Randy L. Vander Wal

Penn State University, University Park, Pennsylvania

Victoria M. Bryg

The Universities Space Research Association

National Center for Space Exploration Research, Cleveland, Ohio

A Survey of Jet Aircraft PM by TEM in APEX III

NASA/CR—2014-218294

June 2014
NASA STI Program . . . in Profi le

Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA Scientifi c and Technical Information (STI) 

program plays a key part in helping NASA maintain 

this important role.

The NASA STI Program operates under the auspices 

of the Agency Chief Information Offi cer. It collects, 

organizes, provides for archiving, and disseminates 

NASA’s STI. The NASA STI program provides access 

to the NASA Aeronautics and Space Database and 

its public interface, the NASA Technical Reports 

Server, thus providing one of the largest collections 

of aeronautical and space science STI in the world. 

Results are published in both non-NASA channels 

and by NASA in the NASA STI Report Series, which 

includes the following report types:

• TECHNICAL PUBLICATION. Reports of 

completed research or a major signifi cant phase 

of research that present the results of NASA 

programs and include extensive data or theoretical 

analysis. Includes compilations of signifi cant 

scientifi c and technical data and information 

deemed to be of continuing reference value. 

NASA counterpart of peer-reviewed formal 

professional papers but has less stringent 

limitations on manuscript length and extent of 

graphic presentations.

• TECHNICAL MEMORANDUM. Scientifi c 

and technical fi ndings that are preliminary or 

of specialized interest, e.g., quick release 

reports, working papers, and bibliographies that 

contain minimal annotation. Does not contain 

extensive analysis.

• CONTRACTOR REPORT. Scientifi c and 

technical fi ndings by NASA-sponsored 

contractors and grantees.

• CONFERENCE PUBLICATION. Collected 

papers from scientifi c and technical 

conferences, symposia, seminars, or other 

meetings sponsored or cosponsored by NASA.

• SPECIAL PUBLICATION. Scientifi c, 

technical, or historical information from 

NASA programs, projects, and missions, often 

concerned with subjects having substantial 

public interest.

• TECHNICAL TRANSLATION. Englishlanguage translations of foreign scientifi c and 

technical material pertinent to NASA’s mission.

Specialized services also include creating custom 

thesauri, building customized databases, organizing 

and publishing research results.

For more information about the NASA STI 

program, see the following:

• Access the NASA STI program home page at 

http://www.sti.nasa.gov

• E-mail your question to help@sti.nasa.gov

• Fax your question to the NASA STI 

Information Desk at 443–757–5803

• Phone the NASA STI Information Desk at

 443–757–5802

• Write to:

 STI Information Desk

NASA Center for AeroSpace Information

 7115 Standard Drive

 Hanover, MD 21076–1320
Randy L. Vander Wal

Penn State University, University Park, Pennsylvania

Victoria M. Bryg

The Universities Space Research Association

National Center for Space Exploration Research, Cleveland, Ohio

A Survey of Jet Aircraft PM by TEM in APEX III

NASA/CR—2014-218294

June 2014

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared under Grant NNX09AD42A
	
	


This work, part of NASA fundamental aeronautics research was supported under the Subsonic Fixed Wing Program, Topic 

A.2.2—Emissions Reduction Program, Subtopic A.2.2.2 Fundamental Experiments, through grant NNX09AD42A with Penn 

State University. The authors gratefully acknowledge the contributions by Chung-Hsuan Huang (Penn State, EME Dept. graduate 

student) for manuscript preparation and discussions.

Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

Available electronically at http://www.sti.nasa.gov

Trade names and trademarks are used in this report for identifi cation 

only. Their usage does not constitute an offi cial endorsement, 

either expressed or implied, by the National Aeronautics and 

Space Administration.

This work was sponsored by the Fundamental Aeronautics Program 

at the NASA Glenn Research Center.

Level of Review: This material has been technically reviewed by NASA technical management OR expert reviewer(s).
NASA/CR—2014-218294 1 

A Survey of Jet Aircraft PM by TEM in APEX III 

Randy L. Vander Wal 

Penn State University 

University Park, Pennsylvania 16802 

Victoria M. Bryg 

The Universities Space Research Association 

National Center for Space Exploration Research 

Cleveland, Ohio 44135 

Abstract 

Based upon field testing during the NASA led APEX III campaign conducted in November 2005 at 

the NASA Glenn Research Center in coordination with Continental Airlines and Cleveland Hopkins 

International Airport. This paper reports observations of particulate emissions collected from a suite of jet 

engine aircraft to assess differences and similarities in soot macro- micro- and nanostructure using 

transmission electron microscopy (TEM). Aggregates are compact, primary particle sizes varied and 

nanostructure mixed. Comparisons are made to more familiar laboratory flame-generated soot as a wellstudied point of reference. Results are interpreted in terms of turbulence interacting with the different 

stages of particle formation and growth. 

Introduction 

This paper reports differences in soot structure across a variety of jet engines on different aircraft. The 

purpose is to identify commonalities of structure across length scales and differences relative to 

laboratory-generated soot. Different studies have suggested suitability of lab-generated soots as surrogates 

(Popovitcheva et al. 2000; Karcher et al. 2007), but comparative metrics between the two, such as 

physical structure are absent. This article reports such, using transmission electron microscopy (TEM) to 

compare across a range of spatial scales for soot produced by a variety of jet engines, as a first such 

survey. Such results can provide a baseline for future reference. 

Prior studies of jet aircraft PM emissions have shown but a few low magnification images of 

collected particulate, but generally lacking detailed examination, certainly of nanostructure. The most 

definitive illustration is that by Popovicheva et al. (2003), who reported crystalline structure within the 

particles near to the combustor exit (~10 cm), but loss of crystalline structure and coalescence of 

morphology farther downstream (~100 cm). Though produced with similar overall equivalence ratio as at 

cruise conditions, gaseous fuels were used in the surrogate combustor, absent design details. High 

resolution TEM and comparisons at any magnification, either across power, fuels or engine class was 

absent. Such mutability for the conditions used warrants further observations. 

