International audienceThe interest in aqueous-based resins for various applications such as coating or in the pharmaceutical field has recently increased due to the need for constant cost reductions and the necessity to control emissions of volatile organic compounds. As most polymers used in these applications are water insoluble, aqueous- based resins generally contain particles in suspension. The average particle size and size distribution are essential data on which the stability and properties of the dispersions depend [1]. Light scattering and cryo-transmission electron microscopy (cryo-TEM) have proved to be complementary techniques to characterise various natural [2] or synthetic [3] polymer nanoparticles dispersed in water. Light scattering is non-destructive and provides fast measurements in a wide range of particle size. However, it cannot analyse double populations, large distributions, absorbent materials, or mixtures of substances with different densities [4]. In this study, cryo-TEM and image analysis were combined to characterise a variety of aqueous polyurethane dispersions. The results were compared with those obtained from dynamic light scattering (DLS) experiments

Durrieu, Vanessa

Gandini, Alessandro

Passas, Raphaël

Putaux, Jean-Luc

English

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Cryo-TEM and Image Analysis of Polymer Nanoparticle
Dispersions
Vanessa Durrieu, Jean-Luc Putaux, Raphaël Passas, Alessandro Gandini
To cite this version:
Vanessa Durrieu, Jean-Luc Putaux, Raphaël Passas, Alessandro Gandini. Cryo-TEM and Image
Analysis of Polymer Nanoparticle Dispersions. Cryoelectron Microscopy, 2004, 18 (1), pp.19-21. ￿hal-
02109636￿
  
 
 
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To cite this version:  
Durrieu, Vanessa  and Putaux, Jean-Luc and Passas, Raphaël and Gandini, 
Alessandro Cryo-TEM and Image Analysis of Polymer Nanoparticle Dispersions. 
(2004) Cryoelectron Microscopy, 18 (1). 19-21. ISSN 0958-1952  
Official URL: 
 https://microscopy-analysis.com/magazine/issues/cryo-tem-and-image-analysis-polymer-
nanoparticle-dispersions 
 
