Detection, Identification, and Quantification of Nonpolar High

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Detection, Identification, and Quantification of Nonpolar High Molecular Weight Contaminants in Jet and Diesel Fuels by Liquid Chromatography Thomas N. Loegel,† Jeffrey A. Cramer,*,† Mark H. Hammond,† Iwona A. Leska,‡ Robert E. Morris,‡ and Kevin J. Johnson† †

Naval Research Laboratory, Code 6181, 4555 Overlook Avenue SW, Washington, D.C., 20375, United States Nova Research Inc., 1900 Elkin Street Suite 230, Alexandria, Virginia 22308, United States

Energy Fuels Downloaded from pubs.acs.org by UNIV DE BARCELONA on 02/04/19. For personal use only.

‡

ABSTRACT: A straightforward analytical methodology has been developed for the identification and characterization of nonpolar high molecular weight contaminants, such as lubricant oils and greases, in jet and diesel fuels. Such contaminants typically contain a distribution of compounds with carbon numbers in the range C25−C40, or 350−600 Da, which are sufficiently larger than fuel constituents to be effectively detected via liquid chromatography. The methodology is based on high performance liquid chromatography, utilizing a xylenes−cyclohexane mobile phase gradient to separate these contaminants from fuels by means of dual porous graphite stationary phase columns and evaporative light scattering detection. The limits of detection for n-pentacosane, n-triacontane, n-pentatriacontane, and n-tetracontane were determined to be approximately 0.1 ppm, with an upper limit of 10% by volume. The newly developed methodology improves upon a previously developed methodology by both enhancing the resolution of high molecular weight contaminants from diesel fuels and eliminating the need for a chlorinated solvent while still minimizing gradient artifacts in the baseline. Uninformative variable elimination− partial least squares modeling was used to construct a comprehensive modeling framework trained with data collected from fuels with known contaminants, to characterize detected contaminants in at least a semiquantitative fashion. The overall methodology was successfully tested for use with various grades of jet and diesel fuel samples.

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fuels9 based on the detection of aromatics and polyaromatics using a photodiode array detector. The present work focuses on developing an analytical methodology to detect and determine nonpolar high molecular weight (NP-HMW) contaminants, which do not contain chromophores, in fuels. Typically, gas chromatography (GC) is the analytical technique of choice for analyzing refined fuel products. However, GC is not amenable to the analysis of high molecular weight compounds due to the impractically high temperatures required to volatilize and elute these analytes into the gas phase. The upper limit of usable GC analyte mass is roughly 350−400 Da, or in the C25−C29 range. Special-purpose metal capillary columns can be used to increase this limit, but the background from stationary phase degradation (i.e., column bleed) and the thermal decomposition of the analytes themselves can obscure the desired signals by overwhelming detectors. High-pressure liquid chromatography (HPLC), on the other hand, does not require analyte volatilization nor greatly elevated temperatures, and was thus a logical technique to investigate for method development. Preliminary high pressure liquid chromatography (HPLC) based methods were previously developed at NRL to analyze NP-HMW contaminants in jet fuels.10 One such method was first adapted from a report in the literature centered on determining polymer standards of sizes from C35 to C110, using

INTRODUCTION Contamination of fuel with even small amounts of high molecular weight materials can lead to practical fuel performance and handling issues. Polar aromatic materials, most notably light cycle oils, can adversely impact fuel stability,1,2 and high amounts of aromatic materials, such as would be present in many high molecular weight contaminants, can adversely impact both fuel stability3 and smoke point measurements.4 Nonpolar high molecular weight contaminants, including, but not limited to, lubricants, turbine oils, hydraulic fluids, transmission fluids, and greases, can potentially be introduced into fuel supplies by pathways ranging from simple handling errors5,6 to intentional adulterations.7,8 The detection and identification of high molecular weight contaminants is a critical component of any fuel failure investigation, and thus a practical detection and characterization methodology is needed to detect and identify as many potential high molecular weight contaminants as possible. However, as petrochemical fuels are complex mixtures of organic compounds, detecting and identifying discrete problematic organic compounds within them remains a significant challenge. Fortunately, these types of organic contaminants have relatively high molecular weights compared to fuel matrices and thus can be separated from typical fuel matrices chromatographically, allowing for detection and identification. Previous work at the Naval Research Laboratory (NRL) has already yielded a test protocol by which to detect and characterize polar aromatic contaminants in jet and diesel © XXXX American Chemical Society

Received: October 25, 2018 Revised: December 20, 2018 Published: January 17, 2019 A

