Complete Group-Type Quantification of Petroleum ... - ACS Publications

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Complete Group-Type Quantification of Petroleum Middle Distillates Based on Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry (GC×GC-TOFMS) and Visual Basic Scripting Maximilian K. Jennerwein,*,†,‡,§ Markus Eschner,*,‡ Thomas Gröger,*,†,§ Thomas Wilharm,*,‡ and Ralf Zimmermann*,†,§ †

Joint Mass Spectrometry Centre, Cooperation Group ’’Comprehensive Molecular Analytics“, Helmholtz Zentrum Muenchen, D-85764 Neuherberg, Germany ‡ ASG Analytik Service GmbH, Trentiner Ring 30, 86356 Neusäss, Germany § Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, D-18057 Rostock, Germany S Supporting Information *

ABSTRACT: The subject of the presented work was the development of a two-dimensional GC×GC−time-of-flight mass spectrometric method (GC×GC-TOFMS) for the complete group-type quantification of petroleum middle distillates. The development of this method was possible due to the inherent features of GC×GC-TOFMS, namely the structured arrangement of compound groups and the mass fragmentation pattern, which provide the possibility of using Visual Basic Scripts as an analytical tool and thereby the classification of several thousand different compounds. The analysis method was focused on common petroleum based fuels from light to heavier middle distillation fractions. For the implementation of an absolute quantification method, a set of standard substances representing the main substance classes and carbon numbers within middle distillates and well-separated and recognizable internal standards were thoroughly chosen in order to obtain individual response factors and group specific response curves. The results of the qualitative and quantitative analysis were compared to wellestablished standard methods used in the petrochemical industry. The quantification of aromatic hydrocarbons was compared to EN 12916, a high performance liquid chromatography (HPLC) method that provides a rough separation between mono-, di-, and higher aromatics. Further, the quantification of the fatty acid methyl ester (FAME) content in diesel fuel was compared to EN 14078 and the distribution of single FAMEs was compared to EN 14103. It could be stated that an absolute quantification as the here presented method has not been reported before and the results were in good agreement with the reference methods. Furthermore, the here presented GC×GC-TOFMS quantification method is able to itemize according substance classes and carbon number. The detection limit of the method allows accurate and sensitive quantification for different limiting values of middle distillates with a single method.

1. INTRODUCTION Middle distillates such as diesel fuel, with a boiling range of 165−350 °C according to the references,1,2 have been used since the development of the diesel engine more than 100 years ago, because of their ignition characteristics. Over the decades the quality of such middle distillates had been improved and regulated because of the growing demand for light oil products and for environmental protection. The physical properties of middle distillates such as the cetane number, the cloud filtration test (CFPP), or the flash point are regulated by EN 590.1 Therefore, the accurate determination of the precise chemical composition is necessary, due to the fact that these properties are caused by it. Further, the chemical composition is responsible for the emission of semivolatile organic compounds and the formation of particulate matter and aerosols3,4 and therefore an important factor concerning environmental pollution and human health problems such as respiratory diseases and myocardial infarction.5−7 Nowadays a special interest is the linkage between the chemical composition of the © 2014 American Chemical Society

fuel with the combustion process and the biological impact of the combustion products. Besides a wide range of different “OMIC” standard tools for the biological characterization as well as a variety of on- and offline tools for the physical and chemical characterization of the combustion aerosol, also a comprehensive characterization of the chemical composition of the fuel is needed (referring to the “Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health” (http://www.hice-vi.eu/)). Partially the chemical composition is regulated by the above-mentioned EN standard, e.g. the content of polycyclic aromatic hydrocarbons (PAH) and fatty acid methyl esters (FAME) as well as the sulfur content. A limited number of parameters could be determined with several different well-established methods existing for the analysis of middle distillates. The content of aromatic compounds plays an Received: June 2, 2014 Revised: August 19, 2014 Published: August 21, 2014 5670

