Transforming fossil fuels into the gasoline used to run a car involves a lot of refining work, including the incorporation of several additives.
Methanol, benzene, and MTBE are all “traditional” additives that have been used to successfully achieve a boost in octane rating. However, this comes with detrimental effects on human health, engine health, and the environment.
As such, there has recently been a drive for new, “non-traditional” renewable gasoline additives (NTGAs) that will provide the fuel properties required without the harmful side-effects.
Some additives found in gasoline are far from beneficial. Several examples of these can be found in the list of cheap octane-boosting compounds that the Asian Clean Fuels Association identified in some Asian gasoline.
Such additives were found to possibly introduce negative effects such as gum formation in the engine. As such, robust analytical techniques are now required for future gasoline monitoring.
For NTGA analysis, it has been proposed that the conditions of ASTM D8071 be used over Detailed Hydrocarbon Analysis (DHA) via ASTM D6730. In the world of NTGA analysis, a key advantage of VUV’s over DHA is how well it can adapt to new compounds. As shown in Figure 1, VUV is similar to other detectors in that it can provide a linear response over a wide concentration range.
Figure 1. Plot of concentration versus response for the NTGA, dimethoxymethane, in gasoline. Even over a range of 20% to 0.05%, the measurements are very linear.
However, GC-VUV identifies compounds using both retention time and spectral matching unlike the GC-FID analysis of ASTM D6730, which identifies compounds using retention time only. Furthermore, as shown in Figure 2, VUV can deconvolve coeluting compounds. New compounds can be easily analyzed using these factors - all that is required is a reference standard.
Figure 2. Acetone, one of the NTGAs analyzed in this experiment, coeluted with iso-pentane. With DHA, this coelution could have proven problematic for proper identification and quantification, but for GC-VUV, they are easily distinguished through spectral deconvolution.
This information has been sourced, reviewed and adapted from materials provided by VUV Analytics.
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