Energy Fuels 2010, 24, 5153–5162 Published on Web 08/31/2010
: DOI:10.1021/ef100504d
Chemical Shifts and Lifetimes for Nuclear Magnetic Resonance (NMR) Analysis of Biofuels
Department of Chemistry, ‡Forest Bioproducts Research Institute, §Department of Chemical and Biological Engineering, and Laboratory for Surface Science and Technology, University of Maine, Orono, Maine 04469 )
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Jincy Joseph,†,‡ Cirila Baker,†,‡ Saikrishna Mukkamala,‡,§ Sedat H. Beis,‡,§ M. Clayton Wheeler,‡,§ William J. DeSisto,‡,§, Bruce L. Jensen,† and Brian G. Frederick*,†,‡,
Received April 22, 2010. Revised Manuscript Received July 21, 2010
Determination of the molecular composition of biofuels is critical to process development. Because biofuels, such as pyrolysis oil, contain hundreds of compounds, quantitative determination of the mixtures is a formidable task and is often not necessary for routine development work. 13C and 1H nuclear magnetic resonance (NMR) offer a reasonable trade-off between functional group identification and analytical measurement effort. However, accuracy depends upon selection of chemical-shift regions, baseline compensation, and correction for incomplete longitudinal relaxation effects. We propose chemical-shift assignments and T1 correction factors based on 13C and 1H NMR measurements of over 50 compounds that have been previously identified in pyrolysis oils and several plant natural products, especially terpenes. The results are intended to allow for a semiquantitative assessment of molecular composition of bio-oils on a time scale of 1-8 h to provide feedback for process development.
therefore, downstream processing may need to be adjusted on the basis of composition. Although some work has been performed on quantitative analysis and determination of standards for production and development purposes,8 our focus here is on more routine analysis as a means of providing feedback to the pyrolysis reactor process and catalytic and upgrading development. The advantages and disadvantages of several techniques, including nuclear magnetic resonance (NMR), infrared (IR), gas chromatography/mass spectrometry (GC/MS), high-performance liquid chromatography (HPLC), and gel permeation, have been thoroughly discussed by Mullen et al.7 The main disadvantage of chromatographic methods are that only a small fraction of the molecular-weight distribution is analyzed by any one technique. Perhaps the major advantages of NMR are that the whole bio-oil can be dissolved in a suitable solvent [such as dimethylsulfoxide (DMSO)-d6] and that a qualitative assessment of the oxygen-containing functionalities can be determined simply from integration of appropriate regions of the 13C and 1H NMR spectra. The method has been popular for some time,9-11 and chemical-shift ranges have
1. Introduction Use of lignocellulosic biomass has the potential to supply a significant fraction of liquid transportation fuels. 1 Fast pyrolysis has gained interest as a means of energy densification, but the bio-oils are unsuitable for use in transportation fuels without upgrading.2,3 The bio-oils are acidic and viscous, polymerize readily, and have about half of the energy density of petroleum fuels because of an approximately 50% elemental oxygen content.4 Pyrolysis oils are very complex mixtures, containing hundreds of compounds, including phenols, carboxylic acids, aldehydes, ketones, aromatics, carbohydrates, and alcohols.5-7 The molecular composition of pyrolysis oils can vary significantly with both feedstock and reactor conditions;6,7 *To whom correspondence should be addressed. Telephone: (207) 5812268. Fax: (207) 581-2255. E-mail:
[email protected]. (1) Perlack, R. D. Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton Annual Supply; Oak Ridge National Laboratory: Oak Ridge, TN, 2005; DOE/GO-1020052135. (2) Huber, G. W. Breaking the Chemical and Engineering Barriers to Lignocellulosic Biofuels: Next Generation Hydrocarbon Biorefineries; Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET), National Science Foundation (NSF): Washington, D.C., 2008. (3) Huber, G. W.; Iborra, S.; Corma, A. Synthesis of transportation fuels from biomass: Chemistry, catalysts, and engineering. Chem. Rev. 2006, 106, 4044–4098. (4) Elliott, D. C. Historical developments in hydroprocessing bio-oils. Energy Fuels 2007, 21, 1792–1815. (5) Garcia-Perez, M.; Chaala, A.; Pakdel, H.; Kretschmer, D.; Roy, C. Characterization of bio-oils in chemical families. Biomass Bioenergy 2007, 31, 222–242. (6) Ingram, L.; Mohan, D.; Bricka, M.; Steele, P.; Strobel, D.; Crocker, D.; Mitchell, B.; Mohammad, J.; Cantrell, K.; Pittman, C. U. Pyrolysis of wood and bark in an auger reactor: Physical properties and chemical analysis of the produced bio-oils. Energy Fuels 2008, 22 (1), 614–625. (7) Mullen, C. A.; Strahan, G. D.; Boateng, A. A. Characterization of various fast-pyrolysis bio-oils by NMR spectroscopy. Energy Fuels 2009, 23 (5), 2707–2718. r 2010 American Chemical Society
(8) Oasmaa, A.; Meier, D. Characterisation, Analysis, Norms and Standards. In Fast Pyrolysis of Biomass: A Handbook; Bridgwater, A. V., Ed.; CPL Press: Newbury, U.K., 2005; Vol. 3, pp 19-59. (9) McKinley, J. W.; Barrass, G.; Chum, H. L. The application of nuclear magnetic resonance to the characterization of biomass liquefaction products. In Research in Thermochemical Biomass Conversion; Bridgwater, A. V., Kuester, J. L., Eds.; Elsevier Applied Science: Amsterdam, The Netherlands, 1988; pp 236-250. (10) Pakdel, H.; Roy, C.; Zeidan, K. Chemical characterization of hydrocarbons produced by vacuum pyrolysis of aspen poplar wood chips. In Research in Thermochemical Biomass Conversion; Bridgwater, A. V., Kuester, J. L., Eds.; Elsevier Applied Science: Amsterdam, The Netherlands, 1988; pp 572-584. (11) P€ ut€ un, A. E.; Uzun, B. B.; Apaydin, E.; P€ ut€ un, E. Bio-oil from olive oil industry wastes: Pyrolysis of olive residue under different conditions. Fuel Process. Technol. 2005, 87, 25–32.