dx.doi.org/10.1021/ef501247h | Energy Fuels 2014, 28, 5670−5681

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The amount of raw data acquired by a GC×GC-TOFMS exceeds the amount of raw data acquired by a conventional one-dimensional system by a factor of 10−100. For this reason automated data deconvolution algorithms and tools are necessary to handle such measurements. In the past few years several different methods have been applied for data reduction and deconvolution concerning group-type and single compound identification. In 2002 Johnson and Synovec presented a method for pattern recognition of jet fuels that allows the interpretation of complex chromatograms.53 Knorr et al. reported an automated, computer-assisted structure identification (CASI) using relative retention times (2DrelRT) in combination with Kovat indices (KI) and boiling points as an alternative to the identification using the NIST library.48 Harvey and Shellie presented an automated data analysis for data reduction based on retention time arrays.39 In this case internal standards were used as marker for the locations of the retention arrays. Also, very recently Castillo et al. described a data analysis tool, using an alignment algorithm called score alignment also based on the retention times and certain mass fragments for group-type identification.49 Computer language for the automated analysis, such as in the aforementioned method, has been reported in several works over the last years, starting with the search of criteria and rules concerning fragmentation patterns of organic compounds in ambient aerosols,42−44,46 household dust,45 or cigarette particular matter.47 One possibility is the use of Visual Basic Scripts (VBS), a userfriendly programming language. In previous works, it has been successfully applied to different scripting based GC×GC-TOFMS analytics for different matrices42,43 including middle distillate.41 Lissitsyna et al.41 showed how VBS can be used for classification of the composition of kerosene, representing the low temperature fraction of middle distillates. Their approach included the use of classification areas in combination with scripts; that is, substance classes were identified through scripting and subdivided by carbon number through the classification areas. The disadvantage of using classification areas is the disregard of the overlap of compound groups. Further, it is shown how an approximate quantification can be done on the basis of peak areas, calculated from certain thoroughly chosen mass fragments corresponding to the different substance classes. The authors conclude that for quantification with TOFMS it is necessary to determine response factors for every compound, which is not possible due to a lack of standards. A solution for this issue can be found in the work of Szulejko et al., who developed a method for the prediction of response factors based on the effective carbon number concept.40 In this method, developed for the analysis of volatile organic compound by 1D GC, the values of the response factors are plotted versus the carbon numbers of three different substance classes. In this work it is shown that it is possible to build up a quantification method by determining response factors on the basis of different sets of standards and interpolating these response factors successfully for absolute quantification. For this purpose dilution series of numerous commercially available standard substances were spiked with different halogenated internal standards and measured with a GC×GC-TOFMS system in order to create several response functions. The qualitative analysis and itemization to substance classes is resting upon an algorithm based analysis of the fragmentation pattern of all found peaks in the peak table after deconvolution. The fragmentation patterns are examined for characteristic and group specific patterns (e.g., ion series, ion ratios, etc.).58−60 First and second dimension retention time are also used as

important role in the whole chemical analysis of middle distillates, because of its influence on the volumetric calorific value, the density, and the smoking tendency. Nowadays the content of PAHs is not allowed to exceed 11%(m/m) following EN 590, in order to prevent the creation of particulate matter. The common method for the analysis of aromatic compounds is described in EN 12916 or ASTM D6591, a HPLC method using refraction index detection that separates saturated and unsaturated components. This method is applied for the separation and quantification of mono-, di-, and higher aromatic compounds. A drawback of this method is that no separation and further distinction is possible within these groups due to the chromatographic preconditions. Especially the quantification of triaromatics and polyaromatics such as pyrene has become difficult, since their content reaches the limit of detection in modern middle distillates. Another point is the fact that the resources of fossil fuels are limited and the petrochemical industry is forced also by legislative reasons to look for adequate alternatives. Diesel fuels are commonly blended with a certain amount of biodiesel, gained from the transesterification of plant oil such as soy or rape seed oil. In Europe the content can be up to 7%(v/v) biodiesel added to common diesel fuel. This content of fatty acid methyl esters (FAMEs) can be measured with the infrared spectroscopy method as it is described in EN 14078 or ASTM D7371; further, the FAME profile can be determined using GC-FID following EN 14103. All these individual analytical methods can be replaced by a comprehensive two-dimensional GC×GC analysis. In numerous previous works, different petrochemical products of fossil and renewable sources have been analyzed using GC×GC with a variety of detection methods,8−18 above all TOFMS and flame ionization detection.19−59 With GC×GC-TOFMS it is possible to get a deeper insight into the chemical composition of complex mixtures, as is the case for petrochemical products, for example concerning biomarkers such as hopanes.27,34 Nevertheless, the analysis of common middle distillates with this method is still an analytical challenge despite modern technology and software. The whole composition of middle distillates can be categorized in more than a dozen substance classes, each containing up to several hundred single compounds. This makes it a challenge, in which even comprehensive two-dimensional separations and sophisticated but nonselective detection techniques soon reach their limits. The combination of GC×GC with TOFMS, contrary to FID for example, offers the possibility to distinguish between compound groups based on their fragmentation pattern where exact retention time information is missing or the chromatographic separation is insufficient. An analysis based on the chromatographic location of different compound groups alone requires the knowledge of all contained groups and their location in a two-dimensional chromatogram. This knowledge has to be gained using the corresponding standards and does not take into account that compound groups are overlapping to a certain degree, corresponding to the chromatographic precondition. Such an approach is described through UOP method 990-11, a comprehensive two-dimensional gas chromatography analysis with flame ionization detection.50 A further approach is coupling of LC and GC×GC for preseparation of saturated and unsaturated compounds.54 This task can also be handled using mass spectrometry for detection and automated pattern recognition. 5671