Vanessa Durrieu1, Jean-Luc Putaux 2, Raphaël Passas1 and Alessandro Gandini 1
1. E.F.P.G., Grenoble, France   2. Centre de Recherches sur les Macromolécules Végétales, CNRS, Grenoble, France
I N T R O D U C T I O N
The interest in aqueous-based resins for vari-
ous applications such as coating or in the phar-
maceutical field has recently increased due to
the need for constant cost reductions and the
necessity to control emissions of volatile
organic compounds. As most polymers used in
these applications are water insoluble, aque-
ous-based resins generally contain particles in
suspension. The average particle size and size
distribution are essential data on which the
stability and properties of the dispersions
depend [1]. Light scattering and cryo-trans-
mission electron microscopy (cryo-TEM) have
proved to be complementary techniques to
characterise various natural [2] or synthetic [3]
polymer nanoparticles dispersed in water.
Light scattering is non-destructive and pro-
vides fast measurements in a wide range of
particle size. However, it cannot analyse dou-
ble populations, large distributions, absorbent
materials, or mixtures of substances with dif-
ferent densities [4]. In this study, cryo-TEM and
image analysis were combined to characterise
a variety of aqueous polyurethane dispersions.
The results were compared with those
obtained from dynamic light scattering (DLS)
experiments. 
M AT E R I A L S  A N D  M E T H O D S
Aqueous polymer dispersions
Polyurethane-urea dispersions were prepared
according to the method described by Lahti-
nen et al. [5]. A polyurethane pre-polymer
with reactive isocyanate chain ends was syn-
thesised and dispersed in water. Chain exten-
sion was carried out and nanoparticles
formed. Several dispersions were studied,
varying the nature and proportion of the raw
materials (di-isocyanates, polyols and emulsi-
fying agents).
Light scattering
DLS was carried out using a Malvern Autosizer
2c photon correlation spectrometer (Malvern
Instruments, Orsay, France) equipped with a
HeNe laser (10 mW, 632.8 nm) and operated at
B I O G R A P H Y
Dr Vanessa Durrieu
received a PhD in materi-
als science from the
National Polytechnique
Institute of Grenoble,
France, in October 2002.
She worked on the
development and char-
acterisation of aqueous
polyurethane dispersions. She also has a
master's degree in materials science and
graduated as an engineer from the French
Engineering School of Printing and Paper-
making in Grenoble. 
A B S T R A C T
Light scattering is routinely used to measure
the average particle size in aqueous polymer
dispersions. However, difficulties arise for
polymodal populations, nanometric particles,
absorbent materials or mixtures of materials
with different densities. A complementary
approach consists in combining cryo-TEM and
image analysis. Using these techniques, we
studied various polyurethane aqueous dis-
persions. The nanoparticles were charac-
terised in terms of morphology and size.
Average diameters, calculated using a semi-
automatic image analysis procedure, were
compared to those obtained from dynamic
light scattering.
K E Y W O R D S
polymer nanoparticles, cryo-TEM, image
analysis, size distribution
A C K N O W L E D G E M E N T S
The authors thank SICPA S.A. (Annemasse,
France) for their financial and technical sup-
port. 
A U T H O R  D E TA I L S
Dr Vanessa Durrieu, Ecole Française de
Papeterie et des Industries Graphiques,
Domaine Universitaire, BP 65, F-38402 St
Martin d'Hères Cedex, France.
Tel : +33 476 82 69 58
Fax : +33 476 82 69 33
Email: raphael.passas@efpg.inpg.fr
putaux@cermav.cnrs.fr
Cryo-TEM and Image Analysis of Polymer
Nanoparticle Dispersions 
an angle of 90˚. The Z-average mean diameter   
DZ(DLS) was calculated using the cumulant
analysis (standard method ISO 13321). Disper-
sions exhibiting a polydispersity index (as
defined by the spectrometer) higher than 0.25
were discarded from our comparative study.
Cryoelectron microscopy
According to the method described elsewhere
[6,7], thin liquid films of 1 mg/ml nanoparticle
dispersions were prepared onto 'lacey' carbon
films (Pelco, USA) and quench-frozen into liq-
uefied ethane (Fig 1). Once transferred into a
Gatan 626 cryoholder, the specimens were
observed at -180˚C using a Philips CM200  cry-
omicroscope operated at 80 kV. Micrographs
were recorded on Kodak SO163 films at mag-
nifications of 11,500 and 20,000, with a defo-
cus of 1-2 µm. 
Image analysis and size distribution 
The negatives were digitised using a Kodak
Megaplus CCD camera. The magnification was
chosen in order to include a large number of
particles in the field of view, while maintain-
ing a sufficient resolution. A sampling rate of
2 nm/pixel was considered to be a good com-
promise. Contrast enhancement and semi-
automatic particle measurements were per-
formed using routines from the Optimas 6.51
software (Media Cybernetics, Inc.) [8]. The
number-average diameter DN , the weight-
average diameter DW , as well as a polydisper-
sity index PD were defined using the following
formulas: 
where Ni is the number of particles at the i th
class in the size-distribution histogram and Wi
= 
∑Di Ni
∑Ni
i
i
Dw
PD
Dw
Dn
=               = = 
∑Wi Di
∑Wi
i
i
∑Ni Di4
∑Ni Di3
i
i
∑Di Ni D 3
∑Ni D 3
i
i
=
Figure 1:  
(a) Production of thin vitrified
films for cryo-TEM: 1. The excess
of suspension was blotted with a
filter paper; 2. The grid was
quench-frozen into liquefied
ethane at -171°C. (b) Schematic
transverse view of a vitreous ice
film containing polymer particles. 
Dn
the molecular weight. For a more consistent
comparison with the data obtained by DLS, a
Z-average mean diameter was calculated from
the TEM data as :
For each sample, size-distribution histograms
were determined from a total of about 1000
particles accumulated from several cryo-TEM
images.
R E S U LT S  A N D  D I S C U S S I O N
TEM observation
Figure 2a shows polyurethane nanoparticles
after negative staining with uranyl acetate.
Since the glass transition temperature (Tg) of
the polymer was lower than room tempera-
ture (varying from -60 to 0˚C depending on the
polymer composition), the soft particles could
deform and flow during drying. Moreover, we
could not decide whether the observed coa-
lescence arose from a drying artifact or if such
an aggregation existed in solution. The cryo-
TEM image in Fig 2b clearly shows unaggre-
gated spheroidal nanoparticles embedded in
vitreous ice. More complex particles were also
observed, as shown in Fig 2c ,where the larger
objects exhibit a peculiar nano-encapsulated
morphology, suggesting that the pre-polymer
synthesis and the chain extension could not
form homogeneous nanospheres. This is a typ-
ical example where the direct visualisation of
the dispersion provided information about the
mechanism of particle formation, whereas DLS