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been suggested, for example, in pyrolysis oil samples dissolved in acetone-d6 by Mullen et al.7 and in DMSO-d6 by Ingram et al.6 The assignments used by Ingram et al. were based on work at the National Renewable Energy Laboratory (NREL) in the 1980s and 1990s (see refs 49 and 50 by Ingram et al.6) and have become a de facto standard. The bio-oils require a polar solvent, but hydroxyl proton shifts vary widely with the concentration in solvents, such as ethanol, in which hydrogen bonding is strong but to a much lesser extent in DMSO-d6.12 Thus, the use of DMSO-d6 as solvent allows both 13C and 1H NMR spectra to be measured and the information correlated. Perhaps the most significant limitation for quantification in 13 C NMR is low sensitivity because of the low (natural abundance) concentration of 13C, which is compounded by long T1 longitudinal relaxation times. We13,14 and others6,7 have typically chosen pulse repetition times, tpp, of 7-8 s, although T1 times for quaternary carbons may be as long as 50-60 s. The time to record 4000 transients necessary to achieve good S/N with full nuclear Overhauser enhancement (NOE) with tpp > 5T1 would exceed 14 days! Thus, a practical approach is needed to estimate the composition within a reasonable period of time. We have measured the 1H and 13C NMR spectra and 13 C longitudinal lifetimes for over 50 compounds, as standards in DMSO-d6, that were identified by Ingram et al.6 and selected natural products, including terpenes. NMR spectra of most compounds have been reported previously, under varying magnetic field strengths and in a variety of solvents. Chemical shifts are relatively insensitive to these factors, except for alcohol protons, which vary strongly with the concentration and solvents other than DMSO-d6. The T1 times vary with field strength and temperature. Thus, for the purpose of generating a consistent database of chemical shifts and T1 times, for assigning chemical shifts and compensating for varying longitudinal relaxation factors, all compounds were measured under similar conditions. The chemical shifts were confirmed by a comparison to predictions using NMR Predicts and by deuteration in a few cases. From the library of chemical shifts for 352 1H and 383 13C nuclei, which is available in the Supporting Information, we have sorted them into functional groups and proposed revised ranges based on the types of compounds present in pyrolysis oil. For 13C nuclei, we have compiled the T1 times into functional group distributions, from which we estimate correction factors as a function of tpp. Measurements of pyrolysis oils are presented to illustrate the method. We have applied our analysis to pyrolysis oils using different pulse repetition times and pulse angles to evaluate the accuracy in semiquantitative analysis for measurement times as short as 1 h, for the purpose of providing rapid feedback to process development. The relative uncertainties because of incomplete relaxation, nuclear Overhauser effects, and baseline corrections are discussed.
2. Experimental Section Most of the standard compounds used to measure NMR were purchased from Sigma-Aldrich, except compound 22 from Eastman Organic Chemicals, compounds 31 and 50 from Acros Organics, compounds 32, 37, and 41 from Supelco, and compound 30 from SAFC. Compound 15 was prepared by the reduction of compound 11 with NaBH4. The NMR solvent, DMSO-d6 [99.9% D þ 0.05% (v/v) tetramethylsilane (TMS)], was obtained from Cambridge Isotope Laboratories, and D2O (99.8 atom % D) was purchased from Aldrich. All standard compounds for 1H, 13C, and T1 experiments were prepared using 20-40 mg of the sample dissolved in 1 mL of DMSO-d6, with TMS as the internal standard in NMR tubes, with 5 mm outer diameter and 178 mm length. High-quality NMR tubes (Kimble Kontes, 400 MHz) were used to measure longitudinal relaxation times (T1). The standard samples were degassed (freeze-pump-thaw method) several times to remove dissolved oxygen, which is paramagnetic and reduces T1. 1H NMR spectra were recorded at 400 MHz, and 13C NMR spectra and T1 times were recorded at 100 MHz on a Varian Unity 9.4 T instrument at 29 C. 1H NMR spectra were recorded with a 51.2 pulse angle, 6000 Hz spectral width, 1 s delay, resulting in a pulse repetition time of 4.744 s/pulse, and 32 transients. 13C NMR were recorded with a 64.5 pulse angle, 25 000 Hz spectral width, 5 s delay, 6.199 s/pulse, and 256 transients. Proton chemical shifts were referenced to TMS, and 13C chemical shifts were referenced to DMSO-d6, at 39.43 ppm. We used the Varian “dot1” macro15 for measurements of 13C T1 times, using a range of d1 times from 0.5 to 30 s, except for compounds 11, 15, 29, and 31, for which an upper limit of 55 s was required. The chemical-shift assignments for 1H and 13C spectra of the standard compounds were compared to the predictions (with DMSO-d6 as the solvent for 1H shifts) from NMR Predicts Desktop, version 1.1 (Modgraph Consultants Ltd.), running within the MestReNova (version 5.2.5, Mestrelab Research SL) platform. Pyrolysis oils were obtained from pyrolyzing Pinus strobus at 500 C in a fluidized-bed reactor16 at the University of Maine, as described previously.14 R-Cellulose (Sigma-Aldrich) was pyrolyzed at 450 C in the same reactor configuration with a total nitrogen flow rate of 8 L/min. The lignin (Lignoboost) was pyrolyzed at 400 C and 8 L/min using the reactor as described recently.13 Samples for NMR were prepared in a 1:1 volume ratio with DMSO-d6 with TMS as the internal standard and measured in 5 mm tubes using a broad-band probe equipped for gradient shimming. Proton NMR spectra were acquired with a 51.2 pulse angle and a 6 s pulse delay, with co-addition of 32 transients. The sweep width was 6000 Hz. The 13C NMR spectra were acquired with a 64.5 pulse angle, full proton decoupling, and a sweep width of 25 000 Hz, corresponding to an acquisition time of 2.56 s. Acquisition of 4000 transients using a 4.5 s pulse delay resulted in good signal/noise ratios after approximately 8 h of total measurement time per sample. Spectra were also measured with pulse delay þ acquisition times of 1 þ 1.2 s = 2.2 s and 1.2 þ 29 s = 30 s for testing our relaxation time correction factors. Spectra of bio-oils were processed for baseline correction using MestreNova by convoluting with a Hanning filter and linear ramp, then apodizing with a 4 Hz exponential, and presenting the spectra as the magnitude.17
3. Results
(12) Abraham, R. J.; Byrne, J. J.; Griffiths, L.; Koniotou, R. 1 H chemical shifts in NMR: Part 22;Prediction of the 1H chemical shifts of alcohols, diols and inositols in solution, a conformational and solvation investigation. Magn. Reson. Chem. 2005, 43, 611–624. (13) Beis, S. H.; Mukkamala, S.; Hill, N.; Joseph, J.; Baker, C.; Jensen, B.; Stemmler, E. A.; Wheeler, M. C.; Frederick, B. G.; van Heiningen, A.; Berg, A. G.; DeSisto, W. J. Fast pyrolysis of lignins. BioResources 2010, 5 (3), 1408– 1424. (14) DeSisto, W. J.; Hill, N.; Beis, S. H.; Mukkamala, S.; Joseph, J.; Baker, C.; Ong, T.-H.; Stemmler, E. A.; Wheeler, M. C.; Frederick, B. G.; van Heiningen, A. Fast pyrolysis of pine sawdust in a fluidizedbed reactor. Energy Fuels 2010, 24, 2642–2651.
