and hence reaction residence time, achieved by maintaining a constant concentration profile over a range of differing flow rates of the scCO2 solvent. We now show that an interesting application of 2DCOR-GC is the identification of overlapping peaks of coeluting species, which may not be easily seen from the chromatograms presented in a

Figure 3. (a) (Top left) M5A homocorrelation and (b) (bottom right) the HSA homocorrelation map. Note the small correlations around the ∼0.1 min CO peak; these signals are due to H2, O2, and N2 (prior to CO) and CH4 (post to CO). The small volume of CH4 produced is due to the increasing residence time within the reactor as the overall gas flow rate decreases. (c) (Top right) the corresponding heterocorrelation plot, which confirms the identity of the major peaks. In all of the 2DCOR-GC plots, red is taken as a positive correlation and blue as a negative correlation.

more conventional manner. As mentioned in the introduction, one of the more interesting products of this reaction is thymol, T in Figure 4, which gives rise to the peak with a retention time of ∼13 min. Panels a and b of Figure 6 show expanded portions of the synchronous and asynchronous maps in the region of this peak. It is immediately clear that the autopeak peak for T is more complicated than the other correlation peaks in this diagram. In 2D-FT-IR, such patterns in the synchronous map can be attributed either to a shift in peak position or to two overlapping peaks changing in opposite directions. This ambiguity is usually resolved in the asynchronous map where, in this case, the pattern observed is exactly the pattern that would be expected for two overlapping peaks if the map were for 2D-FT-IR. However, there is a difficulty; a single-component peak in 2DCOR-GC can also exhibit this asynchronous pattern, because an increasing concentration in GC produces asymmetric peaks which have small differences in the peak maximums, i.e. anti-Langmuir/Langmuir isotherm-type adsorptions. Examination of the raw chromatograms (not il-

lustrated) suggests that there are two components that are moving in the same direction, which is not suggested by either of the correlation maps. A solution to this problem can be to align the T peak to a common maximum and, hence, a constant elution time, because unlike FT-IR, a distance usually separates the peaks in the chromatogram, which is large compared to the size of the peaks themselves. Therefore, a minor correction in the position of one particular peak will not have any effect on the shape, intensity, or retention time of the other peaks in the chromatogram. Although this procedure will clearly alter the absolute value of the retention time, it does not affect the qualitative arguments that we need to apply to these maps. Panels c and d of Figure 6 show the expanded region of the synchronous and asynchronous maps using the chromatograms after the adjustment of the peak assigned to T. The key point is that the synchronous map, Figure 6c, still shows that the autopeak of “corrected” T has a shape quite different from those of the the autopeaks assigned to M and P, respectively. Comparison with Analytical Chemistry, Vol. 76, No. 21, November 1, 2004

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Chart 1. Sequential Order of the Changes in Product Distribution Observeda

Figure 4. (Top) Gas chromatogram of the alkylation products of M-C with isopropyl alcohol. (Bottom) Chemical structures of the compounds produced during the alkylation of M-C with isopropyl alcohol.

the asynchronous map, Figure 6d, now reveals the characteristic pattern for two overlapping peaks in 2DCOR-GC at ∼13 min, and the presence of two components changing in the same direction, is confirmed by the cross-peaks involving T, which show positive correlations. This preeluting component has been identified as o-thymol, O′, and by Gaussian deconvolution, a chromatographic resolution of 0.28 can be determined. Indeed, this overlap can be confirmed by rerunning the samples with a different column and program to resolve the peaks.31 The behavior of O′ can be seen to be closely related to that of T, producing correlation signals of identical sign to T, the strength of which, however, differs in each case. The cross-correlation intensities of the three isomers, O′, T, and M can all be seen to be positive, indicating that their concentrations are increasing or decreasing together during the course of the perturbation. The second application of 2DCOR-GC is to narrow down a sequence in which the various products are formed. Using these aligned chromatograms, full correlation maps can be generated. Figure 5 shows the synchronous and asynchronous maps at high contour resolution. One can now sequence the event with respect to all eight major components of this mixture, by using Noda’s rules. The sequence produced is found to be M-C > E > O′ > T ) P ) D-O > M > D-P; see Chart 1. With a single experiment, it is not possible to draw conclusions as to the order of events between T, P, and D-O and because we have tentatively assigned this sequence based on the strength of the correlation signals. The sequence and correlation maps suggest that D-O is a primarily formed from O′, while D-P arises from a further reaction product of P. The position of M in the sequence suggests that it is formed (31) Amandi, R. Ph.D. Thesis, Nottingham University, Nottingham, U.K., 2004.

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a The perturbation was reaction residence time. From this sequence it can be seen that D-P was the last compound to increase in concentration, as the residence time was decreased, while E was the first compound to change in concentration during the course of the perturbation.

via a mechanistic route different from that of the other isomers, possibly a direct alkylation or a transalkylation. The correlations of E and M-C with all products suggest that M-C and E are in rapid equilibrium; thus, it is difficult to conclude if further reaction products originate from M-C or E. It is most likely a combination of the two. Further possible relationships can be drawn from these correlation maps, and there are several chemically plausible routes to each product, via either direct alkylation, Fries rearrangements, or intramolecular transalkylation reactions. It is important to stress here that this reaction is complex, and one single experiment will not answer all of the scientific questions posed. Moreover, the ease of detection of the O′ isomer, which was previously difficult, even with an extremely detailed examination of the raw data, shows that the use of 2DCOR-GC is a highly worthwhile exercise. CONCLUSIONS We have shown that the generalized 2D correlation approach described by Noda3 can successfully be applied to GC data obtained from reactions performed in scCO2. Although the RWGSR is a simple reaction, the 2D correlation of the GC data validates the use of the correlation method, thus opening the way for further experimentation using this technique. In this paper, the application of FT-IR has been used to relate this technique to the more traditional 2D-FT-IR, while also revealing information about each chromatographic component in the IR. We have applied the 2D correlation method to the GC analysis of supercritical fluid reactors, as a tool to help understand changes in

Figure 5. (a) (Top) Full synchronous correlation map generated from the T aligned chromatograms collected during the scCO2 alkylation of M-C with isopropyl alcohol over a Nafion catalyst. (b) (Bottom) Corresponding asynchronous correlation map.

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Figure 6. Synchronous and asynchronous correlation maps of the thymol, T, region of the chromatogram (12.9-13.45 min). (a) (Top left) Synchronous correlation map of the raw untreated data. (b) (Top right) The corresponding asynchronous correlation map. (c) (Bottom left) Synchronous correlation map of the T aligned data. (d) (Bottom right) The corresponding asynchronous correlation map of the T aligned data.

product distributions as a result of simple perturbations, such as a change in system pressure, temperature, residence times, or organic concentrations. The analysis holds the advantage that it reduces a large number of GC chromatograms into two information-rich maps. Examination of the 2DCOR-GC maps also reveals relationships that may not be apparent from traditional GC analysis due to inconsistent conversions. 2DCOR-GC also enhances the resolution of GC by identification of overlapping responses, which often becomes apparent within the asynchronous 2DCOR-GC map. By understanding the changes in selectivity or conversion with each perturbation in turn, a greater understanding of the chemical processes occurring in SCF reactors can be achieved, and we

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believe this will allow a more rapid optimization of the overall process. ACKNOWLEDGMENT We thank EPSRC (GR/R41644) and Thomas Swan & Co. Ltd. for support. We are grateful to M. Guyler, P. Fields, R. Wilson, S. K. Ross, B. Walsh, P. Licence, R. Amandi, and M. W. George for their help and advice. Received for review April 30, 2004. Accepted July 18, 2004. AC049360L