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In-depth interpretation of mid-infrared spectra of various synthetic fuels for the chemometric prediction of aviation fuel blend properties Sebastian S. Scheuermann, Stephan Forster, and Sebastian eibl Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b03178 • Publication Date (Web): 07 Feb 2017 Downloaded from http://pubs.acs.org on February 8, 2017

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Energy & Fuels

In-depth interpretation of mid-infrared spectra of various synthetic fuels for the chemometric prediction of aviation fuel blend properties

S. S. Scheuermann, S. Forster, S. Eibl Abstract The aim of this work was to predict composition and nine selected physico-chemical properties of fossil/synthetic aviation fuel blends by chemometric analysis of mid infrared spectra. Therefore IR spectra of various mixtures with six different synthetic hydrocarbon fuels were recorded and comprehensively interpreted supported by data from GCxGC-MS analysis of these fuels. Deep insight has been gained on how individual blend components are differentiated in principal component analysis (PCA) and how they influence physicochemical properties by means of partial least squares regression. A chemometric model has been established to determine the amount of an individual synthetic fuel in a blend with a precision of < 1 vol% and a detection limit of < 2 vol %. The quality of prediction of physico-chemical properties is good enough to compete with results obtained by established test methods. Keywords: Synthetic Fuels; MIR-Spectroscopy; Chemometrics; GCxGC-MS

Introduction The use of synthetic hydrocarbons as blend components for fossil aviation

turbine

fuels

is

an

actively

pursued

goal,

either

for

reasons of energy security, or for environmental reasons [1, 2]. Therefore,

most

aviation

turbine

fuel

specifications

allow

the

addition of up to 50 percent by volume of synthetic hydrocarbons to the fossil fuel. However, the respective blend still has to meet the physico-chemical requirements stipulated for neat fossil fuels plus some additional ones [3]. A variety of pathways for the production of synthetic fuels have been

shown

to

be

in

principal

feasible

[4,

1 ACS Paragon Plus Environment

5],

and

some

are

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currently approved to actually produce blend components for aviation turbine fuels. These are the Fischer-Tropsch process, hydrotreatment of esters and fatty acids, and the synthesis of iso-paraffins from hydroprocessed fermented sugars [3]. Production pathways for synthetic aviation fuels in general aim at producing classes of chemical compounds that can as well be found in fossil fuels. Therefore, admixture of such synthetic products to fossil fuels does not basically alter their chemical composition. This in turn allows the use of aviation fuels containing synthetic hydrocarbons in existent systems. From an analytical standpoint the chemical similarity of synthetic and fossil aviation fuels poses a challenge for determining the content of synthetic hydrocarbons in a random fuel sample. Chemometric analysis of fourier transform infrared (FTIR) spectra of fossil fuels – especially in the near infrared region – has proven to be a powerful tool to predict e.g. physico-chemical properties from conveniently obtainable spectroscopic data [6, 7]. Beyond that, it has been shown, that it is possible to predict, whether a fossil fuel contains synthetic components or not [8, 9]. However, it is not self-evident,

that

IR-spectroscopy

is

the

method

of

choice

to

differentiate between hydrocarbons of synthetic and fossil origin, because the latter chemically correspond very well to those present in fossil fuels [10]. Also, more sophisticated techniques such as GCxGC-TOFMS can be used to reliably predict properties of fossil fuels [11, 12]. Within

the

scope

of

this

work,

emphasis

was

laid

on

trying

to

explain why IR-spectroscopy is sensitive towards the presence of synthetic fuels. In contrast to near-infrared, mid-infrared spectra provide

rich

Therefore, fuels

structural

mid-infrared

have

been

information spectra

recorded

and

of

on

a

the

broad

interpreted

respective variety

to

shed

of

analyte. synthetic

light

on

the

spectroscopic differences between these synthetic fuel types. The detailed interpretation of IR-spectra was based on

compositional

details of the fuels, derived from GCxGC-MS analysis [13, 14]. Furthermore, fuels

(Jet

synthetic

IR-spectra A-1)

fuels

of

blended were

a with

recorded

variety varying and

of

fossil

amounts

analysed

aviation of

using

the

turbine

different

partial

least

squares regression and principal component analysis. The aim was to


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Energy & Fuels

predict (i) type and content of synthetic in fossil fuel and (ii) selected

physico-chemical

prediction

of

fuel

properties.

To

calibrate

physico-chemical

properties,

data

for

for

fuel

the

blends

described in a related study were used [15]. Materials The synthetic fuels used for these investigations can be divided into three groups. The first group includes two complex mixtures of n- and mainly iso-alkanes, namely a fuel produced by the coal-toliquids process (CTL; provided by SASOL, Sasolburg, South Africa) and a hydrotreated vegetable oil (HVO; provided by Honeywell UOP, Pasadena]. The second group comprises fuels which almost exclusively contain

one

or

two

trimethyldodecane

(Farnesane,

biotechnologically Emeryville,

specific

CA,

obtained

produced USA)

iso-alkanes.

through

farnesene;

and

a

These

mixture

are

hydrogenation

provided of

2,6,10-

by

basically

of

Amyris, 2,2,4,6,6-

pentamethylheptane and 2,2,4,4,6,8,8-heptamethylnonane (provided by GEVO,

Englewood,

CO,

USA).

The

alcohol-to-jet process through

latter

fuel

is

produced

dehydration of

C2

to C5

by

an

alcohols,

oligomerisation and hydrogenation and is called ATJ-SPK (alcohol-tojet synthetic paraffinic kerosene). Two synthetic fuels containing n-, iso- and cyclo-alkanes, as well as aromatic compounds belong to the

third

group.