3.1. Chemical Shifts. Figure 1 shows the compounds for which 1H and 13C NMR spectra were recorded. NMR chemical (15) Holmes, D. Longitudinal relaxation time (T1) measurement. www2. chemistry.msu.edu/facilities/nmr/handouts/T1%20Measurement. pdf (accessed on March 4, 2010). (16) Hoekstra, E.; Hogendoorn, K. J. A.; Wang, X.; Westerhof, R. J. M.; Kersten, S. R. A.; Swaaij, W. P. M. v.; Groeneveld, M. J. Fast pyrolysis of biomass in a a fluidized bed reactor: In situ filtering of the vapors. Ind. Eng. Chem. Res. 2009, 48 (10), 4744–4756. (17) Cobas, C. Personal communication, 2010.
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Figure 2. Distribution of 1H chemical shifts according to the indicated functional groups from 352 1H nuclei in the 54 compounds measured as standards in DMSO-d6. Overlapping histograms are offset vertically for clarity.
as a solvent. The chemical shifts, predicted shifts for 13C and 1 H in DMSO-d6, are summarized in the Supporting Information. In most cases, the measured shifts agreed well with the predicted values. The hydroxyl groups of compounds 15, 17, 18, 27, 36, 44, and 51 that showed considerable chemicalshift differences with predicted values were verified by D2O exchange. The OH protons of all of the monosaccharides and levoglucosan (42 and 44-47) were also confirmed by deuterium exchange. The 1H and 13C NMR spectra in DMSO-d6 for compounds 1, 2, 44, 52, and 53 are consistent with previous work by Gottlieb et al.18 The 1H and 13C NMR spectra in DMSO-d6 for compound 3 was reported by Jones et al.19 The 1H NMR spectrum of compound 4 was reported in DMSO-d6 by Abraham and Mobli,20 and the 13C NMR spectrum was reported in CDCl3 by Archer and Johnson.21 The 1H NMR spectra of compounds 5, 8, 9, and 14 were reported in DMSO-d6 by Abraham et al.22 The 13C NMR spectra of compounds 18-23 and 28 were reported in CS2 by Jautelat et al.23 The 1H and 13C NMR spectra of compounds 45 and 46 were reported in DMSO-d6 by Hobley et al.24 The 13C NMR spectrum of compound 27 was reported in CDCl3 by Holden and Whittaker.25 The chemical shifts for the protons are summarized in histograms shown in Figure 2. Protons in alkyl groups range (18) Gottlieb, H. E.; Kotlyar, V.; Nudelman, A. NMR chemical shifts of common laboratory solvents as trace imprities. J. Org. Chem. 1997, 62, 7512–7515. (19) Jones, I. C.; Sharman, G. J.; Pidgeon, J. Spectral assignments and reference data: 1H and 13C NMR data to aid the identification and quantification of residual solvents by NMR spectroscopy. Magn. Reson. Chem. 2005, 43, 497–509. (20) Abraham, R. J.; Mobli, M. An NMR, IR and theoretical investigation of 1H chemical shifts and hydrogen bonding in phenols. Magn. Reson. Chem. 2007, 45, 865–877. (21) Archer, R. A.; Johnson, D. W. Carbon-13 nuclear magnetic resonance spectrsocopy of naturally occuring substances. 47. Cannabinoid compounds. J. Org. Chem. 1977, 42, 490–495. (22) Abraham, R. J.; Byrne, J. J.; Griffiths, L.; Perez, M. 1H chemical shifts in NMR: Part 23, the effect of dimethyl sulphoxide versus chloroform solvent on 1H chemical shifts. Magn. Reson. Chem. 2006, 44, 491–509. (23) Jautelat, M.; Grutzner, J. B.; Roberts, J. D. Natural-abundance 13 C nuclear magnetic resonance spectra of terpenes and carotenes. Proc. Natl. Acad. Sci. U.S.A. 1970, 65, 288–292. (24) Hobley, P.; Howarth, O.; Ibbett, R. N. 1H and 13C NMR shifts for aldopyranose and aldofuranose monosaccharides: Conformational analysis and solvent dependence. Magn. Reson. Chem. 1996, 34, 755– 760. (25) Holden, C. M.; Whittaker, D. Conformational studies of monoterpenes II;Conformational studies of bicyclo[3.1.1]heptane derivatives by 13C NMR. Org. Magn. Reson. 1975, 7, 125–127.
Figure 1. Standard compounds (54) identified in bio-oil by Ingram et al.6 and several plant and natural products, especially terpenes, for which 1H and 13C NMR spectra and T1 relaxation times were measured. Note that compound 36 was observed to exist primarily in the enol form.
shifts for these compounds were compared to those predicted by NMR Predicts Desktop, which was used within the MestreNova platform. The good accuracy found here was facilitated by the ability of this software to select DMSO-d6 5155
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Figure 4. Two-dimensional representation of the relationship between the proton and carbon chemical-shift regions for each of the functional groups.
Figure 3. Distribution of 13C chemical shifts according to the indicated functional groups from the 383 nuclei measured within the 54 compounds measured as standards in DMSO-d6. Overlapping histograms are offset vertically for clarity.
