Use of Dual Detection in the Gas Chromatographic Analysis of

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Use of Dual Detection in the Gas Chromatographic Analysis of Oleaginous Biomass Feeds and Biofuel Products To Enable Accurate Simulated Distillation and Lipid Profiling Tonya Morgan,† Eduardo Santillan-Jimenez,† Kelsey Huff,‡ Kazi R. Javed,‡ and Mark Crocker*,†,§ †

Center for Applied Energy Research, University of Kentucky, 2540 Research Park Drive, Lexington, Kentucky 40511, United States Kentucky State University, 400 East Main Street, Frankfort, Kentucky 40601, United States § Department of Chemistry, University of Kentucky, Lexington, Kentucky 40506, United States ‡

S Supporting Information *

ABSTRACT: This contribution describes the development of a chromatographic method capable of simultaneously identifying and quantifying the constituents found in oleaginous biomassincluding algae oiland in biofuel samples through a single costeffective analysis. Major constituents of the aforementioned analytes include oxygen- and/or nitrogen-containing compounds along with fuel-like hydrocarbons. A novel simulated distillation gas chromatographic (SimDist) technique including dual detection capabilitiesspecifically flame ionization (FID) and mass spectrometry (MS)produced identical chromatograms with perfectly aligned signals. FID chromatograms afforded excellent quantitative data while the corresponding MS spectra enabled accurate and thorough compound identification. Simulated distillation data displayed a remarkably linear relationship between retention time and the boiling point of heteroatom-containing compounds in addition to n-alkanes. Indeed, although standard SimDist data were calibrated using the boiling points of n-alkanes, analyses involving other compounds yielded insights into the effect of additional functionalities on both retention time and response factor. Notably, the method developed proved superior relative to commonly employed techniques in the identification and quantification of polyunsaturated fatty acids in algae oil.

1. INTRODUCTION Catalytic deoxygenation processes capable of converting lipidbased feedstocks to fuel-like hydrocarbons1−4 have shown great potential to afford biofuels free of the drawbacks associated with the fatty acid esters that constitute biodiesel. Indeed, while biodiesel synthesis represents the most commonly used approach in the biofuels industry to upgrade oleaginous biomass into a liquid transportation fuel, the high oxygen content of biodiesel causes a number of problems (e.g., poor cold flow properties, limited shelf life, and engine compatibility issues) that render fully deoxygenated hydrocarbon biofuels which are chemically identical and entirely fungible with petroleum-derived fuelsmore attractive.5,6 A previous publication7 has described a method to quantitatively gauge the effectiveness of deoxygenation processes through the analysis of lipid-based feeds and their deoxygenation reaction products, which can be a mixture of different hydrocarbons and unconverted (or partially converted) lipids. This method is based on a simulated distillation gas chromatographic (SimDist) approach that affords data in the form of a boiling point distribution plot (BPDP), a graph illustrating the percentage (by mass) of a mixture that boils at different temperatures. Parenthetically, this analytical approach is widely used in the petroleum industryin the form of simulated distillation GC standard methods of the American Society for Testing and Materials (ASTM) including ASTM D28878 and ASTM D72139due to its time- and costefficiency. The method previously described can be used to analyze samples without the need for the costly and time© XXXX American Chemical Society

consuming derivatization step that is often required to increase the volatility and detectability components in a sample10−18 and employs standard GC-FID instrumentation common to many laboratories. In so doing, this approach avoids the use of less common and/or more expensive instrumentation, including liquid chromatographs (LCs),19−23 gas chromatography−mass spectrometers (GC-MS),10−18,24−39 size exclusion chromatographs (SEC),15,16,40−42 and electrospray ionization−mass spectrometers (ESI-MS).43 Moreover, the previously published method7 is capable of identifying and quantifying not only lipids (i.e., glycerides and fatty acids) and the alkanes that constitute hydrocarbon-based fuels but also other minor components of oleaginous feeds and their deoxygenation reaction products including alkenes, alcohols, aldehydes, and esters. Since the publication of the aforementioned method, significant advances have been made in SimDist method development. Most saliently, researchers including Disanzo et al. and Boczkaj et al. have found that by modifying the column design, the boiling point range measurable via SimDist can be significantly extended and the accuracy with which boiling points are assigned to the components of complex mixtures can be improved.44−46 The original purpose of SimDistto determine the boiling point distribution of petroleum-derived sampleshas expanded to efficiently aid in more comprehenReceived: May 18, 2017 Revised: July 17, 2017 Published: August 2, 2017 A