In

detail

these

are

a

kerosene

produced

by

catalytic hydrothermolysis of triglycerides, other esters or fatty acids

(ReadiJet;

provided

by

Applied

Research

Associates

(ARA),

Panama City, Florida, USA)[16] and an alcohol-to-jet fuel where the synthesis

includes

in

addition

to

the

above

mentioned

steps

an

aromatization step (ATJ-SKA, alcohol-to-jet synthetic kerosene with aromatics; provided by Swedish Biofuels AB, Stockholm, Sweden). Synthetic

fuels

were

volumetrically

mixed

with

each

of

six

commercially available Jet A-1 fuels with aromatic contents of 13.7, 15.1, 16.2, 18.1 (two samples), and 21.6 vol% in proportions given in Tab. 1. Some of these binary mixtures were analyzed for their physico-chemical

properties

(grey

shade

in

Tab.

1;

for

clarity

individual Jet A-1 fuels are not differentiated). Ternary mixtures always containing 50 vol% Jet A-1 were mixed from the same Jet A-1 fuels containing 21.6 vol% aromatics for HVO/Farnesane, HVO/ATJ-SPK,


Energy & Fuels

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HVO/CTL

and

a

Jet

A-1

containing

Page 4 of 31

16.2

vol%

aromatics

for

Farnesane/CTL, Farnesane/ATJ-SPK, ATJ-SPK/CTL (see Tab. 2). In total 228 samples were investigated. Experimental GCxGC-MS analyses were performed on a Shimadzu GC-2010 Plus gas chromatograph (split-mode, and helium as carrier gas), equipped with a ZOEX ZX1 modulator and coupled to a Shimadzu GCMS-QP2010 Ultra mass spectrometer operated in electron ionisation mode. The column combination consisted of 30 m of a Restek Rxi-1ms (0.25 mm i.d.; 0.25 µm film) column coupled to 3 m of a SGE Analytical Science BPX 50 (0.15 mm i.d.; 0.15 µm film) column. GCxGC plots were created with Chromaleont S.r.l. ChromSquare 2.1.01 Software. Infrared spectra were recorded with a Bruker Tensor 27 spectrometer equipped

with

a

100 µm

flow-through

ZnSe-cuvette

at

a

spectral

-1

resolution of 4 cm . Principal Component Analyses (PCA) and Partial Least Square (PLS) regressions were performed with the quant2-module of

the

Bruker

OPUS®

7.0

application

software

and

CAMO

The

Unscrambler® 10.2 [17]. Spectra were pretreated by smoothing, using 5

data

points

and

calculating

the

first

derivative

of

selected

spectral regions (731 data points) given in Fig. 4. PLS are carried out on the basis of cross validation for all samples (centered data, leave-out samples: 1), typically predicting all parameters in one model. The number of latent variables for PLS was typically 8-10 for each parameter, as recommended by the software. Results and Discussion The main goal of the current investigation was to shed light on the IR-spectroscopic differences between fossil and synthetic fuels and thus explain, why chemometry is suited to detect synthetic fuels in mixtures with fossil ones. Therefore it was necessary to initially assess the fuels’ compositions using gas-chromatographic techniques and then use these data to help to interpret IR-spectra of the synthetic and fossil fuels in detail. Gas-chromatographic analyses


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Energy & Fuels

Farnesane and ATJ-SPK consist of just one, respectively two isoalkanes, and the chemical structures of these compounds are given in Tab. 3. CTL and HVO are complex mixtures of exclusively n- and isoalkanes in the range of n-C8 to n-C13 (CTL) and n-C8 to n-C17 (HVO) [15]. The composition of the remaining synthetic fuels ReadiJet and ATJ-SKA, however, is more complex since they contain a variety of substances from different classes of hydrocarbons. Therefore GCxGCMS is the method of choice to analyse these fuels. The respective chromatograms of ReadiJet and ATJ-SKA as well as that of a typical Jet A-1 are shown in Fig. 1 - 3. Apart from prominent peaks for n-alkanes (n-C8 to n-C16), the gaschromatogram of ReadiJet is characterized by peaks of a homologous series of alkyl-substituted cyclohexanes and –pentanes; for reasons of

clarity

only

chromatogram.

two

Aromatic

peak

couples

compounds

have

are

been

primarily

indexed

in

represented

the by

a

homologues series of n-alkylbenzenes with alkyl substituents ranging from 3 to 8 C-atoms. As for other aromatics, weak peaks for indaneand tetrahydronaphthalene-derivatives are present. Naphthalene and alkylnaphthalenes were identified as well. However, their peaks do not appear in the presented chromatograms, because the modulation time

has

been

optimized

for

high

resolution

of

compounds

with

polarities up to those of tetrahydronaphthalenes. The

composition

exhibits

of

significant

ATJ-SKA peaks

is

less

for

complex.

iso-alkanes

The

with

chromatogram

boiling

points

between those of n-C8H18 and n-C13H28. Yet, the most intense peaks group around the total time of retention of n-C11H24. Peaks for some di- and tri-substituted methyl- and ethylbenzenes were identified as well,

whereas

tetrahydronaphthalenes

n-alkanes,

cycloalkanes,

and naphthalenes are

widely

indanes, absent in the

mixture. The chromatogram of the fossil Jet A-1 fuel features peaks for a series of n-alkanes (primarily n-C8 to n-C14), as well as for isoalkanes. Regarding those compounds, ReadiJet and the fossil Jet A-1 are closely related. Cycloalkanes can be found as well, however, in comparison to ReadiJet, their amount appears to be low. Aromatic compounds are represented by di- and tri-substituted methyl- and ethylbenzenes, quite similar to those found in ATJ-SKA. Indanes,


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tetrahydronaphthalenes and naphthalenes found in fossil Jet A-1 are essentially similar to those found in ReadiJet. Infrared-spectroscopic analyses Fig.