More recently extensive aerosol measurements have been applied to characterize exhaust emissions in 

a series of field campaigns, APEX I, II, III, AAFEX I, II and in engine test stands (Kinsey and Hays 

2011; Vander Wal et al. 2013; Huang and Vander Wal 2013). Related techniques have included scanning 

mobility particle sizer/condensation particle counter (SMPS/CPC), multi-angle absorption photometer 

(MAAPs), engine exhaust particle sizer (EEPS) and comparative smoke number measurements. 

From these, key results such as particle number concentrations, aerodynamic size distributions were 

found. With assumptions of sphericity and unit mass density, mass per unit fuel consumed yield particle 

number and mass emission indices (Corporan et al. 2007; Herndon et al. 2005; Schmid et al. 2004; Wey 

et al. 2007; Timko et al. 2010). A common trend is that emission indices in both particle number and 

mass (each per unit fuel consumed) across a series of engines exhibit minima at intermediate powers and 

steep increases at both low and high powers (Timko et al. 2010). 
NASA/CR—2014-218294 2 

TEM is particularly valuable in that it provides directly observable physical structure on multiple 

length scales, as relevant to environmental processes and health impacts. In recent years image analysis 

algorithms have advanced sufficiently so as to quantify such data (Yehliu 2010; Vander Wal et al. 2011; 

Yehliu et al. 2011; Teini et al. 2011). By examining several such images, statistical analyses of structure 

at varied length scales can be made, ranging from nano- to micro- to macro-structure (Vander Wal et al. 

2013; Huang and Vander Wal 2013; Wey et al. 2006). For soot, these length scales pertain to carbon 

lamellae, primary particle size and aggregate morphology, respectively. Each spatial scale is relevant to 

primary particle formation and growth. 

These same length scales are also relevant to the environmental impact of soot. Jet aircraft contribute 

significantly to soot in the upper troposphere and at ground level near airports (Bond et al. 2004; Hagen 

and Whitefield 1996; Penner et al. 1999). Soot aggregate physical size and surface chemistry have direct 

consequences for atmospheric processes (Wuebbles et al. 2007). Soot surface chemistry determines its 

propensity to act as cloud condensation nuclei (CCN) as does its hydrophobic/hydrophilic balance for 

coalescence with inorganics (Khalizov et al. 2009). In turn soot surface chemistry depends upon 

nanostructure (Popovitcheva et al. 2003). Aggregate size and morphology are also relevant to the light 

absorption and scattering properties, and hence soot’s radiative forcing (Liu and Smallwood 2009; 

Li et al. 2010). Aggregate size and surface chemistry also determine atmospheric longevity (Riemer et al. 

2004). 

Understanding soot aggregate size, morphology, surface chemistry and related consequences requires 

direct and detailed examination of soot from gas turbine engines and high-pressure combustors operating 

under realistic conditions. For this purpose, observations are reported as the first, as known to the authors, 

across engines for physical structure and particle composition, to fill knowledge gaps regarding these soot 

characteristics. The data are not presented as a comparison between engines or as a function of power 

level. Observed soot structure is interpreted as reflective of the turbulent, fuel rich environment. This 

knowledge has the potential to contribute to future modeling of soot growth in the non-uniform, fuel-rich 

regions within high-pressure gas turbine engines (Bisetti et al. 2012). 

Experimental 

The engines and aircraft in the field campaign are summarized in Table 1. Operational jet aircraft 

were temporarily loaned by Continental Airlines, in coordination with the Cleveland Hopkins 

International Airport. Soot from these engines was generally sampled at high power, typically 85 percent 

or 100 percent of full power. Fuel for all jets was commercial Jet-A. These and lower powers were 

studied by gas analyzers and varied particle analysis instruments, by other groups. These results are to yet 

be summarized in a future NASA TM. These powers correspond to take-off conditions and are those 

associated with the majority of near-ground level PM emission, and correspond to the data/samples 

collected using a custom sampling probe. 

Soot entrained within the exhaust was sampled directly upon a TEM grid (Vander Wal et al. 1995). 

As shown in Figure 1 the probe held a TEM grid within the flow, and was inserted into a swagelock tee 

for convenience of plumbing into exhaust lines. Essentially it is a 1/4 in. brass rod, approximately 5 cm in 

length with set screw to hold a folded brass clip made from 0.005 in. shim stock. Folded, it retains the 

TEM grid with exposure provided by the 2 mm hole near its end, as shown. The swagelock tee provides 

ease of insert and also a gas-tight seal around the brass rod of standard rod diameter. The key advantage 

to our collection during the APEX campaign was the direct collection of the soot from the aerosol phase. 

This bypasses filter collection with the need for re-dispersal upon a TEM grid. Such processes inherently 

lose aggregate distinction through agglomeration. Soot was collected upon the grid during the entire test 

duration, typically limited to a few minutes or less when running at full power, for reasons of engine 

TABLE 1.—THE OPERATIONAL JET AIRCRAFT AND THE TESTED ENGINES 

Aircraft B737-700 Lear 25 ERJ A300-600 

Engine CFM56-3 CJ610 145-XRJ/AE3007 PW4158 
NASA/CR—2014-218294 3 

Figure 1.—The schematics of the (L) sample probes: particle and gas probes; (R) soot sampling manifold. 

cooling requirements. Complimentary samples include soot collected on quartz fiber filters, which were 

included in the direct sampling line for quantification of soot concentration by absorption measurements 

which were retained for subsequent XPS characterization, to be reported elsewhere (Vander Wal and 

Bryg 2013). 

