Viscosity Measurements of Aviation Turbine Fuels - Energy & Fuels

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Viscosity Measurements of Aviation Turbine Fuels Tara J. Fortin* and Arno Laesecke National Institute of Standards and Technology, Material Measurement Laboratory, Applied Chemicals and Materials Division, 325 Broadway, Boulder, Colorado 80305-3328, United States

Downloaded by GEORGETOWN UNIV on September 8, 2015 | http://pubs.acs.org Publication Date (Web): September 3, 2015 | doi: 10.1021/acs.energyfuels.5b00423

S Supporting Information *

ABSTRACT: Kinematic viscosity has been measured for nine separate aviation turbine fuel samples. The nine samples span the range of fuel types available: conventional petroleum-derived fuels, synthetic fuels derived from the Fischer−Tropsch process, and renewable fuels derived from biomass feedstocks. Results for a tenth sample intended for use in quantifying the total sulfur in fuel oils are also included for comparison. All measurements were made at ambient pressure and over the temperature range of 293−373 K. A significant degree of variability is observed among the measured viscosities. Measurement data for the conventional fuel samples were also compared to the predictions of two existing surrogate mixture models.

1. INTRODUCTION According to the International Air Transport Association (IATA), the world’s airlines carried approximately 3 billion passengers and consumed 73 billion U.S. gallons of fuel in 2012.1 Total fuel costs for 2012 were estimated at $209 billion, an approximately $60 billion increase over 2010 costs resulting from an increase in fuel prices.1 With fuel costs constituting approximately 33% of airline operating costs in 20121 and consumption expected to keep increasing,2 the industry is actively seeking ways to improve efficiencies and secure stable fuel supplies. These objectives have the potential added benefit of helping the industry implement its vision for mitigating greenhouse gas emissions. For example, in 2009, IATA member airlines adopted the following targets: carbon neutral growth from 2020, an average 1.5% per year improvement in fuel efficiency through 2020, and a 50% reduction in carbon dioxide (CO2) emissions relative to 2005 levels by the year 2050.3 Similar goals have been agreed upon by the International Civil Aviation Organization (ICAO) Group on International Aviation and Climate Change (GIACC).4 The pursuit of low carbon, drop-in alternative jet fuels to complement improvements in operational and equipment efficiency is a key component of the industry’s strategy.2−8 Jet fuels are complex mixtures consisting of hundreds of different hydrocarbons; the relative abundance of compound type (paraffin, cycloparaffin, etc.) varies depending on the crude oil from which the fuel was derived and which refining process was used.9 The most common commercial aviation turbine fuels are Jet A, primarily used within the United States (U.S.), and Jet A-1. The two fuels are similar except that Jet A-1 has a lower freeze point and often contains a static dissipater additive.10 Potential alternatives include synthetic fuels manufactured from coal or natural gas using a Fischer− Tropsch process and renewable fuels derived from biomass feedstocks such as vegetable oils, animal fat, waste grease, switch grass, and algae via a variety of processes.7,11 Currently, Fischer−Tropsch fuels and fuels produced from a hydroprocessed esters and fatty acids (HEFA) process (also known as hydrotreated renewable jet (HRJ) fuels) are certified for commercial use as blends with conventional fuels;11,12 addiThis article not subject to U.S. Copyright. Published XXXX by the American Chemical Society

tional conversion processes are under development and could one day be considered for approval.6 Ultimately, whether or not a particular alternative fuel becomes commercially viable depends on a variety of factors, including feedstock availability and processing costs. But more importantly, for a fuel to be considered a viable drop-in candidate, it must first exhibit properties that would allow it to be blended with, or completely replace, Jet A without requiring substantial equipment modification. Some important properties to consider are its density, speed of sound, thermal stability, volatility, and viscosity; we will be discussing viscosity measurements in this work. Viscosity is the measure of a fluid’s ability to transmit momentum resulting from cohesive forces among molecules. These forces appear as shear stresses between fluid layers moving at different velocities. Dynamic, or absolute, viscosity is defined as the ratio between the applied shear stress and the rate of shear of a material and can be thought of as the momentum conductivity.13 The kinematic viscosity of a fluid is defined as the ratio of the dynamic viscosity to the density and can be thought of as the momentum diffusivity since it is the ratio between momentum transport and momentum storage.13 In this work we report measurements of kinematic viscosity at ambient pressure for several aviation turbine fuels of varying origin (conventional, synthetic, and renewable). Since the temperature dependence of viscosity is as important as the viscosity itself, measurements were carried out over the range of 293−373 K.

2. MATERIALS AND METHODS 2.1. Fuel Samples. Nine samples of aviation turbine fuels were obtained from the Air Force Research Laboratory Propulsion Directorate at Wright Patterson Air Force Base. These samples include both commercially available and prototype fuels, and they represent both conventional and alternative fuel sources. For each of these nine samples, the viscosity data presented here are one Received: February 26, 2015 Revised: July 29, 2015

A

DOI: 10.1021/acs.energyfuels.5b00423 Energy Fuels XXXX, XXX, XXX−XXX

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Table 1. Top Ten Components (by Area %) of the Measured Fuel Samples compound

CAS no.

area %

Jet A 360223 n-undecane n-dodecane n-decane n-nonane n-tridecane 4-ethyloctane n-heptane cis-1,3-dimethylcyclohexane n-octane xylene

1120-21-4 112-40-3 124-18-5 111-84-2 629-50-5 15869-86-0 142-82-5 638-04-0 111-65-9 1330-20-7

5.99 5.66 5.50 4.42 4.33 2.85 2.79 2.74 2.54 2.51

1120-21-4 112-40-3 124-18-5 629-50-5 1680-51-9 17301-23-4 13475-81-5 111-84-2 105-05-5

9.80 8.03 7.17 4.91 2.56 2.35 2.33 2.23 2.12 1.92

112-40-3 629-50-5 1120-21-4 124-18-5 629-59-4 535-77-3 1560-97-0 111-84-2 611-14-3 53172-84-2

6.23 5.58 5.46 4.60 3.57 2.69 2.43 2.43 2.15 2.12

1120-21-4 112-40-3 629-50-5 629-59-4 124-18-5 2958-76-1 6975-98-0 13151-34-3 17301-23-4 629-62-9

