ARTICLE pubs.acs.org/EF
Prediction of Crude Oil Properties and Chemical Composition by Means of Steady-State and Time-Resolved Fluorescence Patricia A. Pantoja,† Juan Lopez-Gejo,*,‡ Galo A. C. Le Roux,† Frank H. Quina,§ and Claudio A. O. Nascimento†,|| †
)
Departamento de Engenharia Química, Escola Politecnica da Universidade de S~ao Paulo (USP), Avenida Prof. Luciano Gualberto, 380, Travessa 3, Cidade Universitaria, 05508-900 S~ao Paulo, S~ao Paulo (SP), Brazil ‡ Departamento de Química Organica I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain § Instituto de Química and the Universidade de S~ao Paulo (USP) Research Consortium for Photochemical Technology, Universidade de S~ao Paulo (USP), CP 26077, 05513-970 S~ao Paulo, S~ao Paulo (SP), Brazil Centro de Capacitac) ~ao e Pesquisa em Meio Ambiente (CEPEMA), Universidade de S~ao Paulo (USP), Rodovia C^onego Dom^enico Rangoni KM 270, 11573-000 Cubat~ao, S~ao Paulo (SP), Brazil
bS Supporting Information ABSTRACT: Steady-state and time-resolved fluorescence measurements are reported for several crude oils and their saturates, aromatics, resins, and asphaltenes (SARA) fractions (saturates, aromatics and resins), isolated from maltene after pentane precipitation of the asphaltenes. There is a clear relationship between the American Petroleum Institute (API) grade of the crude oils and their fluorescence emission intensity and maxima. Dilution of the crude oil samples with cyclohexane results in a significant increase of emission intensity and a blue shift, which is a clear indication of the presence of energy-transfer processes between the emissive chromophores present in the crude oil. Both the fluorescence spectra and the mean fluorescence lifetimes of the three SARA fractions and their mixtures indicate that the aromatics and resins are the major contributors to the emission of crude oils. Total synchronous fluorescence scan (TSFS) spectral maps are preferable to steady-state fluorescence spectra for discriminating between the fractions, making TSFS maps a particularly interesting choice for the development of fluorescence-based methods for the characterization and classification of crude oils. More detailed studies, using a much wider range of excitation and emission wavelengths, are necessary to determine the utility of time-resolved fluorescence (TRF) data for this purpose. Preliminary models constructed using TSFS spectra from 21 crude oil samples show a very good correlation (R2 > 0.88) between the calculated and measured values of API and the SARA fraction concentrations. The use of models based on a fast fluorescence measurement may thus be an alternative to tedious and time-consuming chemical analysis in refineries.
’ INTRODUCTION Online remote characterization and real-time classification of crude petroleum are the most important current challenges faced by the petrochemical industry and environmental agencies. A rapid and inexpensive method for the remote analysis and classification of petroleum prior to distillation of the crude would provide chemical information of great importance for real-time adjustment of the critical operational parameters of a refinery, permitting an optimization of the process and resulting in economic and environmental benefits. A variety of spectroscopic techniques have been used over the last few decades for the analysis, characterization, and classification of crude oil in drilling fields, for the analysis of petroleum products, and for the detection of oil spills. The advantages of using such techniques include rapid response, the requirement of minimal sample preparation, and relatively inexpensive equipment costs. Of all of the optical spectroscopic techniques employed, vibrational [infrared (IR) and Raman]1,2 and electronic [ultraviolet visible (UV vis) and fluorescence] spectroscopies have shown the highest potential in the field. Fluorescence is a more complex phenomenon than absorption (UV vis or IR), and effects such as quenching and energy transfer have to be considered.3,4 The r 2011 American Chemical Society