All jet aircraft were fueled by JP-A. Run times varied depending upon the power level. (For 

experimental reasons related to engine cooling, the A300 aircraft was not run at 100 percent power). 

Multi-port particle sample rakes were designed, built, and successfully deployed to sample the exhaust 

plume at 1 and 10 m downstream of the engine exhaust plane. Collection at the 10 m sampling position 

was not tip-diluted. Streams, diluted 10:1 with ambient air were transferred through about 30 m of heated 

(177 °C) sampling line (12.7 mm I.D.) to individual instruments through a distribution manifold. 

Residence time within the sampling systems was less than 10 s, which is within the SAE Aerospace 

Recommended Practice (ARP 1256B) limits. 

The normal diffusion flame was established on a Santoro-style burner, with ethylene as fuel. The fuel 

flow rate was 238 sccm through a center brass tube of 10.1 mm I.D. with an air co-flow of 43.2 slm 

through a surrounding honeycomb (Santoro et al. 1983). This flame has become the defacto system for 

studying soot processes. Further details for the laboratory flame and sampling system have been described 

elsewhere (Vander Wal 1997). 

Given the predominant soot literature based upon laboratory flames, typical data, in the form of TEM 

images is presented for comparison. It is not unique nor is it suggested by such illustration that such a 

system can serve as surrogate for the jet engine. However, commonality of flame-produced particulate, if 

observed would suggest relevance of formation, growth and oxidation studies to jet engine formed soot. 

To access soot aggregate size, shape and internal nanostructure, HRTEM was applied. (The TEM was 

performed in a Phillips CM20 operating at a nominal 200 keV using a LaB6 filament.) Gatan software, 

v. 3.4, was used for microscope operation. Post-processing of images included analysis of geometric 

descriptors using the software package Optimus and ImageJ. Custom analysis algorithms developed on 

the Optimus platform provided quantification of nanostructure metrics, such as lamella length. Generally 

3 to 6 areas were examined upon the grid, with images at a series of magnifications taken at 3 or more 

locations. Images shown here are judged qualitatively representative. 
NASA/CR—2014-218294 4 

Results 

Macrostructure 

To test the suitability of lab-generated soot as a surrogate for jet engine PM as cited elsewhere 

(Popovitcheva et al. 2003; Demirdjian et al. 2007), Figure 2 shows a typical soot aggregate from the laminar 

diffusion flame. The varied magnifications illustrate the relevant length scales. Many other similar images 

from the same flame have been reported elsewhere (Koylu et al. 1994; Koylu et al. 1995). As seen by 

comparison of the images shown in Figure 3, the open, branched structure so often describing flameproduced soot appears largely inapplicable to soot from jet engines. Aggregates are compacted, somewhat 

elongated. Few gaps and little void volume are evident between primary particles, as particularly 

exemplified by the Lear jet soot. Such compact morphology is suggestive of locally ballistic (primary) 

particle aggregation, as would be possible with a high (primary) particle concentration (Shim et al. 2000). 

Figure 4 shows the examination of soots from the B737 aircraft at 4 and 65 percent power suggest similar 

morphology. No observable coating or amorphous liquid-like coating was found on the particles. 

Figure 2.—TEM images at the indicated magnifications of soot sampled from an ethylene gas jet 

diffusion flame at atmospheric pressure, illustrating varied length scales. 

Figure 3.—TEM images at successive magnifications of the soots produced by the 

different aircraft, each at 100 percent power (except where noted). (From left to 

right = CFM 56-3; CJ610; 145-XRJ; PW4158—85 percent.) 
NASA/CR—2014-218294 5 

Figure 4.—TEM images at successive magnifications of the soots produced by 

B737 at 4 percent (top row) and 65 percent (bottom row) power levels. 

Two foremost differences are the fuel and combustion system, being a high pressure, liquid-fueled 

gas turbine. Moreover multiple injectors are often present and recirculation is inherent to designs to 

achieve flame stability. Wall flow cooling further alters the aerodynamics, hence simple interpretations of 

monotonic temperature or equivalence ratio,  with time, as along the center streamline of a lab-scale gas 

jet flame are void. Further complicating comparisons are temperature and pressure differences between 

engines and varied power levels. With paucity of TEM reports but decided impact upon atmospheric 

processes, a survey is presented of results across engines at power levels relevant to take-off and cruise 

operation for comparison of structural similarities and differences between the aircraft platforms. 

As observed for the jet soots, aggregates exhibit a more compact morphology. This is often 

qualitatively observed in soots from other practical combustion sources such as engines, producing soot 

under conditions of elevated pressure. Yet quantitatively distinguishing such differences is difficult, as the 

fractal dimension varies little between quite distinct morphologies. Across the different jet soots, the 

fractal dimension varies between 1.6 and 1.8, less than a 12 percent variation. Therein alternative 

geometric measures were investigated to characterize and quantify the morphologies. The formulas are 

summarized in Table 2. These metrics are commonly used for morphological analysis of objects within 

images (ImageJ 1997). Numerical results for these geometric measures applied to the different soots are 

shown in the bar graph plots of Figure 5. For illustrative comparisons, two soots from the B737 at widely 

different powers (100 and 56 percent) are compared to soots from the Lear and ERJ aircraft, each at full 

power. Results are presented for engines and power levels for which sufficient data existed to permit 

analysis of greater than 20 aggregates for each engine and power level. 

As observed from the plots in Figure 5, aspect ratio exhibit a minimal variation of  20 percent with 

roundness showing a similar range. Compactness, a measure of overall asymmetry provides even less 

difference ( 10 percent). By recognizing the importance of irregular boundaries, the root form factor 

well describes differences in morphology with nearly a 2.5-fold (250 percent) variation between sampled 

aggregates, suggesting it to be the superior metric of morphology, and subtle variations. By contrast the 

aggregates from the ND flame possess an average fractal dimension of 1.74 and pre-factor of 2.2 

(Hu et al. 2003). 

