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MS analysis of pyrolytic oils
J. Anal. Appl. Pyrolysis 74 (2005) 315–326 www.elsevier.com/locate/jaap
Multidimensional GC/MS analysis of pyrolytic oils Andres Fullana *, Jesse A. Contreras, Richard C. Striebich, Sukh S. Sidhu Environmental Engineering, University of Dayton, 300 College Park, Dayton, OH 45469-0114, USA Received 1 July 2004; accepted 3 November 2004 Available online 19 March 2005
Abstract In this work, the multidimensional gas chromatography (MDGC) technique was used to analyze pyrolytic products generated from primary and secondary pyrolysis of cellulose, lignin and sewage sludge samples. These three materials were selected because pyrolysis of these three materials produces compounds with very different polarity, thus making them ideal for MDGC application. The results of this study show that more than 70% of total chromatogram peaks could be identified with MDGC but only 47%, in the best case, with conventional GC. The increase in the number of identified products is due to increased separations. This increased understanding of pyrolytic product distribution can be used to enhance our understanding of the formation mechanisms of important pyrolytic products like PAHs. This study shows that the MDGC technique can be easily implemented in most pyrolysis laboratories. Also, it was shown that easily available software such as Microsoft1 Excel and Visual Basic can be used to effectively handle vast amounts of MDGC data. The data treatment methods discussed in this paper were successful in dividing MDGC data into manageable groups which were then used to obtain insights into the pyrolytic processes. # 2005 Elsevier B.V. All rights reserved. Keywords: Cellulose; Lignin; Sewage sludge; Chromatography; MDGC
1. Introduction Tentative analysis by GC/MS is a widely used technique in the field of pyrolysis. Identification and quantification of compounds formed during pyrolysis provides valuable information about polymer structure, composition and mechanism offormation of undesired products (pollutants) [1]. In the last decade, research in this technique has mainly focused on the pyrolytic process and sample introduction and improvements in chromatographic separation of products which is necessary for their identification and quantification. Improvements in chromatographic separation have typically been done using two procedures: derivatization and selective extraction of certain analytes [2]. In derivatization, pyrolytic products are modified to improve their behavior in the column separation. This is the case for methylation and silylation of polar compounds. Polar compounds generate asymmetric wide peaks when analyzed in a non-polar column (typically used). These * Corresponding author. Tel.: +1 937 229 3951; fax: +1 937 229 2503. E-mail address: [email protected] (A. Fullana). 0165-2370/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jaap.2004.11.036
peaks reduce resolution of mixtures and decrease the number of compounds identified. Derivatizaton agents react with polar groups, reducing polarity of the product and consequently improving chromatographic separation. Another way to improve separation is to remove the specific compound responsible for bad separation. For example, solid phase extraction is used to remove polar compounds and then the mixture is analyzed using a nonpolar column. Similarly, solid phase extraction can also be used to remove non-polar compounds and then analysis could be performed using a polar column. Multidimensional gas chromatography (MDGC) has been known for many years as a good method for improving GC separations [3]. In this technique, two or more independent chromatographic separations are applied to the entire sample. The MDGC instrument consists of a sequential arrangement of two GC columns of different selectivity; distinctive segments of eluant from the first column (first dimension) are transported into a second column (second dimension) to further separate any unresolved components. The key to the MDGC procedure is the modulator, which is the interface between the two
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columns. The modulator continuously accumulates and focuses small fractions of the eluting peaks coming from the primary column (heartcuts) and injects them into the secondary column in a very fast and efficient manner. The MDGC instrument generally uses a non-polar column in the first dimension to separate samples according to volatility, and a shorter polar column for the second dimension, where each fraction is separated by differences in polarity. The secondary separation has to be fast enough to permit the continual introduction of heartcuts from the first column without interference [4]. The outcome of an MDGC run is essentially a series of high-speed second dimension chromatograms. The data analysis of MDGC chromatograms is performed by placing the second dimension chromatograms side by side to a form two-dimensional chromatogram, where the two axes represent the retention times of the solutes on the first and second column, respectively. Using the detector response as the third axis, a three-dimensional surface plot can also be constructed [4,5]. The advantages of MDGC are the large peak capacity, which is close to the product of the peak capacity of the two individual columns [6], an increase in the signal-to-noise (S/ N) ratios as a result of the focusing effect of the modulation [7], and the chemical class separation obtained in the second dimension, which provides an additional tool for the identification of compounds (group type analysis) [8]. In this work, pyrolytic products of cellulose, lignin and sewage sludge were analyzed using MDGC/MS. These materials were selected because they have been widely studied, which ensures that bibliographic data will be easily available to compare with the results of this study. Pyrolysis of these materials also generates compounds with very different polarity, thus making them perfect examples of the applicability of MDGC. The primary objective of this work was to demonstrate the simple application of MDGC to complex samples generated through pyrolysis. To help implementation of this technique in pyrolysis laboratories, only materials available in major pyrolysis laboratories were used. Although a timeof-flight detector was used, similar results can be obtained by using other mass spectrometers [9].
2. Experimental 2.1. Materials The six pyrolysis samples analyzed in this study were prepared by primary and secondary pyrolysis of cellulose, lignin and sewage sludge samples. Cellulose is probably one of the most studied materials in the field of pyrolysis because it is one of the principal components of biomass. Pyrolytic oils from biomass are considered possible substitutes for petroleum [10]. On the other hand, pyrolytic compounds of cellulose are also major products in cigarette smoke and
understanding them is one of the keys to understanding the carcinogenic effects of this smoke [11]. Lignin is the second most abundant natural biopolymer, as cellulose is found in the cell walls of plants. In Europe approximately 50 million tons of technical lignin is produced annually [12]. Kraft lignin is the principal residue in cellulose paste processes. Typically, this residue is dried and burned, and for that reason pyrolysis of this material is very important. The composition of lignin is not defined as in the case of cellulose. This material is basically a phenylpropyne, which is a rich source of phenolic compounds under thermal degradation conditions. The formation of pyrolytic products has been studied to understand its structure, the kinetics of decomposition, and identify possible valuable products and pollutants formed during the burning process. Sewage sludge has been selected as an example of heterogeneous material. The composition of this material varies depending on the original composition of waste water treated, treatment of waste water and posterior treatments of whole sewage sludge. Pyrolysis of sewage sludge is of interest as a method to stabilize this material and analysis of pyrolysis products can be used to characterize different properties of this material [13,14]. Powdered cellulose and alkali lignin used in this work were purchased from Aldrich. Sewage sludge was collected from a physical–chemical wastewater treatment plant located in Alicante (Spain). A complete characterization of this sewage sludge (sludge 7) has been published elsewhere [15]. 2.2. Sample preparation All experiments were performed in a quartz tube (i.d. 7 mm) placed in a horizontal tubular furnace equipped with a temperature controller that allows heating at a constant temperature rate. Nitrogen (100 ml/min) was used to create an inert atmosphere and transport the pyrolytic products. Two different heating conditions were used to produce pyrolytic oils with different composition. Fig. 1 shows a schematic of these two different operating conditions. To produce primary pyrolysis oils, 100 mg of sample was placed in the furnace and heated from ambient to 700 8C at a rate of 10 8C/min (Fig. 1a). Under these conditions, gases formed during pyrolysis are removed from the furnace before the temperature increase. These gases can react in the gas phase (secondary pyrolysis) only if the temperature is high enough to allow such reactions. To produce secondary pyrolysis oils, sample was placed in the quartz tube outside the furnace (Fig. 1b); the sample was introduced into the furnace in the same direction as the nitrogen flow after the furnace had reached 700 8C. In secondary pyrolysis, the products formed at low temperature have to pass through the high temperature zone, which further enhances the rate of secondary pyrolytic reactions. Gases formed during the pyrolysis were absorbed in an Amberlite XAD-2 resin and extracted with 50 ml dichloromethane. Extract was concentrated using nitrogen to 1 ml.