For more complex situations, such as lignin pyrolysis oils,13 we found that the 1H NMR data provided complementary information that was useful when correlated with the 13C NMR spectra for a particular sample. Figure 4 shows the assignments given in Table 1 in a 2D graphical representation that may be helpful in this respect. For example, although aromatic and non-conjugated alkene carbons cannot be distinguished from the 13C shift, they may be distinguished from the proton shifts. 3.2. Lifetime Measurements. The 13C longitudinal relaxation time constants, T1, measured for the standard compounds in DMSO-d6, were compiled into histograms according to the five main functional groups, as shown in Figure 5. Notice that the abscissa is scaled from 0 to 25 s for the upper four groups of carbon nuclei but is increased from 0 to 60 s for the carbonyl carbons. Clearly, under practical measurement conditions (tpp = 5-10 s), most methoxy/hydroxy and all carbohydrate carbons will relax fully, a large proportion of the alkyl carbons will fully relax, but a substantial fraction of aromatic/alkene and carbonyl carbons will not have time to relax. Qualitatively, the intensity of the latter functional group carbons will be underestimated. It has been known since early NMR work26 that lifetimes can vary from milliseconds to tens of seconds, and two general approaches have been developed for quantitation. The standard method26 is to use 90 pulses with a delay of 5 times the longest T1, but this would be prohibitively long. Alternatively, a smaller tip angle and tpp of order T1 can be chosen27-29 to minimize the difference in steady-state magnetization and improve the S/N ratio. Given a discrete set of lines, with known values of T1 and T2, correction factors can be calculated to improve accuracy.30 We are not aware of similar work to this in which statistical distributions of T1 times have been determined according to functional group. While T1 times vary with the temperature and magnetic field strength,31
from 0 to 3 ppm shift (wrt TMS). The region from 0 to 2 ppm is uniquely assignable to aliphatic protons, but signals between 2 and 3 ppm can also be assigned to protons on carbons R to a carbonyl group. The region from 3 to 4 ppm is predominantly associated with ethers, although the chemical shift for protons in ethers extended to 5.5 ppm. Unfortunately, the proton shifts in aliphatic OH groups (4-6.5 ppm) overlap those for the proton shifts in non-conjugated alkenes. Thus, the 4-6 ppm region cannot be unambiguously assigned. Protons in aromatic and conjugated alkenes span the 6-8 ppm range and are unambiguous from 6.5 to 8 ppm. The region from 8.5 to 13 ppm is uniquely identified with carboxylic acids, aldehydes, and phenols. Of course, intensity in this region should be related to intensity in the 2-3.5 ppm region. The 13C NMR chemical shifts are summarized as histograms in Figure 3. Although previous assignments6 suggest that the alkyl region could be divided into primary, secondary, tertiary, and quaternary carbons, we find that these distributions overlap extensively. Although there appears to be some overlap between secondary alkyl carbons and hydroxy/ methoxy carbons, the alkyl carbons were below 54 ppm and the hydroxy carbons appeared above 55 ppm. Therefore, we retain the assignment of Ingram et al.6 of 0-54 ppm for alkyl carbons. The region from 54 to 103 contains carbons that are singly bound to oxygen and can be divided fairly cleanly between hydroxy/methoxy carbons (54-70 ppm) and carbohydrate carbons (70-103 ppm). This is a significant change compared to the DMSO-d6 assignments suggested by Ingram et al.6 As shown in Figure 3, we distinguished between aromatic and non-conjugated alkene carbons in preparing the histograms; however, the distributions overlap completely. Therefore, we assign the region from 103 to 163 to aromatic and alkene carbons. The carbonyl carbons are, not surprisingly, easily distinguished from other functionalities, and we agree with the Ingram et al.6 assignment from 163 to 215 ppm to carbonyl carbons. Although at first sight the carbon NMR spectrum appears to be better for distinguishing the various functional groups, 1 H NMR can be acquired much faster and is worthwhile for two reasons. In cases where the compounds of interest can be uniquely identified, such as aldehydes, the proton NMR will allow for rapid quantitation because of the large signalto-noise ratio and simple sample preparation procedures.
(26) Shoolery, J. N. Some quantitative applications of 13C NMR spectroscopy. Prog. NMR Spectrosc. 1977, 11, 79–93. (27) Ernst, R. R.; Anderson, W. A. Application of Fourier transform spectroscopy to magnetic resonance. Rev. Sci. Instrum. 1966, 37 (1), 93–102. (28) Traficante, D. D. Optimum tip angle and relaxation delay for quantitative analysis. Concepts Magn. Reson. 1992, 4, 153–160. (29) Becker, E. D.; Ferretti, J. A.; Ghambir, P. N. Selection of optimum parameters for pulse Fourier transform nuclear magnetic resonance. Anal. Chem. 1979, 51 (9), 1413–1420. (30) Bharti, S. K.; Sinha, N.; Joshi, B. S.; Mandal, S. K.; Roy, R.; Khetrapal, C. L. Improved quantification from 1H-NMR spectra using reduced repetition times. Metabolomics 2008, 4, 367–376. (31) Shaw, D. Fourier Transform NMR Spectroscopy; Elsevier: Amsterdam, The Netherlands, 1984; Vol. 30.
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Table 1. Chemical-Shift Assignments Determined from Classification of 1H and 13C NMR Measurements for 54 Standard Compounds in DMSO-d6 type of proton
chemical shift (ppm)
type of carbon
chemical shift (ppm)
COOH CHO, ArOH aromatic and conjugated H aliphatic OH, -CdC-, Ar-CH2-Oether, methoxy CH2CdO, aliphatic aliphatic
12.5-11 11-8.25 8.25-6 6-4.2 4.2-3 3-2 2-0
carbonyl aromatic carbohydrate methoxy/hydroxy alkyl hydrocarbon
215-163 163-103 103-70 70-54 54-1
Figure 6. 13C NMR (upper panel) and 1H NMR (lower panel) spectra for pyrolysis oils generated from (A) pine, (B) cellulose, and (C) lignin.