11.05 8.77 7.36 5.83 4.33 2.28 2.13 1.94 1.91 1.68

112-40-3 2216-34-4 1120-21-4 124-18-5 17301-94-9

2.43 2.42 2.28 1.94 1.79 1.73 1.66 1.63 1.59 1.53


Jet A 363823 n-undecane n-dodecane n-decane n-tridecane 1,2,3,4-tetrahydro-6-methylnaphthalene w,x,y,z-tetramethylcyclohexanea 2,6-dimethylundecane 2,2,3,3-tetramethylhexane n-nonane 1,4-diethylbenzene Jet A 465823 n-dodecane n-tridecane n-undecane n-decane n-tetradecane 1-methyl-3-(1-methylethyl)benzene 2-methyldodecane n-nonane 1-ethyl-2-methylbenzene (1-methyl-1-butenyl)benzene SRM 1617b41 n-undecane n-dodecane n-tridecane n-tetradecane n-decane decahydro-2-methylnaphthalene 2-methyldecane 3-methyldecane 2,6-dimethylundecane n-pentadecane S814 n-dodecane 4-methyloctane n-undecane n-decane 4-methylnonane x,y-dimethylundecanea 2,4-dimethylundecane n-tridecane 5-methylundecane n-nonane

17312-80-0 629-50-5 1632-70-8 111-84-2 GTL20

n-decane 2-methylnonane n-nonane 2-methyldecane 3,6-dimethyloctane

124-18-5 871-83-0 111-84-2 6975-98-0 15869-94-0 B

14.5 12.3 10.4 8.2 6.4 DOI: 10.1021/acs.energyfuels.5b00423 Energy Fuels XXXX, XXX, XXX−XXX

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Table 1. continued compound

CAS no.

area %

GTL20 n-undecane 3-methylnonane 4,5-dimethylnonane 2,3,4-trimethylhexane n-hexane

1120-21-4 5911-09-6 17302-23-7 921-47-1 110-54-3

5.1 4.7 4.2 3.5 3.3

62016-14-2

5.3 3.2 2.9 2.8 2.2 2.2 2.1 2.1 2.0 1.9

CTL20


2,5,6-trimethyloctane x-methyldecanea 2,3-dimethyloctane 3,7-dimethylnonane 2,4,6-trimethyloctane x-methyldecanea x-methylnonanea n-decane 2,5-dimethyloctane x-methylundecanea

7146-60-3 17302-32-8 62016-37-9

124-18-5 15869-89-3 CSK21

cis-1-methyl-4-(1-methylethyl)cyclohexane o-, m-, and p-1-methyl-x-(1-methylethyl)benzeneb

trans-1-methyl-4-(1-methylethyl)cyclohexane x,y-dimethyloctanea,c

6069-98-3 (ortho) 527-84-4 (meta) 535-77-3 (para) 99-87-6 1678-82-6

47.14 25.77

2216-34-4 871-83-0 17302-27-1 6975-98-0 1632-70-8 15869-89-3 112-40-3 15869-94-0 2216-30-0 5911-04-6

10.69 8.49 6.15 5.79 5.33 5.13 4.39 4.17 4.15 4.15

124-18-5 1120-21-4 871-83-0 13151-35-4 5911-04-6 2216-34-4 111-84-2 15869-89-3 2847-72-5 2216-33-3

7.94 6.72 6.69 6.04 3.73 3.44 2.97 2.92 2.28 2.07

22.43 4.34

C-HRJ21 4-methyloctane 2-methylnonane 2,5-dimethylnonane 2-methyldecane 5-methylundecane 2,5-dimethyloctane n-dodecane 3,6-dimethyloctane 2,5-dimethylheptane 3-methylnonane Cs-HRJ21 n-decane n-undecane 2-methylnonane 5-methyldecane 3-methylnonane 4-methyloctane n-nonane 2,5-dimethyloctane 4-methyldecane 3-methyloctane a

It is not always possible to determine the position of methyl substitution. In these instances, the position is indicated with a letter. bThe three isomers coelute in a peak centered at a retention time of 5.888 min. The value of x is 2, 3, and 4 for the ortho, meta, and para isomers, respectively. c This component was previously identified as 3,6-dimethyloctane,21 but the manufacturer of the fluid has cited 2,6-dimethyloctane as the more likely isomer considering the synthesis process. The present designation better reflects the uncertainty in the identification of this component. component of a more extensive research effort aimed at characterizing the chemical and thermophysical properties of aviation fuels.14−24 Three of the nine samples represent conventional, petroleum-based aviation turbine fuel. These are three separate samples of Jet A, designated 3602, 3638, and 4658. In the U.S., Jet A is the most commonly used commercial aviation turbine fuel. For Jet A to be eligible for sale, select characteristics of the fuel, such as composition in terms of acidity and aromatic and sulfur content, volatility, and fluidity, among others, must comply with the detailed requirements enumerated in ASTM-D1655.25 This standard is primarily a performance specification since variability resulting from differences

in crude sources and manufacturing processes precludes the formulation of a strict compositional specification.25 The three Jet A samples measured and discussed herein were intended to capture some of the expected variability, with Jet A 4658 intended as an “average” sample. Specifically, Jet A 4658 is a composite mixture of approximately equal volumes of several available batches of Jet A, each from different manufacturers. Three additional samples represent synthetic isoparaffinic kerosenes (S-IPK). All three were produced using the Fischer−Tropsch (FT) process wherein a synthesis gas of carbon monoxide and hydrogen is converted to liquid fuel over a catalyst.26−30 The first, S8, is derived C