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of information used for the scripts are the main fragments and fragmentation pattern that could be observed for the different compound classes and also their locations in the structured twodimensional (2D) chromatograms (Figure 2). Retention time information was therefore integrated directly into the script, unlike the common approach to have a preceding classification. A similar approach, using the retention times for the definition of such a roof tile, has also been reported by Reichenbach et al.46 The practical use of VBS has shown that the different compound groups, defined by their fragmentation patterns, are overlapping in most cases. This has also been observed in almost all previous publications. The degree of these overlaps is corresponding to the content of the several compound groups in the different fuel samples, the carbon number, and of course the column combination and the chromatographic preconditions. After the data processing using the scripts, the degree of the overlap gets visible by the use of adequate plotting. In conclusion, it could be stated that using script functions for the definition of areas within twodimensional chromatograms, instead of manual drawn classification areas, is faster and more precise, considering the large number of subgroups in this special case. For future work the introduction of retention time indices for both dimensions, as described by Marriott et al.51,52 could be applied using Visual Basic Script if the retention information is directly included in the algorithm. 2.3. Standard Mixtures and Quantification Method. For the development of a quantification method, altogether 11 different external standard solutions were made. The substances were roughly organized in the classes of alkanes, iso-alkanes, cyclic alkanes, monoaromatics, diaromatics, polyaromatics, and fatty acid methyl esters. Special attention was paid to the aromatic compounds, because of the possibility of a further subclassification. Monoaromatics were further divided into alkylated benzenes, indanes, tetralines, and other hydrated polyaromatics, leading to four different standard solutions. Diaromatics could be differentiated into naphthalenes, biphenyls, fluorenes, diphenylmethanes, acenaphthenes, and also different hydrated polyaromatics and were split into two standard solutions. For each other substance class, only one solution was made, respectively. A list of all standard substances is given in the Supporting Information. Different compounds with the same carbon number were used if appropriate standards were commercially available. For every substance class a dilution series was made with dichloromethane in order to cover the estimated concentration range within the fuel samples. The dilution series ranged from 10 to 10000 mg/kg, corresponding to the concentrations of the different substance classes, regarding the high concentration differences between them within fuel samples. For each class seven dilutions were made within this range. The obtained dilutions were spiked with corresponding halogenated internal standards before injection, resulting in a concentration of 1000 mg/kg for every internal standard. Afterward 0.4 μL were injected with a split of 1:400 into the GC×GC-TOFMS. Calculation of the peak areas was done by summing up the intensities of certain mass fragments according to the different substance classes (Table 2). In this way response factors could be determined according to the carbon numbers and a response function could be interpolated for compounds where no standard material was available. For the calibration of the FAME content, a diesel fuel without biodiesel content (B0) was mixed with biodiesel produced of rape seed oil. The biodiesel content varied between 0.5% and 20%, and every sample preparation of this dilution series was blended with a 10%(m/m) solution of methyl nonadecanoate (C19:0) as internal standard, resulting in a concentration of 1%(m/m) of the sample. The C19:0 is an artificial FAME that does not appear in natural biodiesel. The mass fragments used for the calculation of the area are also displayed in Table 2. Three different common fuels representing the middle distillate fraction of petroleum products, namely jet fuel A1, diesel fuel blended with biodiesel (B7), and light heating oil, were quantified for method evaluation. Every fuel sample was measured three times, every time with a separate sample preparation, consisting of 900 μL of fuel sample and 100 μL of internal standard solution comprising six internal standards. For cross-validation of the method, different official analysis

qualitative marker and integrated directly to the search algorithm. For the implementation of the algorithm, a wide range of computer languages could be used. In literature approaches using Excel, MatLab and Visual Basic Scripts are described.41−47 LECO ChromaTOF Software allows the direct integration of Visual Basic Scripts into the workflow (scripting toolbox). While the structure and syntax of the scripts are identical to Visual Basic Scripts, the common function library of Visual Basic is extended by a set of predefined functions which are related to the specific application of interpreting the fragmentation pattern. An overview of the predefined functions is given in the Supporting Information. The here-presented self-written scripts use these predefined functions in order to utilize the mass fragmentation pattern and the retention times of compound groups in the first and second dimensions. This approach took the overlapping compound groups under consideration and prevented a false classification of compounds. Thus, a proper qualitative and quantitative analysis has been provided for petroleum middle distillates.