NREL/MSU standard classifications, we measured 13C and 1 H NMR spectra for pyrolysis oils generated from whole pine flour, cellulose, and lignin. The spectra are shown in Figure 6, and the integrated areas are given in Table 2. Prior to integration, baseline correction was necessary. It can be shown that the superposition of many Lorentzian lineshapes leads to a broad hump in the baseline, which is due to true spectral intensity of the Lorentzian tails. However, distinguishing these broad humps from real fluctuations in the baseline because of instrumental effects is difficult and can lead to uncertainties in integrated areas of (5%. We therefore processed data by multiplying the free induction decay (FID) by a linear ramp, which is equivalent to taking the derivative in the frequency domain, apodizing with a Hanning filter, effectively converting the Lorentzian tails into a well-behaved, narrow peak, and then further apodizing with a 4 Hz exponential to improve S/N. The magnitude of the Fourier transformed spectrum then contained a signal superimposed with positive definite noise, which was straightforwardly compensated. We used a linear baseline in
Figure 5. Distribution of T1 times measured according to functional group. Smooth curves approximate a continuous distribution used for the calculation of correction factors.
we suggest that the qualitative differences between the functional groups will be useful under other conditions and, therefore, include the T1 values in the Supporting Information. We have used the T1 distributions to estimate correction factors as a function of the pulse repetition time and pulse tip angle; see section 4.2 below. 3.3. Pyrolysis Oil Measurements. To illustrate the relative changes in chemical functionalities estimated using the proposed chemical-shift regions, as compared to the previous 5157
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Table 2. Comparison of Integrated Spectral Regions of 13C and 1H NMR Data Based on Chemical Shifts by Ingram et al.6 and This Work (Table 1) for Pine, Cellulose, and Lignin Pyrolysis Oils 13
C NMR
ref 6
raw integrated area
this work
raw integrated area
assignments
chemical shifts
pine (%)
cellulose (%)
lignin (%)
chemical shifts
pine (%)
cellulose (%)
lignin (%)
alkyl methoxy/hydroxy carbohydrate aromatic carbonyl
0-54 54-84 84-110 110-163 163-215
19.1 37.3 7.7 30.8 5.2
11.4 64.8 11.4 8.3 4.1
22 11 2 61 4
0-54 54-70 70-103 103-163 163-215
19.1 16.6 22.2 36.9 5.2
11.4 17.8 57.2 9.5 4.1
21.8 10.6 0.8 63.2 3.6
1
H NMR
this work
assignments
chemical shifts
pine (%)
cellulose (%)
lignin (%)
aliphatic CH2CdO, aliphatic ether, methoxy
0-2 2-3 3-4.2
15.9 16.9 27.4
6.2 15.4 31.9
10.7 23.7 30.2
aliphatic OH, -CdC-, Ar-CH2-Oaromatic, conjugated -CdC-
4.2-6
12.1
38.9
5.6
6-8.25
22.3
6.4
24.8
CHO, ArOH COOH
8.25-11 11-12.5
4.6 0.9
1.2 0.0
5.0 0.0
1
H NMR
ref 6
raw integrated area
assignments
chemical shifts
pine (%)
cellulose (%)
lignin (%)
CH3, CH2 CH2, aliphatic OH CH3CdO, CH3-Ar, -CH2-Ar CH3O, CH2O, CHO
0-1.6 1.6-2.2 2.2-3
6.7 18.1 8.6
4.4 3.5 13.7
7.5 15.6 11.4
3-4.2
27.8
31.9
30.2
CHO, ArOH, HC=C (non-conjugated) HC=C (non-conjugated) ArH, HC=C (conjugated) CHO, COOH, downfield ArH
4.2-6.4
16.2
40.3
6.9
6.4-6.8 6.8-8 8-10
8.9 8.4 5.4
1.6 3.1 1.5
16.1 7.1 5.0
which a wavelet transform peak picking algorithm first identified spectral regions that contain peaks.32 Those regions were then represented in a mask that was used to eliminate such regions during baseline fitting to the remaining background segments.32 We note that, apart from the choice of the (4 Hz) exponential apodization time constant, the process requires no arbitrary choices and is both efficient and much more reproducible than baseline corrections requiring choices by an analyst. A comparison of the peak areas obtained with this method agreed very well to more time-consuming (and somewhat arbitrary) baseline subtraction methods tested. Table 2 shows the integrated peak areas of the processed spectra, presented in Figure 6. We compare the relative areas determined according to the proposed assignments in Table 1 to those by Ingram et al.6 For the pine oil 13C NMR assignments, the major changes are in the alcohol-related region. There is a substantial decrease in the intensity assigned to methoxy/hydroxy carbons and a large increase in the intensity assigned to carbohydrate carbons; however, the total of the two oxygen-related regions decreased by only about 5%. The alkene/aromatic region increased by this amount. For cellulose, the relative intensities of these regions changes dramatically. In the lignin bio-oil, there is very little carbohydrate and the only significant change is reassignment of some intensity between 103 and 110 ppm to aromatic carbons. Thus, the assignments that we propose based on the library of standard compounds primarily affect the determination of the relative amounts of methoxy/hydroxy versus carbohydrate carbons, with a smaller effect to increase the estimated amount of the aromatic/conjugated carbon fraction. As the histograms in Figure 2 show, there is much greater overlap between the proton chemical shifts of different functional groups than is the case for 13C NMR. Our assignments are similar in ordering of the functional groups to
raw integrated area
Figure 7. 13C NMR spectra of a pine pyrolysis oil14 measured with pulse to pulse time of tpp = (a) 30 s, (b) 7 s, and (c) 2.2 s using a tip angle of 64.5 and 4000 transients to quantify the effects of incomplete longitudinal relaxation. Spectra normalized to the carbohydrate peak at 76.668 ppm. (d) 13C NMR spectrum measured with inverse-gated decoupling, tpp = 7 s, and 8192 transients.
those by Ingram et al.6 We found that primary, secondary, and tertiary hydrogens cannot be distinguished and that there is overlap from 2 to 3 ppm with protons R to carbonyl carbons. Therefore, the intensity between 0 and 3 ppm is redistributed. The ether, methoxy, and aliphatic OH regions are assigned consistently with those by Ingram et al.6 Our assignment of the aromatic region combines the intensity from the non-conjugated alkene and aromatic protons into one region between 6 and 8.25 ppm. We distinguish carboxylic acids (>11 ppm) from phenols and aldehydes (8.25-11 ppm). Data acquisition time is strongly dependent upon the pulse repetition rate, tpp, number of transients, nt, and signal-tonoise enhancement that can be obtained through NOE. The trade-offs between tpp, nt, and NOE and the ability to correct for incomplete longitudinal relaxation (as discussed below) were investigated by acquiring a set of spectra for a pine pyrolysis oil described previously.14 Figure 7 shows
(32) Cobas, J. C.; Bernstein, M. A.; Martin-Pastor, M.; Tahoces, P. G. A new general-purpose fully automatic baseline-correction procedure for 1D and 2D NMR data. J. Magn. Reson. 2006, 183, 145–151.