4

shows

synthetic

IR

fuels.

spectra

of

Aromatic

the

investigated

components

in

Jet

neat A-1,

fossil

and

ATJ-SKA

und

ReadiJet are indicated by significant bands characteristic for the C-H-valence and benzene ring vibrations at 3020 cm-1, 1607 cm-1 and 1505 cm-1 [18-20]. Their intensity roughly corresponds to the overall content

of

aromatic

compounds

of

21.6

%,

15.8

%,

19.7

%,

respectively [15]. The used six Jet A-1 fuels (not depicted) show slightly varying intensities of bands characteristic for aromatic components contents.

which The

correspond

pattern

of

to

weak

the

respective

bands,

however,

variations does

not

in

differ

significantly among the different fuels and has been assigned in Tab.

4.

Monosubstituted

aromatics

such

as

alkyl

benzenes

are

-1

indicated predominantly by a band at 700 cm . An additional band at 740 cm-1, also characteristic for the monosubstituted benzene ring might be superimposed. In accordance to GC analysis n-alkyl benzenes are prominent in ReadiJet.

Jet A-1 and ATJ-SKA contain di- and

trisubstituted benzenes as indicated by bands in a range from 770 to 815

cm-1.

Especially

a

prominent

band

at

806

cm-1

indicates

a

comparably high content of di- and trisubstituted benzenes in Jet A1, followed by ATJ-SKA. Bands characteristic for aliphatic hydrocarbons can be assigned more easily as compared to the strongly superimposing bands of aromatics. ATJ-SPK

shows

characteristic

intensive for

t-butyl

bands

at

groups

in

1244

cm-1

and

1205

cm-1

2,2,4,6,6-pentamethylheptane

and 2,2,4,4,6,8,8-heptamethylnonane. Among the other fuels, only CTL contains

minor

amounts

of

hydrocarbons

carrying

t-butyl

groups. -1

Terminal i-propyl groups are indicated by a band at 1169 cm , which is

most

intensive

for

farnesane

(2,6,10-trimethyldodecan).

Significantly lower amounts of hydrocarbons with terminal i-propyl groups are detected in CTL, HVO, ATJ-SKA and Jet A-1. A band at 1154 cm-1 representing an isolated methyl substituent in a carbon chain neighbored by two CH2-groups is also most intensive for farnesane and weaker in spectra of CTL, HVO, ATJ-SKA and Jet A-1. Weak bands at


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Energy & Fuels

1169 cm-1 and 1154 cm-1 therefore are indicative for mixtures of isoalkanes in fossil fuels such as Jet A-1. A prominent band at 1124 cm-1 in the spectrum of ATJ-SKA and CTL is attributed to the C-Cvalence

vibrations

of

two

neighbored

groups

each

composed

of

a

single methyl substituent along the carbon chain. This band is not typical

for

iso-alkanes

present

in

fossil

fuels

and

not

for

Farnesane and ATJ-SPK since the latter contain hydrocarbons composed predominantly of t-butyl and i-propyl building blocks. Additional structural information with respect to the presence of tbutyl

and i-propyl groups is provided by bands for -1

vibration of CH3-groups between 971 cm ATJ-SPK

the rocking

-1

to 918 cm . Correspondingly,

(2,2,4,6,6-pentamethylheptan

and

2,2,4,4,6,8,8-

heptamethylnonan) with numerous CH3-groups in its molecules exhibits the

most

intensive

bands

at

971

cm-1

to

925

cm-1,

followed

by

farnesane (2,6,10-trimethyldodecan) with an intermediate intensity of these bands in the spectrum. In the spectra of the remaining fuels, the intensities of these bands decrease further, in the order CTL, HVO, ATJ-SKA, ReadiJet and Jet A-1. Bands at 770 cm-1 and 739-735 cm-1 characteristic for terminal ethyl and n-propyl groups, respectively, cannot be found for ATJ-SPK, as expected. However, they are observed for the remaining non-aromatic fuels (CTL, HVO, Farnesane); whereupon a band at 739 cm-1 may also be attributed to rocking vibrations of less than four subsequent CH2groups present in Farnesane. However, superimposing bands especially of aromatic components do not allow and unambiguous assignment in the spectral region from 730 to 820 cm-1. The band at 722 cm-1 is characteristic for at least four subsequent CH2-groups predominantly found in n-alkanes. According to the IR-spectra, n-alkanes are only observed in HVO, ReadiJet and Jet A-1. ReadiJet contains the highest amounts of n-alkanes and the lowest amounts of methyl branched isoalkanes among the investigated fuels, as nearly none of the above discussed

bands

between

1300

cm-1

und

900

cm-1

are

observed.

In

contrast to that, spectra of ATJ-SKA exhibit no significant band a 722

cm-1

since

the

fuel

contains

predominantly

iso-alkanes.

Cycloalkanes and alkylated cycloalkanes are best identified by a band at 890 cm-1, which is almost exclusively observed in spectra of ReadiJet.

This

finding

is

confirmed

by

GCxGC-MS

analysis

which

shows, that ReadiJet contains significant amounts of cycloalkanes.


Energy & Fuels

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In

summary,

IR-spectroscopy

Page 8 of 31

significantly

indicates

aromatic

and

alkane components and is explicitly able to differentiate alkane components

by

Complementarily, aromatic

means

of

GCxGC-MS

components.

their allows

Yet,

methyl a

substitution

detailed

IR-spectroscopy

pattern.