complexity of the phenomenon should not necessarily be seen as a disadvantage because the contribution of all of these processes to the emission makes the fluorescence spectrum of individual crude oils more sensitive to the composition of the emissive chromophores present in the sample. In addition, fluorescence spectroscopy can provide two-dimensional and time-resolved signals, with much more information than a one-dimensional absorption or steady-state emission spectrum. Thus, several studies have employed total synchronous fluorescence scan (TSFS) and timeresolved fluorescence (TRF) techniques. TSFS has been extensively used for the analysis of crude oils, petroleum products,5,6 aromatic hydrocarbons,7 oil-spill identification,8,9 gasoline adulteration control,10 crude oil classification,11 and petroleum photodegradation12,13 studies. One of the first groups to apply the synchronous fluorescence technique to the analysis of crude oils was John and Souter in 1976,8 who presented a basic comparison between the synchronous spectrum and the standard emission spectra. The importance of energy-transfer processes in Received: April 13, 2011 Revised: June 20, 2011 Published: July 05, 2011 3598
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Energy & Fuels petroleum samples was studied with this technique by Ryder.11 He pointed out that the crude oil TSFS plots show a general diagonal contour trend from short excitation wavelength/large wavelength interval to long excitation wavelength/short wavelength interval, which is largely the result of energy-transfer processes. To the best of our knowledge, no studies have been reported of the use of TSFS for the analysis of crude oil fractions. We believe that the study of the fraction itself will shed light on the different processes that contribute to the fluorescence phenomenon. Moreover, correlations between the fluorescence characteristics (intensity, band shape, emission lifetime, etc.) and chemical composition of the crude oil have been extensively studied, but no models have been developed to predict crude oil properties. Concerning TRF, the requirement of more complex instrumentation (pulsed excitation source and rapid response detectors) may be the reason for the more recent application of this technique to crude oil identification. Thus, only a few but very interesting publications can be found in the literature.14 Ryder et al. measured the lifetime of different crude oils using fast-pulsed light-emitting diodes (LEDs) as the excitation source with emitting wavelengths of 460 and 510 nm and found that the mean lifetime was linearly correlated with American Petroleum Institute (API) grade and aromatic hydrocarbon concentration.14b In another work by the same authors, a good linearity between lifetime and API grade was found using 380 nm as the excitation wavelength.15 Again, no report was found of lifetime measurements on the different petroleum fractions or their mixtures. To develop new characterization and classification methods and physical property prediction models for crude oils based on fluorescence analysis, it is essential to understand the emissive properties of the principal families of compounds that contribute to the fluorescence spectrum and to the emission quenching or attenuation of crude oil fluorescence. In the present work, we report a steady-state and time-resolved study of the fluorescence emission of a series of crude oils of different API grades and their individual saturates, aromatics, resins, and asphaltenes (SARA) fractions, which form the basis for further, more detailed studies of the fluorescence of crude oils. The fluorescence spectra were correlated with API grade and SARA fractions by partial least squares (PLS). The resulting correlations reproduce quite accurately the physicochemical properties of the crude petroleums studied, providing evidence for the technical feasibility of the proposed methodology.