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Fig. 1. (a) Experimental procedure for generating primary pyrolysis products. (b) Experimental procedure for generating secondary pyrolysis products.
A 10 mL internal standard mixture of six compounds (dichlorobenzene-D4, naphthalene-D8, acenaphthene-D10, anthracene-D10, Chrysene-D12, and Perylene-D12) was spiked into the resin for posterior semiquantification of pyrolytic products. 2.3. Multidimensional analysis The MDGC system was housed in a modified HP 5890N (Agilent Technologies) gas chromatograph, equipped with a cryogenic trap system. The detector used was a Pegasus III time-of-flight mass spectrometer (LECO). The column set used was a 0.53-mm i.d., 1-mm film thickness, 30 m Restek MXT-5 (low bleed MS primary column) connected to a 0.1mm i.d., 0.1-mm film thickness 2.5-m SGE BP20 (secondary column). The connection between the two columns was sleeved in a low dead volume tee (J&W Scientific). At the
tee, the flow was divided and part of it was vented, thereby maintaining the required carrier gas velocity in both columns (25 cm/s first dimension optimal velocity and 100 cm/s for fast second dimension separations). A programmed pressure mode was used to maintain the constant split at the tee. The trap system was based on a modified Marriott and Kinghorn design [7,15,16]. Instead of moving the cryogenic trap along a section of a fixed column, the column was moved through a stationary cryogenic zone. The trap operated at approximately 30 8C while heartcutting and was quickly heated to release the solutes from the trap by moving the column out of the cryogenic zone using a computer-controlled linear actuator. Multiple heartcuts were taken during each primary analysis, as had been done on earlier conventional MDGC experiments. The secondary column effluent was directed to the mass selective detector to determine qualitative product identification.
Fig. 2. A diagram describing the trap and release processes using a stationary cryogenic zone and a moving capillary column.
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The carrier gas was high purity helium. The temperature of the GC column was programmed from 40 8C (15 min hold) to 260 8C (15 min hold) at a rate of 4 8C/min. The pressure was programmed from 38 psig (15 min hold) to 55 psig (15 min hold) at a rate of 0.32 psig/min. The modulation time was of 20 s; the cryogenic trap temperature was kept at 30 8C at the beginning and at 40 8C when the GC oven was at 250 8C. The time-of-flight mass spectrometer was operated at a spectrum storage rate of 100 Hz, using a mass range of 40–300 m/z. 2.4. Role of modulator The key part of the MDGC system is the modulator, which refocuses, accumulates, and injects continuously into the second column, fractions (heartcuts) of the first dimension chromatogram. The complicated nature, installation, and cost of typical modulators is one of the reasons that MDGC
technology is difficult to implement. The modulator in this work is of special interest due to its simple design, fast installation, low cost, and for its use in conventional GC-MS systems in laboratories where pyrolysis studies are performed. A complete description of the cryotrap modulator used in this work is given below. During the accumulation phase, solutes moving towards the cryogenically cooled zone essentially stop moving through the column and are refocused in a narrow band, thus removing the effects of zone dispersion within the first column. After a pre-set and constant period of time, the column is quickly moved to a new upstream position. There, the previously focused zone is exposed to the thermal environment of forced flow convection provided by the GC oven and the trapped components are quickly released. On average, it is estimated that the component release time is 30 ms [9]. For this study, the front end of the second dimension column was the column inside the cryotrap. The cryogenic
Fig. 3. A Comparison of GC and MDGC chromatograms.
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319
Fig. 4. Comparison of a small area between GC and MDGC chromatogram.
zone (Fig. 2) was obtained by flowing liquid nitrogen through a copper tube of 1/8 in. o.d. The column was then moved through a Teflon tube attached to the cryogenic zone. The movement of the column through the trap was created by the use of a linear actuating device manufactured by a Swiss company (Sulzer Electronics AG). The linear actuator had an extendable arm that could be computer-programmed to move at various speeds. A brass bar attached to the end of the actuator arm extended into the right side of the GC oven. This bar was attached to the capillary column by two hightemperature silicon septa, which prevented the capillary column from slipping. When the actuator was extended, the system was in ‘‘trap’’ mode and the chemical components within the cold section of the column no longer migrated. As the arm retracted, the trapped section of column was pulled into the heated oven, where the components were released and transported through the secondary column [9]. This trap and release motion is further described in Fig. 2. It was determined that the most effective motion of the actuator was a slow extension followed by a rapid retraction. The slow trapping motion created a continual refocusing of the components at the beginning of the trap, preventing the potential breakthrough or migration of the solutes through the cryogenic trap, and lessened the risk of breaking the column [9,17].
3. Results and discussion 3.1. GC/MS versus MDGC/MS The primary objective of this study was to determine if it was advantageous to use multidimensional gas chromatography with the linear modulator to analyze pyrolysis products. Conventional GC was performed with the same system but an inert column was used instead of the secondary column to obtain a comparative chromatogram. Fig. 3 compares a complete chromatogram of conventional GC versus MDGC for cellulose pyrolytic oil. From this
figure, it can be clearly seen that the number of peaks increase significantly in MDGC analysis. The number of peaks increases for two reasons. One is because during the modulation heartcuts can inject the same peaks several times in to the second column; this effect is more evident in the case of broad peaks that are modulated in several heartcuts. Posterior analysis comparing the mass spectra shows that approximately 25% of peaks result from this split. The increased resolution due to second column causes the major increase in the number of peaks. This point is clearly illustrated in Fig. 4, which shows as an example a 40 s zone of conventional GC that has been further separated by two MDGC heartcuts. In this conventional GC zone it is only possible to identify two compounds, propenyl furan (1) and methyl furanone (2). For the same zone, MDGC is able to show four new compounds: 3,4-dimethyl-2-hexanone (3), p-xylene (4), 2,4-hexadienal (5) and 1-(acetyloxy)-2-propanone (6). It is also important to note that peak 2 appears twice because its elution occurs during both heartcuts. The new compounds identified by MDGC actually coelute with compounds 1 and 2 in conventional GC, but their relative low concentration makes it impossible to identify them. By extracting the principal ions of these compounds (Fig. 5) it is possible to observe different maximums corresponding to all of these compounds. The software resident on the LECO ChromaTOF Workstation also identifies co-elution compounds by identifying maximum of principal ions. This software uses a complex algorithm to automatically identify compounds that are co-eluting. Using this deconvolution software, it is possible to identify thousands of compounds in all of our pyrolysis products, even for conventional GC analysis. The major advantage of MDGC is that this technique produces actual separation of compounds. Identification of compounds comparing their mass spectra with a database of spectra is the key in the non-target analysis which leads to the identification of new compounds in complex mixtures. When compounds are not completely separated, the mass
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Fig. 5. Chromatogram of some principal ions which shows co-elution in conventional GC.