These values can be compared to those found by fractal analysis, which exhibit a minimal range, 

generally from 1.6 to 1.8, as routinely reported for soot aggregates from both engines (Lind et al. 2010) 

and laboratory flames (Gangopadhyay et al. 1991)—traditionally interpreted as reflecting similarity of 

soot (aerosol) processes. The purpose for comparison of geometric measures is not to suggest that these 

particular values represent the different PM emissions from these jet engines but rather to use these varied 

(real) sources to identify a geometric measure that is more sensitive to morphology differences—visually 

evident (by TEM) across the array of engines. A larger dynamic range, as found in the root form factor 

has this potential for compact aggregate morphologies found here, a characteristic of soots produced by 

high pressure combustion (Zhu et al. 2005). 
NASA/CR—2014-218294 6 

TABLE 2.—FORMULAS FOR DIFFERENT GEOMETRIC DESCRIPTORS OF 

SOOT AGGREGATE MORPHOLOGY 

Geometric description Formulas 

Root form factor 

2 P

4

erimeter



Aspect ratio 

y

x

Compactness A 

4

Roundness 

x

A

2

4



x: major axis; y: minor axis; A: area 

Figure 5.—Results of geometric descriptors for soot aggregate morphology. Formulas 

are indicated in corresponding Table 2. 

Aggregate structure bears witness to the aerosol dynamics and mass growth conditions. The degree of 

fusion between individual spherules serves as a clock of aggregation relative to continued growth 

(Gaddam and Vander Wal 2013). Two jet soots well illustrate these differences. The A300 soot exhibits a 

branched structure suggesting late aggregation relative to primary particle growth. By contrast the B737 

produces aggregates with tight compaction suggesting just the opposite occurrence. If growth continues 

well beyond aggregation distinctive substructure is largely lost and only appear, as evident in Figure 3. 

These can be contrasted with the contacting spherule within the ND flame, as seen in Figure 1. Soot from 

diffusion flames typically exhibits a central region or core of less structure or even recognizable nuclei, 

clearly marking stages of coalescence and continued growth (Merchan-Merchan et al. 2012; Zhang and 

Boehman 2013). 
NASA/CR—2014-218294 7 

Microstructure 

Another striking feature of the aggregates is the disparate primary particle sizes, with highlights in the 

middle row of images in Figure 3, and the low magnification images (1st column) of Figure 4. For such 

vastly different sizes to co-exist within the same aggregate is unusual. By analyzing a series of such 

images, the means of the two primary particle populations are 173 and 41. These are statistically 

different by application of the Student’s T-test to 99 percent confidence. Either the growth environments 

were different or the timescales within a given growth region were different to produce such different size 

classes. Such disparate size classes are not observed in atmospheric pressure, laboratory diffusion flames 

(Koylu et al. 1994; Koylu et al. 1995). By either process, to come together from different spatial locations 

requires fast transport as would be facilitated by turbulent mixing (Bisetti et al. 2012), and conjectured 

here. This is particularly true for the small primary particles that are physically linked to the larger 

aggregate, and not merely physically adhered as would occur in agglomeration. Notably aggregate 

collapse to form nodular structures as reported elsewhere was not observed (Popovitcheva et al. 2003), 

likely reflecting differences in fuel, engines, or sampling procedures. 

Nanostructure 

Primary particles exhibit divergent nanostructure, as seen in the last image row of Figure 3. For the 

Lear soot nanostructure appears rather uniform throughout, even more so for the ERJ soot. There is no 

radial dependence to it nanostructure nor recognizable core within “particles” or substructures. This 

suggests uniformity of growth environment, species and temperature, and absence of phases associated 

with primary particle nucleation, coalescence and subsequent growth, as are spatially delineated in an 

ordinary gas-jet diffusion flame (Dobbins et al. 1996). 

This is in contrast to the laboratory-generated soot where a clear radial dependence is observed, 

characteristic of the temperature and species profile within the laminar diffusion flame (Vander Wal and 

Tomasek 2003). Along the center streamline in such buoyancy driven flames, precursor particles form, 

carbonize and further surface mass growth occurs throughout the fuel-rich region (Dobbins et al. 1995; 

Vander Wal 1998). Such a monotonic traverse in temperature and species is reflected in the nanostructure 

and its radial variation. 

In highly turbulent liquid-fueled combustors, soot (particle) formation may not follow such a model 

path (Liu and Smallwood 2009; El-Asrag and Menon 2009). The resulting particle nanostructure, 

specifically core-shell construction would be lost, or at the very least, different. Though seemingly 

homogeneous, the A300 soot contains amorphous regions and extended lamella interspersed throughout 

the particle. Irregular growth, particularly as engendered by varied species underlies such assembly. This 

again is in contrast to the flame soot in which nanostructure is circumferentially uniform (Fig. 1). 

Uniform structure in the Lear soot suggests no such variation or rather sufficiently rapid formation so as 

to be insensitive to the timescale on which such changes occur. Oddly the B737 soot shows multiple 

nucleation centers, indicative of coalescence between multiple precursor particles to form a primary 

particle. High equivalence ratio at high temperature would be conducive to such formation by fostering a 

high density of seed nuclei. Based on the TEM image, the primary particle nanostructure reflects ballistic 

rather than diffusional aerosol dynamics between the soot precursor particles (Chakrabarty 2008). Oddly 

some particles were either composed of two very different nanostructures (as marked by the divots in 

Figure 6) or were juxtaposed. Unlike a laminar diffusion flame, non-monotonic temperature and species 

profiles as imposed by turbulence are interpreted as causative. 
NASA/CR—2014-218294 8 

Figure 6.—A HRTEM image of an individual primary 

particle of the Lear jet soot showing disparate 

nanostructure with a clear geometric boundary 

between. 