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wet algae with 5% HCl and methanol was followed by a series of hexane washes.53 All extraction reagents and solvents were ACS grade and purchased from VWR. Crude algal lipids were then purified by adding activated carbon to the hexane solution recovered and vigorously stirring the resulting mixture prior to separating the activated carbon from the latter via vacuum filtration. The activated carbon filter cake was washed with hexane to improve lipid recovery, after which the solvent was removed from the filtrate using a rotary evaporator. 2.2. Deoxygenation Reactions. Unless otherwise stated, lipid deoxygenation experiments in continuous mode were performed using a 20% Ni−5% Cu/Al2O3 catalyst prepared by aqueous excess wetness impregnation and previously described equipment and reaction conditions.54,55 Notable method variations include the use of a higher feed:solvent ratio and reaction temperature (3:1 and 375 °C, respectively) in most runs, the exception being runs involving hemp seed oil in which the ratio used was 1:3 and the reaction temperature was 350 °C. 2.3. GC Analyses. 2.3.1. Calibration Standards. Calibration standards were prepared using a maximum of two representative constituents from a given functional group, to create a four-point calibration curve within 0.1 ≤ MX/MIS ≤ 2, where MX is the mass of the constituent and MIS is the mass of the internal standard, cyclohexanone. The solutions were diluted with chloroform (10:1) prior to GC analysis. Calibrations solutions were stored at −18 °C until analysis to prevent evaporation or any undesirable reactions. Boiling Point Calibration Sample Kit No. 1 (C5−C40) was used neat to obtain qualitative boiling point calibration curves for the SimDist software. Methyl nonadecanoate was used as an internal standard for the 37 FAME mixture, which was further diluted with toluene (10:1) prior to GC analysis. 2.3.2. Reference GC-FID Measurements. Data obtained using a previously described method7 are employed as a reference throughout this contribution. This previously reported method will henceforth be referred to as method A. 2.3.3. GC-Dual Detection Measurements. An Agilent 7890B GC system equipped with an Agilent 5977A extractor MSD and a FID was used for analyses. A 0.1 μL injection was employed, and helium was used as the carrier gas. The multimode inlet (MMI), containing a helix liner, was run in split mode (split ratio, 15:1; split flow, 48 mL/min) using an initial temperature of 100 °C. Immediately upon injection, the inlet temperature was increased at a rate of 8 °C/min to a final temperature of 380 °C, which was maintained for the duration of the analysis. Similarly, the oven temperature (initially 40 °C) was increased immediately upon injection at a rate of 4 °C/min to 325 °C, followed by a ramp of 10 °C/min to a final temperature of 400 °C which was maintained for 12.5 min. The total run time was 91.25 min. An Agilent J&W VF-5ht column (30 m × 250 μm × 0.1 μm; 450 °C maximum) was used as the primary column. Column eluents were directed to a Siltek MXT connector, which split the flow into two streams: one leading to the MSD (J&W Ultimetal Plus Tubing, 11 m × 0.25 mm i.d.) and one leading to the FID (J&W Ultimetal Plus Tubing, 5 m × 0.25 mm i.d.). MS zone temperaturesincluding those of the MS source (230 °C) and quadrupole (150 °C)remained constant for the duration of the analysis. A 1.75 min solvent delay was implemented and the MSD scanned from 10 to 700 Da. The FID was set to 390 °C with the following flow rates: H2 = 40 mL/min; air = 400 mL/min; makeup He = 25 mL/min. Chromatographic programming was performed using Agilent MassHunter Data Acquisition software. Acquired GC-FID data were processed using SimDis Expert 9 software purchased from Separation Systems, Inc. Solvents such as chloroform and dodecane were subtracted and/or quenched from the chromatogram prior to any calculations. This method will henceforth be referred to as method B. 2.3.4. Lipid Profiling. Extracted algal lipids were further esterified/ transesterified to convert any triglycerides and fatty acids that were not converted to FAMES during the extraction process described in section 2.1 by mixing 0.2 g of crude algal lipids with 3.2 g of methanol and 0.2 g of boron trifluoride diethyl etherate in a glass vial, which was then purged with N2, magnetically stirred and heated to 100 °C for 6

sive oil analyses. Indeed, the determination of several variables of interest to the oil industry has been linked to SimDist methodology, including total acid number47 as well as cetane number, pour point, cloud point, and aniline point.48 Traditionally, this extensive list of variables could only be determined through the use of a suite of instruments including standard chromatographic equipment. However, Koseoglu et al. have recently developed a method capable of predicting a number of these crude oil properties by combining simulated distillation gas chromatography and ultraviolet visible spectroscopy.48 Moreover, merging the working principle of the SimDist technique with GC-MS hardware has been evaluated by Roussis et al. for crude oil analysis.49,50 Against this backdrop, this contribution focuses on the potential of simulated distillation gas chromatography with dual detection capabilities, namely, flame ionization detection (FID) and mass spectrometry (MS). A unique configuration of hardware has been successfully employed to obtain high quality data in the time- and cost-effective analysis of oleaginous biomass feedstocks and their catalytic upgrading products. The versatility and reliability of this technique has been proven through the analysis of a diverse set of samples of varying composition to afford accurate simulated distillation and lipid profiling data. Furthermore, novel correlations between quantitative data and the functionality of well-known biomass constituents are proposed.