identification

is

sensitive

of

towards

substitution patterns of aromatic compounds and is able to nicely differentiate between mono- and di-/tri-substituted aromatics. Principial component analysis (PCA) Principal component analysis (PCA) allows exploring

complex data

sets in order to identify correlations between dependent variables. Here PCA is applied to check, whether fuels of fossil as well as of synthetic origin can be reliably identified and distinguished and to find out how variations in fuel composition are reflected in the corresponding IR-spectra. Basic information on PCA is given in [21]. PCA

was

performed

based

on

IR-spectra

of

all

investigated

fuel

mixtures. Only shaded regions of the spectra (Fig. 4) are used for chemometric analysis, for example, intensive CH2- and CH3-valence and deformation bands in a spectral range from 3040 cm-1 to 2770 cm-1 and 1500 cm-1 to 1340 cm-1 are excluded. Fig. 5 and 6 depict the variance for the five chosen principal components (PC) and the respective scores and loadings for the first two and three PCs, respecitvely. For

a

more

convenient

interpretation

of

the

loadings,

they

are

presented in the integrated form. Usually PCA works with the IRspectra´s first derivatives. The first PC already covers 76 % of the variance, indicating the high quality of the PCA. The score plot allows a clear distinction of the neat fuels. IR-bands characteristic for ATJ-SPK (1244 cm-1, 1205 cm-1, 971 cm-1, 925 cm-1) show pronounced positive loadings on PC1 (Fig. 6). Accordingly PC1 is suited best to differentiate ATJSPK in the scores plot (right side in Fig. 5). In contrast, bands characteristic for aromatic compounds (1607 cm-1, 1505 cm-1, 806 cm-1, 770 cm-1, 700 cm-1) especially in Jet A-1, ReadiJet and ATJ-SKA load negatively on PC1 and the respective loadings are therefore located on the left side in Fig. 5. Aromatic compounds dominate the loadings on PC2, which covers 16% of the variance. Therefore, loadings for fuels with increasing contents of aromatics are found close to the


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Energy & Fuels

bottom in the scores plot. Jet A-1 containing 21.6 % aromatics is followed by ReadiJet (19.7 %) and the other Jet A-1 fuels (18.1 % 13.7

%).

However,

PC2

does

not

allow

to

differentiate

between

monoalkylbenzenes and di-/trialkylbenzenes, because the respective bands (700 cm-1 / 740 cm-1 and 770 to 815 cm-1), all strongly load on PC2. Therefore PC2 does not allow to differentiate between ReadiJet (aromatics fraction: predominantly monoalkylbenzenes) and Jet A-1 as well

as

ATJ-SKA

(aromatics

fraction:

predominantly

di-

/trialkylbenzenes), which is however possible by considering PC4 and PC5 (not depicted in Fig. 6). Bands at 770 to 815 cm-1 strongly load on PC4, whereas bands at 700 cm-1 / 740 cm-1 strongly load on PC5. IR-spectra of Farnesane exhibit most intensive bands at 1169 cm-1 and 1154 cm-1. Spectra of CTL and HVO also feature these bands, but with less intensity. These bands positively load on PC2 and therefore the scores of these fuels are located closer to the top of Fig. 5. PC3 (not depicted in Fig. 5) is most sensitive towards long chain nalkanes as found in Jet A-1, ReadiJet and HVO (intensive band 722 cm-1, positive load). A band at 1124 cm-1 negatively loads on PC3. It is not typical for mineral oil based mixtures of n- and iso-alkanes. However, it

is indicative for

ATJ-SKA and

CTL and

allows their

separation from the other investigated fuels. It

can

provide

be

concluded,

significant

that

IR-spectra

information,

of

which

the

can

investigated

be

used

to

fuels

reliably

discriminate the various synthetic and fossil fuel types. Specific compounds in the fuels and their corresponding IR-spectroscopical features

are

prospects

to

identified

by

discriminate

PCA. the

Advantageously,

fuels

just

by

PCA

the

exceeds

evaluation

the of

selected IR-bands characteristic for certain functional groups or structural peculiarities, as the overall spectral information

is

exploited. Loadings

for

fossil/synthetic

fuel

mixtures

are

located

between

those of the respective neat fuels in the score plot. The loadings of the binary Jet A-1/ATJ-SPK mixtures therefore form a straight line connecting the loadings of neat Jet A-1 and neat ATJ-SPK. Loadings of Jet A-1/ATJ-SPK mixtures prepared from Jet A-1 with lower contents of aromatics and 50 to 100% ATJ-SPK appear above this straight line. Both lines merge at the loading of neat ATJ-SPK. Even loadings of

ternary mixtures all

containing 50% Jet


A-1 can be

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Page 10 of 31

located, where they are expected, according to the observed loadings on PC1 and PC2. However, the density of data points prohibits a detailed attribution in Fig. 5. By

including

determine

all

the

PCs,

content

PCA

of

IR

of

each

spectra fuel

promises

component

by

a

pathway

to

multivariate

statistics. Chemometric quantitative analysis of fuel composition In general chemometric analysis uses chemical data (e.g. IR spectra) which

depend

contents

of

properties).

on

several

different Using

independent

fuels

in

a

variables mixture

multivariate statistics,

(here:

or

types

and

physico-chemical

a correlation can

be

drawn between the experimentally determined properties of a fuel mixture and its composition [22, 23]. The resulting training set forms the basis to predict the respective properties of an unknown fuel sample from its FTIR-spectrum. In the underlying investigation the chemometric method utilizes partial

least

squares regression

(PLS) [24]. A PLS model was established to quantify the amounts of different types of synthetic fuels in a mixture. Fig. 7A exemplarily shows the correlation of a mixture´s true ATJ-SPK content with the predicted. With a Root Mean Square Error of Cross validation (RMSECV) of 0.15 vol%, the average deviation of predicted fuel content from the true value is remarkably low, and the correlation coefficient (R²) is with 0.9999 very close to 1. It therefore can already be concluded from PCA alone, that determination of the amount of ATJ-SPK in a fuel mixture is highly reliable. For the other fuel components similar RMSECV are obtained (Tab. 5), all below 1 %. It has to be mentioned that RMSECV of 0.07 vol% for the aromatics content in all investigated fuel blends is lower, as the true aromatics content only ranges from 0 to 21.6 vol% and not from 0 to 100 vol% (Fig 7B). The detection limit for a fuel component in a mixture can be derived from the inserts in Fig. 7. The calculated contents for ATJ-SPK free mixtures vary in the range from -0.3 to 0.4 vol%, for CTL free mixtures within -0.8 to 1.2 vol% and for Jet A-1 free mixtures within -1.0 to 1.5 vol%, analogue to the increasing RMSECV. Results