Table 1 Percentage of Area with a match quality higher than 600 Material
Pyrolytic conditions
GC/MS
MDGC/MS
Cellulose
Primary Secondary Primary Secondary Primary Secondary
43.8 39.7 47.8 30.8 30.7 32.6
88.9 77.1 73.1 89.4 77.1 86.6
Lignin Sewage sludge
spectra are a combination of the individual spectra which decreases the match quality (quality of identification). Table 1 compares the percentages of area (for each chromatogram) that have a match quality higher than 600 (1000 is a perfect match) in conventional GC versus MDGC for each pyrolytic product sample used in this study. For all experiments more than 70% of the total chromatogram peaks could be identified (match quality greater than 600) with MDGC but only 47%, in the best case, with conventional GC. Another advantage of MDGC is that it introduces polarity as a new separation variable. Compounds are separated in the second column according to polarity, which leads to classification of compounds according to polarity (chemical classification). For example, Fig. 4 shows that the separation between p-xylene and furanone has increased compared to conventional GC (Fig. 5) perhaps due to their difference in polarity. Fig. 6 presents the compounds in a two-dimensional plot where one axis is the time of each heartcut (first dimension) and the second axis is the time since the compounds were injected in the second column (second dimension). As shown in Fig. 6, this representation allows phenols, alkyl-benzenes and polyaromatic hydrocarbons (PAH) to appear in different positions according to their polarity. Alkyl-benzenes and PAHs have a low polarity and hence elute earlier in the second dimension, whereas alkyl-
Fig. 6. Effect of second column on distribution of compounds by polarity: chemical classification.
phenols with higher polarity elute later in the second dimension. Even when the majority of compounds are classified by groups, mass spectrometry is still necessary to identify some overlaps. 3.2. Examples of data processing applied to pyrolysis One of the limitations of MDGC is that it is difficult for researchers to efficiently reduce and process the large amount of data generated. For example, in this study, more than 1000 compounds were identified. Obviously, it is not possible to discuss each compound individually. The three data processing examples discussed in this section are intended to showcase the data processing technique used to handle MDGC data in this study. Only Visual Basic and Microsoft1 Excel software programs were used to process MDGC data. It is possible to obtain specialized commercial software that will further improve data processing, but the wide availability of the former software in almost all
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laboratories is the reason why they were selected for data processing in this study. Discussions of pyrolysis products have been kept brief in this paper because the objective of the study was to show usefulness of application of MDGC technique to pyrolytic processes and not the pyrolytic processes themselves. 3.3. Contour plot Unlike conventional GC chromatograms, the single time responses resulting from MDGC analysis (see Fig. 3) do not give any information at a first glance. The reason for this difficulty is that MDGC total chromatogram is actually a combination of multiple chromatograms generated in the second column which are stacked next to each other. By assembling all of chromatograms side by side it is possible to generate a 2D chromatogram. The axes of this 2D chromatogram represents the retention time on the first and second column. This 2D chromatogram can also be presented as a 3D graphic or contour plot (2D). Microsoft1 Excel is able to generate contour plots and 3D graphics only if the MS signal has been previously converted into a matrix where each column of the matrix is the MS signal from the secondary dimension heartcuts. A very short program written in Visual Basic was used to generate this matrix from the raw data. The contour plots can then be used to obtain a general idea of the sample composition just as from chromatograms of conventional GC. The first dimension time, corresponding to the non-polar column is inversely proportional to the volatility of the compounds and the second dimension, corresponding to the polar column, is directly proportional to the polarity of compounds. Fig. 7 shows the contour plots of the pyrolytic oils investigated in this study. Due to the high content of oxygen in cellulose, its thermal decomposition generates mainly oxygenated compounds, which can have very different polarity depending on the type of oxygenated functional group [18]. A contour plot of cellulose pyrolysis products shows a wide distribution in the second dimension due to different polarity of these oxygenated compounds. As principal components several wide peaks are identified, they correspond to dehydrated glucose derivates: levoglucosenone, 1,4:3,6-dianhydro-a`-Dglucopyranose, levoglucosan and D-allose. It is interesting to note that 1,4:3,6-dianhydro-a`-D-glucopyranose appear to split between the beginning of the end of the second dimension because the high polarity of this compounds prevents it from eluting completely during the modulation time ‘‘wrap around.’’ Secondary gas reactions produce a reduction in the number of detected peaks. An interesting result is the complete destruction of dehydrated glucose derivates identified in the primary pyrolysis products. Only D-allose survives secondary reaction conditions. Lignin also has high oxygen content like cellulose, but its contour plot shows a completely different distribution.
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Lignin oils show a very small distribution in the contour plot due to the relative stability of methoxyphenols and alkylphenol produced during the thermal decomposition of lignin. During primary pyrolysis conditions, these compounds are too stable to react and form a wide variety of products as it happened in the case of cellulose. However, the secondary pyrolytic thermal conditions are able to destroy these phenolic compounds thus increasing the number of peaks. When secondary pyrolytic reactions destroy the hydroxy and methoxy groups, non-polar alkyl benzene compounds are formed but when the hydroxyl group survives, additional polar compounds like phenol are formed. Sewage sludge is a highly heterogeneous material, ranging from simple compounds to very complex polymers. Thermogravimetric studies show that from a pyrolysis point of view, this material can be divided into three big fractions [19,20]. A first fraction is easily biodegradable material and evaporable compounds, a second fraction is derived from cell composition macromolecules like polysaccharides, lipids and proteins, and a third fraction is non-biodegradable materials like cellulose and lignin. The contour plot for sewage sludge also show three major groups of compounds. There is a linear distribution of nonpolar compounds at the beginning of the second dimension. This fraction is formed for linear aliphatics, nitriles and ester compounds. This fraction is probably formed from the decomposition of cellular polymers. The second group formed from polar compounds has a pattern similar to that observed in cellulose, which suggests that this group could have originated from cellulose. The third group is formed by four big peaks at the end of the first dimension. These peaks are fatty acids that have directly evaporated from the sewage sludge. The major effect of secondary pyrolysis conditions in the sewage sludge experiments is the destruction of fatty acids. These compounds evaporate under primary pyrolysis conditions but the high temperature condition of secondary pyrolysis leads to their complete destruction. Another important result from secondary pyrolysis is the formation of phenol which probably is from decomposition of lignin. 3.4. Grouping compounds by formula composition More than 1000 compounds were identified for each pyrolytic oil, thus, a discussion of individual compounds would be a difficult task. It is possible, however, to group the compounds according to different properties. In this section compounds were grouped according to their formula composition as an example of how a large number of identified compounds could be sub-divided into useful groups. Almost all MS data softwares include automatic library search routines that can generate a list of identified compounds, including their formulas and the list can be used to classify the compounds according to their formula composition. In our case, this list was exported to a text file (CSV) and then processed using Microsoft1 Excel. Using
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Fig. 7. Contour plot (generated from TIC) for primary and secondary pyrolytic oils.
the ‘‘Auto Filter’’ capability, compounds were classified according number of carbons (C) and oxygens (O) in the formula. This search routine accepts that a correct match is the first hit which at times can be erroneous. Fig. 8 shows these distributions for the six pyrolytic oils analyzed in this study. The cellulose product distribution shows that compounds with six carbons are the most abundant, which makes sense,
because glucose (cellulose monomer) is a molecule with six carbons. The original C6 carbon structure stays intact because under primary pyrolysis low thermal stress conditions it is difficult to break C–C bonds. However, low stability of chemical water easily generates different anhydrosugar dehydration which explains the distribution of oxygen composition [18].
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Fig. 8. Carbon, oxygen distribution for the pyrolytic oils analyzed in this study.