2. EXPERIMENTAL SECTION 2.1. GC×GC-TOFMS. GC×GC-TOFMS analyses were performed on Pegasus4D (Leco, St. Joseph, MI) with an Agilent Technologies 7890 gas chromatograph (Palo Alto, CA) equipped with a second oven and a nonmoving quad-jet dual-stage modulator. The modulation was achieved with a nonconsumable electrical chiller. The GC column combination consisted of a BPX-1 (60 m length; 0.25 mm inner diameter; 0.25 μm film thickness) and a BPX-50 in the second oven (3 m length; 0.1 mm inner diameter; 0.1 μm film thickness). The two columns were connected through an additional 0.2 m BPX-1 column within the modulator having an inner diameter of 0.1 mm and a film thickness of 0.2 μm. The chromatographic and mass spectrometric conditions are listed in Table 1.

Table 1. Overview of the GC×GC and TOFMS Preconditions, Including the Column Combination GC×GC 1D column modulator column 2D column injection carrier gas and flow rate 1st oven program 2nd oven program modulator temperature offset modulation period TOFMS source temperature transfer line temperature EI ionization aquisition frequency

BPX-1 (60 m × 0.25 mm × 0.25 μm) BPX-1 (0.2 m × 0.1 mm × 0.2 μm) BPX-50 (3 m × 0.1 mm × 0.1 μm) 0.4 μL split 1:400 at 300 °C 1.0 mL/min He; constant flow 60−300 °C; 2 °C/min 60−140 °C; 2 °C/min 140−310 °C; 2.4 °C/min 10 °C relative to second oven 6 s; 0.6 s hot pulse 200 °C 280 °C −70 eV 200 Hz

2.2. Data Acquisition and Analysis. All data acquisition and processing of the GC×GC-TOFMS raw data were done using the ChromaTOF software, version 4.50.8.0 (Leco Corp., St. Joseph, MI) including the software interface for Visual Basic scripting. The parameters of the processing and the peak detection are given in the Supporting Information. As mentioned before, the Visual Basic Script (VBS) software package also includes several functions, which define for example the intensity and abundance of mass fragments or the retention time in both dimensions. All VBSs for the analysis have been written in house using these functions and on the basis of knowledge based fragmentation rules and retention times.60 The decisive pieces 5672


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Table 2. Different Compound Groups and Internal Standards and Their Corresponding Mass Fragments Used for the Calculation of the Peak Areas, along with Further Subdivision of the Compound Groups substance classes alkanes cycloalkanes bicyclic alkanes polycyclic alkanes monoaromatics diaromatics polyaromatics FAME saturated monounsaturated linolic acid methyl ester linolenic acid methyl ester internal standard 1-bromooctane chlorocyclohexane 1.2-dichlorobenzene 1-bromonaphthalene 9-bromoanthracene C19:0

mass fragments 43 41 39 51

+ + + +

57 55 53 65

+ + + +

71 69 67 79

+ + + +

85 83 81 93

+ + + +

possible further subdivision n-/iso-alkanes cyclopentanes/-hexanes

99 97 95 + 109 + 123 107 + 121 + 135 + 149 + 163

tricyclic alkanes, adamantanes, hopanes, steranes 91 + 92 + 104 + 105 + 115 + 116 + 117 + 118 + 119 + 129 + 130 + 131 + 132 + 133 + 143 + 144 + alkyl-benzenes, indanes, teralines, 145 + 146 + 147 + 157 + 158 + 159 + 171 + 172 + 173 + 185 + 186 + 187 + 199 + 200 + 201 + hydrated polyaromatics 207 + 214 115 + 127 + 128 + 139 + 141 + 142 + 143 + 152 + 153 + 154 + 155 + 156 + 157 + 165 + 166 + naphthalenes, biphenyls, fluorenes, 167 + 168 + 169 + 170 + 179 + 180 + 181 + 182 + 183 + 184 + 186 + 193 + 195 + 196 + 197 + acenaphthenes, diphenylmenthanes, 198 + 208 + 210 + 211 + 212 + 225 + 226 hydrated polyaromatics 178 + 189 + 191 + 192 + 202 + 203 + 204 + 205 + 206 + 215 + 216 + 219 + 220 + 230 phenanthrenes, anthracenes, pyrenes, fluoranthenes 41 + 43 + 55 + 74 + 87 41 + 43 + 55 + 69 + 74 41 + 55 + 67 + 79 + 81 + 95

by carbon number

41 + 55 + 67 + 79 + 93 mass 39 + 39 + 50 + 50 + 75 + 41 +

fragments 41 + 43 + 55 + 57 + 69 + 71 + 135 + 137 41 + 54 + 55 + 67 + 82 63 + 74 + 75 + 77 + 126 + 127 + 206 + 208 75 + 111 + 146 + 148 88 + 150 + 151 + 176 + 177 + 256 + 258 55 + 67 + 74 + 79 + 81 + 87

methods in the petrochemical industry were applied to the same set of samples. The content of aromatic compounds was measured following EN 12916 and the content of FAME following EN 14078.