To be noted is that combustion environment or chemical environment refers to both gas-phase and 

surface molecular re-arrangement processes that drive particle growth (Grieco et al. 2000; Howard and 

Richter 2000). Both contribute to the observed nanostructure and their differentiation or relative 

contributions is not the focus here, nor within resolution given “end-product” observations. However both 

will depend upon species and temperature, and rationale for translation of nanostructure reflecting the 

chemical growth “environment”. 

Though sampled from the engine exhaust plume, no evidence was found for extensive surface 

oxidation. In other studies by HRTEM of partially oxidized soots, whether by TGA or lean diesel 

operation, primary particles appear with roughened surfaces (Vander Wal and Tomasek 2003) and are 

readily distinguished. Slower oxidation, such as in diesel particulate traps or via TGA analyses can lead to 

internal burnout or particle restructuring. No such modes were observed in these exhaust entrained 

samples. 

Given the design and operational differences between engines, results are not compared across 

platforms but instead presented as a survey of the emitted soot, at high powers relevant to take-off 

operation. Along with a more compact morphology are less distinct primary particles. Aggregate 

compactness and loss of substructure reflect necessarily the same aerosol dynamics (Puri et al. 1993). 

Late coalescence relative to particle growth forms aggregates with an open, branching structure whereas 

early coalescence followed by continued mass surface growth yields compactly clustered aggregates of 

primary particles (Slowik et al. 2004). The relative timescales of mass (surface) growth versus 

coalescence depend upon species concentration and hence pressure, for temperatures sufficient to activate 

both pyrolysis and free radical generation (Ishiguro et al. 1997). Soot from diffusion flames typically 

exhibits a central region or core of less structure or even recognizable nuclei, clearly marking stages of 

coalescence and continued growth (Raj et al. 2010). This is observed in both the laboratory-generated 

soot and in soot from the 145-XRJ (ERJ) engine. 

In both images arrows point to the inner core and outer perimeter of recognizable primary particles, 

illustrating the contrasting nanostructure. For some of the jet soots, such as from the CFM56-3 engine 

with low power level, such structure is absent. There is no radial dependence to it nanostructure nor 

recognizable core within “particles” or substructures. This suggests uniformity of growth environment, 

species and temperature, and absence of phases associated with primary particle nucleation, coalescence 
NASA/CR—2014-218294 9 

and subsequent growth, as are spatially delineated in an ordinary gas-jet diffusion flame (Frenklach 2002; 

Santoro et al. 1983). For the CJ610-engine (Lear) soots, soot formation as judged by particle 

nanostructure appears to not follow the traditional soot growth stages. This inference does not necessarily 

pertain to rate of particle formation/growth, only to the homogeneity of formation conditions as judged by 

uniformity of nanostructure throughout the aggregate and its subsections. 

Discussion 

Though multi-team programs have comprehensively investigated detailed chemical kinetic models for 

surrogate fuels (Gudiyella et al. 2011; Gudiyella and Brezinsky 2012), and large-scale CFD models have 

incorporated soot models (Tolpadi et al. 1997; Colket et al. 2007), experimental measurements are yet 

needed as reference by which to extend chemical species models to soot nanostructure. As example, 

present models embody the pre-mixing designed into practical systems and the burning of fuel-rich, 

partially premixed regions. Above the critical stoichiometry threshold for soot formation, examination of 

soot under such conditions would be illuminating for the dependence upon mixing of soot structure across 

all lengths. 

More generally a jet engine is designed to burn overall lean with turbulence contributing to the 

mixing. Within turbulent flames soot particles can pass from regions of growth to oxidation to yet again 

growth (Vander Wal 1997). Each such region will interact with the soot particle surface but most telling 

will be multiple growth stages. If the different (growth) regions differ in species and temperature this 

should be reflected in the nanostructure. As observed in Figure 6, the primary particle shows two 

distinctly different nanostructures merged together. The broad interface and conformal knit between the 

two nanostructure types suggests a 2nd stage of growth as opposed to simple physical coalescence 

between two different primary particles. The absence of a “between particle gap” is consistent with this. 

Even if the particle shown in Figure 6 were actually two different primary units that physically came 

together, their varied nanostructure still points to different environments in time and temperature for their 

formation. This has not been reported for laboratory flames but is quite likely in practical systems. Recent 

turbulent models of soot within turbulent and flames anchored by recirculation regions include such nonmonotonic variation in temperature and time (Bisetti et al. 2012; El-Asrag et al. 2007). 

In summary, turbulence breaks up fuel rich regions, lowers their overall equivalence ratio and can 

even move soot particles between rich and lean regions, in either order. Such shifts in chemical 

environments create a complicated and non-monotonic particle history by multiple stages of growth and 

oxidation, of varied temperature and duration (Bisetti et al. 2012). Laboratory studies of this appear 

absent. We conjecture that soot particle structure across length scales will reflect such histories, as 

suggested by the variety of soot structures observed here. 

Conclusions 

Aggregate morphologies attest to aerosol dynamics, at high pressure. Reaction-limited aggregation 

leads to tight clusters whereas diffusion limited, particle-cluster aggregation is more representative of 

atmospheric pressure soot. Turbulence interacts by mixing particles from different growth trajectories. 

The large range of primary particle sizes is indicative of varied nucleation and growth environments. 

Nanostructure is indicative of species and temperature. Differences point to formation in regions of 

significantly different temperature and species so as to be manifest in the soot structure, at all length 

scales. Turbulence mixing is considered the operative factor for crossing particle trajectories from varied 

spatial regions. Models of PM formation in turbulent environments will need to include such variations 

for accurate prediction. 