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Energy & Fuels evaporative light scattering detection (ELSD).11 This detection technique also eschews the need for contaminant-specific features, such as chromophores, to enable detection, only requiring changes in the light scattering properties of nebulized vapor streams. This preliminary method utilized a single HPLC column with a porous graphite stationary phase that was chosen for its high selectivity toward alkanes, its high operating temperature range, and its robustness to a wide variety of mobile phases used in normal phase liquid chromatography. This method was further adapted to address a smaller, narrower molecular size range of alkanes, from C25 to C40, which would be more representative of the molecular size distributions typically seen in NP-HMW contaminants such as lubricating oils and greases. However, this method exhibited significant practical drawbacks that hindered its widespread applicability. First, it required the use of a chlorinated mobile phase which raised safety and instrument longevity concerns. Of more immediate analytical relevance, however, is the simple fact that it did not provide sufficient resolution of high molecular weight contaminants when said contaminants were present in diesel fuel matrices. The HPLC-ELSD method described in the present work was thus developed to resolve both of these shortcomings and yield a single analytical methodology that can be used for both diesel and jet fuel samples and eliminates the use of chlorinated solvents. It will not only be shown in the present work that the developed methodology, relying upon widely available instrumentation and materials for ease of implementation, is effective at basic detection, but also that chemometric analysis can model correlations between the HPLC-ELSD chromatograms of fuels possessing unknown NP-HMW contaminants and reference contaminant chromatograms. These data modeling strategies were employed with the updated HPLCELSD methodology to discriminate between different categories of NP-HMW contaminants, and even provide estimates of the amount of contaminant present. This novel data modeling technique can thus be used to inform further fuel failure investigations and, specifically, the production of contaminantspecific calibration curves.

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from this batch of samples in discrete subsets, and because the instrumentation was reconfigured for other tasks between these discrete data collections, the data also inherently display slight amounts of instrument-based variance, which would be expected to enhance real-world model robustness. An additional batch of samples was also produced to validate the modeling framework thus produced, in terms of both its apparent limit of detection and its capabilities with multiple simultaneous contaminants. This is important because the peak shapes produced by various individual contaminants are likely to overlap when multiple contaminants are present in the same sample, affecting predictive modeling results. Collecting this additional data again required the associated instrumentation, previously configured for a different task, to be reconfigured for the detection of NP-HMW contaminants, introducing instrument-based differences to the data relative to the training data. Coordinating this new data to the constructed models required only slight manual shifts in the data along the x-axis and no additional data transforms or skewing. Three replicates each of four different NP-HMW contaminants, one of which was not explicitly accounted for during any model calibration, present at a concentration of 0.10% in one of the four fuels described previously, were included in this additional batch of samples to evaluate the apparent limit of detection of the modeling framework. Also, three replicates each of 12 different 50/50 blends of previously calibrated samples were produced for this batch of samples to determine how the framework would accommodate instances of complex NP-HMW contamination. Instrumentation. The HPLC instrument consisted of a Waters Acquity ultrahigh pressure liquid chromatography (UHPLC) H-class quaternary pump attached to an autosampler for the serial collection of samples, replicate or otherwise. In the results reported herein, the autosampler injection volume was set to 2 μL, though some evidence exists that detection limits can be improved for some NP-HMW contaminants by increasing the injection volume to 10 μL, which, thanks to the use of dual columns (see below), only minimally increases the chances of column overloading. Individual sample injections were followed by three consecutive rinses with 2-propanol. Samples in the autosampler were kept at a temperature of 40 °C to ensure the solubility of high molecular weight components. A mobile phase flow rate of 0.4 mL/min was maintained throughout each individual run. Two Hypercarb HPLC columns with a diameter of 2.1 mm, a length of 100 mm, and a 5 μm particle size (Thermo Fisher) were mounted in series inside a ChromTech TL-105 HT column oven (Timberland Instruments). The temperature in the column oven was set to 70 °C. The mobile phase gradient profile is shown in Table 1. Either HPLC or spectroscopic grade solvents (Sigma-Aldrich) were used to

EXPERIMENTAL SECTION

Samples. Sample preparation steps were intentionally minimized to promote the widest possible applicability of the methodology. Neat fuel samples were analyzed by pipetting 1 mL aliquots into screw-top 2 mL conical bottom HPLC vials capped with pre-slit Teflon septa. Initial calibration curves were created using n-pentacosane, ntricontane, n-pentatricontane, or n-tetracontane, dissolved in either xylenes or a representative fuel. The method was initially formulated, optimized, and evaluated using samples of typical petroleum JP-5 jet and F-76 diesel fuels, which were dosed with either the aforementioned n-alkane standards or one of two lubricating oils: a helicopter transmission oil and a turbine oil. Once the initial development work was completed, an additional batch of 253 samples was collected for the purposes of constructing a comprehensive partial least squares (PLS) modeling framework capable of characterizing as many potential NP-HMW contaminants as possible. This batch of samples consisted of 69 different contaminants, corresponding to various military specifications and other established performance standards, blended at concentrations of 0.05, 0.10, 0.20, 0.40, and 0.60% (v/v) with a JP-5 jet fuel, a JP-8 jet fuel, an F-76 diesel fuel, an ultralow sulfur diesel (ULSD) fuel, and a hydroprocessed renewable jet (HRJ) fuel. This provided a diverse array of 253 NP-HMW-contaminated samples with which to develop comprehensive models, complete with a diverse array of fuel backgrounds. Because the required HPLC-ELSD data were collected

Table 1. Gradient Profile of HPLC Mobile Phase Composition for NP-HMW Fuel Analysis mobile phase composition (%) time (min)

solvent 1

solvent 2

0 5 20 21

1 1 25 1

99 99 75 99

prevent column plugging. As will be shown in the present work, 1,2dichlorobenzene and heptane were initially used as solvents 1 and 2, respectively, but these were later replaced with xylenes (i.e., a racemic mixture of o-, m-, and p-xylene isomers) and cyclohexane in the finalized method. All gradient ramp changes were linear, and the columns were equilibrated for 10 min at initial conditions between injections. The ELSD detector (Waters Corp., model 2424) utilized ultrahigh purity (UHP) nitrogen as the nebulizer gas with a pressure of 25 psi. The nebulizer temperature was kept at 60 °C, and the drift tube B