The second most abundant group was compounds with five carbons which shows the ease with which volatile compounds lose a carbon. Li et al. [21] found that formation of formaldehyde and CO (both with one carbon atom) is a fast step in a secondary reaction which is consistent with abundance of C5 group. From this figure it is also possible to appreciate how secondary pyrolytic reactions move distribution to lower carbon content. Using oxygen distribution,
it is possible to observe a decrease in oxygen content probably due to water dehydration and formaldehyde and CO formation in secondary pyrolysis reactions. In the case of lignin, compounds with seven, eight and nine carbons are the most abundant. This distribution is due to the phenolic composition of this material which under pyrolytic conditions breaks down to form alkylphenols and methoxyalkylphenols. Secondary pyrolysis reactions pro-
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duce an increase in total yields of compounds with seven and nine carbons (phenols derivates). This increase is probably due to the cracking of macro-molecules (not detected with chromatographic techniques) during secondary reactions. It is interesting to note that in distribution of oxygenated compounds, the importance of monoxygenated compounds increases, probably because demethylation high temperature reactions increases methoxy groups. The product distribution of sewage sludge shows a wide range in contrast to other two samples which is indicative of the complexity of this material. Non-oxygenated compounds weigh more in this distribution, which agrees with the observed low polarity of this sample in two-dimensional plots. The high concentration of C14, C16 and C18 dioxygenated compounds suggests the presence of fatty acids in this sample. Secondary reactions lead to a significant decrease in these compounds which shows the facility of these compounds to be destroyed under secondary pyrolysis conditions. 3.5. Filtering names to group compounds Another easy method of grouping compounds is to generate automatic search list and then filter name using a specific word, which would allow a grouping of compounds according to structure. This method was used to group compounds of pyrolytic oils with special emphasis on PAHs and nitrogenated compounds. As described in the previous section, the second column is able to separate compounds with different polarity. This advantage of multidimensionality is very useful in study of compounds which are present in low concentration and have different polarity than the matrix. This is the case for PAHs present in cellulose pyrolysis products. PAHs are important in study of carcinogens in cigarette smoke (cellulose is an important component of this product). Fig. 6 shows how monoaromatic compounds and PAHs appear at the beginning of the second dimension in comparison with phenols, which have more polarity and a higher second dimension time. For this example, the compounds were identified from the raw data and a list of compounds was generated by an autosearch routine and the list was filtered by the following procedure. The first step considered only compounds without any heteroatom (O, N, S,. . .). After a rapid check of the filtration results, some words like ‘‘benz,’’ ‘‘napth,’’ ‘‘fluo,’’ ‘‘anthr,’’ and ‘‘inde,’’ which form part of compound names, were selected as PAH identifiers. The remainder of the compounds were identified manually. More than 30 PAHs were found using this filtering method (Table 2). Traditional method of analyzing PAHs with target analysis only led to the study of previously observed compounds [2,22,23], however, using MDGC it is also possible detect compounds that are not targeted. This leads to identification of new PAHs which can enhance our understanding of formation mechanisms of these compounds.
Table 2 PAHs identified in cellulose primary pyrolytic oil (yield in mg/kg) CAS
Compound
Similarity Yield
Primary pyrotytic OH 496-11-7 Indane 447-53-0 Naphthalene, 1,2-dihydro824-22-6 IH-indene, 2,3-dihydro-4-methyl767-60-2 1H-indene, 3-methyl767-59-9 1H-indene, 1-methyl4373-13-1 Naphthalene, 1,2-dihydro-4-methy1275-51-4 Azulene 17057-82-8 1H-lndene, 2,3-dihydro-1,2-dimethyl20836-11-7 1H-lndene, 2,3-dihydro-2.2-dimethyl56147-63-8 2-Ethyl-2,3-dihydro-1H-indene 17059-50-6 2-Elhyl-H-indene 66B2-71-9 1H-indene, 2,3-dihydro-4,7-dimethyl2717-44-4 Naphthalene, 1,2-dihydro.3-methyl4373-13-1 Naphthalene, 1,2-dihydro-4-methyJ20836-11-7 1 H-lndene, 2,3-dihydre-2,2-dimenthyl4773-82-4 1H-lndene, 2.3-dimethyl18636-55-0 1H-lndene, 1,1-dimethyl90-12-0 Naphthalene, 1-methyl264-09-5 Benzocycloheptatriene 91-57-6 Naphthalene, 2-methyl4773-83-5 1,2,3-Trimethylindene 92-52-4 Biphenyl 575-37-1 Naphthalene, 1.7-dimethyl575-41-7 Naphthalene, 1,3-dimethyl2027-17-0 Naphthalene, 2-(i-methy1ethyl)2131-41-1 Naphthalene, 1,4,5-trimethyl829-26-5 Naphthalene, 2,3,6-trimethyl2245-38-7 Naphthalene, 1,6,7-trimethyl86-73-7 Fluorene 1855-47-6 1-Isopropenylnaphthalene 1730-37-6 9H-fluorene, 1-methyl120-12-7 Anthracene 4569-45-3 9H-fluorene, 9,9-dimethyl-
730 775 877 875 933 748 742 752 711 631 718 615 637 776 642 900 643 941 623 934 719 674 867 601 659 630 757 737 713 605 837 774 835
51 12 33 187 183 31 866 18 6 2 11 10 39 355 3 26 38 514 167 194 11 36 215 23 11 11 18 40 31 19 46 30 4
Secondary pyrolytic oil 95-13-6 Indene 824-22-6 1H-indene, 2,3-dihydro-4-methyl767-60-2 1H-indene, 3-methyl2177-47-1 2-Methylindene 447-53-0 Naphthalene, 1,2-dihydro17059-50-6 2-Ethyl-H-indene 18636-55-0 1H-indene, 1,1-dimethyl2717-44-4 Naphthalene, U-dihydro-3-methyl90-12-0 Naphthalene, 1-methyl2177-45-9 1H-indene, 1,1,3-trimethyl92-52-4 Biphenyl 1127-76-0 Naphthalene, 1-ethyl575-37-1 Naphthalene, 1,7-dimenthyl92-52-4 Biphenyl 2027-17-0 Naphthalene, 2-(1-methylethyl)86-73-7 Fluorene
935 769 645 939 897 706 691 769 920 625 680 838 708 677 799 661
100 10 0 26 32 2 14 7 27 0 3 5 12 3 1 7
Nitrogenated compounds are typically present in sewage sludge oils and their composition is very important in the formation of NOx during combustion [24]. These compounds are formed from decomposition of proteins and nucleic acids present in the original waste. In the sewage sludge investigated for this study, 140 nitrogenated compounds were identified. Based on their structural names, these nitrogenated compounds were divided in five different groups: nitriles,
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Fig. 9. Distribution of nitrogenated compounds in sewage sludge pyrolytic products.
pyridines, amides, amines and polyaromatic nitrogenated (PAN) compounds. Results for this classification are shown in Fig. 9. The most abundant compounds, nitriles and pyridines, are formed from protein decomposition. Secondary pyrolysis produces an increase in the yield of PANs.
ject Officer – Dr. Farley Fisher) is gratefully acknowledged. We also acknowledge the Fulbright Foundation and Spanish Ministry of Education and Science for their support of AF during his stay at the University of Dayton.
4. Conclusions
References
In this study, the MDGC technique was used to analyze pyrolytic products generated from primary and secondary pyrolysis of cellulose, lignin and sewage sludge samples. The results of this study show that the MDGC technique provides more information about pyrolytic product distribution due to increased chromatographic separation of pyrolytic products. The increased separations also lead to an increase in the number of identified products. This improved understanding of pyrolytic product distribution can be used to enhance our understanding of formation mechanisms of important pyrolytic products like PAHs. This study also shows that the MDGC technique can be easily implemented in most pyrolysis laboratories. Also, widely available software programs such as Microsoft1 Excel and Visual Basic can be effectively used to handle vast amounts of MDGC data. The data treatment methods discussed in this paper were successful in dividing MDGC data into manageable groups which were then used to obtain insights into the pyrolytic processes.
