Fuel 183 (2016) 55–63

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Fuel

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A molecular dynamics study of nanoparticle-formation frombioethanol-gasoline blend emissions

http://dx.doi.org/10.1016/j.fuel.2016.06.0490016-2361/� 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: oan@vestforsk.no (O. Andersen).

Sergio Manzetti a,b, Otto Andersen c,⇑a Fjordforsk AS. Nanofactory, Institute of Science and Technology, Midtun, 6894 Vangsnes, NorwaybComputational Systems and Biology, Biomedical Center, University of Uppsala, 75124 Uppsala, SwedencWestern Norway Research Institute, Postboks 163, 6851 Sogndal, Norway

a r t i c l e i n f o a b s t r a c t

Article history:Received 15 May 2015Received in revised form 6 May 2016Accepted 7 June 2016

Keywords:Molecular dynamics simulationsBiofuelsBio-blendsExhaust emissionsToxicology

Aerosol components and nanoparticles deriving from fuel combustion represent a class of exhaustemissions with critical relevance to environmental studies. In particular, the formation of nanoparticlesis an important theme for environmental assessments of new fuel blends. Here, a set of computersimulations is carried out to study the behaviour of acetaldehyde-phenanthrene nanoparticles in relationto the influences to the three major atmospheric components CO2, O2, N2. The results show thatphenanthrene and acetaldehyde quickly generate nanoparticles with dimensions of 2–5 nm in vacuum.The formed particles are stable in atmospheric conditions and interestingly absorb CO2 from theatmosphere-gas simulations but not O2 and N2. The probability of absorption of CO2 in the formednanoparticles results as 10–20-fold compared to N2 and O2. Furthermore, acetaldehyde appears tolocalize on the surface of the formed nanoparticles, and seemingly acts with the planar geometry ofphenanthrene as a facilitator for CO2 absorption. The results provided show also the properties of formednanoparticle with higher concentrations of acetaldehyde and lower of phenanthrene, where phenan-threne forms the core of the nanoparticle, while acetaldehyde interacts with the surface and subsurfacearea in making their chemistry hydrophilic with a dense aromatic core. The study is important for furtherassessing bioethanol and fuel blends, and also introduces a methodology for studying interactions ofgases and particles at the molecular level, with macroscopic significance. The study reports on growthof nanoparticles by CO2 absorption, introduces a new issue for blending fuels, with implications towardspollution profiles.

� 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Exhaust emissions from automotive fuel combustion representsone of the major public health-threats of modern times and is themajor cause to the worldwide incidence of cardiac problems, pul-monary complications, stroke, allergies, asthma and cancer [1–4].These pathologies are caused by exhaust-emissions of particles[5,6], tar-like and semi-combusted volatile compounds [7], soot,smog and fly-ash and from the release of polluting NOx andsulphur-containing compounds from logistic and industrial fuelcombustion [8–10]. The majority of the fuel types encompass gaso-line and diesel fuels; however biofuels have been gradually imple-mented in the urban and rural car-parks as part of politicalincentives for greener alternatives [10–12]. This has led to the

development of strategies in various countries for implementingbiofuels both in the public and private transport sectors.

Biofuels contain a fossil fuel fraction (e.g. gasoline, diesel),which is mixed with a bio-fraction (e.g. bioethanol, biodiesel) atvarious ratios (e.g. E5, E85, B10, B20) [13,14]. The fraction’s ratioaffects the performance of the engine, the cetane and the octanenumber, and results also in different physico-chemical propertiesof the fuel (viscosity, ignition temperature, etc.) [15]. Biofuelblends are also treated with additives to achieve optimal miscibil-ity of the different chemical phases in the fuel mixture [16].However, blending fossil and biofuels result also in changes ofemission levels, exhaust-components, engine performance, enginedurability, and transport costs and has created concerns as theoverall toxicity is not significantly reduced when compared to fos-sil fuels, and results in the emission of new toxic bio-specificexhaust components such as acetaldehyde and formaldehyde[17,18]. The presence of a fossil fraction contributes with the majorformed PAH in fossil emissions, phenanthrene [19]. Its presence in

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fossil emissions has such a high significance that this study evolveson mapping its interaction with acetaldehyde, which is converselyto phenanthrene - the major component of bioethanol combustion[20], at a molecular level. This is carried out in order to revealpotential chemical properties of the gas-phase of acetaldehydeand phenanthrene after emission from the combustion ofbioethanol and gasoline blends. The approach of using moleculardynamics simulation is of critical importance to emission research,as molecular dynamics is a powerful tool for predicting gas-phaseformations, nanoparticle interactions as well as clustering andaggregation [21,22].