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Energy & Fuels

are expected to become reliable for contents higher than 2 vol% independent of the fact whether a low amount of synthetic fuel is detected in a fossil fuel or vice versa. In

summary,

the

precision

of

the

predicted

composition

is

well

suited for practical application. Chemometric determination of physico-chemical properties Physico-chemical

properties

of

the

synthetic

fuels

and

their

mixtures with fossil fuels are reported in [15]. For the current work viscosity, freezing point and flash point, initial and final distillation temperatures as well as distillation temperatures for fractions of 10, 50 and 90 vol% were taken into account. Tab. 6 summarizes these properties for the neat fuels. Fig. 8 exemplarily shows the results of a chemometric analysis (PLS) by

means

of predicted vs. true values for density

and freezing

point. Tab. 7 summarizes the results with respect to the statistics of

the

single

PLS

for

all

physico-chemical

properties.

Best

correlation is observed for density with a correlation factor R2 = 0.9997, whereas significant point (R

2

observed

for freezing

= 0.8936). Therefore, the relative deviation is lowest for

predicted (7.4%).

deviations are

density

(0.03%),

and

models

are

Chemometric

highest

for

established

the

freezing

typically

point

using

8-10

latent variables. With lower numbers of latent variables, predicted values

are

predicting

less

precise,

properties

but

of

the

fuel

robustness

mixtures

of

not

the

model

included

in

for the

calibration set may be increased. How

the

composition

properties,

is

of

the

fuel

characterized

by

influences the

its

physico-chemical

regression

coefficients.

Weighted regression coefficients (650 – 1300 cm-1) for the prediction of these properties are shown in Fig. 9. In principal, the property of a sample is calculated by summation of the products of regression coefficient and associated absorbance value for each wave number. Therefore examination of curves of regression coefficients allows estimating

the

physico-chemical

influence property.

of

a

For

spectral example

region the

on

the

predicted

predicted

content

of

aromatics is increased by positive regression coefficients at 700 cm-1, 740 cm-1, 806 cm-1, 1505 cm-1 and 1607 cm-1 characteristic for


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

aromatic

compounds.

It

is

Page 12 of 31

decreased

by

negative

regression

coefficients at 722 cm-1, 1124 cm-1, 1154 cm-1 and 1169 cm-1 which indicate the presence of aliphatic compounds. The progression of the regression coefficients representing density is very similar to the one characteristic for the aromatics. Therefore it can be concluded that the content of aromatics strongly influences density of a fuel sample. As diaromatic compounds typically show higher densities than monoaromatics,

regression

coefficients

for

wavenumbers

-1

characteristic for diaromatics (769-815 cm ; especially 806 cm-1) are positive

and

higher

than

those

for

monoaromatics

at

cm-1.

700

ReadiJet which contains a fairly high amount of aromatic compounds therefore

exhibits

the

highest

density

among

the

investigated

synthetic fuels (see Tab. 6). For aliphatic components, cycloalkanes exhibit higher densities compared to n- and iso-alkanes. Therefore slightly

positive

regression

coefficients

are

observed

for

the

-1

wavenumber characteristic for cycloalkanes (890 cm ). In general, wavenumbers characteristic for iso-alkanes (1169 cm-1, 1154 cm-1, 1124 cm-1) are associated with negative regression coefficients. If an iso-alkane, however, carries a t-butyl group, negative influence on density is less pronounced, indicated by a less negative regression coefficient for 1244 cm-1. The lowest regression coefficient is obtained for n-alkanes (722 cm1

). Therefore, PLS-based description of how the different types of

hydrocarbons

influence

density

of

fuel

mixtures

nicely

reflects

reality. The curve of the regression coefficients for initial boiling point shows

a

strongly

negative

value

at

722

cm-1,

meaning

that

predominantly aliphatic compounds decrease the initial distillation temperatures.

Even

though

infrared

spectroscopy

is

not

able

to

differentiate short- and long-chain alkanes in complex mixtures, it is assumed that low boiling, short hydrocarbons are most represented at

722

cm-1.

coefficients

As

distillation

is

observed.

coefficient at 722 cm cm-1

(dominant

-1

Whereas

a

change

the

in

negative

regression regression

loses its influence, those at 770 cm-1 and 806

IR-bands

for

distillation

temperatures.

monoaromatic

compounds,

regression

progresses

coefficients

diaromatics) The

presence

predominantly at

700

contribute

cm-1,

of

indicated is

lower by

responsible


to

high

boiling negative for

lower

Page 13 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

distillation

temperatures.

Within

the

range

of

regression

coefficients characteristic for iso-alkanes, especially at 1169 cm-1 and 1154 cm-1 positive values are observed up to a distillation fraction

of

50

wavenumbers,

%.

the

Because

presence

of of

its

intensive

IR

bands

2,6,10-trimethyldodecane

at

these

(farnesane)

leads to increased predicted distillation temperatures which is in agreement

with

Therefore,

the

high

chemometric

boiling

point

prediction

of

of

the

farnesane

distillation

(249°C). range

is

strongly influenced by farnesane content. Similar observations can be made for ATJ-SPK which consists mainly of two iso-alkanes with comparatively

high

boiling

points

(2,2,4,6,6-pentamethylheptane:

178 °C and 2,2,4,4,6,8,8-heptamethylnonane: 240 °C). Therefore the presence of ATJ-SPK leads to high regression coefficients at 971 cm-1. However, this is not consistent with the strong IR bands of ATJ-SPK, which do not result in positive regression coefficients at 1244 and 1205 cm-1 (terminal t-butyl groups). Approaching

the

final

distillation

temperature,

again

aliphatic

compounds exert a strong influence. However, they are responsible for the prediction of high distillation temperatures due to positive regression coefficients at 722 cm-1. At this stage of distillation, high boiling aliphatic compounds are most represented at 722 cm-1. HVO with dominant IR bands at 722 cm-1 shows the highest distillation temperatures

for

the

90

%

fraction

and

the

highest

final

distillation temperature within the investigated samples (see Tab. 6), because

it contains

high boiling n-alkanes up to 17

carbon

atoms. The influence of farnesane content on regression coefficients at 1169 cm-1 and 1154 cm-1 is strong for distillation temperatures up to the 50 % fractions but less pronounced for higher fractions. For 90

%

distillation

fractions

and

higher,

2,6,10-trimethyldodecane

loses influence since its boiling point lies within the range of typical distillation temperatures for these high boiling fractions. The same trend is observed for regression coefficients at 971 cm-1 attributed

to

the

pentamethylheptane

influence and

of

ATJ-SPK

containing

2,2,4,6,6-

2,2,4,4,6,8,8-heptamethylnonane.

Especially

at this stage of distillation the primary constituent of ATJ-SPK (2,2,4,6,6-pentamethylheptane) has already vapored from the mixture (see above).


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 31

The regression coefficients for flash point are very similar to that for

initial

stages

hydrocarbons

of

distillation

temperature,

as

the

volatile

are responsible for ignition temperature. The flash

point is low in the presence of low boiling aliphatic compounds (722 cm-1) and high in the presence of higher boiling aromatic compounds (740 cm-1, 770 cm-1, 806 cm-1), as to be expected. On the contrary, the curve of the regression coefficients for freezing point is very similar

to

that

of

the

final

distillation

temperature,

as

the

presence of high boiling and melting hydrocarbons is decisive. The regression coefficients for viscosity seem to be very similar to that for distillation temperatures for the 50 % fraction. A detailed attribution of how blend components affect viscosity is difficult. For example, no significant influence of n-alkanes (722 cm-1) is observed and high viscosity values are calculated for di-aromatics (806 cm-1) but low values for mono-aromatics (700 cm-1). However, the temperature value for the 50 % distillation fraction seems to be a good

indicator

for

the

overall

blend

composition

and

therefore

characterizes viscosity well. In summary, the influence of the fuel´s composition on its physicochemical property is well described by the PLS of IR spectra. The predicted values for the investigated properties are mostly precise enough

to

compete

with

traditionally

determined

values

by

the

established test methods (see Tab. 7). A detailed attribution of bands in the IR spectra can be used to identify how certain classes of

substances

properties. bands,

differently

However,

correlation

for

affect

compounds

factors

are

various

in

low

close

to

physico-chemical

amounts zero

with

which

weak

IR

makes

it

difficult to link structural features of fuel compounds with their impact on physico-chemical property. This is especially true for freezing point, for which minority species may be responsible, and therefore

the

large

RMSECV

is

explained

(Fig.

7B).

Moreover,

according to [15] non-linear curve progression of freezing point vs. blend ratio was observed in some cases. Conclusion Six synthetic hydrocarbon fuels from various production pathways have been investigated by mid infrared (MIR) spectroscopy and GCxGC-


Page 15 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

MS.

Chromatographic

interpret

and

MIR-spectra

distinct

structural

individual

mass-spectrometric

in

order

to

peculiarities

synthetic

fuels.

This

data

assign of

spectral

the

approach

were

used

to

features

to

constituents was

chosen

of

to

the

utilize

insights gained from highly sophisticated analytical techniques to improve

performance

of

the

fast

and

routinely

applicable

IR-

spectroscopic method. Based on this, deep insight has been gained on how the different kinds of synthetic fuels can be distinguished by principal component analysis. Beyond that, chemometric analysis of MIR-spectra of various blends of synthetic and fossil fuels proved to be well suited to predict content of synthetic fuel as well as selected physico-chemical fuel properties.

It

is

possible

synthetic fuel in

to

determine

content

and

kind

a blend with a precision of < 1 vol%

of

and a

detection limit of < 2 vol%. As for prediction of physico-chemical properties it was explained how

they

depend

composition

of

on

the

individual fuel

has

blend

been

components:

correlated

with

The

chemical

results

from

partial least squares regression. On this basis it was possible to explain

the

reasons

why

some

parameters

can

be

determined

with

higher precision than others: For example, freezing point, where minority

species

may

play

a

vital

role

but

cannot

be

detected

spectroscopically. In

contrast

to

commonly

used

near

infrared

spectroscopy

in

conjunction with chemometric analysis, the distinct bands in MIR spectra allow to comprehensibly proof, whether predicted results by chemometrics are reliable. However,

with

increasing

availability

of

synthetic

fuels

on

the

market, mixtures of several (new) kinds of synthetic fuels with fossil ones will occur. This means, that it will be necessary to expand the current models from binary and ternary mixtures to cover multi-component mixtures of synthetic fuels. Even though correlation factors might change or parameters of the multivariate data analysis may have to be adapted for new synthetic fuel types, the underlying basic principles of how composition influences property prediction remain valid. With respect to robust field application, a next step would be to transfer this approach to raman-spectroscopy which is promising to


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

perform the analysis directly in the tank of a vehicle and to omit any kind of sample preparation.


Page 16 of 31

Page 17 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

References [1] Wilson, G. R.; Edwards, T.; Corporan, E.; Freerks, R. L. Energy Fuels 2013, 27, 962-966. [2]

Probstein,

R.

F.;

Hicks,

R.

E.