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Energy & Fuels temperature was held at 80 °C. The detector gain was set to 50 (arbitrary units), and data was collected at a rate of 10 Hz. Contaminant Modeling. A modeling framework similar to that which was previously developed in this laboratory for alternative fuel content modeling in jet and diesel fuels12,13 was developed for the present work. In such a modeling framework, classes of samples containing target analytes that produce class-specific data features deviating most obviously from the remaining data are explicitly targeted and removed from subsequent modeling operations to allow for the more precise modeling of less obviously deviating sample classes in subsequent steps. Individual models were constructed in the present work by first quantifying the amounts of contaminants in the training samples from one of eight contaminant classes against not only neat fuel samples, but also samples containing the remaining contaminant classes. Once effectively modeled, the successfully identified contaminated fuel samples from any given class were removed from the sample populations utilized to train subsequent models. Uninformative variable elimination partial least squares (UVEPLS),14 a variant of partial least squares (PLS),15 was utilized to construct the individual models within this framework, to automatically engage in retention time variable selection and thus improve model accuracy. PLS-based data modeling is intended to correlate the underlying linear variance that can be found within multivariate data sets (in the present case, the HPLC-ELSD data collected from training fuel samples with known contamination levels) to vectors of calibration data (in the present case, the known contamination levels) for the purposes of producing predictive models. It should be noted that alternative data modeling techniques such as multivariate curve resolution (MCR),16 the generalized rank annihilation method (GRAM),17 and target factor analysis (TFA)18 exist that forego this indirect approach to more directly focus upon the modeling of underlying chemical variance via the explicit calibration of models toward the presence of pure chemical components. Such data modeling methodologies would have distinct advantages in scenarios in which the detection and quantification of pure chemical components would be both reliable and desirable. However, because realistic NP-HMW contaminants typically have chemical compositions that are not known a priori, and because said chemical compositions would not often be expected to be easily reducible to small numbers of components with characteristic HPLC-ELSD chromatograms, the wide-ranging contaminant characterization challenge being addressed in the present work is more appropriately addressed by exploiting the common underlying linear data variances that can be extracted via techniques such as PLS from realistic and diverse training sample populations. While more focused models produced via the aforementioned alternative modeling techniques might serve distinct needs in more focused contaminant characterization scenarios,16 exploring these possibilities is outside the scope of the present work. In UVE-PLS, the amount of relevant information possessed by individual data variables is determined by using regression coefficients derived from the overall stability found during a leave-one-out crossvalidation modeling procedure. To determine this stability, a number of randomized variables equal to a third of the total number of retention time variables possessed by any given sample’s HPLC-ELSD data are added to the original data. After this, the actual retention time variables determined to be as inconsistently informative as their randomized counterparts during cross-validation are eliminated from the final model. In the present work, being inconsistently informative is defined as having a regression coefficient average/regression coefficient standard deviation ratio lower than that obtained for 85% of the random variables. The value of 85% was chosen to maintain consistency with the statistical F-test which, when applied to the cross-validation’s cumulative predicted residual error sum of square (CUMPRESS) results with an 85% confidence interval and utilizing a maximum of 10 latent variables (LVs), was also used to select a number of LVs to be employed for each of the final UVE-PLS models that would minimize model overfitting.

This modeling framework utilizes both identification and quantification models. While the identification UVE-PLS model at any given step is trained with all of the samples that were not identified as possessing a NP-HMW contaminant at any previous step, the corresponding quantification UVE-PLS model is trained only with uncontaminated, i.e., control, samples and those samples contaminated by the class of NP-HMW contaminants being explicitly modeled at that step. The use of identification and quantification models serves two purposes. First, as implied by their name, quantification models are expected to produce more accurate contaminant quantification predictions, and, in fact, the quantitative predictions of contaminant content reported herein are taken directly from quantification model results. Second, by design, a contaminated sample must be detectable above a given threshold value in both models before actually being reported as being detected, which is at least theoretically capable of providing lower limits of detection and decreased numbers of false positive results. For all UVE-PLS modeling operations, the raw data were first subjected to base 10 logarithmic transforms to more effectively accommodate the larger fuel features that appear near the beginning of many HPLC-ELSD chromatograms. These transformed data were then normalized to unit area and mean centered.

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RESULTS AND DISCUSSION Method Development. The HPLC-based method developed in 2015 suffered from poor resolution between diesel fuel matrices and high molecular weight contaminants. Figure 1 shows an example of this in the form of an HPLC-

Figure 1. Initial single-column HPLC-based method chromatogram of a petrochemical F-76 diesel fuel sample containing 0.5 ppm npentatricontane and n-tetracontane.