’ EXPERIMENTAL SECTION Crude oil samples were obtained from a refinery and stored at 6 °C during the period of analysis. The densities of the samples were measured in g/cm3 with a model DMA 4500/5000 densimeter (Anton Paar, Graz, Austria) to obtain the API degree (which ranged from 32° to 25°) as described in American Society for Testing and Materials (ASTM) D287.16 The crude oil samples, furnished by the refinery, were dead oil samples devoid of the more volatile components, as indicated by the lack of a significant loss of weight upon heating the crude oil to 150 °C for several hours. Steady-state fluorescence emission spectra were recorded on a Hitachi model F-4500 fluorimeter (Tokyo, Japan) employing an excitation wavelength of 337 nm and emission range from 350 to 650 nm (5.0 nm excitation/2.5 nm emission slits). For synchronous fluorescence spectroscopy (TSFS), a Perkin-Elmer (Waltham, MA) model LS50 fluorimeter was employed with excitation from 250 650 nm (15 nm excitation/10 nm emission slits), with an initial Δλ = 25 nm and an increment of 5 nm in Δλ on each scan for a total of 35 scans, at a scan rate
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Figure 1. Steady-state emission spectra (λexc = 337 nm) of several crude oils with API values of (a) 33.0, (b) 31.5, (c) 26.6, (d) 26.9, (e) 25.2, and (f) 25.1. Spectra normalized at the emission maximum are shown in the inset in the upper right-hand corner. of 500 nm/min. All fluorescence spectra were recorded with samples contained in a 1 mm optical path-length quartz cuvettes, employing a front surface configuration to avoid inner filter effects as suggested by Ryder.11 Fluorescence lifetime measurements were performed by the singlephoton counting technique on an Edinburgh Analytical Instruments (Livingston, U.K.) model FS-900 fluorescence lifetime spectrometer with a 370 nm NanoLED pulsed diode (IBH-Horiba, Edison, NJ) as the excitation source. The separation of the crude oils into their SARA fractions was performed by the method described by Jokuty et al.17 Thus, 2 g of a crude oil was dissolved in 25 mL of pentane. After stirring for 12 h, the precipitated asphaltenes were collected by filtration and the filtrate was labeled as maltenes. After removal of the residual pentane under vacuum, the asphaltenes were weighed. A 2 mL aliquot of the maltenes was applied to a chromatographic column prepared with 30 g of silica gel in hexane. The “saturates” fraction of the crude oil was then eluted from the column with 100 mL of hexane. Next, the “aromatics” fraction was eluted with 100 mL of benzene. Finally, the heavier part of the maltenes, the “resins” fraction, was eluted with 100 mL of methanol and 100 mL of dichloromethane. The solvents from all of these different fractions were eliminated under vacuum for 30 min at 50 °C in a rotary evaporator prior to weighing the fractions. The saturates fraction was a colorless liquid; the aromatics fraction was a yellow orange liquid; and the resins fraction was a more viscous oil or solid brown deposit. Concerning data processing, unfolding was applied to TSFS fluorescence spectra to reorder the three-dimensional data into bidimensional data arrays.18 A pretreatment step must be implemented before unfolding because of the discontinuity originated when matrix columns are folded in their original form. All of the spectra were centralized, and a Savitzky Golay filter was applied to smooth the data. PLS was used for model calibration. All models were constructed by cross-validation (leave-one-out mode). This procedure allows for the determination of the optimum number of factors (LV) that must be used in the model development.19
’ RESULTS AND DISCUSSION Fluorescence Spectra of Crude Oil. Figure 1 shows the relative steady-state fluorescence spectra of several crude oils using 337 nm as the excitation wavelength. This excitation wavelength has been extensively used in the literature for practical and technical reasons. High-power and low-cost excitation sources at this wavelength are available because it corresponds 3599
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Table 1. API Grade, Percentage of Each SARA Fraction (Determined for the Different Crude Oils after Precipitation of the Asphaltenes with Pentane, Followed by Chromatographic Separation of the Filtrate or Maltenes into Saturates, Aromatics, and Resins), and Total Recovery of the Initial Crude Oil, in Percent
Figure 2. Steady-state emission spectra of (a) neat crude oil and crude oil diluted (b) 5-, (c) 25-, and (d) 125-fold with cyclohexane. λexc = 337 nm. 20,21