2. MATERIALS AND METHODS 2.1. Reagents. Triolein (99%), methyl stearate (97%), methyl oleate (70%), decane (≥99%), n-heptadecane (99%), boron trifluoride diethyl etherate, a mixture of 37 fatty acid methyl esters (FAMEs), a polyunsaturated fatty acid mixture (PUFA No. 3 from menhaden oil), and Darco KB-G activated carbon were purchased from Sigma-Aldrich. Octadecylamine (98%), tristearin (95%), and palmitic acid (≥96%) were purchased from City Chemical. Dodecane (>99%), cyclohexanone (>99%), 1-octadecanol (97%), oleyl alcohol (80−85%), 1tetradecene (94%), 1-octadecene (90%), 1-dodecanol (98%), 1hexadecanol (98%), and oleic acid (90%) were purchased from Alfa Aesar. Trilaurin, trimyristin (>95%), tripalmitin (>85%), dodecylamine (>97%), tetradecylamine (>95%), hexadecylamine (>95%), 1dodecene (>95%), 1-hexadecene (>90%), stearonitrile (>92%), stearamide (>90%), isophytol (>95%), and stigmasterol (>90%) were obtained from TCI America. Myristic acid (>99.5%), stearic acid (97%), n-undecane (99%), and ergosterol (98%) were purchased from Acros Organics. Chloroform (HPLC grade) was obtained from J. T. Baker. Methanol (HiPerSolv CHROMANORM) was manufactured by BDH Analytical Chemicals. Pentadecane (≥98%) was purchased from Fluka. Dodecanoic acid (98%) and 1-tetradecanol (97%) were obtained from BeanTown Chemical Co. Phytol (distilled) was produced by MP Biomedicals. Stearyl stearate was acquired from Wako Chemical. The Boiling Point Calibration Sample Kit No. 1 was purchased from Agilent Technologies. A mixture of FFAsproduced as a waste stream in the biodiesel industry during the purification of a triglyceride feedstock by steam strippingwas obtained from ESC Energy. Canola, corn, soybean, and coconut oils (all food grade) were purchased from a local supermarket. Yellow grease (used cooking oil) samples were acquired from two different sources, one being the cafeteria of a local school and the other being local households that disposed of cooking oil used during Thanksgiving in a collection event organized in the community. Pure perilla seed oil and virgin flax seed oil were purchased from H&B Oils Center Co. Cold pressed refined organic hemp seed oil was acquired from Dr. Adorable, Inc. Tall oil fatty acid samples (XTOL 100, 101, 300, and 304) were provided by Georgia-Pacific Chemicals. Algal lipids were obtainedfrom Scenedesmus acutus (UTEX B72) grown in a photobioreactor fed with the flue gas of a coal-fired power plant51,52 through an extraction method in which the direct transesterification of B

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Figure 1. Schematic of GC with dual detection capabilities.

quality calibrations reported in the previous publication.7 The system also comprises both an FID and an Agilent 5977A MSD with an extractor EI ion source, the ultimate goal being to obtain two chromatograms simultaneouslyone from each detectorfrom a single injection and a single GC column separation. This requires the eluent stream to be split downstream of the primary separation column. A key feature of the system is a splitter composed of a SilTek MXT “Y” union connectorengineered from a graphite composite materialwith a similar flow path to the traditional glass Y splitters that seal the columns using a press-tight connection (glass splitters being unstable under thermal cycling conditions). Finally, in order to avoid deformation of the nut and ferrule at the transfer line due to temperature cycling, the MSD is distanced from the GC oven so the connection hardware can be located outside of the oven. 3.2. Method Development and Verification. Method B was developed in conjunction with the hardware described in section 3.1 to identify and quantify the components of feeds and productsas well as intermediates and byproductsin the deoxygenation of lipids to hydrocarbon biofuels. Using method A as a starting point, restrictor lengths, flow rates, and temperatures were optimized to (1) obtain equivalent retention times and optimal resolution for each compound of interest in both the FID and MSD; (2) establish the linear relationship between retention time and boiling point of individual nalkanes needed to acquire simulated distillation data on both the FID and MSD; and (3) attain the same linear relationship for other functional groups. The same column stationary phase and dimensions are utilized in method A and method B. The first major difference between these methods is the splitter used in method B which divides the eluent stream of the GC column, directing part of it to the FID and the rest to the MSD. The restrictors connecting the dual end of the splitter to the FID and the MSD are identical in their internal diameter (0.25 mm) in order to send equal streams to each detector. Similarly, the lengths of the restrictor columns were optimized to result in equal retention times across both detectors, meaning that restrictors of different lengths were used to account for the respective presence and

h. The resulting algal FAMEs were purified with an activated carbon slurry (see section 2.1) and analyzed by two different methodsa standard method involving an HP-88 column and a FID outlined in a previous contribution55 and the dual detection method described in section 2.3.3 (method B)in order to enable a comparison between the data obtained by each method.