ELSD chromatogram resulting from the analysis of an F-76 diesel containing 0.5 ppm n-pentatriacontane and n-tetracontane. The contaminants, which were C35 and larger, while successfully detected, were barely resolved from the F-76 sample matrix. This is problematic because organic constituents from turbine and transmission oils are known to be encompass hydrocarbons in the C25−C40 range, which would overlap with those eluting from diesel fuels, which can contain compounds as large as C29. To increase the separation between a diesel fuel matrix and alkane contaminants within this size range, the method was modified to use two HPLC columns in series, with the temperature of these columns maintained above ambient by means of an external column oven. To further improve the peak shape and resolution in this size range, the mobile phase gradient program, which still consisted of 1,2-dichlorobenzene C

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Energy & Fuels and heptane at this stage in development, and the ELSD settings were optimized to the parameters described in the Experimental Section. These changes resulted in an acceptable separation of the contaminant-range alkanes from the bulk fuel matrix down to n-pentacosane (C25) as can be seen in Figure 2.

Figure 4. HPLC chromatogram of an F-76 diesel fuel with a 0.5 ppm n-tetracontane standard using a xylenes−heptane mobile phase. Endogenous n-pentacosane from the diesel fuel is indicated for reference. Baseline artifacts in the ELSD signal due to the solvent gradient are visible in the center of the elution region for high molecular weight components.

Figure 2. Improved HPLC-based method chromatogram of an F-76 diesel fuel sample with 0.5 ppm n-pentacosane added.

While this improved method demonstrated sufficient chromatographic resolution, the decision was made to replace the 1,2-dichlorobenzene solvent in the mobile phase gradient, in order to address safety, waste disposal, and instrument robustness concerns because chlorinated solvents are particularly hazardous to operators and are known to cause premature wear on instrument seals. Since xylene has a similar polarity index compared to 1,2-dichlorobenzene, substituting xylenes proved to be suitable as a drop-in replacement with only small increases in peak tailing observed. Figure 3 shows a

differences in these properties between the two mobile phase solvents as the solvent profile (Table 1) changes throughout the course of the chromatographic run. In order to minimize such baseline artifacts, cyclohexane was substituted for heptane, at least in part because cyclohexane has a polarity similar to that of heptane but a slightly higher refractive index that is more closely matched to that of xylenes. After this new xylenes−cyclohexane mobile phase was established, calibration curves were developed for n-pentacosane, n-triacontane, and n-pentatriacontane, as shown in Figure 5. The lower limit of detection (LOD) of approximately 0.1 ppm is limited by the nonlinear instrument response characteristics of the ELSD detector, which has a reduced sensitivity to molecules under 1000 Da when they are present at less than 5 ppm. More generally, the correlation of peak areas to concentrations of discrete compounds, such as is shown in Figure 5, will be affected by such nonlinearities, which are caused by the tendencies of larger particles to scatter more light.19 Such considerations could very well arise when attempting to characterize multicomponent contaminants. To further evaluate the analytical method, typical fuel contamination scenarios were simulated by dosing a standard F-76 diesel fuel with one of two available lubricants: a helicopter transmission oil and a turbine oil. Chromatograms obtained from the analysis of these NP-HMW contaminated diesel fuels are shown in Figures 6 and 7, respectively. Analysis of a neat F-76 diesel is also shown in Figure 8 for the purposes of comparison. A comparison of the neat fuel chromatogram in Figure 8 with the chromatograms in Figures 6 and 7 clearly indicates the presence of NP-HMW contaminants in the latter. Contamination with such unknown materials can be quantified by comparing the appropriate peak areas (e.g., from 7.5 to 20 min in diesel fuel) with a standard calibration curve derived from a set of alkane standards and then using the determined total n-alkane concentration as a proxy for contaminant mixture concentration.

Figure 3. Calibration curve from HPLC analysis of samples containing 0.05−1 ppm n-pentatricontane in F-76 diesel using a xylenes−heptane mobile phase gradient.

calibration curve of n-pentatricontane in F-76 diesel using a xylenes−heptane mobile phase gradient, which provided a detection limit of 0.05 ppm (50 ppb). However, with this solvent pairing, a large baseline drift was observed throughout the mobile phase gradient as the proportion of solvent 1 is increased, as can be seen in Figure 4. ELSD instrument responses can be impacted by both the boiling points and viscosities of the solvents utilized.19 Being a light-based detection technique, the refractive indices of solvents are also likely to play a role in ELSD instrument responses. Thus, the chromatographic baseline drift shown in Figure 4 is likely the result of some combination of the D

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Figure 7. Chromatogram of F-76 diesel fuel containing 5% (v/v) turbine oil.

Figure 5. Calibration curves from 0.05 to 1 ppm for n-pentacosane (C25), n-triacontane (C30), and n-pentatricontane (C35) alkane standards in F-76 diesel fuel.