to the emission line of the N2 laser. At the same time, most of the polycyclic aromatic hydrocarbons (PAHs) of the crude oil have a high absorption at 337 nm and quartz optic fiber presents high transmission of this wavelength, which permit the design of remote-monitoring equipment. The heavier crude oils exhibit a less intense emission than the lighter crude oils because of the quenching processes, similar to other reports in the literature.22,23 The second effect observed is a red shift in the emission upon going from lighter to heavier crude oils, which can be more readily seen when the spectra are normalized at their emission maxima (inset of Figure 1), which may indicate the presence of quenching or energy-transfer processes.24 Charge-transfer absorption and/ or quenching processes are implicitly assumed to be unimportant in crude oils.25 Measurements of the fluorescence quantum yield of crude oils can potentially provide valuable information about quenching and energy-transfer processes. In this context, the quantum yield has been reported to decrease with an increasing excitation wavelength.26 Downare et al. measured fluorescence quantum yields of crude oil over a wide range of excitation and emission wavelengths before and after dilution with solvent.22 Even after substantial dilution of the crude oil to reduce the efficiency of energy transfer, the fluorescence quantum yields of crude oil were still less than 0.1, which indicates that most of the chromophores present in crude oil are not highly fluorescent. Nevertheless, all of the quantum yields were much larger for the diluted crude oils than those of the neat crude oils, indicating that there is a substantial amount of energy transfer and quenching in neat crude oil. For pure crude oils, Downare et al. calculated that, for short excitation wavelengths, 90% of the emission comes from energy transfer, whereas for long excitation wavelengths close to the IR, the contribution of energy transfer decreases to 0%.22 The effect of dilution with cyclohexane on the steady-state fluorescence spectrum of a typical crude oil is shown in Figure 2. Particularly noteworthy is the increase in overall emission intensity, the marked blue shift of the emission maximum, and the appearance of some weak vibrational structure superimposed on the otherwise structureless emission. All of these dilutioninduced effects are consistent with the occurrence of concentration-dependent energy transfer and/or quenching processes. Despite the various studies pointing to the importance of energy-transfer processes in crude oil, only a few studies have focused on the identification of the emitting species or fractions, typically by comparing the fluorescence spectra of individual aromatic compounds to the fluorescence spectra of crude oil recorded under the same experimental conditions. Lambert et al.
saturates
aromatics
resins
asphaltenes
total
sample
API
(%)
(%)
(%)
(%)
(%)
P68
25.1
43
40
13
6
102
P102
26.2
50
30
13
5
98
P24 P43
26.3 26.4
46 51
33 30
14 13
4 5
97 99
P104
27.7
49
37
12
5
102
P63
28.0
50
36
12
5
103
P62
28.3
47
32
11
5
96
P54
29.6
48
34
11
4
97
P19
32.2
62
31
7
2
103
P53
32.2
61
29
8
2
100
P50
34.5
63
27
7
3
99
suggested that the fluorescence signal is attributable to a combination of PAHs, in which the relative contributions of each PAH are not equal.27 Other groups used the same strategy but with separation of the different fractions of the petroleum to simplify the fluorescence spectra.28 As expected, the aromatic compounds have the highest fluorescence intensity relative to the total oils and condensates. Among the highly fluorescent aromatic compounds, Prader et al. found that the highest quantum yield fluorophores are the smaller unsubstituted aromatic molecules and not the substituted polycyclic aromatic molecules.29 Because of the intermolecular interactions, a summation of the emission spectra of the individual fractions does not reconstitute the spectrum of the crude oil, again providing a clear indication of the contribution of quenching and energy transfer to the fluorescence spectra. To determine the importance of the quenching and energy-transfer processes, as well as the contribution of each fraction to the overall emission spectra, the individual emission properties of the fractions and their mixtures were studied. SARA Fraction Determination. SARA results for 11 of the 21 samples investigated in this work are shown in Table 1. As expected, the heavier crude oils, with lower API, had higher asphaltene and resin contents. Qualitative gas chromatography mass spectrometry (GC MS) analysis (see Figure 2S in the Supporting Information) confirmed that there was a reasonable separation of the fractions into the desired chemical classes. Thus, most of the identifiable compounds in the saturates fraction were saturated hydrocarbons with some olefinic compounds but with some potential contamination by benzene and naphthalene derivatives, as well as traces of anthracene or its derivatives. Three-ring and higher polycyclic hydrocarbons dominated the aromatics fraction, while the resins fraction consisted almost entirely of relatively nonvolatile high-molecular-weight compounds. Spectroscopic Study of the SARA Fraction. After the three SARA fractions of the maltene were obtained, they were diluted to 2 mL with hexane to obtain solutions in which the final concentration of the fraction was comparable to that in the original crude oil. The fluorescence of the asphaltene fraction was 3600