best compromise for a complex mixture of compounds with strongly deviating polarity, as is the case for petroleum distillates. Regarding the different substance classes found in middle distillates, the π−π interaction of the 50% phenylsubstituted column is suitable for the separation of the dominant class of saturated compounds from the unsaturated compounds. Nevertheless, to achieve an appropriate separation of both saturated and unsaturated compounds, a long length of 3 m for the second column was used. In order to avoid a wraparound of very polar compounds such as polyaromatics, an independent temperature program for the second GC oven was applied, that allows increasing the temperature of the second column separately from the first column. The temperature program for the second oven (Table 1) showed the best compromise to fulfill the demanded properties of the whole two-dimensional separation, due to the increasing temperature offset relative to the first oven. By this approach, the interior part of the separation space could be optimally used, while a wraparound was avoided completely. 3.2. Script Development and Validation. The development of VBS was based on previous works of Vogt et al. and Welthagen et al.42,43 and took up the point of commonly known fragmentation pattern and rules for electron ionization (EI); 70 eV electron ionization will produce molecular ions with an excess of internal ionization. Depending on the structure of the ion, it will fragment to stable ions which are characteristic for certain substance classes. The fragmentation reaction could include cleavage, rearrangement, and also neutral loss.60 For the given algorithm, relative abundances of the fragments were used and the intensity of the base peak was set to 100%. A commented exemplary script is given in the Supporting Information and is explained here in detail. This example for classification of alkanes uses the mass fragments with the highest

3. RESULTS AND DISCUSSION 3.1. Method Development and Optimization. The nonpolar × semipolar (or polar) column combination used for the method development is a very common combination for the analysis of petroleum fractions, and its application is described in the literature.9−36,41−52 In most cases, columns with 5% phenyl-substitution, being more temperature stable than pure dimethylpolysiloxane, and 30 m length are used for the first dimension, often in combination with 50% phenylsubstituted columns in the second dimension with varying length. First dimension columns with a length of more than 30 m are more common nowadays, due to the advantageous separation of complex samples with enormous numbers of isomers. Using a 100% dimethylpolysiloxane column, the sample is predominantly separated in the first dimension based on the boiling point, so it can be recognized as a simulated distillation. Therefore, differentiation of the carbon number was done based on the separation along the first dimension. However, unlike other approaches, the retention time information for the different carbon numbers was integrated directly into script functions in contrast to the traditional approach using classification areas. Another possibility to distinguish the carbon number would be the integration of the molecular ion, which in theory would make the developed method independent from retention time shifts and the fundamental GC×GC setup. This lacks practicality due to the fact that in most cases the molecular ion has little to nothing of intensity or, on the contrary, appears as a background signal. For the second dimension, a 50% phenyl-substituted column was chosen because of its stability, and it appears to be the 5673


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Figure 1. Exemplary visualization of the fundamental approach for the subdivision of alkylated benzenes by carbon number within in a common diesel fuel (B7), using distinct functions of the 1st and 2nd dimension.

Figure 2. Complete 2D chromatogram (TIC) of a common diesel fuel (B7) processed using the Visual Basic Scripts. Internal standards are highlighted and the background color brightened up for a better visualization of the different colors according to the compound groups.

is addressed with the predefined function “Rank(1)”. The integer “1” specifies the highest abundance (base peak), while higher

abundance, occurring from electron ionization. The fragment with the highest abundance within a single fragmentation spectra 5674


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Figure 3. (a) Average response factors for different isomers were calculated and plotted against the corresponding carbon number, resulting in response functions. Displayed in this diagram are the consolidated response functions for n-/iso-alkanes, cyclic alkanes (comprising also isomers of bi- and tricyclic alkanes), and monoaromatics (comprising isomers of alkylated benzenes, indanes, tetralines, and further naphthobenzenes). (b) Displayed in this diagram are the consolidated response function of diaromatic standards (comprising isomers of naphthalenes, biphenyls, diphenylmethanes, acenaphthenes, fluorenes, bibenzyls, and partially hydrated triaromatics) and the consolidated response function of tri- and tetraaromatics (comprising isomers of phenanthrenes, anthracenes, pyrene, fluoranthene, and terphenyls).