Though lacking the model macro-, micro- and nanostructure of model flame soot, on all levels these 

physical scales appears similar to soot produced under other high pressure, rapid timescale engines, such 

as diesel despite vast differences in design, combustion conditions and fuels. 
NASA/CR—2014-218294 10 

The familiar model system, a laboratory scale gas-jet diffusion flame is very fuel-rich throughout the 

particle formation and nucleation regions. Such flames do not encompass the timescales and pressures of 

operation within engines and combustors. Indeed earlier studies of aircraft engine soot suggest substantial 

differences (Anderson et al. 2011). Yet other studies claim adequacy of laboratory-generated soots as 

surrogates (Popovitcheva et al. 2003). With relatively few studies, soot structure, i.e., aggregate structure, 

morphology, substructure and nanostructure remain largely unknown from jet aircraft. As shown here, the 

variation between the soots from several engine platforms across length scales show that aggregates 

retained their structure and did not under morphological rearrangement, were not coated nor evidently 

oxidized. Overall, soots from these aircraft as surveyed are similar in structure to other engine-produced 

soot, as found by many comparisons in the literature (Song et al. 2006; Vander Wal et al. 2007; Vander 

Wal and Muller 2006). 

Abbreviations 

NASA National Aeronautics and Space Administration 

TEM transmission electron microscopy 

References 

Anderson, B. E., Beyersdorf, A. J., Hudgins, C. H., Plant, J. V., Thornhill, K. L., Winstead, E. L., 

Ziemba, L. D., Howard, R., Corporan, E., Miake-Lye, R. C., Herndon, S. C., Timko, M., Woods, E., 

Dodds, W., Lee, B., Santori, G., Whitefield, P., Hagen, D., Lobo, P., Knighton, W. B., Bulzan, D., 

Tacina, K., Wey, C., Vander Wal, R., Bhargava, A., Kinsey, J., Liscinsky, D. S. (2011). Alternative 

aviation fuel experiment (AAFEX), NASA/TM—2011-217059, NASA Langley Research Center. 

Bisetti, F., Blanquart, G., Mueller, M. E., Pitsch, H. (2012). On the formation and early evolution of soot 

in turbulent nonpremixed flames. Combust. Flame 159:317-335. 

Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J.-H., Klimont, Z. (2004). A technology 

based global inventory of black and organic carbon emissions from combustion. J. Geophys. Res. 

109:D14203-43. 

Chakrabarty, R. K. (2008). On the morphology and optics of carbonaceous aerosols. Dissertation of 

Chemical Physics, University of Nevada. 

Colket, M., Edwards, T., Williams, S., Cernansky, N. P., Miller, D. L., Egolfopoulos, F., Seshadri, K., 

Dryer, F. L., Law, C. K., Friend, D., Lenhert, D., Pitsch, H., Sarofim, A., Smooke, M., Tsang, W. 

(2007). Development of an experimental database and kinetic models for surrogate jet fuels. 45th 

AIAA Aerospace Sciences Meeting and Exhibit 8 - 11 January, Reno, Nevada. AIAA 2007-770. 

Corporan, E., DeWitt, M. J., Belovich, V., Pawlik, R., Lynch, A. C., Gord, J. R., Meyer, T. R. (2007). 

Emissions characteristics of a turbine engine and research combustor burning a Fischer-Tropsch jet 

fuel. Energy Fuels 21:2615-2626. 

Demirdjian, B., Ferry, D., Suzanne, J., Popovicheva, O. B., Persiantseva, N. M., Shonija, N. K. (2007). 

Heterogeneities in the microstructure and composition of aircraft engine combustor soot: Impact on 

the water uptake. J. Atmos. Chem. 56:83-103. 

Dobbins, R. A., Fletcher, R. A., Lu. W. (1995). Laser microprobe analysis of soot precursor particles and 

carbonaceous soot. Combust. Flame 100:301-309. 

Dobbins, R. A., Govatzidakis, G. J., Lu, W., Schwartzman, A. F., Fletcher, R. A. (1996). Carbonization 

rate of soot precursor particles. Combust. Sci. Technol. 121:103-121. 

El-Asrag, H., Lu, T., Law, C. K., Menon, S. (2007). Simulation of soot formation in turbulent premixed 

flames. Combust. Flame 150:108-126. 

El-Asrag, H., Menon, S. (2009). Large eddy simulation of soot formation in a turbulent non-premixied jet 

flame. Combust. Flame 156:385-395. 

Frenklach, M. (2002). Reaction mechanisms of soot formation in flames. PCCP. 4:2028-2037. 
NASA/CR—2014-218294 11 

Gaddam, C. K., Vander Wal, R. L. (2013). Physical and chemical characterization of SIDI engine 

particulate. Combust. Flame (in press); DOI: 10.1016/j.combustflame.2013.05.025. 

Gangopadhyay, S., Elminyawi, I., Sorensen, C. M. (1991). Optical structure factor measurements of soot 

particles in a premixed flame. Appl. Opt. 30:4859-4864. 

Grieco, W. J., Howard, J. B., Rainey, L. C., Vander Sande, J. B. (2000). Fullerenic carbon in combustiongenerated soot. Carbon 38:597-614. 

Gudiyella S., Brezinsky K. (2012). High pressure study of n-propylbenzene oxidation. Combust. Flame. 

159:940-958. 

Gudiyella S., Malewicki T., Comandini A., Brezinsky K. (2011). High pressure study of m-xylene 

oxidation. Combust. Flame 158:687-704. 

Hagen D. E., Whitefield P. D. (1996). Particulate emissions in the exhaust plume form commercial jet 

aircraft under cruise conditions, J. Geophys. Res.: Atmos. 101:19551-19557. 