3. RESULTS AND DISCUSSION 3.1. Hardware Design. As previously noted, Roussis et al. have incorporated mass spectrometry into a traditional simulated distillation gas chromatographic approach. In a 1998 patent, a procedure for profiling crude oil using GC-MS was disclosed, providing insights into the molecular composition of sample fractions with a given boiling point.49 While this technique is superior to methods outlined in our previous contribution7 due to its ability to make more unambiguous identifications, it leads to results grouped in chemical classes instead of individual constituents andas is discussed in section 3.3its MS results are quantitatively inferior to those obtained with less expensive detectors. In general, the use of FID is particularly well suited for quantitative studies, while MS excels at compound identification. Similarly, information stemming from SimDist analysis in the form of BPDPs is highly informative, which is why this technique is so commonly used in the petroleum industry. Incorporating GC, FID, MS, and SimDist capabilities into a single analytical device would certainly require an initial investment with regard to the equipment cost; however, the prospect of considerably increasing data quantity and quality while simultaneously reducing the analysis time and the consumables requiredby combining these analytical capabilities into a single technique, would ultimately justify that investment. Efforts to develop a single instrument with the aforementioned capabilities began with the Agilent 7890B GC system schematized in Figure 1. In this system, a 7693 automatic liquid sampler mixes and heats samples to 50 °C to ensure homogeneity prior to injection. Downstream from the injector, a multimode inlet allows for independent inlet temperature control, which makes possible the acquisition of the high C

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Figure 2. Gas chromatograms from dual detection of a sample containing representative lipid deoxygenation products acquired using method B: (a) FID and (b) MSD.

Figure 3. Calibration plots for n-alkanes (C10−C40) obtained via method A and method B.

shown, linear plots were obtained for both the FID and MSD, the r2 values being ≥0.9976. Method B was also evaluated for the ability to obtain quantitative data from complex mixtures containing a variety of functional groups. Table 1 compares the actual composition of the sample corresponding to the chromatograms in Figure 2 against the results of its quantitative analysis by method A and method B, the latter method being applied in triplicate to assess method B’s precision and accuracy. Tellingly, the percent error for method B-FID was ≤10% for all compounds. In contrast, method B-MSD displayed percent errors as high as 85%, which further highlights the value of dual detection. Similarly, method A, an FID technique, also resulted in major discrepancies in the quantification of several compounds, specifically fatty acids and triglycerides. This discrepancy is attributed to the lack of sample pretreatment rather than the detection method,

absence of vacuum on the MSD and the FID. Using the parameters described in section 2.3.3, the difference in the retention times obtained with the two detectors was found to be method A-FID > method B-MSD. The data in Figure S2 further confirm this trend, as the same continuum was observed when a series of samples containing both a triglyceride and its corresponding free fatty acid were analyzed. These results clearly show method B-FID to constitute the best approach to obtain reliable quantitative data among the three methods considered. Moreover, while method B-MSD was found to be lacking in its ability to yield dependable quantitative data, the fact that the MSD could afford chromatograms showing the same retention times and resolution obtained with the FID offers a unique opportunity to simultaneously and unambiguously identify all compounds quantified through method BFID. This prospect is discussed below in detail. 3.3. Analysis of Representative Oleaginous Biomass Samples with method B. A variety of oleaginous biomass feedstocks were successfully analyzed with method B, including several edible oils (canola, coconut, corn, and soy) and two different yellow grease (used cooking oil) samples, the resulting chromatograms being shown in Figure S3. A number of highly unsaturated oilsnamely, flax, hemp, and perillawere also analyzed with method B, resulting in the chromatograms included in Figure S4. In addition to triglyceride-based feeds, free fatty acid-containing feeds, including tall oil fatty acid and a fatty acid waste stream produced in the biodiesel industry, were also successfully analyzed (see Figure S5). The tall oil fatty acid was determined to be composed primarily of oleic acid, whereas the fatty acid waste stream was a mixture of oleic, stearic, and palmitic acids and their corresponding triglycerides. Method B was also successfully applied to analyze more complex biomass samples such as algal lipids, the analysis of which can be challenging, particularly since the applied extraction, purification, and upgrading methods can induce changes in an already complex feedstock. Columns specifically designed for FAME analysis such as HP-88 are traditionally

paired with a FID and the Supelco 37 FAMEs mixture to accomplish lipid profiling;7 however, method B afforded more detailed results. Figure 4, which is scaled to show >98% of the

Figure 4. Gas chromatograms of algal FAMEs, the calibration standard mixture containing 37 FAMEs and the analytical standard containing PUFA FAMEs from menhaden oil.