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not studied in this work because the asphaltenes are highmolecular-weight species that have a propensity to aggregate and are very difficult to redissolve. Thus, after the asphaltenes are precipitated, the closest that one can get to a solution is a dispersion of small aggregates. Studies dealing with the spectroscopic properties of asphaltenes can be found elsewhere.30 Absorption spectra of the three fractions of maltene (see Figure 1S in the Supporting Information) show that the resins absorb strongly at wavelengths shorter than 650 nm, while the aromatic and saturates fractions absorb strongly below 450 and 350 nm, respectively. The steady-state emission spectra of the three fractions of the maltene of a crude oil are presented in Figure 3. Our saturates fraction presents a partially structured emission at wavelengths where no emission is detected for crude oil. Because none of the aliphatic and olefinic constituents of petroleum should emit under these conditions, it is clear that the emission from our saturates fraction is due to contamination with small amounts of aromatic compounds (probably anthracene-like, given the vibronic structure present in the spectrum in Figure 3). This conclusion is reinforced by the results of Prader et al., who detected no emission from their saturates fraction.27 The emission of the aromatics fraction is not only the most intense of that of the three fractions but also very similar in shape to the emission spectrum of the neat crude oil, suggesting that the aromatics are the major emitting species in neat crude oil. Finally, despite their strong absorption in the visible region, the resins
present a much weaker emission that extends over the same spectral region as the aromatics. More detailed information on the fluorescence properties of the fractions is provided by the TSFS spectral maps shown in Figure 4, which are quite distinct for each fraction. Our saturates fraction presents a strong emission at short excitation wavelengths (between 250 and 350 nm) and small excitation emission wavelength separation (Δλ = ca. 50 nm). Concerning the other two fractions, aromatics show emission at longer wavelengths and smaller Δλ relative to the resins, which exhibit maximum emission at shorter excitation wavelengths but larger Δλ. Thus, the TSFS maps in Figure 4 emphasize the differences in the fluorescence characteristics of these two fractions much more clearly than the steady-state emission spectra in Figure 3. Both the aromatics and resins exhibit an emission maximum at short excitation wavelength and larger Δλ, which means that there is a substantial synchronous emission shift between excitation and emission. The steady-state fluorescence spectrum of the aromatics registered with excitation at 337 nm and shown in Figure 3 corresponds to the region in the upper left-hand corner of the TSFS map in Figure 4. Significantly, the steady-state emission spectrum with excitation at 337 nm fails to reveal the much more intense maximum for the aromatics fraction in the lower right-hand corner of the TSFS map (excitation at ca. 600 nm and a small Δλ of 50 nm). The time-resolved fluorescence decays of the fractions, measured by single-photon counting with excitation at 370 nm, were
Figure 3. Steady-state emission spectra of (a) crude oil (API = 29.4°) and three of its SARA fractions: (b) saturates, (c) aromatics, and (d) resins. λexc = 337 nm.
Figure 5. Decay of the fluorescence of the three SARA fractions. λexc = 370 nm, and λemi = 400 nm in air-saturated solutions.
Figure 4. TSFS spectra of the three SARA fractions: (a) saturates, (b) aromatics, and (c) resins. Range of λexc = 250 650 nm and an initial wavelength interval of Δλ = 25 nm with an increment of Δλ = 5 nm on each scan for a total of 35 scans, with 5.0 nm excitation and 2.5 nm emission slits. 3601
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Figure 6. Fluorescence spectra of (a) saturates, (b) aromatics, and (c) saturates plus aromatics. λexc = 337 nm in air-saturated solutions.