ester shows interfering mass fragments. An effective way to distinguish between cyclic alkanes and unsaturated FAME is the integration of exclusion criterions. This has been realized using the mass fragment m/z = 74 as a disqualifier, since it is characteristic for FAME and not present in cyclic alkanes. The further subdivision of the so defined substance classes into different carbon numbers was done by subsequent scripts, defining areas in the two-dimensional separation space containing so-called roof-tiles which describe the elution pattern of the peaks.33,34,41,56 The principal approach of the subdivision is exemplarily shown for alkylated benzenes in Figure 1, and an exemplary script concerning alkanes is given in the Supporting Information. As mentioned before, a certain degree of overlap can be observed for different compound groups. In those cases, compounds of different substance classes are coeluting in the same chromatographic space. First of all, it can be recorded that the scripts used for the classification of all compound groups

integers (2, 3, ...) address the second, third, et cetera highest abundant fragments in the spectra. For this purpose, a “Select Case” statement was created, comprising the most typical high abundance masses of alkanes as selection criterion. The retention times in both dimensions are also predefined functions within the software and referred to as “RetentionTime(1) and RetentionTime (2)”. In the given example (Supporting Information) a user defined function was created, where the retention time in the first dimension is directly limited by absolute values and in the second dimension as a function of the first dimension. Finally, a third function combines the functions, defining the fragmentation pattern and the chromatographic region, and in order to decide whether or not the examined mass spectra belongs to this class. Furthermore, intensities of certain masses above or below certain values were used as qualifiers, too. A differentiation between cyclic alkanes and olefins, to name an example, could be a difficult task on the basis of mass spectra. Although there are no olefins within common diesel fuel, unsaturated fatty acid methyl 5675


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Figure 4. Diagram showing the complete quantitative results of a common diesel fuel (B7) using the Visual Basic Script for the peak classification and different internal standards as a circle diagram. Minor components and the amount of unknown components are highlighted separately for proper visualization. Diphenylmethanes, acenaphthanes, fluorenes, and partially hydrated triaromatics are summarized as various diaromatics.

occur. The remaining approximately 1% of peaks that were not classified by the scripting had no influence on the quantification. These peaks are yet not identified compounds or artifacts of the deconvolution to a certain extent. 3.3. Quantitative Results and Validation. Every standard solution was measured three times, and the areas were calculated using the mass fragments listed in Table 2. With the average values for the areas of standards and internal standards and the known concentrations, it was possible to calculate response factors for the different substance classes within the range of carbon numbers that could be found for middle distillates. Average response factors were calculated for compounds of the same substance class and carbon number. These determined response factors were plotted on the y-axis against the carbon number on the x-axis in x/y-diagrams in Microsoft Excel, and the resulting linear regressions were created, which were later used as response functions for quantification. These resulting response functions for paraffins, naphthenes, and monoaromatics are displayed in Figure 3, and the two functions for diaromatics and higher aromatic compounds are separately displayed in Figure 3 to ensure a proper visualization. For the calibration of FAMEs, a linear regression of response factors depending on the carbon number could not be determined, which was probably caused by an inconsistent abundance of the chosen mass fragments. Average response factors could be observed according to the degree of

showed a very high precision. More than 99% coverage of all peaks could be observed for most of the analyzed sample. Only the jet fuel values between 98% and 99% are recorded due to the fact that this sample contains a considerable amount of sulfur compounds, which were not in the center of the investigation of the composition. Nevertheless, the main part of these compounds could clearly be identified as benzothiophenes because of the chromatographic location and the fragmentation pattern. In addition to this, the sulfur content of the jet fuel was measured following EN ISO 20884, an X-ray fluorescence analysis, and resulted in approximately 900 mg/kg (as elemental sulfur). Up to now, the qualitative and quantitative analysis method, including the scripting, has not been extended on sulfur compounds as they are not considered to be the main components of middle distillates as a final product. Generally, no contradiction between the different classified compound groups was observed; that is, no peak has been assigned to more than one compound group. This is a great advantage toward the FID, where overlapping compound groups are disregarded. It could be assumed on the basis of the evidence of previous studies on the structured chromatograms and the basically known composition of middle distillates (Figure 2) and,furthermore, on the basis of the script structure based on commonly known fragmentation rules and the accordance with standard substances in the classification that no false positive or negative peak assignment happened to 5676


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Figure 5. Displayed in this diagram is a comparison of the quantitative results of the aromatic contents of the three different fuels, used for the method development, namely jet fuel A1, diesel fuel (B7), and light heating oil. The detailed quantitative results for the different types of aromatic compounds obtained using GC×GC-TOFMS are summarized to mono-, di-, and higher aromatics in order to compare them with the reference method EN 12916.

Table 3. Comparison between the Quantitative Results of the FAME Profile, Obtained with the Reference Method EN 14103 and Using GC×GC-TOFMSa C14:0 C16:0 C16:1 C18:0 C18:1 C18:2 C18:3 C20:0 C20:1 C22:0 C22:1 various a

GC×GC -TOFMS

EN 14103

0.35% 12.29% 0.55% 2.51% 54.27% 18.92% 7.79% 0.47% 1.02% 0.19% 0.12% 1.50%

0.36% 12.06% 0.57% 2.36% 54.86% 18.99% 7.75% 0.54% 0.93% 0.22% 0.14% 1.18%

All results are normalized to 100%.