Various groups have worked on computational analysis ofcombustion formed nanoparticles, particularly on diesel andgasoline fuels, as well as for biodiesel blends [23,24]. Nevertheless,no studies have been published in context with bioethanol – gaso-line nanoparticle formation. Also, the known release of aldehydeand formaldehyde in bioethanol emissions, which are toxiccompounds, represents a cornerstone of the computational analy-sis of exhaust emission nanoparticles from bioethanol-gasolineblends, which has not been undertaken before in a simulationstudy. Given the limited availability of nanoparticle studies in rela-tion to exhaust emissions, and none existing in context withbioethanol, a series of simulations here have been undertaken withthe aim of mapping the effects of nanoparticles formation betweenacetaldehyde and phenanthrene, study the stability of the particlesand ultimately, map the reactions with atmospheric components,oxygen, nitrogen and carbon dioxide, at the particle agglomerationlevel.

2. Materials and methods

2.1. Vacuum simulations

Molecules of phenanthrene and acetaldehyde were prepared inthe program Acpype [25] and imported for simulation in the GRO-MACS 5.0 package [26], as this system is ideal for chemical simula-tions of similar type to this study [27–29]. The GAFF force field wasapplied for all topologies and steps throughout the simulation. 4blends of the two emission products phenanthrene (PHN) andacetaldehyde (ACD) were prepared in a virtual box of dimensions10 � 10 � 10 nm3. The four blends consisted of (a) 100 moleculesof each compounds 50%/50% ratio, (b) 100 molecules of ACD and200 molecules of PHN, (c) 100 molecules of ACD and 400 moleculesof PHN and (d) 100 molecules of ACD and 640 molecules of PHN.The linear increment in phenanthrene content was prepared toreflect the increase of fossil fuel fraction in blends, against the con-tent of bioethanol emission components, acetaldehyde. This gavethe respective four mixtures, 50%PHN-50%ACD; 75%PHN-33%ACD; 80%PHN-20%ACD; 87%PHN-13%ACD. In other words, theemission products were mixed in a similar fashion as their fuelblend is mixed as implementation of bioethanol-gasoline blends.An energy minimization of all systems was carried out subse-quently followed by a 10 ns simulation of their interaction at con-stant volume and zero pressure, at 300 K. This is a preliminaryoptimization, which is required to relax the system of molecules,and does not necessarily reflect the engine conditions. It is notthe engine, but the outlet of the exhaust pipe environment thatis simulated, for the engine-derived emission componentsacetaldehyde and phenanthrene, which are interacting with CO2,O2 and N2 (see in the atmosphere simulations – next chapter).The simulation does not take into account the events in theexhaust pipe, as this requires a separate study. We are focusingon the open atmosphere and interaction with the outlet from withpredominantly N2, O2 and CO2 levels. Details of the molecular sim-ulations are given: Step-size set to 2 fs, a Coulomb type for the sim-

ulation was used with the Particle-Mesh Ewald algorithm and avan der Waal type set by cut-off (1 nm). The temperature wassimulated using a temperature coupling using the V-rescalealgorithm and the environment was simulated without pressurefor preliminary relaxation of the system. All bonds were simulatedwith the LINCS algorithm. Boxes were set to 10 � 10 � 10 nm forall blends except for blend (d) 640 phn + 100 acd, where it wasset to 12 � 12 � 12 nm.