Figure 8. Chromatogram of a neat petroleum F-76 diesel fuel according to the final HPLC analytical method. Alkane peaks are labeled from n-heneicosane to n-nonacosane.

dilutions of the contaminant thus identified. The development of a modeling framework to assist in this task was thus also undertaken in the present work. PLS Modeling Framework Development. While it has been shown thus far that direct calibration and validation methods can be developed and employed as needed to more definitively identify and quantify NP-HMW contaminants in fuels, the efficient implementation of such methods would be further enhanced with an initial screening to predict possible contaminant identity and quantity, via chemometric modeling, so as to inform further, more definitive, investigations. The model framework thus developed is diagrammed in Figure 9. There are eight pairs of UVE-PLS models that are utilized to interrogate fuel samples contaminated with one of eight classes of contaminants. As indicated previously, these model pairs are applied in a deliberate order, with the first model pair constructed to detect and quantify those contaminants that are most obvious as a class, i.e., those that produce data features that most obviously deviate from what one would expect from an uncontaminated sample, and each subsequent model pair constructed to do the same with sequentially less obvious contaminant classes. These less obvious classes are more directly focused upon in each sequential model pair by removing from the training sample

Figure 6. Chromatogram of F-76 diesel fuel containing 5% (v/v) helicopter transmission oil.

The distinct chromatographic features produced by the two lubricants in Figures 6 and 7 indicate that it should be possible to at least partially characterize lubricant base oils by modeling HPLC-ELSD data collected from a library of known potential NP-HMW contaminants. Once a contaminant has been identified, a more precise quantification could then be made in a straightforward fashion by calibrating against standard E

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Figure 9. Modeling framework developed for the characterization of NP-HMW fuel contamination in jet and diesel fuels via HPLC-ELSD data. Identification and quantification models are both utilized and are thus indicated separately within this figure.

Table 2. Details of Classes Modeled by UVE-PLS Modeling Framework, as Well as Root Mean Square Error of Prediction (RMSEP) Performance Metrics of Quantification Model Predictions contaminant class modeling order

RMSEPa

1. silicone/mineral oil/PAO silicone greases (MIL-L-15719), mineral oil lubricants (MIL-PRF-2104), poly-α-olefin lubricants (SAE-J1899) 2. PAO/ester polyol ester lubricants (MIL-PRF-7808), fatty acid salts/esters greases (MIL-G-21164), diester greases (MIL-PRF-23827), poly-α-olefin greases (MIL-PRF-81322) 3. metal deactivator/ester metal deactivator (DMD), polyol ester lubricants (MIL-PRF-7808) 4. CI/LI/brake fluid/inst. oil/coolant/PCTFE/ester CI/LI, coolants (MIL-PRF-87252), mineral instrument oils (MIL-PRF-6085E), polychlorotrifluoroethylene lubricants (DOD-PRF-24574), polyol ester lubricants (unknown spec), silcone brake fluid (MIL-PRF-46167) 5. ester/mineral oil/PAOb ester lubricants (DOD-PRF-85734), mineral oil hydraulic fluids (MIL-PRF-6083), polyol ester lubricants (MIL-PRF-23699, MIL-PRF-7808, unknown spec), poly-α-olefin hydraulic fluids (MIL-PRF-83282), fatty acid methyl ester greases (MIL-PRF-81322), mineral oil lubricants (SAEJ2360, SAE-J1966, MIL-PRF-6081, MIL-PRF-21260, MIL-PRF-46167) 6. graphite/esterc graphite greases (MIL-G-81827), polyol ester lubricants (MIL-PRF-23699), poly-α-olefin hydraulic fluids (MIL-PRF-46170) 7. mineral oil/ester/PAO mineral oil lubricants (MIL-PRF-3150, MIL-PRF-2104), polyol ester lubricants (MIL-PRF-23699, MIL-PRF-7808), biodegradable ester hydraulic fluids (MIL-PRF-32073), lithium complex greases (MIL-PRF-10924), poly-α-olefin hydraulic fluids (MIL-PRF-46170), mineral oil greases (MILG-25537), PAO/esters hydraulic fluids (MIL-PRF-87257) 8. mineral oil mineral oil hydraulic fluids (MIL-PRF-17672, MIL-DTL-32353), mineral oil lubricants (MIL-PRF-17331, MIL-PRF-9000)

0.0464 0.0318

0.0526 0.0574

0.0407

0.0486 0.0269

0.0397

a Quantification modeling, known >0% samples only, not including misidentifications. bAll ≥0.10% “ester/mineral oil/PAO” contaminated samples detected, but 0.20% “mineral oil/ester/PAO” in F-76 also misidentified as 0.29% “ester/mineral oil/PAO”. cAll ≥0.10% “graphite/ester” contaminated samples detected, but 0.40% “mineral oil/ester/PAO” in HRJ also misidentified as 0.15% “graphite/ester”.

population the contaminated fuel samples corresponding to those that should have been detected by using previous model pairs. Cutoff values are also reported in Figure 9. Cutoff values indicate the minimum amount of contamination that must be

detected in both the identification and quantification model of any given model pair to confirm the presence of that class of NP-HMW contaminants. These cutoff values were chosen manually based on the reliable detection of samples possessing 0.10% NP-HMW contamination. Attempts were made to F

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Table 3. NP-HMW Identification/Quantification Results Obtained from Separate Validation Samples, Undertaken To Assess the Lower Limit of Detection contaminant (spec)

contam classa

contam concn (%, v/v)

fuel

rep 1 2 3 1

6 (includes MIL-PRF-23699) 7 (includes MIL-PRF-23699) 5 2 (includes poly-α-olefin greases)