The response factors for the only polyunsaturated FAME were found to be even higher than those for monounsaturated FAME with values of 2.25 for methyl linoleate ester and 2.34 for methyl linolenate. These are the main components of a common biodiesel, and their ratio provides the information needed to determine the plant oil used for the production. For further processing, the peak tables generated within ChromaTOF were exported to Excel for each group and carbon number, including the areas. All peak areas were calculated by summation of the most abundant mass fragments according to each compound group (Table 2). The individual peak areas were transferred to absolute amount by applying the corresponding response function at the corresponding carbon number. The results of the quantification are shown exemplary for diesel fuel in Figure 4 in %(m/m). In accordance to the percentage number of classified peaks >99%, the summation of the quantitative results also provided values between 99 and

Figure 6. Comparison of the quantitative and qualitative results concerning the FAME content and profile using GC×GC-TOFMS and the two reference methods, namely EN 14078 for the quantification and EN 14103 for the FAME profile. The total content is displayed above the columns. The FAME profile is visualized through different colors; detailed information about single FAME is given in Table 3, normalized to 100%.

unsaturation, for saturated and monounsaturated fatty acid methyl esters resulting in values of 1.04 and 2.07, respectively. 5677


5678

0.006% 0.14% 0.75% 1.32% 1.58% 1.45% 1.56% 1.64% 2.12% 2.12% 1.79% 1.61% 1.81% 1.16% 1.20% 0.58% 0.35% 0.13% 0.057% 0.034% 0.022% 0.014% 0.009%

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 various total

21.45%

nalkanes

carbon no.

13.27%

0.07% 0.30% 0.89% 0.91% 0.56% 0.94% 0.92% 1.21% 1.53% 0.89% 1.46% 1.54% 0.59% 0.59% 0.22% 0.36% 0.18% 0.087% 0.024% 0.006%

isoalkanes

29.66%

0.032% 0.25% 1.06% 2.61% 1.85% 2.00% 2.85% 3.01% 2.99% 3.40% 3.22% 2.72% 1.68% 1.13% 0.60% 0.25% 0.010%

monocyclics

6.38%

0.008% 0.09% 0.76% 0.69% 0.86% 0.73% 0.71% 0.46% 0.38% 0.36% 0.67% 0.47% 0.18% 0.006%

bicyclics

0.06%

0.001% 0.012% 0.023% 0.022% 0.007%

tricyclics

7.08%

0.028% 0.19% 0.64% 0.93% 0.91% 0.77% 0.82% 0.81% 0.58% 0.37% 0.29% 0.26% 0.16% 0.21% 0.084% 0.037%

alkyl benzenes

11.82%

0.16% 0.60% 1.77% 1.99% 2.59% 2.11% 0.94% 0.76% 0.18% 0.13%

indanes

0.60%

tetralines

1.61%

0.072% 0.17% 0.31% 0.34% 0.34% 0.28% 0.090%

naphthalenes

1.01%

0.033% 0.16% 0.44% 0.35%

biphenyls

0.013%

diphenylmethanes

0.006%

acenapthenes

0.26%

0.009% 0.059% 0.083% 0.110%

fluorenes

0.36%

0.032% 0.114% 0.141% 0.075%

triaromatics

Table 4. Summary of the Quantification Results in %(m/m) and Identified Compound Groups Exemplarily Displayed for Diesel Fuel (B7)

0.04%

0.037% 0.007%

polyaromatics

0.12% 6.83%

0.024% 0.84% 0.038% 0.18% 3.67% 1.31% 0.53% 0.036% 0.067% 0.017% 0.009%

FAME

total 0.07% 0.65% 2.99% 7.79% 7.89% 8.00% 10.08% 10.18% 9.99% 8.92% 6.90% 10.52% 6.97% 3.80% 2.51% 1.15% 0.73% 0.32% 0.14% 0.06% 0.03% 0.01% 0.01% 0.12% 99.83%

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100%. For this reason the missing amounts were added to the circle charts as “unknowns”. Minor components are displayed in the bar graph for a proper visualization. The results of the quantification method were compared with the results of quantification methods described by EN 12916 for the aromatic content (Figure 5) and EN 14078 for the FAME content (Figure 6) and EN 14103 for the FAME profile (Table 3). The latter, of course, only concerned the analyzed diesel fuel (blended with approximately 7% FAME). The measurements of the aromatic content following EN 12916 were also repeated three times with separate sample preparations. The results of the reference method and the quantification using GC×GC-TOFMS were in very good accordance for the monoaromatic content considering the standard deviation of both methods. Only for the jet fuel sample could a slightly higher content of monoaromatic and diaromatic compounds be found in comparison to the EN 12916 method. This deviation could be caused by the different compositions of both the monoaromatic and diaromatic contents. In comparison to diesel fuel and heating oil, a higher amount of alkyl-substituted benzenes compared to indanes, tetralines, and different naphthobenzenes (Figure 7)

Figure 8. In this diagram the content of diaromatic compounds is displayed for the three different distillation fractions. The higher content within light heating oil is visible, compared to diesel fuel. Further, a clearly deviating composition of the jet fuel can be reported, that is concealed by the reference method EN 12916.

