The LCGC Blog: Addressing the Complexities of Fuels Using GC–VUV


The LCGC Blog

I am quite excited about some recent work we and others have performed related to fuels analysis using gas chromatography and vacuum ultraviolet spectroscopy (GC–VUV).

I am quite excited about some recent work we and others have performed related to fuels analysis using gas chromatography and vacuum ultraviolet spectroscopy (GC–VUV). When we reported our first applications of the GC–VUV system in 2014, we used gasoline as a naturally complex mixture (1). We highlighted issues where gas chromatography–mass spectrometry (GC–MS) has problems, particularly in distinguishing isomers and some coeluted constituents. Since the commercialization of the VUV detector, this effort has greatly expanded. In fact, fuels analysis appears to be among the most enticing applications of the technology; the majority of systems sold have been into this sector of the market.

When fuels are refined from crude oils, it becomes highly desirable to determine their compositions with respect to relative content of different hydrocarbon classes. This composition will dictate the performance of the fuel, but it can also give information regarding the presence of compounds, such as some aromatics, which can be toxic to humans or the environment. Importantly, vacuum ultraviolet–ultraviolet spectra (in the gas phase) of similarly classed compounds are similar. Thus, scrolling through a chromatographic trace and monitoring the spectral response, one can get a good idea of the relative content of saturates, olefins, and aromatics. This makes GC–VUV ideal for PIONA analysis, which is regularly performed for gasoline to determine the paraffin (linear alkane), isoparaffin (branched alkane), olefin (unsaturated hydrocarbon), naphthene (cyclic alkane), and aromatic content of the fuel. Coupling this class-specific response with the relative ease of deconvolving overlapping responses, which are simply additive in nature, provides a recipe for rapid automated class and species analysis in gasoline. Typically, this analysis would be performed using various complex fractionation and detection schemes (according to up to 10 different ASTM methods) that can be difficult to operate and prone to error. Recently, we have developed a time interval deconvolution (TID) scheme and applied it for automated PIONA analysis using GC–VUV. We expect a report of this method to be available in the literature shortly, at which time I can talk more about it (2).

There are many technologies and methods that can be applied for gasoline analysis. However, as you move to heavier fuels, such as jet fuel and diesel fuel, the number of available analysis choices declines rapidly. To perform the GC–VUV-based PIONA classification mentioned previously for gasoline, it is necessary to have good coverage of spectra for representative hydrocarbons logged into the VUV spectral library. Moving into diesel and jet fuels, very quickly a range of high-carbon-number hydrocarbons are encountered for which standards do not exist to populate the library. The number of isomers and mixed-class compounds also increases. This complexity and lack of high-carbon-number standards make for a much greater challenge. Luckily, much can still be done to amplify the content of certain classes of compounds using spectral filters. Because the full absorption spectrum from 120 to 240 nm is collected at up to a 100-Hz acquisition speed across the entire chromatogram, these data can be easily filtered during a run or postrun to project different regions of absorption. The 120–160 nm filter emphasizes a region of absorption where saturates absorb the greatest. The 180–220 nm filter emphasizes aromatics. Recent research from the Zimmermann group (3) using two-dimensional GC (GCxGC) and from the Harynuk group (4) using GC and ionic liquid column chemistries in combination with VUV detection have focused nicely on classification of components in diesel fuels using variations of the spectral filter concept.

Very recently, we used some common components in diesel and jet fuels, namely dimethylnaphthalene isomers, to evaluate the dynamic range over which coeluted compounds having similar absorption could be deconvolved (5). There are 10 isomers of dimethylnaphthalenes. They are very difficult to separate completely, and their MS spectra are very similar. Their VUV spectra are also very similar, but they are unique and distinguishable. For three sets of commonly coeluted isomer pairs, we prepared various compositional ratios from 50:50 to 99.5:0.5. Ultimately, the relative amounts of the isomers control the ratio, which can be distinguished, but we found that even having very similar absorption spectra, we could still detect 1% of one isomer in the presence of 99% of another. I think this starts to provide some very nice guidelines for the operational capabilities of GC–VUV. This article was selected to be a featured article on the cover of an upcoming issue of Analytica Chimica Acta.

We also computed absorption spectra for the dimethylnaphthalene isomers using time-dependent density functional theory and compared the computed spectra with experimental spectra. The agreement was quite good, but there is still more work to be done in that field. The upside of such a capability is something to consider. Once the theoretical computations are further refined, it may be possible to generate the absorption spectra for molecules without measuring a standard experimentally. That would go a long way toward solving the issue mentioned previously regarding the PIONA analysis of diesel and other heavier fuels.

I believe that the VUV will continue to find more and more application in the fuels analysis arena. We have been working together with the Mondello group at the University of Messina and Chromaleont to evaluate flow-modulated GCxGC–VUV. The focus was on fatty acid methyl esters (FAMEs), but these are commonly found and desired to be characterized in biodiesel fuels. The spectral signatures of the FAMEs are quite unique, especially in the C22–C26 range, relative to diesel components. When you couple spectral features with a GCxGC separation, very exceptional clarity can be obtained to characterize biodiesel. Overall, I believe that GCxGC–VUV can be a very powerful technique for complex mixture analysis, especially when moving to heavier fuels. Luckily, VUV now has an extended operating temperature up to 420 °C (and an extended spectral range) with a new version of the instrument, and this will further aid complex fuel analysis (6).


Disclaimer: KAS is a member of the Scientific Advisory Board for VUV Analytics, Inc.


  • K.A. Schug, I. Sawicki, D.D. Carlton Jr., H. Fan, H.M. McNair, J.P. Nimmo, P. Kroll, J. Smuts, P. Walsh, and D.A. Harrison, Anal. Chem.86, 8329–8335 (2014).

  • P. Walsh, M. Garbalena, and K.A. Schug, submitted.

  • T.M. Gröger, B. Gruber, D. Harrison, M.R. Saraji-Bozorgzad, M. Mthembu, A.C. Sutherland, and R. Zimmermann, Anal. Chem.88, 3031–3039 (2016).

  • B.M. Weber, P. Walsh, and J.J. Harynuk, Anal. Chem.88, 5809–5817 (2016).


Kevin A. Schug is a Full Professor and Shimadzu Distinguished Professor of Analytical Chemistry in the Department of Chemistry & Biochemistry at The University of Texas (UT) at Arlington. He joined the faculty at UT Arlington in 2005 after completing a Ph.D. in Chemistry at Virginia Tech under the direction of Prof. Harold M. McNair and a post-doctoral fellowship at the University of Vienna under Prof. Wolfgang Lindner. Research in the Schug group spans fundamental and applied areas of separation science and mass spectrometry. Schug was named the LCGCEmerging Leader in Chromatography in 2009 and the 2012 American Chemical Society Division of Analytical Chemistry Young Investigator in Separation Science. He is a fellow of both the U.T. Arlington and U.T. System-Wide Academies of Distinguished Teachers.

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