Synthetic

fuels,

Dover

Publication Inc, Mineola N.Y., 2006. [3] ASTM D7566-14c; Standard Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons [4] Wang, W.-C.; Tao, L. Renewable and Sustainable Energy Reviews 2016, 53, 801-822. [5] Damartzis, T.; Zabaniotou, A. Renewable and Sustainable Energy Reviews 2011, 15, 366-378. [6] Fodor, G.E.; Kohl, K.B.; Mason, R.L. Anal. Chem. 1996, 68 (1),

23–30. [7] Fodor, G. E.; Kohl, K. B. Energy Fuels 1993, 7, 598-601. [8] Morris, R. E.; Hammond M. H.; Cramer, J. A.; Johnson, K. J.; Giordano, B. C.; Kramer, K. E.; Rose-Pehrsson, S. L. Energy Fuels, 2009, 23, 1610–1618. [9] Cramer J.A.; Morris, R. E.; Giordano, B.; Rose-Pehrsson S. L. Energy Fuels, 2009, 23 (2), 894–902. [10] Morris, R. E.; Hammond, M. H.; Shaffer, R. E.; Gardner W. P.; Rose-Pehrsson, S. L. Energy Fuels, 2004, 18 (2), 485–489. [11] Kehimkar, B.; Hoggard, J. C.; Marney, L. C.; Billingsley M. C.; Fraga, C. G.; Bruno, T. J.; Synovec, R. E. J. Chromatogr A, 2014, 1327, 132-140.

[12] Kehimkar, B.; Parsons, B. A.; Hoggard, J. C.; Billingsley M. C.; Bruno, T. J.; Synovec, R. E. Anal. Bioanal. Chem., 2015, 407, 321-320. [13] Fraga C. G.; Prazen, B. J.; Synovec, R. E. Anal. Chem., 2000, 72 (17), 4154–4162. [14] Van der Westhuizen, R.; Ajam, M.; De Coning, P.; Beens, J.; de Villiers, A.; Sandra, P. J. Chrom. A, 2011, 1218 (28), 4478–4486. [15] Zschocke, A.; Scheuermann, S.; Ortner, J. High Biofuel Blends in Aviation; www.hbba.eu; ENER/C2/2012/420-1. [16] Lixiong, L.; Coppola, E.; Rine, J.; Miller, J. L.; Walker, D. Energy Fuels, 2010, 24 (2), 1305–1315. [17]

Camo

Software

AS,

The

Unscrambler

Gaustadalléen 21, Oslo, Norway, 2016.


®

10.2

Handbook,

Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 31

[18] Hummel, O.; Scholl, F. Atlas of Polymer and Plastics analysis, Vol. 2; Carl Hanser Verlag Munich, 1988. [19] Lin-Vien, D,; Colthup, N.B.; Fateley, W.G.; Grasselli, J.G. The Handbook of Infrared and Raman characteristic Frequencies of Organic Molecules,

Academic

Press

San

Diego,

New

York,

Boston,

London,

Sydney Tokyo Toronto, 1991. [20]

Socrates

G.

Infrared

and

Raman

characteristic

group

frequencies, 2nd Ed.; John Wiley & Sons Chichester, 1994. [21] Kessler, W., Multivariate Datenanalyse für die Pharma-, Biound Prozessanalytik; Wiley-VCH Weinheim, 2007. [22] Mark, H.; Workman, J. Chemometrics in spectroscopy, Academic Press-Elsevier, 2007. [23]

Varmuza

statistical

K.;

analysis

Filzmoser, in

P.

Introduction

Chemometrics;

CRC

Press,

to

multivariate

Boca

Raton

2009. [24] Geladi, P.; Kowalski B.R. Anal. Chim. Acta, 1986, 185, 1-17.


FA,

8 7 6 5

retention time [s]

4 3 2 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

9

Page 19 of 31

5

10

15

20

25

30

35

40

retention time [min] Figure 1: GCxGC plot of ReadiJet


45

50

55

8 7 6 5

retention time [s]

4 3 2 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 31

9

Energy & Fuels

5

10

15

20

25

30

35

40

retention time [min] Figure 2: GCxGC plot of ATJ-SKA


45

50

55

8 7 6 5

retention time [s]

4 3 2 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

9

Page 21 of 31

5

10

15

20

25

30

35

40

retention time [min] Figure 3: GCxGC plot of Jet A-1


45

50

55

806 770 740 722 700

971 925 890

1505

1607

∫∫

A B

absorbance [a.u.]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 31

1244 1205 1169 1154 1124

Energy & Fuels

C D E F G ∫∫ 3000

2500

1500

-1

1000

wavenumbers [cm ] Figure 4:

IR spectra of the neat fuels: A: Jet A-1 B: ATJ-SKA C: ReadiJet D: Farnesane E: CTL F: HVO G: AJT-SPK (Spectral regions used for chemometric analyses are shaded. Spectra are vertically shifted.)


Page 23 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

0.3

PC2

Farnesane

0.2 HVO

CTL

0.1 ATJ-SPK 0 -0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5 80%

Jet A-1 (13.7%)

0.7

PC1

70%

ATJ-SKA (15.6%) -0.1

60%

Jet A-1 (16.2%)

50% 40%

Jet A-1 (18.1%)

Readi Jet

0.6

30%

-0.2 20%

(19.7%)

Jet A-1 (21.6%)

-0.3

Figure 5: Score plot (PC1 vs PC2) and variance of a principal component analysis based on IR-spectra of all investigated samples. Neat fuels are marked with large squares. For neat fuels containing aromatics the aromatics content is given. ATJ-SPK content is indicated in Jet A-1/ATJ-SPK mixtures.