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Acknowledgements The partial financial support of this project by the National Science Foundation (Grant #CTS-0202764; Pro-
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Multidimensional GC/MS analysis of pyrolytic oils Andres Fullana *, Jesse A. Contreras, Richard C. Striebich, Sukh S. Sidhu Environmental Engineering, University of Dayton, 300 College Park, Dayton, OH 45469-0114, USA Received 1 July 2004; accepted 3 November 2004 Available online 19 March 2005
Abstract In this work, the multidimensional gas chromatography (MDGC) technique was used to analyze pyrolytic products generated from primary and secondary pyrolysis of cellulose, lignin and sewage sludge samples. These three materials were selected because pyrolysis of these three materials produces compounds with very different polarity, thus making them ideal for MDGC application. The results of this study show that more than 70% of total chromatogram peaks could be identified with MDGC but only 47%, in the best case, with conventional GC. The increase in the number of identified products is due to increased separations. This increased understanding of pyrolytic product distribution can be used to enhance our understanding of the formation mechanisms of important pyrolytic products like PAHs. This study shows that the MDGC technique can be easily implemented in most pyrolysis laboratories. Also, it was shown that easily available software such as Microsoft1 Excel and Visual Basic can be used to effectively handle vast amounts of MDGC data. The data treatment methods discussed in this paper were successful in dividing MDGC data into manageable groups which were then used to obtain insights into the pyrolytic processes. # 2005 Elsevier B.V. All rights reserved. Keywords: Cellulose; Lignin; Sewage sludge; Chromatography; MDGC
1. Introduction Tentative analysis by GC/MS is a widely used technique in the field of pyrolysis. Identification and quantification of compounds formed during pyrolysis provides valuable information about polymer structure, composition and mechanism offormation of undesired products (pollutants) [1]. In the last decade, research in this technique has mainly focused on the pyrolytic process and sample introduction and improvements in chromatographic separation of products which is necessary for their identification and quantification. Improvements in chromatographic separation have typically been done using two procedures: derivatization and selective extraction of certain analytes [2]. In derivatization, pyrolytic products are modified to improve their behavior in the column separation. This is the case for methylation and silylation of polar compounds. Polar compounds generate asymmetric wide peaks when analyzed in a non-polar column (typically used). These * Corresponding author. Tel.: +1 937 229 3951; fax: +1 937 229 2503. E-mail address: [email protected] (A. Fullana). 0165-2370/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jaap.2004.11.036
peaks reduce resolution of mixtures and decrease the number of compounds identified. Derivatizaton agents react with polar groups, reducing polarity of the product and consequently improving chromatographic separation. Another way to improve separation is to remove the specific compound responsible for bad separation. For example, solid phase extraction is used to remove polar compounds and then the mixture is analyzed using a nonpolar column. Similarly, solid phase extraction can also be used to remove non-polar compounds and then analysis could be performed using a polar column. Multidimensional gas chromatography (MDGC) has been known for many years as a good method for improving GC separations [3]. In this technique, two or more independent chromatographic separations are applied to the entire sample. The MDGC instrument consists of a sequential arrangement of two GC columns of different selectivity; distinctive segments of eluant from the first column (first dimension) are transported into a second column (second dimension) to further separate any unresolved components. The key to the MDGC procedure is the modulator, which is the interface between the two
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columns. The modulator continuously accumulates and focuses small fractions of the eluting peaks coming from the primary column (heartcuts) and injects them into the secondary column in a very fast and efficient manner. The MDGC instrument generally uses a non-polar column in the first dimension to separate samples according to volatility, and a shorter polar column for the second dimension, where each fraction is separated by differences in polarity. The secondary separation has to be fast enough to permit the continual introduction of heartcuts from the first column without interference [4]. The outcome of an MDGC run is essentially a series of high-speed second dimension chromatograms. The data analysis of MDGC chromatograms is performed by placing the second dimension chromatograms side by side to a form two-dimensional chromatogram, where the two axes represent the retention times of the solutes on the first and second column, respectively. Using the detector response as the third axis, a three-dimensional surface plot can also be constructed [4,5]. The advantages of MDGC are the large peak capacity, which is close to the product of the peak capacity of the two individual columns [6], an increase in the signal-to-noise (S/ N) ratios as a result of the focusing effect of the modulation [7], and the chemical class separation obtained in the second dimension, which provides an additional tool for the identification of compounds (group type analysis) [8]. In this work, pyrolytic products of cellulose, lignin and sewage sludge were analyzed using MDGC/MS. These materials were selected because they have been widely studied, which ensures that bibliographic data will be easily available to compare with the results of this study. Pyrolysis of these materials also generates compounds with very different polarity, thus making them perfect examples of the applicability of MDGC. The primary objective of this work was to demonstrate the simple application of MDGC to complex samples generated through pyrolysis. To help implementation of this technique in pyrolysis laboratories, only materials available in major pyrolysis laboratories were used. Although a timeof-flight detector was used, similar results can be obtained by using other mass spectrometers [9].
2. Experimental 2.1. Materials The six pyrolysis samples analyzed in this study were prepared by primary and secondary pyrolysis of cellulose, lignin and sewage sludge samples. Cellulose is probably one of the most studied materials in the field of pyrolysis because it is one of the principal components of biomass. Pyrolytic oils from biomass are considered possible substitutes for petroleum [10]. On the other hand, pyrolytic compounds of cellulose are also major products in cigarette smoke and
understanding them is one of the keys to understanding the carcinogenic effects of this smoke [11]. Lignin is the second most abundant natural biopolymer, as cellulose is found in the cell walls of plants. In Europe approximately 50 million tons of technical lignin is produced annually [12]. Kraft lignin is the principal residue in cellulose paste processes. Typically, this residue is dried and burned, and for that reason pyrolysis of this material is very important. The composition of lignin is not defined as in the case of cellulose. This material is basically a phenylpropyne, which is a rich source of phenolic compounds under thermal degradation conditions. The formation of pyrolytic products has been studied to understand its structure, the kinetics of decomposition, and identify possible valuable products and pollutants formed during the burning process. Sewage sludge has been selected as an example of heterogeneous material. The composition of this material varies depending on the original composition of waste water treated, treatment of waste water and posterior treatments of whole sewage sludge. Pyrolysis of sewage sludge is of interest as a method to stabilize this material and analysis of pyrolysis products can be used to characterize different properties of this material [13,14]. Powdered cellulose and alkali lignin used in this work were purchased from Aldrich. Sewage sludge was collected from a physical–chemical wastewater treatment plant located in Alicante (Spain). A complete characterization of this sewage sludge (sludge 7) has been published elsewhere [15]. 2.2. Sample preparation All experiments were performed in a quartz tube (i.d. 7 mm) placed in a horizontal tubular furnace equipped with a temperature controller that allows heating at a constant temperature rate. Nitrogen (100 ml/min) was used to create an inert atmosphere and transport the pyrolytic products. Two different heating conditions were used to produce pyrolytic oils with different composition. Fig. 1 shows a schematic of these two different operating conditions. To produce primary pyrolysis oils, 100 mg of sample was placed in the furnace and heated from ambient to 700 8C at a rate of 10 8C/min (Fig. 1a). Under these conditions, gases formed during pyrolysis are removed from the furnace before the temperature increase. These gases can react in the gas phase (secondary pyrolysis) only if the temperature is high enough to allow such reactions. To produce secondary pyrolysis oils, sample was placed in the quartz tube outside the furnace (Fig. 1b); the sample was introduced into the furnace in the same direction as the nitrogen flow after the furnace had reached 700 8C. In secondary pyrolysis, the products formed at low temperature have to pass through the high temperature zone, which further enhances the rate of secondary pyrolytic reactions. Gases formed during the pyrolysis were absorbed in an Amberlite XAD-2 resin and extracted with 50 ml dichloromethane. Extract was concentrated using nitrogen to 1 ml.