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Table 3. Effects of the Tip Angle and Pulse Repetition Time, tpp, on Integrated Spectral Regions for Pine Pyrolysis Oil Spectra Measured with Constant S/N Using Full NOE To Evaluate Correction Factors nt = 4000
64.5 tip angle
full NOE assignments alkyl methoxy/hydroxy carbohydrate aromatic carbonyl
tpp = 30 s chemical shifts 0 54 70 103 163
90 tip angle
tpp = 7 s
tpp = 2.2 s
tpp = 7 s
relative area (%)
corrected area (%)
relative area (%)
corrected area (%)
relative area (%)
corrected area (%)
relative area (%)
corrected area (%)
21.2 15.4 20.6 36.6 6.3
21.0 15.2 20.2 36.7 6.9
19.1 16.6 22.2 36.9 5.2
18.4 16.1 19.7 38.8 7.0
17.0 17.2 24.1 38.6 3.0
16.8 16.9 17.5 43.5 5.2
17.7 17.7 23.8 36.1 4.7
16.9 17.1 20.2 38.9 6.9
54 70 103 163 215
Table 4. Effects of NOE on the Integrated Spectral Regions for Pine Pyrolysis Oil Spectra full NOE, nt = 4000 tpp = 7 s assignments alkyl methoxy/hydroxy carbohydrate aromatic carbonyl
64.5 tip angle chemical shifts 0 54 70 103 163
54 70 103 163 215
inverse-gated decoupling, nt = 8192
90 tip angle
64.5 tip angle
90 tip angle
relative area (%)
corrected area (%)
relative area (%)
corrected area (%)
relative area (%)
corrected area (%)
relative area (%)
corrected area (%)
19.1 16.6 22.2 36.9 5.2
18.4 16.1 19.7 38.8 7.0
17.7 17.7 23.8 36.1 4.7
16.9 17.1 20.2 38.9 6.9
19.7 14.0 17.3 40.9 8.1
19.0 13.5 15.4 43.1 10.9
18.1 15.5 19.0 39.1 8.4
16.8 14.5 15.6 41.0 12.1
13
C NMR spectra obtained with a tip angle of 64.5, 4000 transients, and full NOE for pulse repetition times of (a) 30 s (total acquisition time of 33 h), (b) 7 s (8 h), and (c) 2.2 s (2.5 h). The approximately 9 decrease in the signal-to-noise ratio using inverse-gated decoupling was partially compensated in spectrum d by increasing the number of transients to 8192 (16 h). The decrease in S/N in spectrum d compared to b was consistent with these estimates. The spectra were normalized to the prominent carbohydrate peak at 76.668 ppm on the basis that the longitudinal relaxation times for carbohydrate carbons were found to be the shortest (see Figure 5) and should be fully relaxed for all but the smallest tpp. The effect of incomplete relaxation is clearly evident in the intensity of the carbonyl carbon peaks, for example, at 209.17 and 172.2 ppm. Notice that the smaller peaks in the carbonyl region become lost in the noise as tpp decreases from 30 to 2.2 s. A comparison of the spectra with full NOE (a-c) and with inverse gating (d) reveals a few peaks with significantly different relative intensities. Table 3 shows the integrated peak areas for the processed pyrolysis oil spectra presented in a-c of Figure 7 (tip angle of 64.5) and a spectrum measured with a 90 tip angle (not shown), before and after correction for incomplete longitudinal relaxation (as described below). As the time between pulses decreased, the relative area (i.e., raw area for the baseline-corrected spectra) of the carbonyl carbons, which generally have the longest T1 times, decreased significantly, while the relative area of the carbohydrate and methoxy/ hydroxy regions increased systematically. A comparison between spectra measured with full NOE (4000 transients) and inverse-gated decoupling (8192 transients) is summarized in Table 4 for tip angles of 64.5 and 90. There are systematic trends in the relative areas between the spectra measured with full NOE and with inverse-gated decoupling. However, NOE provides an improvement in S/N by a factor of roughly 3, while the increase in the number of transients from 4000 to 8192 is only a factor of 1.4. Therefore, the S/N in the full NOE spectra is higher by approximately 2.1.
Table 5. Effects of S/N on Integrated Spectral Regions for Pine Pyrolysis Oil Spectra Measured with 512, 2048, and 8192 Transients tpp = 7 s, full NOE assignments alkyl methoxy/ hydroxy carbohydrate aromatic carbonyl
chemical shifts
nt = 512
nt = 2048
nt = 8192
relative area (%)
relative area (%)
relative area (%)
54 70
0 54
11.8 19.4
12.9 18.5
13.8 18.1
103 163 215
70 103 163
28.3 31.8 8.7
27.0 35.5 6.2
25.0 36.7 6.4
To achieve a similar S/N in the inverse-gated spectra would have required an acquisition time of approximately 64 h (33 000 transients). To differentiate the effects of signal-to-noise from NOE, we show results in Table 5 for the integrated areas for spectra (not shown) in which the number of transients was increased by factors of 4 to double the S/N ratio. Spectra with 512 transients were acquired in about 1 h, as compared to 4 h for nt = 2048 and 8 h for nt = 8192. The relative areas for nt = 2048 and 8192 are similar and close to the reproducibility of baseline correction procedures, suggesting that the S/N achieved after 4 h of scanning was sufficiently better that only marginal improvements in accuracy were made for longer scanning times. However, for 512 transients, there were significant changes in relative peak areas. Although we expected the weaker carbonyl peaks to be lost in the noise for fewer transients, the relative intensity in the carbonyl region was greatest in the spectrum with 512 transients. Further quantitative analysis of the peak areas and correction for longitudinal relaxation will be given in the discussion. 4. Discussion 4.1. Chemical-Shift Assignments. The data reported in this paper summarizes the analysis of 54 standard compounds, including some compounds already identified in bio-oil6 and several plant natural products, especially the terpenes. Creating a library of compounds actually found in bio-oil should 5159