Herndon, S. C., Onasch, T. B., Frank, B. P., Marr, L. C., Jayne, J. T., Canagaratna, M. R., Grygas, J., 

Lanni, T., Anderson, B. E., Worsnop, D., Miake-Lye, R. C. (2005). Particulate emissions from in-use 

commercial aircraft. Aerosol Sci. Tech. 39:799-809. 

Howard, J. B, Richter, H. (2000). Formation of polycyclic aromatic hydrocarbons and their growth to soot 

– a review of chemical reaction pathways. Prog. Energy Combust. Sci. 26:565-608. 

Hu, B., Yang, B., Koylu, U. O. (2003). Soot measurements at the axis of an ethylene/air non-premixed 

turbulent jet flame. Combust. Flame 134:93-106. 

Huang, C. H., Vander Wal, R. L. (2013). AAFEX II: Effect of soot structure evolution from commercial 

jet engine burning petroleum based JP-8 and synthetic HRJ, FT fuels. Energy Fuels (submitted). 

ImageJ. (1997). http://rsb.info.nih.gov/ij/ 

Ishiguro, T., Takatory, Y., Akihama, K. (1997). Microstructure of diesel soot particles probed by electron 

microscopy: First observation of inner core and outer shell. Combust. Flame 108:231-234. 

Karcher, B., Mohler, O., DeMott, P. J., Pechtl, S., Yu, F. (2007). Insights into the role of soot aerosols in 

cirrus cloud formation. Atmos. Chem. Phys. 7:4203-4227. 

Khalizov A. F., Zhang, R., Zhang, D., Xue, H., Pageis, J., McMurry, P. H. (2009). Formation of highly 

hygroscopic soot aerosols upon internal mixing with sulfuric acid vapor. J. Geophys. Res. 

114:D05208. 

Kim, W. W., Lienau, J. J., Van Slooten, P. R., Colket, M. B., Malecki, R. E., Syed, S. (2004). Towards 

modeling lean blow out in gas turbine flameholder applications. J. Eng. Gas Turbines Power 128: 

40-48. 

Kinsey, J. S., Hays, M. D. (2011). Chemical characterization of the fine particle emissions from 

commercial aircraft engines during the aircraft particle emissions experiment (APEX) 1 to 3. Environ. 

Sci. Technol. 45:3415-3421. 

Koylu, U. O., Faeth, G. M. (1994). Optical properties of soot in buoyant laminar diffusion flames. J. Heat. 

Transfer 116:971-979. 

Koylu, U. O., Faeth, G. M., Farias, T. L., Garvalho, M. G. (1995). Fractal and projected structure 

properties of soot aggregates. Combust. Flame 100:621-633. 

Li, H., Liu, C., Bi, L., Yang, P., Kattawar, G. W. (2010). Numerical accuracy of equivalent spherical 

approximations for computing ensemble-averaged properties of fractal soot aggregates. J. Quant. 

Spectrosc. Radiat. Transfer 111:2127-2132. 

Lind, T., Ammar, Y., Dehbi, A., Guntay, S. (2010). De-agglomeration mechanisms of TiO2 aerosol 

agglomerates in PWR steam generator tube rupture conditions. Nucl. Eng. Des. 240: 2046-2053. 

Liu, F., Smallwood, G. J. (2009). Radiative properties of numerically generated fractal soot aggregates: 

The importance of configuration averaging. J. Heat Transfer 132:023308-023313. 

Merchan-Merchan, W., Sanmiguel, S. G., McCollam, S. (2012). Analysis of soot particles derved from 

biodiesels and diesel fuel air-flames. Fuel 102:525-535. 

Penner J. E., Lister D. H., Griggs D. J., Dokken D. G. J., McFarland M. (1999). Aviation and the Global 

Atmosphere, Intergovernmental Panel on Climate Change Rept., Cambridge Univ. Press, Cambridge, 

England, U.K., p. 373. 
NASA/CR—2014-218294 12 

Popovitcheva, O., Persiantseva, N. M., Kuznetsov, B. V., Rakhmanova, T. A., Shonija, N. K., Suzanne, 

J., Ferry, D. (2003). Microstructure and water adsorbability of aircraft combustor soots and kerosene 

flame soots: Toward an aircraft-generated soot laboratory surrogate. J. Phys. Chem. A 107:10046-

10054. 

Popovitcheva, O., Persiantseva, N. M., Trukhin, M. E., Rulev, G. B., Shonija, N. K., Buriko, Y. Y., 

Starik, A. M., Demirdjian, B., Ferry, D., Suzanne, J. (2000). Experimental characterization of aircraft 

combustor soot: Microstructure, surface area, porosity and water adsorption. PCCP. 2:4421-4426. 

Puri, R., Richardson, T. F., Santoro, R. J. (1993). Aerosol dynamic processes of soot aggregates in a 

laminar ethane diffusion flame. Combust. Flame 92:320-333. 

Raj, A., Man, R. L. W., Totton, T. S., Snader, M., Shirley, R. A., Kraft, M. (2010). New polycyclic 

aromatic hydrocarbon (PAH) surface processes to improve the model prediction of the composition of 

combustion generated PAHs and soot. Carbon 48:319-332. 

Riemer, N., Vogel, H., and Vogel, B. (2004). Soot aging time scales in polluted regions during day and 

night. Atmos. Chem. Phys. 4:1885-1893. 

Santoro, R. J., Semerjian, H. G., Doblins R. A. (1983). Soot particle measurements in diffusion flames. 

Combust. Flame 51:203-218. 

Schmid O., Hagen, D., Whitefield, P. D., Trueblood, M. B., Rutter, A. P., Lilenfeld, H. V. (2004). 