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Fig. 1. (a) Experimental procedure for generating primary pyrolysis products. (b) Experimental procedure for generating secondary pyrolysis products.
A 10 mL internal standard mixture of six compounds (dichlorobenzene-D4, naphthalene-D8, acenaphthene-D10, anthracene-D10, Chrysene-D12, and Perylene-D12) was spiked into the resin for posterior semiquantification of pyrolytic products. 2.3. Multidimensional analysis The MDGC system was housed in a modified HP 5890N (Agilent Technologies) gas chromatograph, equipped with a cryogenic trap system. The detector used was a Pegasus III time-of-flight mass spectrometer (LECO). The column set used was a 0.53-mm i.d., 1-mm film thickness, 30 m Restek MXT-5 (low bleed MS primary column) connected to a 0.1mm i.d., 0.1-mm film thickness 2.5-m SGE BP20 (secondary column). The connection between the two columns was sleeved in a low dead volume tee (J&W Scientific). At the
tee, the flow was divided and part of it was vented, thereby maintaining the required carrier gas velocity in both columns (25 cm/s first dimension optimal velocity and 100 cm/s for fast second dimension separations). A programmed pressure mode was used to maintain the constant split at the tee. The trap system was based on a modified Marriott and Kinghorn design [7,15,16]. Instead of moving the cryogenic trap along a section of a fixed column, the column was moved through a stationary cryogenic zone. The trap operated at approximately 30 8C while heartcutting and was quickly heated to release the solutes from the trap by moving the column out of the cryogenic zone using a computer-controlled linear actuator. Multiple heartcuts were taken during each primary analysis, as had been done on earlier conventional MDGC experiments. The secondary column effluent was directed to the mass selective detector to determine qualitative product identification.
Fig. 2. A diagram describing the trap and release processes using a stationary cryogenic zone and a moving capillary column.
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The carrier gas was high purity helium. The temperature of the GC column was programmed from 40 8C (15 min hold) to 260 8C (15 min hold) at a rate of 4 8C/min. The pressure was programmed from 38 psig (15 min hold) to 55 psig (15 min hold) at a rate of 0.32 psig/min. The modulation time was of 20 s; the cryogenic trap temperature was kept at 30 8C at the beginning and at 40 8C when the GC oven was at 250 8C. The time-of-flight mass spectrometer was operated at a spectrum storage rate of 100 Hz, using a mass range of 40–300 m/z. 2.4. Role of modulator The key part of the MDGC system is the modulator, which refocuses, accumulates, and injects continuously into the second column, fractions (heartcuts) of the first dimension chromatogram. The complicated nature, installation, and cost of typical modulators is one of the reasons that MDGC
technology is difficult to implement. The modulator in this work is of special interest due to its simple design, fast installation, low cost, and for its use in conventional GC-MS systems in laboratories where pyrolysis studies are performed. A complete description of the cryotrap modulator used in this work is given below. During the accumulation phase, solutes moving towards the cryogenically cooled zone essentially stop moving through the column and are refocused in a narrow band, thus removing the effects of zone dispersion within the first column. After a pre-set and constant period of time, the column is quickly moved to a new upstream position. There, the previously focused zone is exposed to the thermal environment of forced flow convection provided by the GC oven and the trapped components are quickly released. On average, it is estimated that the component release time is 30 ms [9]. For this study, the front end of the second dimension column was the column inside the cryotrap. The cryogenic
Fig. 3. A Comparison of GC and MDGC chromatograms.
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Fig. 4. Comparison of a small area between GC and MDGC chromatogram.
zone (Fig. 2) was obtained by flowing liquid nitrogen through a copper tube of 1/8 in. o.d. The column was then moved through a Teflon tube attached to the cryogenic zone. The movement of the column through the trap was created by the use of a linear actuating device manufactured by a Swiss company (Sulzer Electronics AG). The linear actuator had an extendable arm that could be computer-programmed to move at various speeds. A brass bar attached to the end of the actuator arm extended into the right side of the GC oven. This bar was attached to the capillary column by two hightemperature silicon septa, which prevented the capillary column from slipping. When the actuator was extended, the system was in ‘‘trap’’ mode and the chemical components within the cold section of the column no longer migrated. As the arm retracted, the trapped section of column was pulled into the heated oven, where the components were released and transported through the secondary column [9]. This trap and release motion is further described in Fig. 2. It was determined that the most effective motion of the actuator was a slow extension followed by a rapid retraction. The slow trapping motion created a continual refocusing of the components at the beginning of the trap, preventing the potential breakthrough or migration of the solutes through the cryogenic trap, and lessened the risk of breaking the column [9,17].
3. Results and discussion 3.1. GC/MS versus MDGC/MS The primary objective of this study was to determine if it was advantageous to use multidimensional gas chromatography with the linear modulator to analyze pyrolysis products. Conventional GC was performed with the same system but an inert column was used instead of the secondary column to obtain a comparative chromatogram. Fig. 3 compares a complete chromatogram of conventional GC versus MDGC for cellulose pyrolytic oil. From this
figure, it can be clearly seen that the number of peaks increase significantly in MDGC analysis. The number of peaks increases for two reasons. One is because during the modulation heartcuts can inject the same peaks several times in to the second column; this effect is more evident in the case of broad peaks that are modulated in several heartcuts. Posterior analysis comparing the mass spectra shows that approximately 25% of peaks result from this split. The increased resolution due to second column causes the major increase in the number of peaks. This point is clearly illustrated in Fig. 4, which shows as an example a 40 s zone of conventional GC that has been further separated by two MDGC heartcuts. In this conventional GC zone it is only possible to identify two compounds, propenyl furan (1) and methyl furanone (2). For the same zone, MDGC is able to show four new compounds: 3,4-dimethyl-2-hexanone (3), p-xylene (4), 2,4-hexadienal (5) and 1-(acetyloxy)-2-propanone (6). It is also important to note that peak 2 appears twice because its elution occurs during both heartcuts. The new compounds identified by MDGC actually coelute with compounds 1 and 2 in conventional GC, but their relative low concentration makes it impossible to identify them. By extracting the principal ions of these compounds (Fig. 5) it is possible to observe different maximums corresponding to all of these compounds. The software resident on the LECO ChromaTOF Workstation also identifies co-elution compounds by identifying maximum of principal ions. This software uses a complex algorithm to automatically identify compounds that are co-eluting. Using this deconvolution software, it is possible to identify thousands of compounds in all of our pyrolysis products, even for conventional GC analysis. The major advantage of MDGC is that this technique produces actual separation of compounds. Identification of compounds comparing their mass spectra with a database of spectra is the key in the non-target analysis which leads to the identification of new compounds in complex mixtures. When compounds are not completely separated, the mass
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Fig. 5. Chromatogram of some principal ions which shows co-elution in conventional GC.