2.2. Atmospheric simulations

All systems (nanoparticles) deriving from the vacuum simula-tion were subsequently subject to new 10 ns simulations withGROMACS in interaction with three atmospheric components:100 molecules of oxygen, 500 molecules of nitrogen and 50molecules of carbon dioxide. All parameters were set equal tothe vacuum simulations, 300 K and 1 atmosphere pressure usingthe Berendsen pressure coupling scheme [30]. The nanoparticlesfrom system (d) (above) was merged with the atmospheric mole-cules in a larger box than the used, consisting of x, y, z dimensionsof 12 � 12 � 12 nm3.

2.3. Trajectory and simulation analysis

All analyses performed with GROMACS package [26]. All graphsgenerated with XmGrace [31].

3. Results

3.1. Phenanthrene-acetaldehyde simulations

Three simulations for each blend were run for phenanthreneand acetaldehyde, each of 10 ns periods. During the 10 ns periods,the molecules aggregated at various rates and speeds, withphenanthrene providing the agglomerating effects on the overallmixtures. The effects of agglomeration resulted in the formationof single clusters for each simulation with spherical dimensionsof 15–25 Å in diameter (1.5–2.5 nm) (Fig. 1). The positioning ofthe particles inside each formed particle revealed an interestingpattern of the nanoparticle formation dynamics. The acetaldehydemolecules were gradually engulfed with increasing count ofphenanthrene molecules, even when the volume of the boxes wereincreased and an extensive availability of free space allowedacetaldehyde to move about the empty spaces. This pattern wasrepeated for the triplicate runs of the simulations, indicating thatphenanthrene has an extensively ‘‘sticky” chemistry, whichquickly binds acetaldehyde (which is amphiphilic) and engulfs itinside its arrangement of aromatic moieties. This very interestingeffect was not expected, as acetaldehyde has a part polar - partapolar chemistry, and was expected to form clusters separatelyfrom phenanthrene. The positions of the acetaldehyde moleculesinside the particles were revealed at sub-nanometer resolution,by a nanoparticle slice-analysis through the use of radial distribu-tion function plotting (Fig. 2). The four blends of emission productswere plotted for probability of being located at the core of theformed particles in relationship to one another, and the radial dis-tribution plots show that phenanthrene is gradually reduced inradial probability inside the clusters, as this probability is gradu-ally replaced by acetaldehyde. Fig. 1 shows this pattern ofnanoparticle formation dynamics, where the as the phenanthrenecontent decreases with the increasing content of acetaldehyde,the radial probability for each ‘‘slice” of the nanoparticles(x-axis, Fig. 2) is reduced for phenanthrene (and increased foracetaldehyde).

Fig. 1. Formed single nanoparticles after triplicate runs of the molecular dynamics of acetaldehyde’s spontaneous interaction with phenanthrene. (A) 50% PHN - 50% ACD; (B)75% PHN - 33% ACD; 80% PHN - 20% ACD; 87% PHN - 13% ACD. The images show three particles per row, each formed during its individual 10 ns simulation. The pattern showsthat the nanoparticles engulf acetaldehyde the more phenanthrene increases in concentration, independently of the box dimension (see Fig 2).

Fig. 2. The radial distribution of phenanthrene respective to acetaldehyde. The image shows the relative distribution of PHN and ACD by probability. High y-values indicatehigh PHN probability at the given r-position (x-axis). At r = 0.6 nm (red-arrow), the content of PHN drops more intensively from black towards blue plots. This implies that thecontent of PHN is decreasing at the average distance of 0.6 nm (6 Å) from the centre, and replaced by acetaldehyde. The curves of the plots can be compared to one another,which shows that the top peaks for each plot designates the PHN-dense core of the nanoparticles (blue arrow), while the increasingly deeper local minimum at 0.6 nm (redarrow) shows that this region is more acetaldehyde-rich. Moving towards higher x-values, the following local maximum at 0.8–0.9 nm shows that at this distance from thecore, the nanoparticles is again populated more by phenanthrene than acetaldehyde. The subsequently sinking slope shows that PHN content decreases the more one movesaway from the nanoparticle core. This intriguing relationship illustrates that acetaldehyde is increasingly absorbed in the nanoparticles when PHN levels rise, however it islocalized slightly beneath the nanoparticle surface (4 Å deep from the surface). The outmost periphery of the nanoparticles (0.8–0.9 nm) is coated with PHN. This forms aprotective layer by the phenanthrene, which evidently shields acetaldehyde from being exposed to the environment around the particle. This can have implications for theparticle toxicity, as acetaldehyde is a suspected carcinogen. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of thisarticle.)