Methodology for particle characterization in the exhaust flows of gas turbine engines. Aerosol Sci. 

Tech. 38:1108-1122. 

Shim, H.-S., Hurt, R. H., Yang, N. C. (2000). A methodology for analysis of 002 lattice fringe images and 

its application to combustion-derived carbons. Carbon 38:29-45. 

Slowik, J. G., Stainken, K., Davidovits, P., Williams, L. R., Jayne, J. T., Kolb, C. E., Worsnop, D. R., 

Rudich, Y., DeCarlo, P. F., Jimenez, J. L. (2004). Particle morphology and density characterization 

by combined mobility and aerodynamic diameter measurement. Part 2: Application to combustiongenerated soot aerosols as a function of fuel equivalence ratio. Aerosol Sci. Technol. 38:1206-1222. 

Song, J., Alam, M., Boehman, A. L., Kim, U. (2006). Examination of the oxidation behavior of biodiesel 

soot. Combust. Flame 146:589-604. 

Teini, P. D., Karwat, D. M. A., Atreya, A. (2011). Observations of nascent soot: Molecular deposition 

and particle morphology. Combust. Flame 158:2045-2055. 

Timko, M. T., Yu, Z., Onasch, T. B., Wong, H.-W., Miake-Lye, R. C., Beyersdorf, A. J., Anderson, B. E., 

Thornhill, K. L., Winstead, E. L., Corporan, E., DeWitt, M. J., Klingshirn, C. D., Wey, C., Tacina, K., 

Liscinsky, D. S., Howard, R., Bhargava, A. (2010). Particulate emissions of gas turbine engine 

combustion of a Fischer-Tropsch synthetic fuel. Energy Fuels 24:5883-5896. 

Tolpadi, A. K., Correa, S. M., Burrus, D. L., Mongia, H. C. (1997). Monte carlo probability density 

function method for gas turbine combustor flowfield predictions. J. Propul. Power 13:218-225. 

Vander Wal, R. L. (1997). LIF-LII measurements in a turbulent gas-jet flame. Exp. Fluids 23:281-287. 

Vander Wal, R. L. (1998). Soot precursor carbonization: Visualization using LIF and LII and comparison 

using bright and dark field TEM. Combust. Flame 112:607-616. 

Vander Wal, R. L., Bryg, V. M. (2013). Chemistry characterization of jet aircraft engine particulate by 

XPS: results from APEX III. Aerosol Sci. Technol. (submitted). 

Vander Wal, R. L., Bryg, V. M., Huang, C. H. (2013). Aircraft engine particulate: Macro- micro- and 

nanostructure by HRTEM and Chemistry by XPS. Combust. Flame (submitted). 

Vander Wal, R. L., Jensen, K. A., Choi, M. Y. (1997). Simultaneous laser-induced emission of soot and 

polycyclic aromatic hydrocarbons within a gas-jet diffusion flame. Combust. Flame 109:399-414. 

Vander Wal, R. L., Lee, K. O., Choi, M. Y. (1995). The effects of rapid heating of soot: Implications 

when using laser-induced incandescence for soot diagnostics. Combust. Flame 102:200-204. 

Vander Wal, R. L., Mueller, C. J. (2006). Initial investigation of effects of fuel oxygenation on 

nanostructure of soot from a direct-injection diesel engine. Energy Fuel 20:2364-2369. 

Vander Wal, R. L., Tomasek, A. J. (2003). Soot oxidation: Dependence upon initial nanostructure. 

Combust. Flame 134:1-9. 
NASA/CR—2014-218294 13 

Vander Wal, R. L., Tomasek, A. J., Street, K., Hull, D. R., Thompson, W. K. (2004). Carbon 

nanostructure examined by lattice fringe analysis of high-resolution transmission electron microscopy 

images. Appl. Spectrosc. 58:230-237. 

Vander Wal, R. L., Yezerets, A., Currier, N. W., Kim, D. H., Wang, C. M. (2007). HRTEM study of 

diesel soot collected from diesel particulate filters. Carbon 45:70-77. 

Wey, C. C., Anderson, B. E., Hudgins, C., Wey, C., Li-Jones, X., Winstead, E., Thornhill, L. K., Lobo, 

P., Hagen, D., Whitefield, P., Yelvington, P. E., Herndon, S. C., Onasch, T. B., Miake-Lye, R. C., 

Wormhoudt, J., Knighton, W. B., Howard, R., Bryant, D. (2006). Aircraft particle emissions 

experiment (APEX), NASA TM-2006-214382. 

Wey, C. C., Anderson, B. E., Wey, C., Miake-Lye, R. C., Whitefield, P., Howard, R. (2007). Overview 

on the aircraft particle emission experiment. J. Propul. Power 23:898-905. 

Wuebbles, D., Gupta, M., Ko, M. (2007). Evaluating the impacts of aviation on climate change. EOS, 

Trans. Am. Geophys. Union 88:157-168. 

Yehliu, K. (2010). Impacts of Fuel Fromulation and Engine Operating Parameters on the Nanostructure 

and Reactivity of Diesel Soot. Desrtation of Energy and Mineral Engineering, Pennsylvania State 

Univeristy. 

Yehliu, K., Vander Wal, R. L., Boehman, A. L. (2011). Development of an HRTEM image analysis 

method to quantify carbon nanostructure. Combust. Flame 158:1837-1851. 

Zhang, Y., Boehman, A. L. (2013). Oxidation behavior of soot generated from the combustion of methyl 

2-butenoate in a co-flow diffusion flame. Combust. Flame 160:112-119. 

Zhu, J., Lee, K. O., Yozgatligil, A., Choi, M. Y. (2005). Effects of engine operating conditions on 

morphology microstructure, and fractal geometry of light-duty diesel engine particulates. P. Combust. 

Inst. 30:2781-2789.