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required to achieve good S/N with inverse gating, the total acquisition time would be over 8 weeks. From this perspective, we considered methods for semiqualitative analysis by correcting for differences in the distributions of longitudinal relaxation times of the components that are distinguishable according to the 13C chemical-shift regions proposed in Table 1. We assume that the Bloch equations31,33 describe the evolution of the macroscopic magnetization and that each nucleus in the mixture reaches steady state according to its longitudinal relaxation time, T1, the tip angle, R, and the time between pulses, tpp. The magnetization vector immediately after the pulse, at steady state, has components Mx and My31
lead to greater accuracy in bio-oil sample quantification. The solvent dependence of 1H NMR chemical-shift values in chloroform versus DMSO-d6 has been reported by Abraham et al.22 The extent of both inter- and intramolecular hydrogen bonding is found to be dependent upon the pH, concentration, solvent, and temperature. Consequently, OH protons (both aromatic and aliphatic) can appear anywhere in the 1H NMR spectral window when using chloroform-d as the solvent. In contrast, the OH proton shifts are found to be more reproducible and, hence, of predictive value when DMSO-d6 is used as the solvent. Furthermore, one detailed study of 1H NMR analysis of hydrogen bonding of alcohols in DMSO-d6 included a conformational component to the chemical shift found in monosaccharides, levoglucosan, and inositols.22 We note that the standard compound, 3-methyl-1,2cyclopentane-dione, 36, was found to exist completely in its most stable enolic form. This enol places CdC into the most highly substituted position and displays a hydrogen-bonded OH peak at 8.79 ppm. This structural feature was confirmed by deuterium oxide exchange. The downfield shift by the OH peak is due to the intramolecular hydrogen bonding and leads to a cyclic five-membered ring. Chemical-shift data, as presented in the histograms of Figure 2, allowed us to clearly distinguish between carboxylic acid protons, aldehyde protons, and phenolic (aromatic) protons. Carboxylic acid protons ranged from 11.96 to 12.95 ppm, while aldehyde and phenolic protons appeared in the 8.63-10.26 ppm range. In the 13C NMR spectra summarized in Figure 3, ketone carbonyls absorbed further downfield (190-210 ppm) than the carbonyls from aldehydes and carboxylic acids. Furthermore, heterocyclic aldehyde carbonyls (furfural derivatives) were found to absorb upfield in comparison to those from benzenoid aromatic aldehydes. The proton shifts clearly distinguish between aromatic carbons and those from unsaturated carbons, which is not possible in 13C NMR. Therefore, the proton NMR chemical-shift region can provide complementary information for distinguishing aromatic and conjugated CdC bonding from non-conjugated C-C bonding. The vinylic protons from non-conjugated carbon-carbon double bonds appear at 4.68-5.95 ppm in 1H NMR. Most of the aliphatic OH protons are found within the region between 4 and 5 ppm; however, a few appeared as far as 6.52 ppm downfield. The carbohydrate carbons appear in the 67102.3 ppm region. Most methoxy and ether protons absorb in the region of 3.0-4.2 ppm in the proton NMR, while several others, such as Ar-CH2-O, are found further downfield (4.2-6 ppm) because of a combination of the anisotropic effect from the aromatic ring and electron-withdrawing effect from oxygen. Methoxy and hydroxyl carbon functionalities appear between 48.74 and 72 ppm in the 13C NMR. The protons that are attached to the R carbon of carbonyl functionalities absorb at 2.0-3.3 ppm. This region overlaps with the higher end of the alkyl group (1, 2, and 3) protons (0.79-2.36 ppm). Fortunately, it is frequently not necessary to distinguish between alkyl carbons because they span a very long region of 1-54 ppm. 4.2. Estimation of Correction Factors. As is evident from the distribution of longitudinal relaxation times shown in Figure 5, a true quantitative analysis26 of complex mixtures, such as pyrolysis oil, is prohibitively time-consuming. If pulse repetition times of 250 s (at least 5 times the longest T1, on the order of 50 s) were used and perhaps 20 000 transients were
Mx ¼ ðM0 ð1 - E1 ÞE2 sin R sin θÞ=D
ð1Þ
My ¼ ðM0 ð1 - E1 Þð1 - E2 cos θÞ sin RÞ=D
ð2Þ
where M0 is the equilibrium magnetization along the z axis, θ is the angle through which the nucleus will precess between pulses, and the factors E1 and E2 depend upon the longitudinal and transverse relaxation times, T1 and T2, respectively, where E1 ¼ expð- tpp =T1 Þ and E2 ¼ expð- tpp =T2 Þ
ð3Þ
and the factor in the denominator is given by D ¼ ð1 - E1 cos RÞð1 - E2 cos θÞ - ðE1 - cos RÞðE2 - cos θÞE2
ð4Þ
In practice, the detectable signal decays exponentially in time because of dephasing much more quickly than the time required for longitudinal relaxation, and hence, there may be advantages in choosing tpp and R carefully to optimize the S/N.28 The y component of the signal is given by My ðtÞ ¼ My, 0 cosð2πνi tÞexpð- t=T2 Þ þ Mx, 0 sinð2πνi tÞexpð- t=T2 Þ
ð5Þ
where νi is the offset frequency of nucleus i from the pulse frequency and My,0 and Mx,0 denote the components of magnetization immediately after the pulse, according to eqs 1 and 2.31 If the acquisition time, ta, is long compared to T2, then the integrated signal, S, Z ta My ðtÞdt ð6Þ S ¼ 0
is numerically equal to T2My,0, if ta . T2. If the signal from all nuclei dephases with the same time constant, the factor exp(-ta/T2) is the same for all nuclei and this factor cancels in calculating the relative sensitivities. In this case, the sensitivity for a particular nucleus, i, is essentially 1 - exp(-tpp/T1,i). For generality, we define the average NMR sensitivity factor for a distribution of nuclei, Savg, as Z
¥
¼ 0
"Z
Savg ðt; T1 , T2 , tpp , ta , R, θÞ ta
#
My ðt; T1 , T2 , tpp , ta , R, θÞdt pðT1 ÞdT1 ð7Þ
0
where p(T1) is a normalized probability distribution describing the range of T1 values for each type of carbon. The T1 (33) Fukushima, E.; Roeder, S. B. W. Experimental Pulse NMR: A Nuts and Bolts Approach; Addison-Wesley Publishing Company, Inc.: Reading, MA, 1981.