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3.2. Chemical properties of the phenanthrene-acetaldehyde blend

Properties of the formed nanoparticles were calculated usingGROMACS [26]. The properties of surface tension are critical tobe estimated in order to measure the level of stability the particleshave as an agglomerate. Surface tension described the ability of theparticles to hold the shape and agglomerate together and is mea-sured by the unit pressure (bar) pr. length unit (nm) in GROMACS.The analysis shows that the particles with higher content of

Fig. 3. Surface tension dynamics of the formed particles during the last 500 ps of one ofshow a predominating effect, where the blue and green lines have higher amplitudes thaincreased levels of PAH (phenanthrene) in the emission gas blend. (For interpretation of tof this article.)

Fig. 4. Hydrophobicity interactions for each emission-blend. The Lennard-Jones potentiaof the triple simulations carried out for each blend. The energies show that the higherfavourable energies result between groups of phenanthrene and acetaldehyde. This meanof physical terms (surface tension) and in terms of chemical properties (higher packing

phenanthrene manifest the highest amplitudes of surface tension(Fig. 3), depicting that the increasing content of the aromaticphenanthrene from the fossil fuel combustion generates nanopar-ticles with stronger intra-molecular stability and potentially longerhalf-life. The increased surface tension forms stable nanoparticles,which are held together by the strong aromatic-aromatic interac-tions. These interactions are quantified using further GROMACSanalysis. The analysis evolves on mapping the state of the energiesbetween acetaldehyde and phenanthrene (Fig. 4), which clearly

the triple simulations. X-axis time frame, Y-axis: surface tension. The mixed curvesn the red and black. The amplitude of the surface tension evidently increases by thehe references to colour in this figure legend, the reader is referred to the web version

ls are shown in kJ/mole (y-axis) during the 10 ns simulation periods (x-axis) for onethe levels of phenanthrene in the formed particle are, the more optimal and mores that higher phenanthrene (fossil) content increases particle stability, both in termsof molecules).

Table 1Surface tension of the formed nanoparticles at given ratios of acetaldehyde andphenanthrene. The ‘‘Vacuum” column represents the intercept values of the surfacetension, generated from the plots in complete vacuum in a 10 ns period. ‘‘Atmo-spheric conditions” reports the intercept values of the surface tension under influencefrom N2, O2 and CO2 in a 10 ns period. The table shows that the nanoparticles aregenerally experiencing a reduction in surface tension, which is caused by theabsorption of CO2 (Fig 6). Each value is derived through running linear regressionanalysis of the 10 ns surface tension plot in XmGrace.

Particle system Vacuum (bar/nm) Atmospheric conditions (bar/nm)

PHN/ACD 50–50% 21 �2.45PHN/ACD 67–33% �2 �7.05PHN/ACD 80–20% 84 �15.2PHN/ACD 87–13% 55 11

Mean value 39.5 �8.92

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shows that these are in favourable interactions (negative energies).The energies depict that the agglomerated nanoparticles areretained by very strong intermolecular forces and that these areexerted by the aromatic chemistry of phenanthrene, which mostlikely results in that these particles are in a solid-state, and not liq-uid. This is based on the foundation of that phenanthrene has amelting point at 101 �C, and as these particles are formed duringsimulations at 300 K, their solid state further confirms on theirutter stability in circulation in an open trafficked environment.