Figure 7. Fluorescence spectra of (a) saturates, (b) resins, and (c) saturates plus resins. λexc = 337 nm in air-saturated solutions.
multi-exponential, with mean lifetimes in the range of 3.5 4.2 ns (Figure 5), and not particularly useful for discriminating between the fractions. Particularly significant results are provided by the fluorescence spectra of one-to-one mixtures of the fractions. Thus, Figure 6 compares the steady-state fluorescence spectra of saturates, aromatics, and a mixture of the two. Figure 7 compares the steady-state fluorescence spectra of saturates, resins, and their mixture. Figure 8 compares the steady-state fluorescence spectra of aromatics, resins, and their mixture. In all three cases, the emission of the mixture is very similar to that of the fraction that absorbs more strongly at 337 nm, i.e., the aromatics in Figure 6 and the resins in Figures 7 and 8. This can be most readily explained by assuming that there is an inner filter effect of the more strongly absorbing fraction rather than a specific quenching effect. Indeed, for the mixtures of saturates and aromatics and saturates and resins, the mean fluorescence lifetimes of the mixture are identical to those of the aromatics or resins fractions alone (3.9 and 3.5 ns, respectively). However, for the mixture of aromatics and resins, the mean fluorescence lifetime (3.7 ns) is intermediate between that of these two fractions alone, suggesting participation of both fractions in the emission of the mixture. Prediction Models. Independent PLS predictive models based on TSFS data of pure (nondiluted) crude oil samples were built for API gravity (density) and their SARA fractions (saturates, aromatics, and resins). Because our focus was on the applicability of the technique to predict crude oil properties in
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Figure 8. Fluorescence spectra of (a) aromatics, (b) resins, and (c) aromatics plus resins. λexc = 337 nm in air-saturated solutions.
the field, no dilution was applied even though the diluted samples present a more structured and more informative spectrum. The TSFS spectra of crude oil samples with no pretreatment were recorded using the same conditions as for the fractions (see Figure S3 in the Supporting Information). Concerning the mathematical data pretreatment prior to modeling, a smoothing, first derivative and normalization were applied to the set of samples to be calibrated. SARA fractions were also normalized to properly fit the overall mass balance (100% weight). To assess the quality of the SARA models, a validation process was carried using 18 of the 21 oil samples for calibration, with the remaining 3 for external validation. The percentages of the three SARA fractions (saturates, aromatics, and resins) were satisfactorily predicted for the 3 validation samples. The results for the prediction of density were also satisfactory, as shown in Figure 9a, but given the well-known relationship between API and emission spectra (Figure 1), this result is not unexpected. Predictive models of chemical composition based on spectral information are clearly desirable, because chemical composition analyses are generally tedious, expensive, and time-consuming. Models were developed for predicting the SARA fractions of the samples in a quasi-real-time mode after measuring the TSFS spectral map of the crude oil. Panels b and c of Figure 9 present parity plots for saturates (%) and resins (%). As expected, considering the contribution of the resins fraction to the crude oil fluorescence, the prediction of the percentage of resins (Figure 9c) by the model is extremely good over the range of concentrations investigated. Interestingly, despite the lack of significant emission of the saturates in pure crude oil, the values of this fraction can also be predicted with excellent accuracy. Although saturates themselves do not emit, they do have a significant effect on the absorption, especially at short wavelengths, and potentially on the efficiency of energy transfer between the other fractions. The selection of the number of PLS factors used to construct the models is extremely important to avoid overfitting. Hence, the optimal order of differentiation (OD) and number of loading vectors (LV) for each model were chosen according to the wellestablished leave-one-out (PRESS) criterion. Thus, the prediction with one sample removed is compared to that with all samples present, and the squared errors are used to calculate the root-mean-square error of cross-validation (RMSECV). The selection of the number of PLS factors (LV) is based on the lowest RMSECV error. The performance of the models also 3602
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Figure 9. Parity plots (measured versus predicted) for (a) API density, (b) saturates (%), and (c) resins (%). Gray dots, calibration data; black dots, validation data.