Table 1 Percentage of Area with a match quality higher than 600 Material
Pyrolytic conditions
GC/MS
MDGC/MS
Cellulose
Primary Secondary Primary Secondary Primary Secondary
43.8 39.7 47.8 30.8 30.7 32.6
88.9 77.1 73.1 89.4 77.1 86.6
Lignin Sewage sludge
spectra are a combination of the individual spectra which decreases the match quality (quality of identification). Table 1 compares the percentages of area (for each chromatogram) that have a match quality higher than 600 (1000 is a perfect match) in conventional GC versus MDGC for each pyrolytic product sample used in this study. For all experiments more than 70% of the total chromatogram peaks could be identified (match quality greater than 600) with MDGC but only 47%, in the best case, with conventional GC. Another advantage of MDGC is that it introduces polarity as a new separation variable. Compounds are separated in the second column according to polarity, which leads to classification of compounds according to polarity (chemical classification). For example, Fig. 4 shows that the separation between p-xylene and furanone has increased compared to conventional GC (Fig. 5) perhaps due to their difference in polarity. Fig. 6 presents the compounds in a two-dimensional plot where one axis is the time of each heartcut (first dimension) and the second axis is the time since the compounds were injected in the second column (second dimension). As shown in Fig. 6, this representation allows phenols, alkyl-benzenes and polyaromatic hydrocarbons (PAH) to appear in different positions according to their polarity. Alkyl-benzenes and PAHs have a low polarity and hence elute earlier in the second dimension, whereas alkyl-
Fig. 6. Effect of second column on distribution of compounds by polarity: chemical classification.
phenols with higher polarity elute later in the second dimension. Even when the majority of compounds are classified by groups, mass spectrometry is still necessary to identify some overlaps. 3.2. Examples of data processing applied to pyrolysis One of the limitations of MDGC is that it is difficult for researchers to efficiently reduce and process the large amount of data generated. For example, in this study, more than 1000 compounds were identified. Obviously, it is not possible to discuss each compound individually. The three data processing examples discussed in this section are intended to showcase the data processing technique used to handle MDGC data in this study. Only Visual Basic and Microsoft1 Excel software programs were used to process MDGC data. It is possible to obtain specialized commercial software that will further improve data processing, but the wide availability of the former software in almost all
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laboratories is the reason why they were selected for data processing in this study. Discussions of pyrolysis products have been kept brief in this paper because the objective of the study was to show usefulness of application of MDGC technique to pyrolytic processes and not the pyrolytic processes themselves. 3.3. Contour plot Unlike conventional GC chromatograms, the single time responses resulting from MDGC analysis (see Fig. 3) do not give any information at a first glance. The reason for this difficulty is that MDGC total chromatogram is actually a combination of multiple chromatograms generated in the second column which are stacked next to each other. By assembling all of chromatograms side by side it is possible to generate a 2D chromatogram. The axes of this 2D chromatogram represents the retention time on the first and second column. This 2D chromatogram can also be presented as a 3D graphic or contour plot (2D). Microsoft1 Excel is able to generate contour plots and 3D graphics only if the MS signal has been previously converted into a matrix where each column of the matrix is the MS signal from the secondary dimension heartcuts. A very short program written in Visual Basic was used to generate this matrix from the raw data. The contour plots can then be used to obtain a general idea of the sample composition just as from chromatograms of conventional GC. The first dimension time, corresponding to the non-polar column is inversely proportional to the volatility of the compounds and the second dimension, corresponding to the polar column, is directly proportional to the polarity of compounds. Fig. 7 shows the contour plots of the pyrolytic oils investigated in this study. Due to the high content of oxygen in cellulose, its thermal decomposition generates mainly oxygenated compounds, which can have very different polarity depending on the type of oxygenated functional group [18]. A contour plot of cellulose pyrolysis products shows a wide distribution in the second dimension due to different polarity of these oxygenated compounds. As principal components several wide peaks are identified, they correspond to dehydrated glucose derivates: levoglucosenone, 1,4:3,6-dianhydro-a`-Dglucopyranose, levoglucosan and D-allose. It is interesting to note that 1,4:3,6-dianhydro-a`-D-glucopyranose appear to split between the beginning of the end of the second dimension because the high polarity of this compounds prevents it from eluting completely during the modulation time ‘‘wrap around.’’ Secondary gas reactions produce a reduction in the number of detected peaks. An interesting result is the complete destruction of dehydrated glucose derivates identified in the primary pyrolysis products. Only D-allose survives secondary reaction conditions. Lignin also has high oxygen content like cellulose, but its contour plot shows a completely different distribution.
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Lignin oils show a very small distribution in the contour plot due to the relative stability of methoxyphenols and alkylphenol produced during the thermal decomposition of lignin. During primary pyrolysis conditions, these compounds are too stable to react and form a wide variety of products as it happened in the case of cellulose. However, the secondary pyrolytic thermal conditions are able to destroy these phenolic compounds thus increasing the number of peaks. When secondary pyrolytic reactions destroy the hydroxy and methoxy groups, non-polar alkyl benzene compounds are formed but when the hydroxyl group survives, additional polar compounds like phenol are formed. Sewage sludge is a highly heterogeneous material, ranging from simple compounds to very complex polymers. Thermogravimetric studies show that from a pyrolysis point of view, this material can be divided into three big fractions [19,20]. A first fraction is easily biodegradable material and evaporable compounds, a second fraction is derived from cell composition macromolecules like polysaccharides, lipids and proteins, and a third fraction is non-biodegradable materials like cellulose and lignin. The contour plot for sewage sludge also show three major groups of compounds. There is a linear distribution of nonpolar compounds at the beginning of the second dimension. This fraction is formed for linear aliphatics, nitriles and ester compounds. This fraction is probably formed from the decomposition of cellular polymers. The second group formed from polar compounds has a pattern similar to that observed in cellulose, which suggests that this group could have originated from cellulose. The third group is formed by four big peaks at the end of the first dimension. These peaks are fatty acids that have directly evaporated from the sewage sludge. The major effect of secondary pyrolysis conditions in the sewage sludge experiments is the destruction of fatty acids. These compounds evaporate under primary pyrolysis conditions but the high temperature condition of secondary pyrolysis leads to their complete destruction. Another important result from secondary pyrolysis is the formation of phenol which probably is from decomposition of lignin. 3.4. Grouping compounds by formula composition More than 1000 compounds were identified for each pyrolytic oil, thus, a discussion of individual compounds would be a difficult task. It is possible, however, to group the compounds according to different properties. In this section compounds were grouped according to their formula composition as an example of how a large number of identified compounds could be sub-divided into useful groups. Almost all MS data softwares include automatic library search routines that can generate a list of identified compounds, including their formulas and the list can be used to classify the compounds according to their formula composition. In our case, this list was exported to a text file (CSV) and then processed using Microsoft1 Excel. Using
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Fig. 7. Contour plot (generated from TIC) for primary and secondary pyrolytic oils.
the ‘‘Auto Filter’’ capability, compounds were classified according number of carbons (C) and oxygens (O) in the formula. This search routine accepts that a correct match is the first hit which at times can be erroneous. Fig. 8 shows these distributions for the six pyrolytic oils analyzed in this study. The cellulose product distribution shows that compounds with six carbons are the most abundant, which makes sense,
because glucose (cellulose monomer) is a molecule with six carbons. The original C6 carbon structure stays intact because under primary pyrolysis low thermal stress conditions it is difficult to break C–C bonds. However, low stability of chemical water easily generates different anhydrosugar dehydration which explains the distribution of oxygen composition [18].
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Fig. 8. Carbon, oxygen distribution for the pyrolytic oils analyzed in this study.