3.3. Interaction with atmospheric gases

The interaction with atmospheric gases was studied by addingoxygen, nitrogen and carbon dioxide to the simulated environ-ments from the previous section. The composition of moleculeswas set to emulate the emissions escaping the exhaust pipe, witha high content of CO2 than the regular atmospheric composition.In order to give a discrete analysis, nitrogen content was thereforeset to 76%, oxygen was set to 15% while the rest (CO2) was set to9%. Although an underestimate of 9%, the content was notincreased as, due to the dilution effect, the concentration of thegases dramatically decreases through the travel out of the exhaustpipe to reach ppm levels. The effect on the nanoparticles by theatmospheric gases was assessed by deriving the surface tensionof the systems. The average surface tension for each particle iscompared to the surface tension of the nanoparticles withoutatmospheric components (Table 1). The values are approximateand result from simulation of the gas phase atmospheric particles,which result in a generic reduction of the surface tension of theformed particles. The reduction in the surface tension implies thatthe nanoparticles are altering their chemical composition in reac-tion with the atmospheric gases. Visual inspection shows thatCO2 is responsible for this change in surface tension of the formednanoparticles, (Fig. 5) as it is the most recurrent absorbed gasinside the particles. This effect is revealed by performing a radialdistribution function analysis (Fig. 6) on the relative positions ofthe gas molecules hence to the positions of the phenanthrene moi-eties (which represent �80% of the nanoparticles composition).The analysis (Fig. 6) shows a dramatic increase in CO2 presenceinside the particles after 10 ns, and an absence of the same gas in

Fig. 5. Nanoparticle surface during atmospheric conditions. A nanoparticle selected frdisplayed with N2, O2 and CO2 (all three gases in blue spheres). Given software limitatiomolecules are however seen as triple-sphered compounds located mostly at the inner pathis figure legend, the reader is referred to the web version of this article.)

the peripherical regions of the simulated gas-phase environments.Oxygen and nitrogen remain instead at an equal subdivision acrossthe box, and show that the probability for these molecules to belocalized in the nanoparticles is virtually zero (Fig. 6). The prefer-ence of CO2 to be absorbed in the nanoparticles is related to itsmore amphiphilic nature, which is a molecule with a slightly pos-itively charged carbon at the epicentre of two negatively chargedoxygen atoms. This gives CO2 a weak dipole moment, which inter-acts well with the quadrupole moment of phenanthrene (as formost PAHs) [32]. Oxygen and nitrogen which both have more loneelectrons and no positive centre between them, have a sole nega-tive electrostatic potential which is repulsive to the aromatic neg-ative partial charges of the phenanthrene molecules. This effectcontributes to the reduction of the surface tension of the particles(Fig. 3), and increases the particle size with CO2 rising levels. Thegrowth of the nanoparticles in contact with CO2 is an interestingfinding in this study and can have direct implications for nanopar-ticles size when combustion of bioethanol/fuel blends is per-formed. Analyses of CO2-induced nanoparticle growth shouldtherefore be conducted, as the size of nanoparticles directly deter-mine their mode of toxicity [33]. Also, recent studies show that theamount and size of nanoparticles is affected directly by the ratio ofbioethanol and gasoline in the fuel [15] and can therefore haveimplications for the toxicity of PAH-rich blends, which generate

om the simulation with 87% phenanthrene (green) and 13% acetaldehyde (red) isns, the differentiation in colours between the three gases was impossible. The CO2

rts of the nanoparticles (see Fig 6). (For interpretation of the references to colour in

Fig. 6. A radial distribution analysis of the relative positions of CO2 (blue), N2 (red) and O2 (green) in respect with the nanoparticles core, in the simulated atmosphericenvironment. Compare red, green and black plots. The y-axis shows the probability of finding CO2, N2 and O2 among the phenanthrene moieties (at the core of the particle).The x-axis shows the respective distance from the centre of the nanoparticle (x = 0) to the given probability value. The data is reported for the four nanoparticles types (A:PHN/ACD 50–50%; B: PHN/ACD 67–33%; C: PHN/ACD 80–20%; PHN/ACD 87–13%). The exact radial position and its probability density of CO2 is found at the local minimum at0.5 nm from the core, which is visible to be five times higher than for N2 and O2. This comparison shows that CO2 gas has a five times higher probability of being absorbed in aPAH-rich nanoparticle compared to O2 and N2, which are virtually absent in the nanoparticles. The general lower probability density for increasing PHN content (A? D) isrelated to the higher number of PHN particles found in the boxes, and as being relative, is a negligible factor. (For interpretation of the references to colour in this figurelegend, the reader is referred to the web version of this article.)