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Figure 1. Schematic representation of the hydrocarbon classification analysis of all measured samples.20,21,23,41,44 Analysis is based on ASTM Method D-2789,42 which classifies hydrocarbon samples into six families: paraffins (P), monocycloparaffins (MCP), dicycloparaffins (DCP), alkylbenzenes (AB), indanes and tetralins (I&T), and naphthalenes (N). Numbers shown represent the measured percent volume fraction for each of the six families. Volume fractions of less than 1% are not labeled. from natural gas.31 It was developed for the United States Air Force as a synthetic substitute for JP-8 and has been certified for use in 50/50 blends with JP-8 in B-52 aircraft.31 The second FT fuel sample is also derived from natural gas and has been tested in 50/50 blends with JP-8 by the U.S. Air Force.31 It will herein be referred to as GTL (gas-toliquid). The final FT fuel sample is derived from coal.31 This fuel, herein referred to as CTL (coal-to-liquid), has been used in blends at O. R. Tambo International Airport in Johannesburg, South Africa, since 1999.31 The final three aviation turbine fuel samples are derived from various biomass feedstocks. The first of these is obtained from plant isoprenoids via the fermentation reaction of a sugar with a recombinant host cell.21,32 This fuel will herein be referred to as cellular synthetic kerosene, CSK. The remaining two bioderived samples are produced from two different oilseed crops, camelina (Camelina sativa)33 and castor (Ricinus communis).34 Both are hardy plants that provide relatively high oil yields making them attractive as potential energy crops.34−36 In fact, camelina-derived jet fuels have been used in blends during test flights by two commercial airlines37,38 and the U.S. military.39 Because the production of the finished fuel requires hydrodeoxygenation and hydroprocessing, these oilseedderived fuels are often referred to as hydrotreated renewable jet (HRJ) fuels. Therefore, throughout the remainder of this work, the camelina sample will be referred to as C-HRJ and the castor sample as Cs-HRJ. In addition to the aforementioned fuel samples, a tenth sample was measured and the results are also included here. That sample was obtained from the Analytical Chemistry Division of the Material Measurement Laboratory at the National Institute of Standards and Technology (NIST). The sample is a candidate for a Standard

Reference Material (SRM) to quantify the total sulfur in fuel oils or materials of similar matrix.40 As such, it would be part of a suite of liquid fuel SRMs provided by NIST. Once certified, it will replace SRM 1617a,40 which is currently out of stock. This sample will be referred to as SRM 1617b. As part of earlier research efforts, the chemical composition of each of the nine jet fuel samples was analyzed using gas chromatography− mass spectrometry (GC-MS) and the results have been published elsewhere.14,15,20,21,23 The chemical composition of SRM 1617b was determined in conjunction with the measurements reported here.41 For each of the ten samples, a subset consisting of the top ten observed components is shown in Table 1. Chemical names and corresponding CAS registry numbers are listed, sorted by the uncalibrated chromatographic peak area. The one notable exception is CSK, which only has four components listed; together, those four components account for more than 99% of the measured chromatographic peak area. For the remaining samples, the components listed in Table 1 account for approximately 19% to 73% of the total measured chromatographic peak area. As Table 1 shows, all but CSK are composed primarily of linear and branched alkanes (or paraffins); CSK is composed primarily of closely boiling cyclic compounds and a small quantity of x,y-dimethyloctane. Further comparison among the fuel samples is possible with the aid of a classification method based on ASTM Method D-278942 where mass spectral fragments are used to classify hydrocarbon samples into six different types or families: paraffins (P), monocycloparaffins (MCP), dicycloparaffins (DCP), alkylbenzenes (AB), indanes and tetralins (I&T), and naphthalenes (N). A discussion of the limitations and uncertainties of this method can be found in Smith and Bruno.43 D


1.907 1.750 1.614 1.495 1.391 1.299 1.216 1.143 1.077 1.017 0.963 0.913 0.870 0.829 0.792 0.757 0.725

293.15 298.15 303.15 308.15 313.15 318.15 323.15 328.15 333.15 338.15 343.15 348.15 353.15 358.15 363.15 368.15 373.15

2.006 2.008 2.011 2.009 2.007 2.004 1.998 1.996 1.995 1.995 1.998 2.002 2.009 2.013 2.018 2.021 2.024

t95

b

Jet A 3602 0.006 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.007 0.008 0.009 0.009 0.011 0.012 0.013 0.015 0.016

2 −1

U(ν̅) (mm ·s ) c

ν̅ (mm ·s ) 1.650 1.523 1.412 1.314 1.232 1.155 1.086 1.023 0.967 0.916 0.869 0.826 0.788 0.752 0.718 0.688 0.660

2 −1

2.011 2.014 2.015 2.015 2.000 1.996 1.994 1.995 2.000 2.004 2.009 2.013 2.018 2.021 2.024 2.028 2.030

t95b

Jet A 3638 2 −1

U(ν̅) (mm ·s ) 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.008 0.009 0.010 0.011 0.012 0.013 0.015 0.016 0.018 0.020 c

ν̅ (mm ·s ) 1.953 1.790 1.649 1.525 1.418 1.322 1.238 1.162 1.095 1.033 0.978 0.928 0.882 0.840 0.801 0.766 0.734

2 −1

2.007 2.008 2.009 2.011 2.006 2.003 1.999 1.995 1.995 1.995 1.998 2.002 2.007 2.012 2.015 2.021 2.024

t95b

Jet A 4658 2 −1

U(ν̅) (mm ·s ) 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.008 0.009 0.009 0.010 0.011 0.013 0.014 0.016 c

ν̅ (mm ·s ) 1.957 1.794 1.652 1.527 1.419 1.323 1.237 1.159 1.091 1.029 0.973 0.922 0.876 0.834 0.795 0.759 0.726

2 −1

2.012 2.011 2.008 2.008 2.008 2.005 2.004 2.002 1.998 1.996 1.994 1.994 1.995 1.997 2.000 2.003 2.008

t95b

SRM 1617b U(ν̅)c (mm2·s−1) 0.007 0.007 0.007 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.007 0.007 0.008 0.008 0.009 0.010 0.011

a Ambient pressure during measurements was ∼83 kPa. bCoverage factor from the t-distribution for each corresponding degrees of freedom and a 95% level of confidence. cU(ν̅) is the expanded uncertainty at the 95% confidence level for kinematic viscosity.