FAME distribution can be measured. The result of the analysis and the comparison to the reference method are given in Table 3, normalized to 100%, and also in Figure 6, representing the real content within the sample. Although some deviations from the reference method appear in the FAME profile obtained using GC×GC-TOFMS, the results correspond well for all fatty acid methyl esters. The results of the quantification of the different classified compound classes are summarized exemplary for diesel fuel in Table 4. All concentrations are given in %(m/m) and are ordered according to compound class and carbon number.

4. CONCLUSION Within this work, the advantages of the GC×GC-TOFMS method above established standard methods in the petrochemical field, in the form of higher precision and selectivity, were shown. Using this technique, a deeper insight into the quantitative composition of different common middle distillates was provided. For the huge amount of data, produced with this approach, it was indispensable to introduce automated analytical tools. This was realized by the successful application of Visual Basic Scripting as an analytical tool for the analysis of mass spectral data that allowed classifying more than 98% of the composition of different fuels. The combined application of mass spectral data and retention times in both dimensions within the automated scripts turned out to be a practical tool that made it possible not only to itemize a two-dimensional chromatogram but also to avoid false classifications. The overlap of different compound groups could be displayed within the chromatogram by color-coding. Up to now, a clear differentiation between all compound groups using chromatographic separation and flame ionization detection could not be realized. Despite its advantage of nearly consistent response factors, and thus easy quantification, using a flame ionization detector has the disadvantage that the often mentioned grouptype structures54−57 of the chromatograms do not take into account the overlap of different compound groups. Further,

Figure 7. In this diagram a comparison between light heating oil, diesel fuel (B7), and jet fuel A1 concerning the distribution of alkylated benzenes versus indanes, tetralines, and further naphthobenzenes is displayed. Jet fuel A1 clearly shows a different distribution of aromatic compounds compared to the other fuels. This difference can only be detected using GC×GC-TOFMS.

could be found. Furthermore, besides naphthalenes, just a low content of other diaromatics could be found (Figure 8). In what way these differences in the composition influence the standard method could not be clarified at this point. The diesel fuel sample was tested for its FAME content following EN 14078 standard. The measurement was repeated three times in order to derive a standard deviation. The result of the GC×GC-TOFMS quantification method was slightly higher than with the reference method, but still within the analytical uncertainty of the standard method. The advantage of the GC×GC-TOFMS method is the combined measurement of the total FAME content and the distribution of single fatty acid methyl esters. For conventional analysis following EN 14103 it is necessary to separate the FAME content first, before the 5679


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it would be an inconvenient, if not impossible, task to determine unknown peaks without using GC×GC-MS, regarding the total number of different compounds and compound groups within common fuels. Finally, it was shown within this work that an interpolation of response factors, or using average response factors in the case of FAME, covering the main compounds and carbon numbers is possible and can be used for a complete quantification of middle distillates. The negligence of sulfur compounds in the qualitative and quantitative analysis results from the fact that they were not the focus of the method development. However, the jet fuel was the only fuel sample with a considerable amount of sulfur compounds, while it was not possible to detect sulfur compounds within the other middle distillates using GC×GCTOFMS. The results of the presented quantification method were in good accordance with the reference methods and within the same analytical uncertainty. Furthermore, concerning diesel blended with biodiesel, a quantification of the FAME content and simultaneous determination of the FAME distribution was possible, whereas with the common method two different analyses are necessary.

■

ASSOCIATED CONTENT

S Supporting Information *

Overview of the predefined functions, parameters of the processing and the peak detection, list of all standard substances, commented exemplary script, a user defined function, and an exemplary script concerning alkanes. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. Phone: +49(0)89 450 423 20. *E-mail: [email protected]. Phone: +49(0)381 498 646 0. *E-mail: E-mail: [email protected]. Phone: +49(0)89 450 423 10. *E-mail: [email protected]. Phone: +49(0)89 450 423 19. *E-mail: [email protected]. Notes

The authors declare no competing financial interests.

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ACKNOWLEDGMENTS Financial support from the ASG Analytik-Service GmbH is gratefully acknowledged. REFERENCES

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