Automation of the picoSpin 80 1 H NMR benchtop spectrometer for high throughput determination of the research octane number of fuels
The fully automated sample workflow, i.e. the preparation, injection, 1H NMR spectral acquisition and sample cell purging, for determining the research octane number (RON) of fuels by means of low field 1H NMR spectroscopy (80 MHz proton Larmor frequency) is described. To this aim, a Thermo ScientificTM picoSpinTM 80 1H NMR benchtop spectrometer is coupled to a GERSTEL(r) MultiPurpose Sampler MPS (GERSTEL GmbH Co. KG) in combination with the MAESTRO software. The automated sample preparation by the MPS involves the mixing of fuel samples and NMR reference compounds. After sample preparation and injection into the picoSpin flow cell via standard LC tubing, MAESTRO software transfers the sample ID as file name and initiates the NMR data acquisition. After termination of the NMR experiment, the tubing and cell are rinsed and prepared for the subsequent sample by the MPS. N o A N 5906 APPLICATION NOTE To meet the high quality requirements of fuels for modern combustion engine, product streams from refinery processes are analyzed before delivery with respect to the components contained.3 The large number of samples requiring analysis demands an automated solution for sample preparation and analysis where the time factor plays a special role in the product release.4 For motor protection, fuel additives are also added to prevent corrosion and to increase anti knock properties.5 The current standard analysis for determining RON relies on the Cooperative Fuel Research (CFR) engine or gas chromatography, which are both usually slow methods for determining the RON for any liquefied petroleum gas mixture.6 9 In this note, a fast NMR method is described to quickly derive RON by using the low field benchtop picoSpin 80 1H NMR spectrometer where its NMR flow cell has been automated with a GERSTEL multipurpose sample (MPS) robot. The GERSTEL MAESTRO software controlled the MPS and initiated the NMR data acquisition through an executable. The samples were prepared and injected from 10 mL glass vials with septum closure arranged in a 32 position sample tray. The procedure was fully automated through this coupling. Subsequently, multivariate data analysis was applied. The analyzed fuels were classified according to their composition by means of principal component analysis (PCA) Quantitation was achieved by principal component regression, which was validated following the leave one out cross validation method.10 12 Experimental The picoSpin 80 1H NMR benchtop spectrometer was operated at 82 MHz proton Larmor frequency and at T 34 degC. Its sample cell had a total volume of 40 mL and an active volume of 0.2 mL, which was suitable for flow experiments. Due to the effective sensitivity and an electronic lock system, neat liquids can be investigated without the need for deuterated solvents. The picoSpin 80 flow cell was connected via standard PEEK tubing to a GERSTEL MultiPurpose Sampler MPS 3, length 80 cm, equipped with a 1 mL syringe, a VT 32 rack for 10 mL vials, a Vici(r) CheminertTM LC valve and a Fast Wash Station (SpectraLab Scientific, Inc. using methanol as the wash solvent. The MPS was controlled by GERSTEL MAESTRO software version 1.4.2.25. An example set up of the MPS coupled to the picoSpin 80 spectrometer is shown in Figure 1. Both devices were operated using a laptop computer running under Windows(r) 7 software. MAESTRO software controlled the MPS and initiated the NMR spectral acquisition via an executable, which was invoked automatically as a shell script from a command line in the MAESTRO program. All MPS methods and steps as well as the sample sequence, and the sample unique identifier were defined in MAESTRO software. A sample volume comprising all the tubing and the sample cell volume (i.e. in our set up a little less than 1 mL) was drawn from 10 mL sample vials in the 32 tray of the MPS by the 1000 mL glass syringe and inserted into the injection valve. The liquid sample was then driven to the flow cell through a PEEK capillary (Figure 1) The NMR experiment was started as soon as MAESTRO software switched the multi port valve. The start signal was passed to the NMR spectrometer via a LAN cable. The NMR acquisition program "one pulse," which was initiated from the executable within the MAESTRO software and started the NMR experiment, is part of the picoSpin 0.9.1 software version. The fuels were then transferred into the NMR spectrometer via the multi port valve of the MPS with 100 mL of tetramethylsilane (TMS) added as reference without prior dilution by deuterated solvents. As stated above, a turn of this valve was interpreted as the start command by the executable, and this initiated the NMR experiment that was defined in the picoSpin software. Figure 1: Low field picoSpin 80 H NMR spectrometer with a MultiPurpose Sampler (GERSTEL) for automated sample preparation and injection of fuel samples for determining the research octane number (RON) For this study, a simple one pulse experiment followed by acquisition and a sufficiently long relaxation delay, i.e. 7 s, were employed. Sixty four spectra per sample were accumulated. The NMR program stored the resulting digitized free induction decays (FID) in a file of 4 K data points each and copied them into a predefined directory on the laptop computer where MestReNova(r) 9.0.1 software (Mestrelab Research Inc. processing was performed on the same computer. The sample was removed and transferred to waste through injection of the following sample and overfilling of sample cell and tubing. The overall experiment time was about 7 min per sample. The standard RON determination using the combustion motor usually takes about 25 min per sample without warming up period, with the possible reduction of the latter if a sample series is tested. Common gas chromatographic (GC) methods employ run times between 45 up to 90 min. Using this MPS and picoSpin 80 configuration, a total of 81 fuel samples were investigated. The spectra were prepared for PCA and PLS and analyzed using MATLAB(r) version R2015b (MathWorks, Inc. The combined automation of sample preparation, injection and analysis would allow the system to process more than 200 samples in a 24 h period. For a truly automated workflow without any requirement for manual interference, the MPS would need to be equipped with multiple sample trays. The sequence of processing and spectral recording of the fuel samples was programmed graphically through MAESTRO software. The function "prep ahead" allowed the following sample to be prepared while NMR experiments of the current sample were recorded. For a complete sample tray, a total of 90 minutes can thus be economized. A graphical representation of the automatically staggered "Prep and Shoot" workflow within MAESTRO Scheduler program is displayed in Figure 2. Results and discussion A collection of the 1H NMR spectra of twelve fuel samples with research octane numbers between 95 and 102 is shown in Figure 3. RONs are rounded scientifically to the nearest integer. At first glance the spectra appear quite similar. Aromatic signals near 7 ppm from the aromatic protons resonances of benzene, toluene, and xylene, all common components of fuel are clearly visible and difficult to distinguish, as are the aliphatic resonances of the CH3, CH2 and CH groups found between 0.6 ppm and 2.3 ppm (Figure 3) However, closer examination reveals a number of resonances characteristic of the predominant additives used to adjust RONs and comply with national regulations on commercial fuels. The predominant additives, that are typically used to adjust RONs and comply with national regulations in commercial fuels, are ethanol, methyl tert butylether (MTBE) and ethyl tert butylether (ETBE) In the spectra of fuels having RON 95 and RON 98 a strong signal arising from methyl tert butylether (MTBE) was observed at 3.1 ppm. Fuel spectra of RON 99 and RON 102 exhibited a quartet signal around 3.3 ppm stemming from ethyl tertbutylether (ETBE) In lower RON fuels, ethanol signals at 4.2 and 1.2 ppm were regularly observed. When inspecting the stacked spectra in Figure 3, it is obvious that an easy classification of the various fuel samples with respect to RON is not possible. Therefore, a principal component analysis (PCA) as a well known and Figure 2: Graphical representation of the automated RON determination workflow in the GERSTEL MAESTRO Scheduler program. After adding TMS as a reference substance (green) the sample is mixed (orange) and then injected (red) The syringe is rinsed (purple) during the measurement (apricot) Figure 3: Low field H NMR spectra (80 MHz, T 34 degC) of neat fuels with different RONs. hence widespread, simple yet robust method, was chosen from the variety of multivariate data analysis techniques to distinguish fuels according to their RON (Figure 4) Using PCA, four RON ranges were clearly distinguishable. A very good separation was achieved for RON 102. To a slightly lesser extent, RON 98 and 99 fuels were found to be separate as well. The spectra with lower RON had features too similar, i.e. ethanol content varying from 4 9% volume. Hence, samples with RON 95, 96, and 97 could not clearly be separated from each other. A multivariate Partial Least Squares regression (PLS or PLS R) goes beyond univariate calibration models and is therefore appropriate for the quantitative analysis starting from complex spectra. Multicomponent systems with varying concentrations of different components can be examined. For the samples under investigation, a suitable model could be established and tested using the data set containing 27 samples. Using a leave oneout validation approach, the RON of the ith sample, i.e. the left out one, was predicted. The results yielded RON values with a deviation of 0.2 or smaller for approximately 31% of the samples, a deviation between 0.2 and 0.5 for just over 48% between 0.6 and 0.7 another 14% and a deviation over 0.8 for the remaining 7% The 27 predi