ν̅ (mm ·s )

T (K)

2 −1

Table 2. Measured Kinematic Viscosities for Conventional Petroleum-Derived Samples at Ambient Pressurea


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Table 3. Measured Kinematic Viscosities for Synthetic Fuel Samples at Ambient Pressurea S8


T (K) 293.15 298.15 303.15 308.15 313.15 318.15 323.15 328.15 333.15 338.15 343.15 348.15 353.15 358.15 363.15 368.15 373.15

ν̅ (mm2·s‑1) 1.753 1.613 1.490 1.384 1.290 1.207 1.133 1.069 1.008 0.954 0.904 0.860 0.819 0.782 0.748 0.717 0.687

GTL

t95b

U(ν)̅ c (mm2·s−1)

ν̅ (mm2·s−1)

t95b

2.009 2.011 2.013 2.013 2.010 2.006 2.002 1.996 1.995 1.997 1.999 2.003 2.008 2.013 2.017 2.021 2.024

0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.008 0.009 0.010 0.011 0.012 0.013 0.014 0.016

1.199 1.118 1.047 0.984 0.926 0.875 0.828 0.785 0.746 0.711 0.678 0.648 0.620 0.594 0.569 0.547 0.526

2.028 2.032 2.032 2.032 2.032 2.035 2.035 2.035 2.035 2.035 2.035 2.035 2.035 2.035 2.035 2.035 2.035

CTL U(ν)̅ c (mm2·s−1) 0.006 0.021 0.022 0.022 0.022 0.022 0.022 0.022 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.024 0.024

ν̅ (mm2·s−1) 1.498 1.387 1.288 1.201 1.124 1.056 0.994 0.938 0.889 0.843 0.801 0.763 0.728 0.695 0.665 0.638 0.612

t95b 2.015 2.020 2.021 2.020 2.017 2.012 2.005 2.000 1.997 1.997 1.998 2.002 2.006 2.011 2.014 2.020 2.023

U(ν)̅ c (mm2·s−1) 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.008 0.008 0.009 0.010 0.011 0.012 0.014 0.015

Ambient pressure during measurements was ∼83 kPa. bCoverage factor from the t-distribution for each corresponding degrees of freedom and a 95% level of confidence. cU(ν̅) is expanded uncertainty at the 95% confidence level for kinematic viscosity.

a

The results of the hydrocarbon classification for the nine jet fuel samples have previously been reported elsewhere;20,21,23,44 the classification results for SRM 1617b were determined for inclusion here.41 The results for all ten samples are represented schematically in Figure 1. For all but CSK, linear and branched paraffins dominate; together they account for approximately 29% to 40% (v/v) of the total composition for the four petroleum-derived samples. Those values increase to approximately 71% to 80% (v/v) for the three synthetic and two HRJ samples. In contrast, paraffins account for just 5% (v/v) of the total composition for CSK, while cycloparaffins dominate at approximately 45% and 23% (v/v) for monocycloparaffins and dicycloparaffins, respectively. Cycloparaffins are also present in substantial quantities for the other nine samples. For the three Jet A samples and SRM 1617b, both monocycloparaffins and dicycloparaffins are significant, accounting for approximately 25% to 31% (v/v) and 10% to 13% (v/v), respectively. For the remaining five samples, only monocycloparaffins appear to be present in sizable quantities, ranging from approximately 17% to 23% (v/v) of the total composition. However, it should be noted that the apparent quantities of monocycloparaffins may be artificially high since some mass spectral fragments, such as CH2CHCH2• at m/z = 41, can be produced from paraffinic species but are only included in the monocycloparaffin summation.45 Finally, while the four conventional samples and CSK have appreciable quantities of aromatics, the remaining five samples contain only minor quantities of aromatic compounds. 2.2. Kinematic Viscosity Measurements. A commercial automated open gravitational glass capillary viscometer was used with our own measurement protocol to determine kinematic viscosity (ν) at ambient atmospheric pressure (∼83 kPa in Boulder, CO, USA) from 293 to 373 K. The instrument was previously used for measurements of rocket propellants and biofuel SRMs.46,47 A photograph of the instrument with principal components labeled can be found in Figure A2.1 of the Supporting Information. At the core of the instrument is a suspended-level Ubbelohde glass capillary comprised of two timing bulbs with a combined measurement range of approximately 0.3 mm2·s−1 to 30 mm2·s−1. The lower, smaller bulb (bulb 1) is used for liquids with kinematic viscosities of approximately 3−30 mm2·s−1, while the upper, larger bulb (bulb 2) is used for measurements from approximately 0.3−3 mm2·s−1. All measurements reported herein were made utilizing bulb 2. The capillary tube is immersed in a thermostated bath filled with silicone oil, the temperature of which is controlled with the combination of a stirrer, thermoelectric Peltier elements, and an internal temperature sensor.

Additionally, an external platinum resistance thermometer (PRT) is suspended in the bath for use as a temperature reference. Bordering either side of the two timing bulbs are three thermistor sensors that detect the passing of the sample liquid meniscus; the time required for the meniscus to pass between two corresponding thermistors, the efflux time (τ), is the measurand for this instrument. For measurements utilizing bulb 2, τ is determined between the thermistors located above bulb 2 (thermistor 3) and below bulb 1 (thermistor 1). Additional discussion concerning the impedance flow characteristics of the instrument can be found in Laesecke et al.47 The measured efflux time is related to kinematic viscosity via the equation ν = cτ −

ε τ2

(1)

where ν and τ were previously defined and c and ε are parameters that are specific to each timing bulb and are determined via instrument calibration. The instrument is calibrated by measuring several certified viscosity reference standards (CVRS) at temperatures from 20 to 100 °C. The CVRS used during the calibration of bulb 2 were N.4, N1.0, S3, S6, and N14 obtained from Cannon Instrument Co. Combined, these standards cover the approximate viscosity range of 0.4−3.4 mm2·s−1 for measurements using bulb 2; however, it was determined that only a subset of the CVRS measurements was needed to optimize calibration results. Specifically, only those data points with 49 s ≤ τ < 140 s, corresponding to certified viscosities between approximately 0.5 and 1.3 mm2·s−1, were fit to determine c and ε. By limiting the range of fitted points in this manner, we were able to maximize the number of data points whose measured values then fell within the reported expanded uncertainties of the CVRS samples (0.16% to 0.21%, depending on temperature and viscosity), including those whose viscosities exceed both the fit range and the range of jet fuel viscosities measured in this work.47 Additional details concerning the calibration procedures and the parameter values c and ε used in this work can be found in Appendix A1 of the Supporting Information. For each measurement sample, an aliquot of approximately 15 mL is transferred to a new, clean 20 mL glass vial. The vial is placed in the instrument’s sample holder and raised into place so that the instrument’s sampling tube is submerged in the sample. During a measurement, sample is drawn up into the capillary tube to a level above the bulb of interest (bulb 2) and held there for 5 min to allow for temperature equilibration. The sample holder is equipped with a heater, so to aid in temperature equilibration all samples were F