0.21 0.41 0.36 0.13

2 3 1 2 3 1 2 3

2 2 4 4 4 4 7 6

0.16 0.13 0.17 0.17 0.22 0.16 0.43 0.22

polyol ester lubricant (MIL-PRF-23699)

5

0.10

ULSD

poly-α-olefin hydraulic fluid (MIL-PRF-46170)

7

0.10

JP-8

coolant (MIL-PRF-87252)

4

0.10

HRJ

mineral oil grease

N/Ab

0.10

F-76

pred concn (%, v/v)

pred class

(includes poly-α-olefin greases) (includes poly-α-olefin greases)

(includes mineral instrument oils) (includes mineral oil greases)

a

See Table 2. bNot calibrated.

reliably detect samples possessing 0.05% NP-HMW contamination as well, corresponding to the 0.05% samples also utilized during framework development, but this was not found to be achievable in the present work. Given the approximate 0.1 ppm (or 0.00001% by volume) limit of detection found for the parent HPLC-ELSD methodology, it is thus possible that a NP-HMW contaminant can be detected by this methodology and yet remain undetected when applying the framework. However, this simply means that there is a range of low contamination levels within which a NP-HMW contamination can be known to exist in a sample, via the parent methodology, but cannot immediately be characterized via chemometric modeling. While suboptimal, such a situation is not deemed to be a critical flaw in the overall methodology. Only two training samples were misidentified in the modeling framework thus constructed. These misidentifications, while being removed from consideration during subsequent modeling steps, were treated as actually possessing 0% contamination when calculating the respective quantification model root mean square error of prediction (RMSEP) results, as found in Table 2. The actual predicted contamination levels of the two misidentifications are also reported in Table 2. Modeling Framework Validation. The results of introducing the HPLC-ELSD data collected from the validation sample sets to the UVE-PLS modeling framework can be found in Tables 3 and 4. With regard to these modeling results, it should be mentioned that predicted quantification results are almost always higher than what one would expect them to be, regardless of which class identity is determined via the modeling framework. For example, in Table 3, a 0.10% polyol ester lubricant sample is predicted as possessing between 0.21 and 0.41% NP-HMW contamination, depending on the replicate analyzed. These quantitative results would seem to indicate that the RMSEP results shown in Table 1 might be overly optimistic as realistic predictions of error. Furthermore, the inaccuracy of these quantitative predictions only reinforces the notion that quantification results arising from the use of this framework should only be considered semiquantitative and used to inform more focused analytical efforts, as opposed to being relied upon with no additional validation. This is especially true in the case of the multicontaminant samples due to potentially overlapping

peak shapes. However, these semiquantitative overestimations are considered acceptable in the present context because there are very few corresponding instances of underestimation, and no instances in Tables 3 and 4 in which a NP-HMWcontaminated sample is not detected by the modeling framework. Validation of Framework Limit of Detection. All four single NP-HMW contaminants shown in Table 3, present in the newly collected samples at the lower detection limit of 0.10%, were detected in all three associated replicates. Further, although the exact class one might expect for any given validation sample contaminant is not necessarily accurately predicted, the contaminants within the classes that are so predicted have at least some compositional similarities to the validation sample contaminant for the majority of associated replicates. For example, although the poly-α-olefin lubricant contaminant is not correctly predicted as being a member of class 7 in any replicate sample, it is consistently predicted as being a member of class 2, which consists of, at least in part, poly-α-olefin greases. This can still be useful information when considering further investigations. It is suspected that these instances of misidentification are partly due to the fact that these samples possess contamination levels at the lower limit of detection. Multiple Simultaneous Contaminants. In Table 4 it is shown that the blended NP-HMW contaminants, when at least one contaminant is not accurately identified, are often predicted as possessing a class 5 contaminant, even if neither of the individual contaminants belongs to class 5. This makes the most sense when considering somewhat higher concentrations of contaminants (0.20% or more), because, as was indicated in Table 2, the framework has the known potential to falsely correlate relatively high concentrations of non class 5 NP-HMW contaminants to class 5. This potential, as well as the overall tendency toward class 5 contaminant identification regardless of concentration level, might have something to do with the fact that class 5 is one of the more diverse classes of concurrently modeled contaminants, resulting in a lack of predictive precision. Unfortunately, efforts undertaken during method development to separate the constituents of class 5 into distinct subclasses were deemed unsuccessful. Regardless, as this phenomenon does not fundamentally interfere with the detection of NP-HMW contaminants in fuel samples, it is not G

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Energy & Fuels Table 4. NP-HMW Identification/Quantification Results from Blended Calibration Samples first sample in 50/50 blend 0.40% mineral oil lubricant (SAE-J1966, class 5) in JP-8

second sample in 50/50 blend 0.20% mineral oil hydraulic fluid (MIL-PRF-17672, class 8) in JP-8

0.40% CI/LI additive (class 4) in F-76

0.10% CI/LI additive (class 4) in F-76

0.20% mineral oil hydraulic fluid (MIL-PRF-17627, class 8) in ULSD

0.20% mineral oil lubricant (MIL-PRF-17331, class 8) in ULSD

0.20% lithium complex grease (MIL-PRF-10924, class 7) in F-76

0.05% ester lubricant (DOD-PRF-85734, class 5) in F-76

0.20% graphite-based grease (MIL-G-81827, class 6) in ULSD 0.05% silicone-based grease (MIL-L-15719, class 1) in ULSD