lipids in the samples, illustrates the pitfalls of the original method by comparing the chromatogram of the algal FAMEs with that of the commonly used standard containing 37 FAMEs. Indeed, the peaks in the algal lipids chromatogram with a retention time ≤ 26 min cannot be readily identified by the traditional method due to the absence of peaks in that region in the chromatogram of the 37 FAME standard. In addition, the coelution of several unsaturated C18 lipids also renders proper peak identification difficult when the traditional FID-based method is used. Notably, the MS data obtained with method B confirmed that the elution order of the FAMEs in the mixture (see Table S2) was the same as the elution order E

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Energy & Fuels observed with method A,7 thereby facilitating accurate identification of the polyunsaturated FAMEs. This was further confirmed with the use of the PUFA No.3 standard (see Figure 4). Consequently, the dual detection method also yields more reliable data from a quantitative standpoint, as is clearly illustrated by the data in Table 2. The ability of method B to

observed. Similarly, method B was also applied in the analysis of feeds and reaction products of two deoxygenation processes, one involving hemp seed oil and the other yellow grease as the feedstock, to yield the data shown in Figure S6. These results demonstrate the ability of method B to afford high quality data when applied to the analysis of feedstocks and products in the deoxygenation of a wide variety of lipids to hydrocarbon-based biofuels. 3.4. Quantitative Correlations. In preceding sections, the boiling points of the components in samples of interest were determined based on the retention time of n-alkanes per two different detectors (see Figure S1 and Table S1). Henceforth, the discussion will focus exclusively on results acquired with the FID given that this detector is capable of affording superior quantitative data as illustrated by the information presented in Table 1. As mentioned earlier, biofuel and biomass samples do not only contain straight chained hydrocarbons; rather, they comprise a plethora of other compounds, which highlights the need for accurate boiling point determination and for reliable quantitation across a wide array of compounds displaying different functionalities. In an effort to address this dual need, compounds containing functional groups commonly found in biomass and alkyl moieties representative of diesel and jet fuel-like hydrocarbonsnamely, C12, C14, C16, and C18were evaluated. Functional groups investigated comprised terminal alkenes, fatty acids, fatty alcohols, and fatty amines, as well as saturated and unsaturated FAMEs. As shown in Figure 6, using a calibration curve built with nalkanes as a reference, the boiling points of compounds containing different functional groups were estimated with reasonable accuracy (within 15 °C of the actual value). Tellingly, the boiling points of fatty acids, fatty alcohols, and fatty amines (the compounds in Figure 6a) were typically underestimated, while the boiling points of saturated and unsaturated FAMEs, along with terminal alkenes (the compounds in Figure 6b) were slightly overestimated. Underestimation of the boiling points for compounds containing polar functional groups (i.e., carboxylic acids, alcohols, or amines) is unsurprising. Indeed, using the boiling point of nC18 (316 °C) as a reference, compounds with functional groups elevating their polarity display a retention time up to 2 min shorter while maintaining a linear correlation, which is

Table 2. Lipid Profile of Algal Lipids by the Traditional Method and by the Dual Detection Method

a

fatty acid chain (X:Y)a

traditional anal (%)

dual detection anal (%)

myristic (14:0) palmitic (16:0) palmitoleic (16:1) hexadecadienoic (16:2) hiragonic (16:3) hexadecatetraenoic (16:4) stearic (18:0) linolelaidic (18:2t) linoleic (18:2c) α-linolenic (18:3n3) γ-linolenic (18:3n6) stearidonic (18:4) gadoleic (20:1) cis-docosadienoic (22:2) nervonic (24:1) DEHP

2.6 37.0 9.9

0.3 18.3 2.9 4.1 1.2 10.5 0.7

24.0 13.7 5.2

3.8 3.9

9.6 46.8 0.6 3.5 0.5 0.5 0.6

X:Y = carbon number:number of double bonds.

resolve and quantify the methyl ester of α-linolenic acid (ALA or C18:3n3) from other C18 species as well as the C16:4 FAME is particularly notable given that these valuable, highly unsaturated lipids, which constitute almost 70% of the algal lipids, are not correctly identified by the traditional method. Also of note are the chromatograms and the boiling point distribution plots of algal lipids and a product mixture recovered during their catalytic upgrading, which were obtained with method B and which are shown in Figure 5. The algal lipids used in this study were primarily algae-derived FAMES, the chains of which typically showed a length of 16−18 carbon atoms and some degree of unsaturation. The deoxygenation products were primarily comprised of n-alkanes, the most abundant being C15 and C17, although stearyl alcohol was also

Figure 5. GC chromatograms (left) and boiling point distribution plots (right) obtained by applying method B to the analysis of algal FAMEs (a) and the product mixture recovered from a deoxygenation reaction involving this feed after 8 h of time on stream (b). F

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Figure 6. Boiling point calibration plots obtained using C12:0, C14:0, C16:0, and C18:0 with different functional groups (a), as well as C14:1, C16:1, and C18:1 unsaturated FAMEs (b).