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times shown as histograms in Figure 5 were fit to a function of the general form
sensitivity factors shown in Figure 8 do not differ as much with tpp as for 90; therefore, although the absolute sensitivity is lower, the measured intensities are less skewed by incomplete longitudinal relaxation. The optimal choice of R involves a trade-off between the number of transients that can be acquired and the variation in signal for different nuclei.27-29 For the average lifetimes of the alkene (6.8 s) and carbonyl (12.2 s) groups and a tpp of 7 s, the optimal tip angle varies from 68 to 55, respectively. Calculation of the standard deviation in the relative areas from the four spectra in Table 3 after correction for tpp and R gives an average of 1.6%. In comparison to the mean composition of each class of compounds, this corresponds to an average relative standard deviation of 8.6%, which includes true statistical measurement uncertainties, errors in baseline correction, and uncertainties in the lifetime correction factors. In general, we conclude that the estimated correction factors appear to be consistent within a few percent. In any case, the calculated average sensitivity factors provide an estimate of the errors involved in ignoring the longitudinal relaxation, which has not been quantified previously. The effects of NOE and the decrease in S/N when inverse gating is used are less easily quantified. Because these complex mixtures contain hundreds of compounds, there will be many nuclei that are not present in sufficient quantities to be detectable at low S/N levels and NOE increases the fraction of nuclei that can be detected in a given data measurement time. The potential disadvantage of using NOE is that the enhancement varies from nucleus to nucleus, depending upon the number of nearby protons. To the extent the average enhancement factor varies from one functional class to another, the quantitation will be skewed. The increase in the relative area of the carbonyl signal as the S/N decreases, as shown in Table 5, suggests that the differences in estimated composition in Table 4 for the full NOE and the inversegated decoupling measurements may have as much to do with S/N and baseline correction errors as variations in the average effects of NOE on a functional group of carbon nuclei. Notwithstanding that, there are particular peaks whose intensities differ significantly between the NOE and inverse-gated decoupling spectra.
pðT1 Þ ¼ NT1 a exp½- ðT1 =bÞc
5. Conclusions
Figure 8. Average relative sensitivity factors, Savg/T2, for the indicated tip angles and types of carbon nuclei as a function of the pulse repetition time, tpp. The longitudinal relaxation time distributions used in these plots are those shown in Figure 5. The acquisition time ta . T2 was typical of the pyrolysis oil samples.
ð8Þ
We have measured, as standards in DMSO-d6, over 50 compounds that have been previously identified in pyrolysis oil and are of interest as natural plant products. The chemical shifts of the 1H and 13C nuclei were confirmed through prediction software and, in some cases, by deuteration. On the basis of this library of 352 proton and 383 carbon chemical shifts, we proposed revised chemical-shift ranges for assignment of the NMR data: for 13C NMR, we assign (a) carbonyls, 215163; (b) aromatic, 163-103; (c) carbohydrate, 103-70; (d) methoxy/hydroxy, 70-54; and (e) alkyl, 54-1. The reassignment of the carbohydrate and methoxy/hydroxy regions can have major effects on the functional group assignment of the cellulosic component, while the distinction between aromatic and carbohydrates has a smaller effect. We have demonstrated these effects with cellulosic, lignic, and whole wood biooils and recommend use of the shifts for all lignocellulosic pyrolysis oils. For the 1H NMR spectra, we recommend scanning from 0 to 12.5 ppm (wrt TMS) and have simplified the proton chemical-shift region as follows: carboxylic acid, 12.5-11; aldehyde and phenols, 11-8.25; aromatic and conjugated
where a, b, and c were fitting constants and N is a normalization constant. The average sensitivity for each type of carbon was then evaluated for a particular pulse repetition time and tip angle. The integrated intensities (e.g., in Tables 3 and 4) were corrected by dividing by the associated sensitivity factor, and then the total intensity was renormalized to obtain percent composition. The behavior of the sensitivity factors for tip angles of 90, 75, and 60 as a function of the pulse repetition time is shown in Figure 8. In these plots, the acquisition time of ta was taken to be large compared to the value of T2, which was the case for pyrolysis oils. Representative values of the sensitivity factors and the fitted values for a, b, and c are also provided in the Supporting Information. Examining the raw data and the corrected areas in Table 3 shows that there are systematic trends in the raw data as a function of tpp and that the corrected areas nicely compensate for these effects to give more consistent relative areas. The sensitivity for carbonyl carbons is, of course, the smallest and accounts for a decrease in the raw area by a factor of 2 between tpp = 30 and 2 s. For a tip angle of 60, the average 5161
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alkenes, 8.25-6; aliphatic alcohols, non-conjugated alkenes, and aromatic ethers (Ar-CH2-O-), 6-4.2; ethers and methoxy, 4.2-3; protons R to carbonyls and aliphatic protons, 3-2; and aliphatic protons, 2-0. We have also compiled a large database of longitudinal relaxation times for the 383 carbon nuclei that allowed us to quantify the average sensitivity of the five distinguishable groups of chemical functionalities. The sensitivity factors yield relative composition that appears to be consistent within a few percent, which is of the order of magnitude of other uncertainties, particularly baseline correction and variation in NOE effects. The values of the sensitivity factors for selected tip angles and pulse repitition times (tpp = at þ d1) are shown graphically in Figure 8 and given in the Supporting Information. Raw baselinecorrected areas may be divided by these factors, and the percent compositions may be renormalized to 100% to obtain semiquantitative estimates of composition (within a few percent). As a practical approach for relatively rapid analysis of pyrolysis oil, we recommend dissolving the oil 1:1 in DMSO-d6 and measuring both 1H and 13C NMR spectra. We find that an acquisition time of 2.56 s, a pulse delay of 4.5 s, a tip angle of 60, full NOE decoupling, and 512 transients are sufficient to provide semiquantitative analysis in a 1 h measurement time
that is useful for rapid feedback for process development work. The functional group classification can be compared qualitatively from sample to sample. For more quantitative analysis of particular samples generated under optimal process conditions, increasing the number of transients to 4000 with NOE or >20 000 with inverse gating is necessary to give more accurate information as the S/N improves. The 1H NMR spectra can be collected rapidly and provide complementary data. Acknowledgment. We thank the Department of Energy, under Grant DE-FG02-07ER46373, for financial support of this work. We thank David LaBrecque and Nathan Hill for technical assistance and Carlos Cobas and Rachel Austin for helpful discussions. Supporting Information Available: Names and structures of the 54 compounds comprising the library (Figure S1), 1H and 13 C chemical-shift assignments, predicted shifts, and 13C T1 lifetimes for the 54 compounds (Tables S1 and S2), and fitting parameters obtained for the histograms of T1 lifetimes shown in Figure 5 and representative sensitivity factors (Tables S3 and S4). This material is available free of charge via the Internet at http:// pubs.acs.org.
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