0.20% mineral oil lubricant (MIL-PRF-46167, class 5) in ULSD

0.05% mineral oil lubricant (MIL-PRF-6081, class 5) in ULSD

0.20% poly-α-olefin grease (MIL-PRF-81322, class 2) in JP-8

0.05% diester grease (MIL-PRF-23827, class 2) in JP-8

0.20% polyol ester lubricant (MIL-PRF-7808, class 7) in JP-8

0.05% polyol ester lubricant (MIL-PRF-23699, class 5) in JP-8

0.10% lithium complex grease (MIL-PRF-10924, class 7) in JP-8

0.10% mineral oil lubricant (MIL-PRF-46167, class 5) in F-76

0.10% PCTFE-based lubricant (DOD-PRF-24574, class 4) in HRJ

0.05% mineral oil lubricant (MIL-PRF-2104, class 7) in HRJ

0.10% polyol ester lubricant (MIL-PRF-7808, class 3) in JP-8

0.10% FAME-based grease (MIL-PRF-81322, class 5) in F-76

0.10% polyol ester lubricant (MIL-PRF-23699, class 5) in HRJ

0.05% ester hydraulic fluid (MIL-PRF-32073, class 7) in HRJ

considered a critical flaw in the present context, though, of course, it must be kept in mind when class 5 contaminants are identified in fuel samples. Given the class 6 misidentification also seen in Table 2, and the class 6 misidentification results obtained from both the 0.20% lithium complex grease/0.05% ester lubricant blend and the 0.05% mineral oil lubricant/ 0.05% ester hydraulic fluid lubricant blend in Table 4, a similar set of caveats might also be kept in mind for class 6 identifications. Even accounting for these tendencies, some misidentifications still bear specific mention. Overall, these identification inconsistencies would seem to indicate that the collection of replicate data is advisible when utilizing this method: • One of the 0.20% graphite based grease/0.05% silicone based grease replicates is misidentified as being a member of class 4. This class encompasses several NP-HMW contaminants, including silicone-based brake fluids. As a silicone-based

rep

pred class

pred concn (%, v/v)

1

5

0.35

2 3 1 2 3 1

5 5 4 5 5 8

0.31 0.39 0.23 0.41 0.38 0.18

2 3 1

5 5 6

0.34 0.38 0.19

2 3 1 2 3 1

5 5 5 4 5 5

0.41 0.44 0.43 0.31 0.45 0.41

2 3 1 2 3 1

5 5 2 2 2 2

0.43 0.43 0.36 0.37 0.42 0.27

2 3 1

4 1 4

0.19 0.14 0.15

2 3 1

4 5 5

0.26 0.23 0.46

2 3 1

5 5 7

0.47 0.44 0.17

2 3 1 2 3

5 2 7 6 7

0.23 0.06 0.39 0.07 0.39

grease is a part of the original NP-HMW contamination, this misidentification has at least some bearing on the composition of the actual contaminants, and would thus still be of some use in realistic scenarios. • While the replicates representing the 0.20% polyol ester lubricant/0.05% polyol ester lubricant are each misidentified as a different class, two of these class misidentifications do, in fact, accurately relay the possible presence of a polyol ester lubricant. Again, such information would be of use in realistic scenarios. • In the case of the 0.10% lithium complex grease/0.10% polyol ester lubricant blend, one of the three replicates is identified as possessing a class 5 contaminant, whereas two replicates indicate the presence of a class 4 contaminant. While neither class 5 nor class 4 would be a completely accurate characterization of either contaminant, both class 5 and class 4 indicate the possible presence of a polyol ester lubricant of at H

DOI: 10.1021/acs.energyfuels.8b03746 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

and ensure that it has adequate detection limits to properly serve its intended purpose in realistic scenarios.

least some kind. By cross-referencing the constituents of multiple identified classes from multiple replicates, it would appear that contaminant identity might thus be estimated in difficult cases. This same assessment also applies to the identifications found for the 0.10% polychlorotrifluoroethylenes (PCTFE)-based lubricant/0.10% polyol ester lubricant blend, as even the two inaccurate identifications still indicate the possible presence of a polyol ester lubricant. As was the case for the results found in Table 3, inconsistencies in class identification for these two blended NP-HMW contaminations might be related to the fact that both of their overall NP-HMW contamination levels are at the limit of detection for the modeling framework. Despite the inaccuracies that can be found in Tables 3 and 4, the modeling framework functions well enough with the validation data to justify not only its proposed present use as a kind of intermediate screening tool, but also potential future development with additional contaminants and/or samples. Critically, contaminants never completely evade categorization into one of the defined classes, and these categorizations rarely appear to be completely arbitrary, even in those cases in which two NP-HMW contaminants are present at the same time. In conjunction with the HPLC-ELSD, chemometric modeling provides at least preliminary evidence of possible NP-HMW contaminant identity to inform further investigations.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: jeff[email protected]. ORCID

Thomas N. Loegel: 0000-0003-0186-8023 Jeffrey A. Cramer: 0000-0002-6959-755X Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was funded by the Defense Logistics Agency Energy (DLA-E). The program managers for the NRL study are Philip Chang and Cheng Yen.