detected in a given time interval, longer alkyl chains should lead to larger areas and thus higher response factors, as is in fact observed. However, as shown in Figure 7, the response factor is also affected by the presence of different functional groups. Of the compounds investigated, n-alkanes present the highest response factors, which is in line with the fact that the carbon atom concentration is highest. Naturally, n-alkanes are closely followed by terminal alkenes due to their lack of heteroatoms. The presence of oxygen atoms associated with ester, acid, and alcohol groups significantly lowers the response factor associated with these compounds. No significant differences were observed among these compounds despite the higher oxygen concentration of esters, the effect of which is counterbalanced by the presence of an additional carbon in the methyl group. Finally, the effect of nitrogen presence in the form of amines was also investigated; however, the amines used proved to be unstable during analysis and consistently resulted in chromatograms including compounds with nitrile functionalities per the MS data, further highlighting the benefits of dual detection.

consistent with the fact that polar compounds interact more weakly than nonpolar compounds with the nonpolar column used in the method, resulting in a depressed boiling point estimate. Terminal alkenes are akin in polarity to n-alkanes, so the linear boiling point trend closely tracks that of the reference, although the presence of a double bond increases the interaction with the column, which in turn slightly raises the retention time and the boiling point estimate. Interestingly, although the ester functionality within FAMES adds a degree of polarity and raises the boiling point estimate, the effect is dampened by the presence of the additional methyl group. The latter increases the interactions with the column and results in a retention time up to 2 min longer than the same n-C18 reference while maintaining the linear correlation, which yields a slightly boosted boiling point estimate. These findings are also summarized in Table S3. In order to obtain quantitative information, the use of response factors is required. Cyclohexanonewhich shows a retention time of 1.8−2.5 min in method Bwas chosen as internal standard in order to create linear calibration curves with respect to peak areas (r2 ≥ 0.99 being obtained in all cases). Response factors, shown in Table S4, were calculated according to the formula R = (AX/AIS)/(MX/MIS), where R is the response factor, AX is the peak area of the analyte, AIS is the peak area of the internal standard, MX is the mass of the analyte, and MIS is the mass of the internal standard. The response factors calculated using this approach for a representative sample that mimics a typical lipid deoxygenation reaction mixture are both listed in Table S4 and applied to the calculations presented in Table 1. Figure 7 illustrates the correlation of two variables, namely, chain length and response factor, for different functional groups. Given that the response of the detector is governed by the number of carbon ions

4. CONCLUSIONS In this study, the potential of SimDist-GC with simultaneous FID and MS was evaluated for the analysis of a range of lipid and lipid-derived biofuel mixtures. A key feature of the system was the use of a SilTek MXT splitter, used to divide the eluent stream downstream of the separation column for FID and MS detection. Resulting chromatograms proved that equivalent retention times were achieved with both detectors. The quantitative accuracy of the developed method was studied and the FID was shown to provide accurate compound quantitation, whereas the MS data were only semiquantitative. However, the equivalent retention times resulting from this method allowed for simultaneous MS compound identification, proving the unique benefits of merging these two analytical techniques. This was exemplified by the ability of the developed method to resolve and quantify polyunsaturated fatty acids that can go unidentified in algal lipid mixtures when traditional methods are employed. Moreover, simulated distillation data displayed a remarkably linear relationship between retention time and boiling point for compounds containing diverse functional groups, in addition to n-alkanes.

Figure 7. Quantitative correlation between retention time and response factors for compounds containing different functional groups. G