The second most abundant group was compounds with five carbons which shows the ease with which volatile compounds lose a carbon. Li et al. [21] found that formation of formaldehyde and CO (both with one carbon atom) is a fast step in a secondary reaction which is consistent with abundance of C5 group. From this figure it is also possible to appreciate how secondary pyrolytic reactions move distribution to lower carbon content. Using oxygen distribution,
it is possible to observe a decrease in oxygen content probably due to water dehydration and formaldehyde and CO formation in secondary pyrolysis reactions. In the case of lignin, compounds with seven, eight and nine carbons are the most abundant. This distribution is due to the phenolic composition of this material which under pyrolytic conditions breaks down to form alkylphenols and methoxyalkylphenols. Secondary pyrolysis reactions pro-
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duce an increase in total yields of compounds with seven and nine carbons (phenols derivates). This increase is probably due to the cracking of macro-molecules (not detected with chromatographic techniques) during secondary reactions. It is interesting to note that in distribution of oxygenated compounds, the importance of monoxygenated compounds increases, probably because demethylation high temperature reactions increases methoxy groups. The product distribution of sewage sludge shows a wide range in contrast to other two samples which is indicative of the complexity of this material. Non-oxygenated compounds weigh more in this distribution, which agrees with the observed low polarity of this sample in two-dimensional plots. The high concentration of C14, C16 and C18 dioxygenated compounds suggests the presence of fatty acids in this sample. Secondary reactions lead to a significant decrease in these compounds which shows the facility of these compounds to be destroyed under secondary pyrolysis conditions. 3.5. Filtering names to group compounds Another easy method of grouping compounds is to generate automatic search list and then filter name using a specific word, which would allow a grouping of compounds according to structure. This method was used to group compounds of pyrolytic oils with special emphasis on PAHs and nitrogenated compounds. As described in the previous section, the second column is able to separate compounds with different polarity. This advantage of multidimensionality is very useful in study of compounds which are present in low concentration and have different polarity than the matrix. This is the case for PAHs present in cellulose pyrolysis products. PAHs are important in study of carcinogens in cigarette smoke (cellulose is an important component of this product). Fig. 6 shows how monoaromatic compounds and PAHs appear at the beginning of the second dimension in comparison with phenols, which have more polarity and a higher second dimension time. For this example, the compounds were identified from the raw data and a list of compounds was generated by an autosearch routine and the list was filtered by the following procedure. The first step considered only compounds without any heteroatom (O, N, S,. . .). After a rapid check of the filtration results, some words like ‘‘benz,’’ ‘‘napth,’’ ‘‘fluo,’’ ‘‘anthr,’’ and ‘‘inde,’’ which form part of compound names, were selected as PAH identifiers. The remainder of the compounds were identified manually. More than 30 PAHs were found using this filtering method (Table 2). Traditional method of analyzing PAHs with target analysis only led to the study of previously observed compounds [2,22,23], however, using MDGC it is also possible detect compounds that are not targeted. This leads to identification of new PAHs which can enhance our understanding of formation mechanisms of these compounds.
Table 2 PAHs identified in cellulose primary pyrolytic oil (yield in mg/kg) CAS
Compound
Similarity Yield
Primary pyrotytic OH 496-11-7 Indane 447-53-0 Naphthalene, 1,2-dihydro824-22-6 IH-indene, 2,3-dihydro-4-methyl767-60-2 1H-indene, 3-methyl767-59-9 1H-indene, 1-methyl4373-13-1 Naphthalene, 1,2-dihydro-4-methy1275-51-4 Azulene 17057-82-8 1H-lndene, 2,3-dihydro-1,2-dimethyl20836-11-7 1H-lndene, 2,3-dihydro-2.2-dimethyl56147-63-8 2-Ethyl-2,3-dihydro-1H-indene 17059-50-6 2-Elhyl-H-indene 66B2-71-9 1H-indene, 2,3-dihydro-4,7-dimethyl2717-44-4 Naphthalene, 1,2-dihydro.3-methyl4373-13-1 Naphthalene, 1,2-dihydro-4-methyJ20836-11-7 1 H-lndene, 2,3-dihydre-2,2-dimenthyl4773-82-4 1H-lndene, 2.3-dimethyl18636-55-0 1H-lndene, 1,1-dimethyl90-12-0 Naphthalene, 1-methyl264-09-5 Benzocycloheptatriene 91-57-6 Naphthalene, 2-methyl4773-83-5 1,2,3-Trimethylindene 92-52-4 Biphenyl 575-37-1 Naphthalene, 1.7-dimethyl575-41-7 Naphthalene, 1,3-dimethyl2027-17-0 Naphthalene, 2-(i-methy1ethyl)2131-41-1 Naphthalene, 1,4,5-trimethyl829-26-5 Naphthalene, 2,3,6-trimethyl2245-38-7 Naphthalene, 1,6,7-trimethyl86-73-7 Fluorene 1855-47-6 1-Isopropenylnaphthalene 1730-37-6 9H-fluorene, 1-methyl120-12-7 Anthracene 4569-45-3 9H-fluorene, 9,9-dimethyl-
730 775 877 875 933 748 742 752 711 631 718 615 637 776 642 900 643 941 623 934 719 674 867 601 659 630 757 737 713 605 837 774 835
51 12 33 187 183 31 866 18 6 2 11 10 39 355 3 26 38 514 167 194 11 36 215 23 11 11 18 40 31 19 46 30 4
Secondary pyrolytic oil 95-13-6 Indene 824-22-6 1H-indene, 2,3-dihydro-4-methyl767-60-2 1H-indene, 3-methyl2177-47-1 2-Methylindene 447-53-0 Naphthalene, 1,2-dihydro17059-50-6 2-Ethyl-H-indene 18636-55-0 1H-indene, 1,1-dimethyl2717-44-4 Naphthalene, U-dihydro-3-methyl90-12-0 Naphthalene, 1-methyl2177-45-9 1H-indene, 1,1,3-trimethyl92-52-4 Biphenyl 1127-76-0 Naphthalene, 1-ethyl575-37-1 Naphthalene, 1,7-dimenthyl92-52-4 Biphenyl 2027-17-0 Naphthalene, 2-(1-methylethyl)86-73-7 Fluorene
935 769 645 939 897 706 691 769 920 625 680 838 708 677 799 661
100 10 0 26 32 2 14 7 27 0 3 5 12 3 1 7
Nitrogenated compounds are typically present in sewage sludge oils and their composition is very important in the formation of NOx during combustion [24]. These compounds are formed from decomposition of proteins and nucleic acids present in the original waste. In the sewage sludge investigated for this study, 140 nitrogenated compounds were identified. Based on their structural names, these nitrogenated compounds were divided in five different groups: nitriles,
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Fig. 9. Distribution of nitrogenated compounds in sewage sludge pyrolytic products.
pyridines, amides, amines and polyaromatic nitrogenated (PAN) compounds. Results for this classification are shown in Fig. 9. The most abundant compounds, nitriles and pyridines, are formed from protein decomposition. Secondary pyrolysis produces an increase in the yield of PANs.
ject Officer – Dr. Farley Fisher) is gratefully acknowledged. We also acknowledge the Fulbright Foundation and Spanish Ministry of Education and Science for their support of AF during his stay at the University of Dayton.
4. Conclusions
References
In this study, the MDGC technique was used to analyze pyrolytic products generated from primary and secondary pyrolysis of cellulose, lignin and sewage sludge samples. The results of this study show that the MDGC technique provides more information about pyrolytic product distribution due to increased chromatographic separation of pyrolytic products. The increased separations also lead to an increase in the number of identified products. This improved understanding of pyrolytic product distribution can be used to enhance our understanding of formation mechanisms of important pyrolytic products like PAHs. This study also shows that the MDGC technique can be easily implemented in most pyrolysis laboratories. Also, widely available software programs such as Microsoft1 Excel and Visual Basic can be effectively used to handle vast amounts of MDGC data. The data treatment methods discussed in this paper were successful in dividing MDGC data into manageable groups which were then used to obtain insights into the pyrolytic processes.
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Acknowledgements The partial financial support of this project by the National Science Foundation (Grant #CTS-0202764; Pro-
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