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REFERENCES

(1) Arfelli, W.; Power, A. J.; Solly, R. K. The Storage Stability of Automotive Distillate Fuel. SAE Tech. Pap. Ser. 1987, No. 871269. DOI: 10.4271/871269. (2) Mužíková, Z.; Procháska, F.; Pospíšil, M. Fuel 2010, 89 (11), 3534−3539. (3) Fathoni, A. Z.; Batts, B. D. Energy Fuels 1992, 6 (6), 681−693. (4) ASTM International. ASTM D1322-18: Standard Test Method for Smoke Point of Kerosene and Aviation Turbine Fuel; ASTM International: 2018. (5) Mazivila, S. J.; Gontijo, L. C.; de Santana, F. B.; Mitsutake, H.; Santos, D. Q.; Borges Neto, W. Energy Fuels 2015, 29 (1), 227−232. (6) Omrani, H.; Barnes, J. A.; Dudelzak, A. E.; Loock, H.-P.; Waechter, H. Analyst 2012, 137 (12), 2782−2790. (7) Babu, V.; Krishna, R.; Mani, N. Mater. Today: Proc. 2017, 4, 1723−1729. (8) Tharby, R. Catching Gasoline and Diesel Adulteration (English); South Asia Urban Air Quality Management Briefing Note, No. 7; Tharby, R., Ed.; World Bank: Washington, DC, 2002. (9) Morris, R. E.; Loegel, T. N. Improved Analytical Methods to Detect and Characterize Light Cycle Oil Contaminants in Jet Fuel; NRL Ltr Rpt 6180/0212A; Naval Research Laboratory: Oct 8, 2014. (10) Loegel, T. N.; Morris, R. E.; Myers, K. M.; Leska, I. A. Analytical Protocol for Detection of Nonpolar High Molecular Weight Fuel Contaminants; NRL Ltr Rpt 6180/0162; Naval Research Laboratory: Aug 24, 2015. (11) Mekap, D.; Macko, T.; Brull, R.; Cong, R.; deGroot, A. W.; Parrott, A.; Yau, W. Macromolecules 2013, 46 (15), 6257−6262. (12) Cramer, J. A.; Hammond, M. H.; Myers, K. M.; Leska, I. A.; Morris, R. E. Energy Fuels 2015, 29 (11), 7026−7035. (13) Cramer, J. A.; Morris, R. E.; Giordano, B.; Rose-Pehrsson, S. L. Energy Fuels 2009, 23 (2), 894−902. (14) Centner, V.; Massart, D. L.; de Noord, O. E.; de Jong, S.; Vandeginste, B. M.; Sterna, C. Anal. Chem. 1996, 68, 3851−3858. (15) Geladi, P.; Kowalski, B. R. Anal. Chim. Acta 1986, 185, 1−17. (16) Câmara, A. B. F.; de Carvalho, L. S.; de Morais, C. L. M.; de Lima, L. A. S.; de Araújo, H. O. M.; de Oliveira, F. M.; de Lima, K. M. G. Fuel 2017, 210, 497−506. (17) Faber, K.; Lorber, A.; Kowalski, B. R. J. Chemom. 1997, 11, 95− 109. (18) Miao, L.; Cai, W.; Shao, X. Talanta 2011, 83, 1247−1253. (19) ACQUITY UPLC Evaporative Light Scattering Detector Getting Started Guide, 71500109303/revision D; Waters Corp.: 2014; section 1, pp 3−4.

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CONCLUSIONS A straightforward analytical methodology, intended for widespread implementation, was developed to detect and quantify nonpolar materials of size C25 up to C110 (approximately 350− 1500 Da) in both jet and diesel fuels to an approximate limit of detection of 0.1 ppm. The effective separation of NP-HMW components from both diesel and jet fuels was achieved via a dual-column chromatographic methodology which utilizes standard HPLC equipment and materials while not requiring a chlorinated solvent mobile phase as was required when utilizing a previous single-column methodology that was less capable of resolving NP-HMW contaminants from diesel fuels. Accurate quantification was demonstrated by calibrating against n-alkane standards in the appropriate molecular size ranges, and there is no reason to suspect that more precisely targeted calibration curves cannot also be constructed for more complex sources of contamination. The UVE-PLS modeling framework developed to complement the parent HPLC-ELSD methodology only allows for a limit of detection of 0.10% at present, despite the current implementation of calibration samples possessing contamination levels as low as 0.05%. However, at and above this limit of detection, the framework has been shown to perform acceptably well at characterizing NP-HMW contaminants in jet and diesel fuels. These predictions can be used to inform more targeted contaminant assessments in the context of broader fuel failure investigations. It is, however, generally recommended that all samples and standards to be used in such circumstances be analyzed in the form of at least three replicates to ensure accurate and robust results. The widespread applicability of this analytical method, while important in and of itself, also motivates a need to perform new research to examine allowable contamination levels for specific fuel/contaminant scenarios in order to support and maintain fuel property, stability, or performance specifications. In turn, knowing circumstance-specific minimum contamination thresholds can be used to further validate this method I

DOI: 10.1021/acs.energyfuels.8b03746 Energy Fuels XXXX, XXX, XXX−XXX