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(15) Simakova, I.; Rozmysłowicz, B.; Simakova, O.; Mäki-Arvela, P.; Simakov, A.; Murzin, D. Top. Catal. 2011, 54, 460−466. (16) Mäki-Arvela, P.; Rozmysłowicz, B.; Lestari, S.; Simakova, O.; Eränen, K.; Salmi, T.; Murzin, D. Y. Energy Fuels 2011, 25, 2815− 2825. (17) Hollak, S. A. W.; Bitter, J. H.; Haveren, J. v.; Jong, K. P. d.; Es, D. S. v. RSC Adv. 2012, 2, 9387−9391. (18) Madsen, A. T.; Ahmed, E. H.; Christensen, C. H.; Fehrmann, R.; Riisager, A. Fuel 2011, 90, 3433−3438. (19) MacDougall, K. M.; McNichol, J.; McGinn, P. J.; O’Leary, S. J. B.; Melanson, J. E. Anal. Bioanal. Chem. 2011, 401, 2609−2616. (20) Foglia, T. A.; Jones, K. C. J. Liq. Chromatogr. Relat. Technol. 1997, 20, 1829−1838. (21) Holčapek, M.; Jandera, P.; Fischer, J.; Prokeš, B. J. Chromatogr. A 1999, 858, 13−31. (22) Di Nicola, G.; Pacetti, M.; Polonara, F.; Santori, G.; Stryjek, R. J. Chromatogr. A 2008, 1190, 120−126. (23) Lechner, M.; Bauer-Plank, C.; Lorbeer, E. J. High Resolut. Chromatogr. 1997, 20, 581−585. (24) Priecel, P.; Kubička, D.; Č apek, L.; Bastl, Z.; Ryšań ek, P. Appl. Catal., A 2011, 397, 127−137. (25) Chiappero, M.; Do, P. T. M.; Crossley, S.; Lobban, L. L.; Resasco, D. E. Fuel 2011, 90, 1155−1165. (26) Na, J.-G.; Han, J. K.; Oh, Y.-K.; Park, J.-H.; Jung, T. S.; Han, S. S.; Yoon, H. C.; Chung, S. H.; Kim, J.-N.; Ko, C. H. Catal. Today 2012, 185, 313−317. (27) Ford, J.; Immer, J.; Lamb, H. Top. Catal. 2012, 55, 175−184. (28) Sari, E.; Kim, M.; Salley, S. O.; Ng, K. Y. S. Appl. Catal., A 2013, 467, 261−269. (29) Peng, B.; Yao, Y.; Zhao, C.; Lercher, J. A. Angew. Chem., Int. Ed. 2012, 51, 2072−2075. (30) Morgan, T.; Grubb, D.; Santillan-Jimenez, E.; Crocker, M. Top. Catal. 2010, 53, 820−829. (31) Morgan, T.; Santillan-Jimenez, E.; Harman-Ware, A. E.; Ji, Y.; Grubb, D.; Crocker, M. Chem. Eng. J. 2012, 189−190, 346−355. (32) Santillan-Jimenez, E.; Morgan, T.; Lacny, J.; Mohapatra, S.; Crocker, M. Fuel 2013, 103, 1010−1017. (33) Zuo, H.; Liu, Q.; Wang, T.; Ma, L.; Zhang, Q.; Zhang, Q. Energy Fuels 2012, 26, 3747−3755. (34) Liu, Q.; Zuo, H.; Wang, T.; Ma, L.; Zhang, Q. Appl. Catal., A 2013, 468, 68−74. (35) Wang, C.; Tian, Z.; Wang, L.; Xu, R.; Liu, Q.; Qu, W.; Ma, H.; Wang, B. ChemSusChem 2012, 5, 1974−1983. (36) Yang, Y.; Ochoa-Hernández, C.; de la Peña O’Shea, V. A.; Coronado, J. M.; Serrano, D. P. ACS Catal. 2012, 2, 592−598. (37) Onyestyák, G.; Harnos, S.; Szegedi, Á .; Kalló, D. Fuel 2012, 102, 282−288. (38) Veriansyah, B.; Han, J. Y.; Kim, S. K.; Hong, S.-A.; Kim, Y. J.; Lim, J. S.; Shu, Y.-W.; Oh, S.-G.; Kim, J. Fuel 2012, 94, 578−585. (39) Kim, S. K.; Han, J. Y.; Lee, H.-s.; Yum, T.; Kim, Y.; Kim, J. Appl. Energy 2014, 116, 199−205. (40) Trathnigg, B.; Mittelbach, M. J. Liq. Chromatogr. 1990, 13, 95− 105. (41) Arzamendi, G.; Arguiñarena, E.; Campo, I.; Gandía, L. M. Chem. Eng. J. 2006, 122, 31−40. (42) Kittirattanapiboon, K.; Krisnangkura, K. Eur. J. Lipid Sci. Technol. 2008, 110, 422−427. (43) Catharino, R. R.; Milagre, H. M. S.; Saraiva, S. A.; Garcia, C. M.; Schuchardt, U.; Eberlin, M. N.; Augusti, R.; Pereira, R. C. L.; Guimarães, M. J. R.; de Sá, G. F.; Caixeiro, J. M. R.; de Souza, V. Energy Fuels 2007, 21, 3698−3701. (44) DiSanzo, F. P.; Freund, H. J.; Moy, E. A. U. S. Patent Appl. 20150198545 A1, 2015. (45) Boczkaj, G.; Momotko, M.; Przyjazny, A.; Kamiński, M. J. Sep. Sci. 2016, 39, 748−755. (46) Boczkaj, G.; Kamiński, M. Anal. Bioanal. Chem. 2013, 405, 8377−8382. (47) Green, J. B.; Snelling, R. E.; Young, L. L.; Embry, D. L.; Pinnick, H. R. Eur. Patent Appl. EP 2629092 A2, 2013.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.7b01445. Boiling points and retention times, list of representative lipid deoxygenation products, chromatographic overlays, actual and analyzed compositions, and chromatograms (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: +1 859 257 0295. Fax: +1 859 257 0220. ORCID

Eduardo Santillan-Jimenez: 0000-0002-1627-2719 Mark Crocker: 0000-0002-6560-8071 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This material is based upon work supported by the National Science Foundation under Grant Nos. 1531637, 1437604, and 1305039. This work was also supported in part by a Seed Grant of the University of Kentucky Center for Applied Energy Research. We thank Brad Davis of ESC Energy and Jeffery Yost of Georgia-Pacific Chemicals for providing FFA and tall oil fatty acid samples, respectively. The Sayre School in Lexington, KY, USA is acknowledged for their assistance with the acquisition of yellow grease samples. Molly Frazar is thanked for assistance with sample preparation.