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Table 4. Measured Kinematic Viscosities for Biomass-Derived Fuel Samples at Ambient Pressurea CSK


T (K) 293.15 298.15 303.15 308.15 313.15 318.15 323.15 328.15 333.15 338.15 343.15 348.15 353.15 358.15 363.15 368.15 373.15

ν̅ (mm2·s−1) 1.257 1.173 1.096 1.029 0.968 0.913 0.863 0.818 0.777 0.739 0.704 0.672 0.642 0.614 0.589 0.564 0.542

t95b 2.023 2.026 2.024 2.026 2.020 2.014 2.009 2.002 1.998 1.997 1.998 2.002 2.006 2.010 2.014 2.018 2.023

C-HRJ U(ν)̅ c (mm2·s−1) 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.007 0.008 0.008 0.009 0.010 0.011 0.012 0.014 0.015

ν̅ (mm2·s−1) 1.474 1.364 1.269 1.184 1.109 1.043 0.983 0.929 0.880 0.835 0.794 0.758 0.724 0.692 0.663 0.636 0.612

t95b 2.015 2.020 2.021 2.021 2.013 2.009 2.004 1.998 1.997 1.997 1.999 2.002 2.008 2.012 2.017 2.021 2.024

Cs-HRJ U(ν)̅ c (mm2·s−1) 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.008 0.008 0.009 0.010 0.011 0.013 0.014 0.016

ν̅ (mm2·s−1) 1.888 1.739 1.597 1.479 1.374 1.282 1.200 1.127 1.063 1.004 0.950 0.901 0.856 0.817 0.779 0.745 0.714

t95b 1.999 2.006 2.002 2.002 2.001 1.998 1.994 1.992 1.992 1.995 1.999 2.003 2.008 2.014 2.020 2.023 2.023

U(ν)̅ c (mm2·s−1) 0.007 0.007 0.006 0.006 0.006 0.006 0.007 0.007 0.008 0.009 0.009 0.010 0.011 0.013 0.014 0.016 0.018

Ambient pressure during measurements was ∼83 kPa. bCoverage factor from the t-distribution for each corresponding degrees of freedom and a 95% level of confidence. cU(ν̅) is the expanded uncertainty at the 95% confidence level for kinematic viscosity.

a

preheated to a temperature of 313 K for all measurements at ≥313 K. After the temperature equilibration period, the liquid is released and its progress through the tube is timed. During the measurements reported here, each separate 15 mL sample aliquot was measured in a programmed scan from 293 to 373 K in 5 K increments. At each temperature, efflux time measurements were repeated until three consecutive tests agreed within 0.25%. Additionally, for each fuel tested, at least one other 15 mL sample was prepared and measured to check for reproducibility between sample aliquots. With the exception of Cs-HRJ, all kinematic viscosity results reported in the tables included herein are from the final sample aliquot measured; the second of three samples is reported for Cs-HRJ because a valid measurement result at 343 K was missing for the third sample. The observed sampleto-sample reproducibility was taken into consideration during the calculation of reported uncertainties. Because the sample remains open to the atmosphere over the course of a full temperature scan, a period of 9 h or more, changing sample composition resulting from the loss of more volatile components with time and temperature is a concern. Measurements of multiple sample aliquots help us arrive at a reasonable estimate of the corresponding uncertainty contribution. This is discussed in further detail in Appendix A3 of the Supporting Information. The instrument is thoroughly cleaned and dried between each new measurement sample. Since previous work illustrated the potential for sample cross-contamination when following the manufacturer’s recommended cleaning procedure,48 a more thorough cleaning procedure combining both a bottom up and top down approach was implemented during this work. Details are given in Appendix A2 of the Supporting Information.

where t95(dfν) is the coverage factor taken from the tdistribution for dfν degrees of freedom and 95% confidence level, and u(ν̅) is the combined standard uncertainty for the kinematic viscosity measurements. In the absence of software with statistical analysis capabilities, the corresponding value of t95(dfν) can be determined from Table G.2 of the Guide to the Expression of Uncertainty in Measurement49 by interpolation. The corresponding values of t95(dfν) are included in Tables 2−4 for clarity. Additional details regarding the uncertainty analysis employed in this work are given in Appendix A3 of the Supporting Information. The expanded uncertainties reported in Tables 2−4 range from an overall minimum of 0.006 mm2·s−1 to an overall maximum of 0.024 mm2·s−1. In terms of relative expanded uncertainties, our estimates range from an overall minimum of approximately 0.31% (Jet A 4658 at 293 K) to an overall maximum of approximately 4.5% (GTL at 373 K). While such values may seem larger than perhaps expected, we believe the comprehensive uncertainty analysis approach outlined in Appendix A3 of the Supporting Information results in a reasonable estimate of the uncertainty associated with the measurement of the volatile samples reported here. In fact, the resulting expanded uncertainties are in alignment with the observed sample-to-sample variability, which was as low as 0.002% and as high as 2.0%. To facilitate fuel-to-fuel comparisons, the kinematic viscosity results reported in Tables 2−4 are plotted as a function of temperature in Figure 2. It is clear from this figure that the observed variability in kinematic viscosity is statistically significant and varies with temperature; the overall spread among the ten samples ranges from 38.7% at 293 K to 28.3% at 373 K. While the differences in chemical composition among the fuels are undoubtedly responsible for the observed variability, no clear trends are readily apparent when comparing the viscosity results (Figure 2) with the available composition information (Table 1 and Figure 1). Specifically, it is clear from Figure 2 that it is not possible to make simple statements regarding trends in petroleum-derived fuels compared to S-IPK fuels compared to bioderived fuels since not all fuels of a given

3. RESULTS AND DISCUSSION Kinematic viscosity measurement results are presented in Tables 2−4; conventional petroleum-derived samples are in Table 2, synthetic fuel samples are in Table 3, and biomassderived fuel samples are in Table 4. As was previously mentioned, tabulated viscosities are an average (ν)̅ of three consecutive measurements of a given sample at each temperature. Also included in the tables are the associated expanded uncertainty estimates (U(ν)), ̅ which are calculated according to the expression U (ν ̅ ) = t 95(dfν) u(ν ̅ )

(2) G



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Figure 3. Molecular size, shape, and charge distribution of representative compounds found in the fuels measured in this study. Representative aromatics include the following: a) 1,2,3,4-tetrahydro6-methylnaphthalene, b) 1-methyl-3-(1-methylethyl)benzene, and c) (1-methyl-1-butenyl)benzene. Representative mono- and dicycloparaffins include the following: d) trans-1-methyl-4-(1-methylethyl)cyclohexane, e) decahydro-2-methylnaphthalene, and f) cis-1,3dimethylcyclohexane. Representative paraffins include the following: g) 2,2,3,3-tetramethylhexane, h) 2,4,6-trimethyloctane, and i) 2,6dimethylundecane.