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high CO2 levels at high cetane levels. Furthermore, idling andhigh-speed driving trigger different levels of CO2 emissions, andnano-particle growth can therefore be particularly related tohigh-speed areas such as freeways. Trafficked regions such asurban centres can instead generate less CO2 by lower enginerevolution number, and thus generate smaller nanoparticles.

The molecular dynamics simulations performed here show thatPAH phenanthrene, which is abundant in gasoline exhaust formsreadily particles with acetaldehyde – which derives from bioetha-nol combustion. These particle retain stability and shape in vacuo,however when they are exposed to atmospheric and the majorcombustion gas CO2, these particle grow in size by absorbingCO2. Other gases in the system of combustion, such as NOx mayaffect this effect, however NO2 for instance has a high partialcharge evenly subdivided across the OAN@O, and has a negativeelectrostatic moment. This is not the case for CO2, which has aweak positive electrostatic charge on the carbon atom, leading tothe formation of a charge-to-charge compatible molecule with pla-nar PAHs, such as phenanthrene, who inversely have a negativecharge in the centre and a slight positive charge on the peripheralhydrogen atoms. NOx are therefore a potential candidate for inter-action with acetaldehyde at the surface, though a poorer candidatefor intercalation with PAHs.

4. Discussion

4.1. On the shapes, sizes and properties of nanoparticles

Studies on nanoparticle formation from fuel combustion showthat the smallest particles behave like molecules and not like par-ticles [34]. The small size of the particles gives them a dynamicbehaviour, which is similar to the dynamic behaviour of gases,while larger particles have considerably large nuclei and thereforetend to have a dust-like dynamic behaviour. The density andweight of the nanoparticle plays therefore a central role innanoparticle behaviour, as well as the morphology [35]. Sphericalparticles, as found in emissions [36] and as resulted from the sim-ulations in this study, have uniform dimensions and shapes andtend to share the same aerodynamic behaviour and direction whenpropagated by spreading from air-fluxes and air-currents, as theirshape does not result exert any particular non-uniform counter-force towards the air currents. This means in a few terms thatthe more spherical the particles are, the more they share the samedirection of transport in the air. Contrary to this, particles witharbitrary shapes and forms tend to spread in random patterns,and are less trivial to track for aero-dynamic behaviour [35]. Thisfeature has direct consequences for the pathway of inhalation

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the formed nanoparticles in exposed subjects to traffic exhaust,such as the simulated particles in this study. This exposure toformed particles from combustion is more efficient the smallerthey are, and also the more round-shaped and uniform theybecome. Nanoparticles as the type simulated here are thereforeexpected to represent a type of particles formed in during theinteraction of PAHs with acetaldehyde and CO2. No available datafrom the studies show any limits to their sizes, and the effects ofmoist and temperature furthermore play a central role in thedynamics of particle size and shape. Larger (PM5-PM10) and lessuniformly shaped particles have, on the contrary to nanoparticles,a greater tendency of being localized to mucus membranes as theirdisordered shape/size ratio leads to more random aerodynamicbehaviour during inhalation [35]. In other words, the sphericalshape of the simulated particles, particularly the ones formed at50% fossil fuel deriving phenanthrene and 50% bioethanol derivingacetaldehyde, gives them a higher probability of being able to bedirectly transferred to the lung alveoli, passing the mucal tissues.

The periods required for absorption and transfer of the particlesin the lungs is calculated to 1.29 s after the start of an inhalationstep in the subject, and has highest probability of deposition inthe tracheobronchial region for large particles, while ultra-fine par-ticles deposit in the deeper regions of the lungs [35]. The last phaseof intoxication is transfer to the blood and is dependent on that theparticles are transferred to the alveoli (being of spherical andnanometer character) and that these have a chemistry, whichmerges with cellular membranes. Absorption and uptake in theblood results therefore the particle’s membrane-permeating prop-erties, which is directly dependent on the content of lipophiliccomponents on the surface of the nanoparticles (e.g. PAHs, semi-combusted hydrocarbons and other hydrophobic compounds)[37–39]. At this stage, the intoxication has reached a cellular level,and several health risks result [40].