Table 2. Coefficient of Determination for the TSF Technique Tested for API and SARA Components (R2), OD, and LV SARA composites LV/OD
API
LV/OD
saturates
LV/OD
aromatics
LV/OD
resins
LV/OD
asphaltenes
3/0
0.935
4/1
0.977
6/0
0.961
4/0
0.982
2/0
0.886
Figure 10. TSFS spectra of (a) crude oil and (b) the corresponding unfolded spectra. Loadings of LV1 of the prediction models for (c) API, (d) saturates, and (e) resins.
depends upon the coefficient of determination (R2). The optimum number of PLS factors, the order of differentiation (OD), and the coefficients of determination of the models for API gravity and SARA fractions are shown in Table 2. An analysis of the loadings for the different fractions can give valuable information concerning the part of the spectrum that contains the most information. The fact that an unfolding was applied to TSFS fluorescence spectra to reorder the threedimensional data into bidimensional data arrays complicates this analysis. Figure 10 presents an original TSFS map (3D) of (a)
crude oil together with (b) the corresponding unfolded spectra. In the same figure, the main loading vectors for (c) API, (d) saturates, and (e) resins are plotted. As expected, the contributions of the different spectral regions to the prediction model vary from one fraction to the other. These results illustrate the potential of fluorescence techniques for the development of prediction models for SARA. Further work using TSFS is underway, and it appears to be a potentially useful technique for the development of prediction models. Technical limitations and the cost of these fluorescence 3603
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’ CONCLUSION The advances in petroleum hydrocarbon fingerprinting and data interpretation methods and approaches in the last 2 decades allow for detailed qualitative and quantitative characterizations of oils. An analysis of the fluorescence characteristics of crude oil and its fractions is presented in this work. A study of the fluorescence of the three maltene fractions (saturates, aromatics, and resins) shows that the fluorescence spectrum of crude oil is dominated by the emission from resins and aromatics. TSFS maps appear to be a particularly powerful technique for the discrimination of crude oil fractions and should therefore be sensitive to variations in the relative concentrations of aromatics and resins, which is particularly interesting for the development of fluorescence-based methods for the characterization and classification of crude oils. Calibration models were built to predict the saturate and resin contents of crude oil. The coefficients of determination for the prediction of physicochemical properties from fluorescence spectra are very impressive (R2 > 0.886) and point to the potential of employing these spectroscopic data to construct valid models for the remote, quasi-real-time and online classification of crude oil. More detailed studies, using a much wider range of excitation and emission wavelengths and a larger number of samples, are necessary to determine the utility of fluorescence lifetime data for this purpose. ’ ASSOCIATED CONTENT
bS
Supporting Information. Absorption spectra together with GC MS analysis of the fractions (Figures S1 S3). This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Telephone: +34-91-394-5204. E-mail:
[email protected].
’ ACKNOWLEDGMENT The authors acknowledge Prof. Guillermo Orellana head of the Chemical Optosensors and Applied Photochemistry Group (GSOLFA) from Universidad Complutense of Madrid for the facilities to carry out lifetime and synchronous measurements. This project has been funded by FAPESP, CNPq, and Petrobras. Frank H. Quina thanks CNPq for the fellowship support. ’ REFERENCES (1) Hannisdal, A.; Hemmingsen, P. V.; Sj€oblom, J. Ind. Eng. Chem. Res. 2005, 44, 1349–1357. (2) Mullins, O. C.; Rodgers, R. P.; Weinheber, P.; Klein, G. C.; Venkataramanan, L.; Andrew, A. B.; Marshall, A. G. Energy Fuels 2006, 20, 2448–2456. (3) Ellingsen, L.; Fery-Forgues, S. Rev. Inst. Fr. Pet. 1998, 53, 201–216. (4) Ryder, A. G. Rev. Fluoresc. 2005, 2, 169–198. (5) Patra, D.; Mishra, A. K. Anal. Chim. Acta 2002, 454, 209–215. (6) Patra, D.; Misha, A. K. Anal. Bioanal. Chem. 2002, 373, 304–309. (7) Taylor, T. A.; Patterson, H. H. Anal. Chem. 1987, 59, 2180–2187.
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