Figure 2. Ambient pressure kinematic viscosity measurements for 10 fuel samples plotted as a function of temperature.

source type cluster together. For example, while the three samples exhibiting the highest viscosities (SRM 1617b, Jet A 4658, and Jet A 3602) are all derived from petroleum, the fourth petroleum-derived sample (Jet A 3638) exhibits significantly lower viscosities. The maximum observed spread among the aforementioned three petroleum-derived samples ranges from 2.6% at 293 K to 1.1% at 373 K, yet the maximum spread between the petroleum-derived sample with the highest viscosity and Jet A 3638 ranges from 15.7% at 293 K to 10.1% at 373 K. When trying to relate observed thermophysical properties to fluid composition, it is necessary to take into consideration all factors that can contribute to intermolecular interactions. To that end, Figure 3 shows the molecular size, shape, and charge distribution of nine compounds that were chosen to represent aromatics, cycloparaffins, and paraffins found in several of the fuels measured in this work. The molecules are shown in terms of their electron density distribution with the electrostatic potential color-mapped onto it. Details of this rendering have been described previously.50 It should be noted that the colorization of all nine compounds in Figure 3 corresponds to the same electrostatic potential scale shown in the bottom right corner of the figure. In the most general terms, the presence of more larger, heavier hydrocarbons would be expected to increase the viscosity of a fluid relative to one containing more smaller, lighter compounds. Additionally, viscosity would also be expected to increase with the presence of aromatics and cycloparaffins. In contrast, branched paraffins would typically be expected to decrease viscosity. All three phenomena can be explained in terms of how molecular size and shape influence intermolecular interactions. Specifically, the van der Waals forces primarily responsible for the intermolecular interactions of nonpolar compounds increase with increased surface area. In the case of two straight-chain paraffins such as n-undecane and n-nonane, the larger n-undecane provides a larger surface area thereby increasing the opportunities for molecular interaction. Cyclic structures further enhance the effective surface area by reducing conformational freedom. An acyclic compound can

have numerous conformational possibilities (e.g., zigzag vs syn), which ultimately decreases the likelihood that any two molecules in the vicinity of one another will have compatible conformations. However, since cyclic compounds exhibit more limited conformations, the likelihood of favorable interactions is increased; this is particularly true for aromatic compounds since they are unable to change conformation. In contrast, branched paraffins are more compact than their unbranched counterparts and the decreased surface area results in weaker intermolecular interactions. Unfortunately, while the preceding considerations can contribute to the discussion of how composition contributes to observed viscosities, they do not necessarily allow for simple explanations when dealing with complex mixtures such as fuels. For example, it is tempting to attribute the large differences between Jet A 3638 and the other petroleum-derived samples to the fact that Jet A 3638 is known to have an unusually low aromatic content relative to typical Jet A specimens51 or that it contains more branched compounds than the others, both of which should decrease viscosity. However, in terms of contributions that should increase viscosity, the cyclic and aromatic content of Jet A 3638 is comparable to the other petroleum-derived samples. Additionally, in terms of the average molecular weight of compounds present in each fuel, the expected order for the four petroleum samples from highest to lowest viscosity would be SRM 1617b, Jet A 4658, Jet A 3638, and Jet A 3602. Finally, it is interesting to note that the differences in composition between the four petroleum-derived samples are small relative to the dramatically different composition of S8 compared to Jet A 3638 (Figure 1), yet these two samples exhibit relatively similar viscosities; the spread between S8 and Jet A 3638 ranges from 5.9% at 293 K to 4.0% at 373 K. In addition to the preceding observations, the following general trends are readily apparent in Figure 2. Cs-HRJ exhibits H



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viscosities that are more similar to Jet A 3602 (1.0% at 293 K) than they are to either C-HRJ (21.9% at 373 K) or CSK (33.4% at 373 K). The deviation relative to CSK may not be surprising given its vastly different chemical composition (Figure 1). The deviation relative to C-HRJ is harder to reconcile given their similarities; the one difference apparent in Table 1 is that there are more straight-chain alkanes present in the top ten components of Cs-HRJ. Viscosities for the S-IPK fuels are similarly distributed: among the biobased fuels the overall spread is 33.4% at 293 K and 24.0% at 373 K; for the S-IPK fuels those values are 31.6% and 23.5%, respectively. Interestingly, the maximum spread within the S-IPK fuels is between S8 and GTL, the two FT fuels derived from natural gas; the deviations between S8 and CTL are approximately half those observed for GTL. The final two clusters of data that are apparent in Figure 2 are CTL and C-HRJ, with a spread of 1.6% at 293 K and 0.1% at 373 K, and the two lowest viscosity fuels CSK and GTL, with a spread of 4.6% at 293 K and 3.0% at 373 K. The similarities between CTL and C-HRJ are possibly explained by the fact that for both fuels all but one of the ten major components are branched alkanes (Table 1). A similar reasoning cannot be applied in the case of CSK and GTL. In addition to a significant number of branched alkanes, GTL also has a lower average molecular weight than the other two S-IPK samples, perhaps explaining why it has the lowest viscosity of the three. However, the low observed viscosity of CSK is surprising. While its average molecular weight is similar to that of GTL, that of Jet A 3602 is even lower and yet that fuel has one of the highest viscosities. Furthermore, CSK is predominantly composed of cyclic and aromatic compounds, which generally increase viscosity. Finally, it is interesting to note that the viscosity trends observed in Figure 2 do not strictly follow trends previously observed in other fuel properties. To illustrate this, the results from previous measurements of density and speed of sound18,22,24 are plotted in Figures 4 and 5, respectively, for all but SRM 1617b. As is seen with the viscosity data (Figure 2), there is a general order of Jet A samples with the largest