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REFERENCES

(1) Lestari, S.; Mäki-Arvela, P.; Beltramini, J.; Lu, G. Q. M.; Murzin, D. Y. ChemSusChem 2009, 2, 1109−1119. (2) Choudhary, T. V.; Phillips, C. B. Appl. Catal., A 2011, 397, 1−12. (3) Serrano-Ruiz, J. C.; Ramos-Fernandez, E. V.; SepulvedaEscribano, A. Energy Environ. Sci. 2012, 5, 5638−5652. (4) Santillan-Jimenez, E.; Crocker, M. J. Chem. Technol. Biotechnol. 2012, 87, 1041−1050. (5) Honeywell Green Diesel. http://www.uop.com/processingsolutions/renewables/green-diesel/#biodiesel (accessed March 2017). (6) How is NESBTL different from biodiesel? https://www.neste. com/fi/en/node/20465 (accessed March 2017). (7) Morgan, T.; Santillan-Jimenez, E.; Crocker, M. Energy Fuels 2014, 28, 2654−2662. (8) Standard Test Method for Boiling Range Distribution of Petroleum Fractions by Gas Chromatography, ASTM Standard D2887; ASTM International: West Conshohocken, PA, USA, 2013. (9) Standard Test Method for Boiling Range Distribution of Petroleum Distillates in the Boiling Range from 100 to 615°C by Gas Chromatography, ASTM Standard D7213; ASTM International: West Conshohocken, PA, USA, 2012. (10) Kubička, D.; Kaluža, L. Appl. Catal., A 2010, 372, 199−208. (11) Ping, E. W.; Pierson, J.; Wallace, R.; Miller, J. T.; Fuller, T. F.; Jones, C. W. Appl. Catal., A 2011, 396, 85−90. (12) Na, J. G.; Yi, B. E.; Han, J. K.; Oh, Y. K.; Park, J. H.; Jung, T. S.; Han, S. S.; Yoon, H. C.; Kim, J. N.; Lee, H.; Ko, C. H. Energy 2012, 47, 25−30. (13) Berenblyum, A.; Podoplelova, T.; Shamsiev, R.; Katsman, E.; Danyushevsky, V. Pet. Chem. 2011, 51, 336−341. (14) Rozmysłowicz, B.; Mäki-Arvela, P.; Tokarev, A.; Leino, A. R.; Eränen, K.; Murzin, D. Y. Ind. Eng. Chem. Res. 2012, 51, 8922−8927. H

DOI: 10.1021/acs.energyfuels.7b01445 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels (48) Koseoglu, O. R.; Al-Hajji, A.; Adam, F. M. U. S. Patent Appl. 20150106032 A1, 2015. (49) Roussis, S. G.; Fedora, J. W.; Fitzgerald, W. P. U. S. Patent US5808180 A, 1998. (50) Roussis, S. G.; Fitzgerald, W. P. Anal. Chem. 2000, 72, 1400− 1409. (51) Wilson, M. H.; Groppo, J.; Placido, A.; Graham, S.; Morton, S. A., III; Santillan-Jimenez, E.; Shea, A.; Crocker, M.; Crofcheck, C.; Andrews, R. Appl. Petrochem. Res. 2014, 4, 41−53. (52) Wilson, M. H.; Mohler, D. T.; Groppo, J. G.; Grubbs, T.; Kesner, S.; Frazar, E. M.; Shea, A.; Crofcheck, C.; Crocker, M. Appl. Petrochem. Res. 2016, 6, 279−293. (53) Laurens, L. M. L.; Quinn, M.; Van Wychen, S.; Templeton, D. W.; Wolfrum, E. J. Anal. Bioanal. Chem. 2012, 403, 167−178. (54) Loe, R.; Santillan-Jimenez, E.; Morgan, T.; Sewell, L.; Ji, Y.; Jones, S.; Isaacs, M. A.; Lee, A. F.; Crocker, M. Appl. Catal., B 2016, 191, 147−156. (55) Santillan-Jimenez, E.; Loe, R.; Garrett, M.; Morgan, T.; Crocker, M. Effect of Cu promotion on cracking and methanation during the Ni-catalyzed deoxygenation of waste lipids and hemp seed oil to fuellike hydrocarbons. Catal. Today 2017, DOI: 10.1016/j.cattod.2017.03.025

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DOI: 10.1021/acs.energyfuels.7b01445 Energy Fuels XXXX, XXX, XXX−XXX