4.2. Mechanisms of nanoparticle formation

According to two theories on the formation of nanoparticles,one theory reports that the nanoparticles are formed throughliquid droplets that coalesce completely at small sizes but do nothave sufficient time for fusion as the particle size increases [41].The other theory states that the nearly spherical shape of thenanoparticles is the product of the simultaneously occurring coag-ulation and surface growth [42,43]. However the reports fromFrenklach and Mitchell [44] show that particles are formed viacollisions of smaller particles, which lead to the particle surfacegrowth. This pattern is also observed in earlier molecular simula-tions performed by the authors [19]. This pattern of formation ishowever highly dependent on the chemistry of the componentsformed. This chemical composition is however difficult to deter-mine accurately, and the properties of exhaust-emission nanopar-ticles are normally limited to element content (e.g. total carboncontent, sulphur, etc). However an extensive study by D’Anna[34] shows that pure-gas (ethylene) flame combustion generatesparticles of minimum size of 4–8 nm, which of the chemical com-position is not known. This demonstrated presence of nanoparti-cles 4–8 nm in pure ethylene combustion delineates that theprobability of finding particles of similar and larger diameters ishigh during the combustion of fossil or ‘‘green” fuels. This intro-duces that nanoparticles formed from bioethanol/gasoline com-bustion or any other fuel for that matter, are of sizes starting at10 nm in diameter and quickly growing and reaching 10 lm, andalso becoming parts of microparticles. Indeed, pure acetylenecombustion as reported by D’Anna represents a highly efficientcombustion process, which is superior in full combustion percent-age compared to car-engines, car-catalyzers and other car-relatedincinerators, such as diesel particulate filters (DPF).

The role of PAHs in this process is expected to contributeconsiderably to nanoparticle formation, as seen in the molecularsimulations, and these compounds can interestingly be found innanoparticles as small as 3 nm [34]. In parallel, PAHs play perhapsthe most critical role of giving the formed particles membrane-permeable properties, as they provide the aromatic characterwhich favours nucleation of nanoparticles via a coagulationmechanism and exclusion of oxygen species from the formingnanoparticles [34].

Even more interestingly, the nanoparticles from car engines donot derived from the fuel combustion process, but from thecondensation process between the catalyzer and the exhaust pipe[45,46]. This condensation and nanoparticle formation is preciselyfacilitated by PAHs, particularly the planar congeners (phenan-threne, benzene, naphthalene, anthracene) [34,47], which arederiving directly from the fossil fraction. Nanoparticle diametersin the engine have a distribution of diameters with a maximumsize of 3 nm during the early stages of combustion, which increasesto 3–7 nm during combustion and can reach a maximum size of20 nm during combustion, as characterized through AFM andTEM analysis [48].

4.3. Computational and theoretical studies

The simulation results presented above represent one part ofthe chemical interactions of combustion products of bioethanoland gasoline, however several other parts of the combustion pro-cess affect the properties and characteristics of exhaust emissionproducts. The exhaust pipe is one part of the combustion system,which represents a condensation process, of the volatile carbona-ceous particles and gases from the catalyzer. The role of theexhaust pipe on the formed particles is expect to be by contribut-ing to the growth, therefore affecting the simulated environment inthis study by enlarging the particles, and expectedly by mergingmoist from the atmospheric condensation to their chemical com-position. Nevertheless, simulating this vast multitude of reactionsis virtually impossible, however the extraction of principal featuresof the chemistry involved can give fruitful answers to emissionstudies. The approaches by other simulation studies [19,44,49–52] are similar to this extensive study. In this referred studiestheoretical and computational description of the molecular distri-bution during combustion has been derived, and the coupling ofthe physical and chemical routes of molecular changes haveallowed for the derivation of particle nucleation based on theknown chemistry of the selected molecular species. Chemistry ofcombustion relies principally on the formation of radicals and bythe dissolution of bonds, which follow a specific route of reaction.This route of reaction is defined that the weakest bonds (polarizedbonds such as OAH, and less polarized such as OAC) are dissolvedfirst and the bonds with lower bond energy (CAC, CAH) follow.Interestingly, during combustion, other molecules are formedthrough fusion reactions during the release of electric charges inthe chamber [36]. These reactions include the formation ofaromatic compounds such as phenyl, benzene, naphthalene andat last phenanthrene, the most abundant toxic compound fromfossil emissions, through the following possible reactions:

n-C4H3 þ C2H2 ! Phenyl ð1Þ

n-C4H5 þ C2H2 ! benzeneþH ð2Þ

C5H5 þ C5H5 ! Napthalene ð3Þ

Napthaleneþ C2H2 ! PhenanthreneþH ð4Þ(H indicate radicals)

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The formation product phenanthrene is rather stable and isfound abundantly in exhaust emissions [9].

Aromatics formed in the cylinders during fuel combustion canfurthermore grow to soot and particles through the process of H-abstraction and C2H2 addition (HACA), which is a repetitive reac-tion sequence, which leads to the formation of aromatics fromsmall hydrocarbons, such as ethene. The presence of ethene ingasoline fuel is expected to be negligible, however the presenceof small hydrocarbons such as n-heptane is known [53] andcan also participate in the growth of aromatics from gasoline fuelcombustion. Formed aromatics in fuel combustion can alsoinclude cyclopentane species and radicals [36]. The aromaticcompounds found in gasoline are also combusted duringelectrification in the cylinders of the engine, and the mechanismof their oxidation is affected by O2 content, and not by other oxy-gen species such as OH [36] and interestingly the oxygen contentin the engine cylinders promotes the formation of soot by build-ing up the radical pool and specifically single H atom radical[49,54].

These chemistries are altered during blending, and the gasolinefraction contributes in a blend with a higher start concentration ofPAHs (as these are commonly found in fossil fuels), which resultsin a larger nanoparticles with higher gasoline ratio, as PAHs pro-mote aggregation and nucleation [34,36]. The bioethanol fractionon the other hand, is virtually devoid of PAHs, and instead con-tributes with polar components, which result in the generation ofacetaldehyde and formaldehyde, frequently found in ethanol-blend fuel emissions [55,56]. This blending of fuels leads to onecentral preserved pattern of chemistry: the generation of PAHand acetaldehyde, which have not been mapped for interactionand nucleation properties empirically. This computational studyhowever, gives an introductory and computationally valid imageof the fate of acetaldehyde and phenanthrene, and how the atmo-spheric compounds contribute further to this.

5. Conclusions

An extensive computational study has been carried outfollowed by an in-depth review of the molecular and chemicalcharacteristics of exhaust emissions, nanoparticle formation andnucleation processes. The computational study shows interestingresults, which rely on the chemical properties of most planarPAHs, with particular emphasis on phenanthrene. These resultsrest in that phenanthrene nucleates rapidly to form nanoparti-cles in vacuo and absorbs acetaldehyde, which derive frombioethanol. The absorption of acetaldehyde by phenanthrene,which both are generated in bioethanol/gasoline blends formsnanoparticles of minimum sizes of 2 nm in diameter whichpreserve acetaldehyde and chemically shield it from the environ-ment. This effect is retained when the nanoparticles are exposedto atmospheric gases, however CO2 is equally efficientlyabsorbed, which leads to nanoparticles growth and changes insurface tension. The generic shape of the particles is not affectedby the absorbed molecules and at large governed by thearomatic forces of the PAH, in this case phenanthrene. Theresults show therefore that PAHs from the fossil fuel fractionin a blend can have conserving effects on the carcinogenicacetaldehyde molecules from ethanol, and prolong its half life,and furthermore carry it to the deepest parts of the lungs, beingof small and spherical dimensions. Computational studies arecritical to reveal effects, which are not easily found throughempirical experiments, such as the nanoparticles growth thatCO2 catalyzes when interacting with PAH-rich particles. Thiscritical finding can serve to explain the size of nanoparticles inbioethanol/gasoline fuel blends.

Funding

This work has been funded through the EEA Grants –Norway Grants Financial System, with the Grant No.Pol-Nor/199100/6/2013.

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