Figure 5. Previously measured18,22,24 ambient pressure speed of sound data for nine fuel samples plotted as a function of temperature.

values, GTL with the lowest values, and the rest falling somewhere in between. But whereas Jet A 4658 has the highest viscosity values, it is Jet A 3602 that exhibits the highest overall values for both density and speed of sound. Furthermore, the relative order of the remaining samples varies with no two properties showing the same order of compounds; the greatest difference is for CSK, which exhibited some of the lowest viscosity values but some of the highest density and speed of sound values. Very little data could be found in the literature31,52,53 to which we could compare our viscosity results. What does exist is typically reported only at 253 K since the aviation turbine fuel specification allows for a maximum viscosity of 8 mm2·s−1 at that temperature.25 Only Bessee et al.52 report data that overlaps at all with the results reported here, two data points each for CTL and C-HRJ. Specifically, they report viscosities of 1.17 mm2·s−1 at 313 K and 0.62 mm2·s−1 at 373 K for CTL, and viscosities of 1.35 mm2·s−1 at 303 K and 1.10 mm2·s−1 at 313 K for C-HRJ.52 The deviations of our data relative to that of Bessee et al.52 range from 0.8% to as much as 6%, with only one (CTL at 373 K) falling within our reported uncertainties. When dealing with complex fluid mixtures, particularly ones containing highly volatile components, such as the fuels studied here, it is likely that such large discrepancies can be attributed to differences in composition among samples, either as they were received, due to source variability, or as a result of distinctions in handling and measurement practices. In addition to the limited comparisons discussed above, select viscosity data can be compared to the predictions of surrogate mixture models. Figure 6 shows experimental data for the four petroleum-based samples, Jet A 3602, Jet A 3638, Jet A 4658, and SRM 1617b, and one synthetic fuel, S8, along with the aforementioned model predictions. The models, one for Jet A 3638, one for Jet A 4658, and one for S8, were developed at NIST and have been implemented within the framework of the Reference Fluid Thermodynamic and Transport Properties (REFPROP) program.16,19,54 Jet A viscosity values from the Handbook of Aviation Fuel Properties9 have also been included in Figure 6 for comparison. A couple of observations are apparent

Figure 4. Previously measured18,22,24 ambient pressure density data for nine fuel samples plotted as a function of temperature. I



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Figure 7. Percent deviations of ambient pressure kinematic viscosity measurements from Jet A and S8 surrogate mixture models16,19 plotted as a function of temperature. Jet A 3638 and S8 measurements are compared to their respective models, while Jet A 3602, Jet A 4658, and SRM 1617b are compared to the Jet A 4658 model. Jet A viscosity values from the Handbook of Aviation Fuel Properties9 were compared to the Jet A 4658 model and are included for comparison.

Figure 6. Ambient pressure kinematic viscosity measurements for Jet A and S8 fuel samples plotted as a function of temperature. Measurement results for SRM 1617b, a conventional kerosene product, are included for comparison. Additionally, predictions from the surrogate mixture models16,19 for Jet A 3638 (dotted line), Jet A 4658 (solid line), and S8 (dashed−dotted line), as well as Jet A viscosity values from the Handbook of Aviation Fuel Properties9 (dashed line), are included for comparison.

has an AAD of 0.9% and maximum and minimum deviations of 2.1% and −1.3%, respectively. As expected, since the model is specific to the fuel in question, the deviations are somewhat smaller for Jet A 3638 with an AAD of 0.6%, a maximum of 1.3%, and a minimum of −1.1%. For all five aforementioned samples, the deviations exceed the corresponding experimental uncertainties in many cases. However, for all but one point (Jet A 4658 at 373 K), the deviations are within the reported model uncertainty of ≥3%.16,19 In contrast to the experimental data reported here, the viscosity values from the Handbook of Aviation Fuel Properties9 exhibit increasing deviations with decreasing temperature relative to the Jet A 4658 model. The AAD for these data is 0.8%, and the maximum and minimum deviations are 3.5% and −0.1%, respectively. The variability observed in Figure 7, particularly the differences between Jet A 4658 and Jet A 3602 relative to the Jet A 4658 model, can be taken as further evidence23 of the need for more flexible models that can account for the compositional sensitivity of fuel properties. Such work is currently underway.

when looking at Figure 6. First, the Jet A viscosities reported in the Handbook of Aviation Fuel Properties9 are in very good agreement with Jet A 3602, SRM 1617b, and particularly with the composite fuel, Jet A 4658. Second, the viscosity predictions of both Jet A surrogate mixture models are in very good agreement with their respective fuel, while the predictions of the S8 model are noticeably low relative to the measurement results reported here. Figure 7 shows more clearly the deviations between the experimental measurements and their appropriate corresponding model. Specifically, the Jet A 3638 measurements are compared to the Jet A 3638 model and the S8 measurements are compared to the S8 model, while the remaining samples are compared to the Jet A 4648 model. This was done since the Jet A 4658 and, by extension, its surrogate mixture model are meant to represent a typical Jet A sample. Also included in Figure 7 are the calculated deviations for the viscosity values from the Handbook of Aviation Fuel Properties9 compared to the Jet A 4658 model. For Jet A 4658, SRM 1617b, and S8, the experimental values are consistently higher than the Jet A 4658 and S8 model predictions. For Jet A and SRM 1617b, a slight temperature dependence is observed wherein the deviations primarily increase with increasing temperature; no significant temperature dependence is observed for S8. More specifically, the absolute average deviation (AAD) for Jet A 4658 is 0.7% with a maximum deviation of 3.3% and a minimum deviation of 0.8%; for SRM 1617b the values are 0.3%, 2.2%, and 0.9% for AAD, maximum, and minimum, respectively. For S8, the AAD, maximum, and minimum deviations are 0.3%, 3.9%, and 2.9%, respectively. For Jet A 3602 and Jet A 3638, experimental values are increasingly lower than the corresponding model predictions as the temperature decreases from 338 to 293 K, but above 338 K the experimental values are increasingly higher than model predictions as the temperature increases. Jet A 3602