Energy & Fuels

PC3

1.2 806

700

1.6

971

1169 1154 1124

2

PC2 770

1205

1244

PC1

0.8 0.4

925

1505

1607

722

∫∫

0 -0.4 -0.8 1700

1500

∫∫

-1.2 1300

1200

1100

1000

900

800

700

600

wave numbers [cm-1] Figure 6:

Loadings of a principal component analysis based on IRspectra of all investigated samples for PC1 to PC3. (Loadings were integrated for a better comparison with the IR spectra)

100

A

calc. aromatics content [vol%]

calc. ATJ-SPK content [vol%]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 31

80 60 40 RMSECV= 0.15 vol%

20

R²= 0.9999 0 0

20

40

60

80

100

true AtJ-SPK content [vol%]

Figure 7:

B

20

15

10 RMSECV= 0.07 vol%

5

R²= 0.9998 0 0

5

10

15

20

true aromatics content [vol%]

Exemplary results for the chemometric quantification of ATJ-SPK (A) and aromatics content (B) in the investigated fuel mixtures. Insets show very low contents. Root mean square error of cross validation (RMSECV) and correlation coefficient (R2) are given.


Page 25 of 31

825

-40

calc. freezing point [°C]

R²= 0.9997

800

775

RMSECV= 0.23 kg/m3

750 750

B

-45

A calc. density [kg/m3]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

775

800

true density [kg/m3]

-50

R²= 0.9836

-55 -60 -65 -70 -75

RMSECV= 3.0 vol%

-80 825

-80

-70

-60

-50

-40

true freezing point [°C]

Figure 8: Exemplary results for the chemometric determination of the density (A) and the freezing point (B) of the investigated fuel mixtures. Root mean square error of cross validation (RMSECV) and correlation coefficient (R2) are given.


weighted regression coefficients [-]

770 760 740 722 700

806

Page 26 of 31

890

971

1124

1169 1154

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1244

Energy & Fuels

0

viscosity

0

freez. pt.

0

flash pt.

0

dist.final

0

dist.90%

0

dist.50%

0

dist.10%

0

dist.ini.

0

aromatics

0

density

1300

1200

1100

1000

900

wavenumbers

800

700

600

[cm-1]

Figure 9: Weighted regression coefficients for the prediction of various physico-chemical properties based on IR-spectra of all investigated samples (PLS was calculated not using the first derivatives of the spectra for a better comparison with IR spectra


Page 27 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Tables Table 1: Contents of fuel components [vol%] of the investigated binary mixtures, each containing Jet A-1 (complete data set for physico-chemical properties is only determined for grey shaded mixtures) Jet A-1

CTL

ATJ-SPK

HVO

Farnesane

ReadiJet

AtJ-SKA

0 0.05 0.1 0.5 1 5 10 20 30 40 50 60 70 (65) 80 (75) 90 95 100

100 99.95 99.9 99.5 99 95 90 80 70 60 50 40 30 20 10 5 0

100 99.95 99.9 99.5 99 95 90 80 70 60 50 40 30 20 10 5 0

100 99.95 99.9 99.5 99 95 90 80 70 60 50 40 30 20 10 5 0

100 99.95 99.9 99.5 99 95 90 80 70 60 50 35 20 10 0

100 90 70 50 25 0

100 99 95 90 80 70 60 50 40 30 20 10 0

Table 2: Contents of fuel components [vol%] for the investigated ternary mixtures; Combinations of synthetic fuels 1 and 2: HVO/Farnesane; HVO/CTL; HVO/ATJ-SPK; Farnesane/CTL; Farnesane/ATJSPK; CTL/ATJ-SPK

Jet A-1

Synthetic Fuel 1

Synthetic Fuel 2

50 50 50 50 50 50 50

1 5 10 25 40 45 49

49 45 40 25 10 5 1


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 31

Table 3: Contents of components identified by IR spectroscopy and GCxGC-MS

ATJ-SPK Farnesane a)

b)

GCxGC-MS

IR

HVO

CTL

alkanes (C8-C17)

alkanes (C8-C13)

Readi Jet

ATJ-SKA

isocomplex mixture: alkanes + cf. Fig. 1 mono aromatics: cf. Fig. 2

Jet A-1 complex mixture: cf. Fig. 3

n-alkane

-

-

h

l

h

-

m

iso-alkane

h

h

m

h

l

m

l

-

-

-

-

m

-

l

-

-

-

-

h

m

m

-

-

-

-

l

l

h

cyclo alkane mono aromatics diaromatics

a) ATJ-SPK contains: 2,2,4,6,6-pentamethyl-heptane (~80%), 2,2,4,4,6,8,8-heptmethylnonane (~10%) and C12-iso-alkanes (~10%) b) 2,6,10-trimethyl-dodecane

h: high; m: medium; l: low


Page 29 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 4: Assignment of IR-bands (bands of aromatic components are grey shaded)

Group frequency [cm-1]

Functional group / assignment

ATJSPK

1339

τ (CH2), δas (CH)

-

w

w

vw

w

w

w

1244

ν (C-C)* in

vs

-

-

w

vw

-

-

1205

ν (C-C)* in

s

-

-

vw

-

-

-

1169

ν (C-C)* in

vw

m

w

w

m

-

vw

1154

ν (C-C)* in

vw

m

w

w

m

-

vw

1124

ν (C-C)* in

-

-

-

m

m

-

-

971

ϱ (CH3)

s

w

-

-

-

-

vw

967

ϱ (CH3)

-

m

w

w

w

w

-

935

ϱ (CH3)

-

m

-

-

-

-

-

925

ϱ (CH3)

m

-

-

-

-

-

-

918

ϱ (CH3)

-

m

vw

w

w

-

-

890

ν (C-C) in cycloalkanes

-

-

-

-

-

w

vw

770 769-815

δ (CH2)-CH3

-

w

vw

vw

m

w

m

740

ƴ (CH) in di-, tri-aromatics

FarneHVO sane

CTL

ATJ- Readi SKA Jet

Jet A-1

-

-

-

-

m

s

m

vw

m

sh

vw

-

-

-

722

ƴ (CH) in mono-aromatics ϱ R-(CH2)n

In-Depth Interpretation of Mid-Infrared Spectra of ... - ACS Publications

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