4. CONCLUSIONS In this work, viscosity measurements for nine aviation turbine fuel samples, representing both conventional and alternative fuels, have been presented. A tenth, petroleum-derived sample has also been included for comparison. All measurements were made at ambient pressure (∼83 kPa) and over the temperature range of 293−373 K. Significant variability was observed among the ten samples; the overall spread in viscosity ranged from 38.7% at 293 K to 28.3% at 373 K. Three of the four petroleum-based samples exhibited the highest viscosities, while CSK and GTL exhibited the lowest. No decisive correspondence between sample composition and observed viscosity trends could be found, highlighting the difficulties in definitively linking composition and properties when dealing with highly complex fluid mixtures. The situation is likely J


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Systems Center: Cambridge, MA, USA, 2012; http://ntl.bts.gov/lib/ 46000/46500/46597/DOT-VNTSC-FAA-12-01.pdf. (9) Handbook of Aviation Fuel Properties, CRC Report No. 635; Coordinating Research Council (CRC): Alpharetta, GA, USA, 2004. (10) World Jet Fuel Specifications with Avgas Supplement; ExxonMobil Aviation: Leatherhead, U.K., 2005; http://www.exxonmobil.com/ AviationGlobal/Files/WorldJetFuelSpecifications2005.pdf. (11) Winchester, N.; McConnachie, D.; Wollersheim, C.; Waitz, I. Market Cost of Renewable Jet Fuel Adoption in the United States: A PARTNER Project 31 Report, PARTNER-COE-2013-001; The Partnership for Air Transportation Noise and Emissions Reduction (PARTNER): Cambridge, MA, USA, 2013; http://web.mit.edu/ aeroastro/partner/reports/proj31/proj31-jetfuel-market-costs.pdf. (12) Standard Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons. Book of Standards, ASTM Standard D7566-13; American Society for Testing and Materials: West Conshohocken, PA, USA, 2013. (13) Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity). Book of Standards, ASTM Standard D445-12; American Society for Testing and Materials: West Conshohocken, PA, USA, 2012. (14) Bruno, T. J.; Smith, B. L. Improvements in the Measurement of Distillation Curves. 2. Application to Aerospace/Aviation Fuels RP-1 and S-8. Ind. Eng. Chem. Res. 2006, 45, 4381−4388. (15) Smith, B. L.; Bruno, T. J. Improvements in the Measurement of Distillation Curves. 4. Application to the Aviation Turbine Fuel Jet-A. Ind. Eng. Chem. Res. 2007, 46, 310−320. (16) Huber, M. L.; Smith, B. L.; Ott, L. S.; Bruno, T. J. Surrogate Mixture Model for the Thermophysical Properties of Synthetic Aviation Fuel S-8: Explicit Application of the Advanced Distillation Curve. Energy Fuels 2008, 22, 1104−1114. (17) Widegren, J. A.; Bruno, T. J. Thermal Decomposition Kinetics of the Aviation Turbine Fuel Jet A. Ind. Eng. Chem. Res. 2008, 47, 4342−4348. (18) Outcalt, S. L.; Laesecke, A.; Freund, M. B. Density and Speed of Sound Measurements of Jet A and S-8 Aviation Turbine Fuels. Energy Fuels 2009, 23, 1626−1633. (19) Huber, M. L.; Lemmon, E. W.; Bruno, T. J. Surrogate Mixture Models for the Thermophysical Properties of Aviation Fuel Jet-A. Energy Fuels 2010, 24, 3565−3571. (20) Bruno, T. J.; Baibourine, E.; Lovestead, T. M. Comparison of Synthetic Isoparaffinic Kerosene Turbine Fuels with the CompositionExplicit Distillation Curve Method. Energy Fuels 2010, 24, 3049−3059. (21) Bruno, T. J.; Baibourine, E. Comparison of Biomass-Derived Turbine Fuels with the Composition-Explicit Distillation Curve Method. Energy Fuels 2011, 25, 1847−1858. (22) Outcalt, S. L.; Fortin, T. J. Density and Speed of Sound Measurements of Two Synthetic Aviation Turbine Fuels. J. Chem. Eng. Data 2011, 56, 3201−3207. (23) Burger, J. L.; Bruno, T. J. Application of the Advanced Distillation Curve Method to the Variability of Jet Fuels. Energy Fuels 2012, 26, 3661−3671. (24) Outcalt, S. L.; Fortin, T. J. Density and Speed of Sound Measurements of Four Bioderived Aviation Fuels. J. Chem. Eng. Data 2012, 57, 2869−2877. (25) Standard Specification for Aviation Turbine Fuels. Book of Standards, ASTM Standard D1655-13; American Society for Testing and Materials: West Conshohocken, PA, USA, 2013. (26) Fischer, F.; Tropsch, H. The Synthesis of Petroleum at Atmospheric Pressures from Gasification Products of Coal. Brennst.Chem. 1926, 7, 97−104. (27) James, O. O.; Chowdhury, B.; Mesubi, M. A.; Maity, S. Reflections on the Chemistry of the Fischer−Tropsch Synthesis. RSC Adv. 2012, 2, 7347−7366. (28) Jahangiri, H.; Bennett, J.; Mahjoubi, P.; Wilson, K.; Gu, S. A Review of Advanced Catalyst Development for Fischer−Tropsch Synthesis of Hydrocarbons from Biomass Derived Syn-Gas. Catal. Sci. Technol. 2014, 4, 2210−2229.

further complicated by additional contributing factors that cannot be accounted for utilizing the compositional characterization discussed herein. For example, a fuel sample’s history (e.g., its exposure to air and heat), as well as the presence of even small quantities of oligomers or gums, may affect a fuel’s viscosity. Finally, results for the four petroleum-based samples and one S-IPK sample were compared to three corresponding surrogate mixture models,16,19 one for Jet A 4658, representing a typical Jet A sample, one for Jet A 3638, and one for S8. The calculated deviations were within the reported model uncertainties, but the observed discrepancies indicate the development of a more general, tunable model is warranted.

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ASSOCIATED CONTENT


S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.5b00423. Details regarding instrument calibration, the cleaning protocol, and uncertainty calculations (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Phone: 1-303-497-3522. Fax: 1-303-497-6682. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Dr. John Molloy of NIST Gaithersburg for the sample of SRM 1617b and Dr. Tara Lovestead and Dr. Tom Bruno of NIST Boulder for performing the chemical analysis of SRM 1617b. Dr. Benjamin Harvey of the Naval Air Warfare Center in China Lake is acknowledged for useful comments that helped improve the manuscript.

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REFERENCES

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