would be insensitive to this type of morpho-
logical feature. 
Image analysis
The contrast between the polymer particles
and the embedding ice was generally low in
cryo-TEM images, partly due to the strong
inelastic electron scattering. Contrast
enhancements were performed but they also
increased the contribution of the background
noise from the negatives. On the other hand,
variations in the ice thickness induced back-
ground intensity modulations. Consequently,
the grey-level histogram of the cryo-TEM
micrographs generally exhibited a broad peak
(Fig 3a) and it was difficult to define lumi-
nance classes corresponding to specific contri-
butions from the supporting membrane, the
particles and the embedding medium. To limit
these problems, the treatments were per-
formed on smaller regions of interest (ROI)
selected by the user (Fig 3b). To get a better
contrast, the TEM micrographs were recorded
with a negative defocus, generating fresnel
fringes around the ice-embedded objects.
Considering the high background noise, only a
white fringe located outside of the particle
surface could be seen. However, as we mostly
used 1 to 2 mm defoci, we assumed that fres-
nel effects would not significantly influence
the particle detection.
The main steps of the image processing pro-
cedure were: 
1. Manual selection of a rectangular ROI (Fig
3b, step 1). 
2. Local adaptive background smoothing to
homogenise the luminance and improve
object detection: this routine fits a polynomial
surface to the sampled background and cor-
rects the ROI luminance by subtracting this sur-
face [8].
3. Manual thresholding based on a visual con-
trol, followed by an image binarisation (Fig 3b,
step 2).
4. Elimination of background heterogeneities
using successive erosions/dilatations. 
5. Edge detection of the particles (Fig 3b, step
3) and separation process.
6. Fitting of the individual objects with equiv-
alent ellipses, the diameter being calculated
as the mean between major and minor axes.
In order to validate our semi-automatic pro-
cedure, we checked that 'manual' measure-
ments performed on the same images and
numbers of particles yielded similar results.
However, as expected, the semi-automatic
image analysis generally ran four times faster.
An example or size distribution histogram is
presented in Fig 4b. 
Surface tension effects may influence the
particle distribution during sample prepara-
tion. Controlling the thickness of the vitreous
ice film is generally difficult and depends on
the diluent composition (salts, surfactants,
organic solvents), the room humidity level, the
blotting efficiency, etc. Thickness values that
are proper for TEM imaging typically vary from
50 to 200 nm [7]. The presence of a single layer
of particles in the film is important, as illus-
trated in Fig 5a: the larger nanospheres are
more or less isolated, while the smaller ones
overlap along the observation axis. In that
case, size-distribution analysis is hardly possi-
ble, although the operator still has a good
general 'impression' on the suspension disper-
sity.
In addition, due to surface tension effects,
the frozen film often exhibits a gradient of
thickness. Consequently, the average back-
ground intensity in the image varies over the
field of view. As explained earlier, binarisation
can only be properly carried out after back-
ground fitting and correction. Another conse-
quence is the fast reorganisation of the parti-
cles in the liquid film, just before freezing. The
largest particles tend to accumulate near the
edges of the carbon membrane where the
water film is thicker. The smaller particles
remain closer to the centre of the film.
Depending on the local concentration and
steepness of the gradient, regular distribu-
tions may be observed, the nanospheres reor-
ganising along iso-thickness lines according to
their size (Fig 5b). However, we cannot rule
out the fact that some bigger objects may
have migrated towards areas that are too
thick for proper observation. These 'hidden'
particles would thus be absent from the statis-
tic treatment, which would yield an error in
the mean diameter calculation. When the film
nm nm nm nm
1.25 37.9 47.4 51.5 55.6
1.32 22.2 29.3 35.6 68.7
1.4 24.8 34.8 40.6 76.3
1.5 33.2 49.9 76.8 130.4
DNPD DW DZ(TEM) DZ(DLS)
Table 1: 
Mean diameters and polydispersity indexes calculated from cryo-TEM images and DLS experiments for four different polyurethane dispersions.
Figure 2:  
(a) TEM image of polyurethane particles after negative staining. (b) Similar dispersion observed by cryo-TEM. (c) Cryo-TEM of a polymer dispersion with a different composition, showing particles with a complex inner morphology. 
= 
∑Ni Di6
∑Ni Di5
i
i
DZ(TEM)
Figure 3:  
(a) Grey-level histogram of a typical cryo-TEM image of ice-embedded polymer nanoparticles. (b) Stages of the semi-automatic procedure for particle
size measurement: 1. manual selection of the ROI; 2. manual thresholding; 3. detection of the particles and fit by ellipses.  
Figure 5: 
Effect of the embedding liquid thickness on the distribution of polydis-
perse particles. (a) The thickness is more or less constant. Particles are
mixed but the smaller ones overlap along the observation direction. (b)
Regular reorganisation of the nanospheres in a thickness gradient. The
larger particles are closer to the carbon membrane where the liquid film
is thicker.
Figure 4: 
(a) Cryo-TEM micrograph of a polyurethane suspension. (b) Corre-
sponding size-distribution histogram obtained by semi-automatic image
analysis, for a total of about 1000 particles. The shoulder in the low
diameter region suggests a bimodal distribution.
thickness is more or less constant, particles can
mix more randomly, at the risk of overlapping
(Fig 5a). 
With a Tg lower than room temperature,
the suspended nanoparticles are soft in the liq-
uid film and thus sensitive to the surface ten-
sion effects. If their size in the dispersion is
larger than the local thickness of the liquid
film, and provided that they cannot 'migrate'
to thicker areas, the nanoparticles are
squeezed within the thin film. Their apparent
diameter thus increases, yielding an incorrect
spread of the size-distribution histogram
towards larger diameters. Indeed, it is difficult
to visualise such an effect and judge if the par-
ticles are actually deformed. In our experi-
ments, we assumed that the particle size might
be unreliable above 100-120 nm. 
After binarisation, the image analysis algo-
rithm performs several erosions/dilatations
which, depending on the particle shape, may
generate an error on the contours. This error
was estimated to be around 1 pixel, i.e. 2 nm,
considering our sampling rate. Moreover, the
successive erosions, carried out during the
image 'cleaning', eliminated objects smaller
than 5-6 pixels. Consequently, particles smaller
than 10 nm were not properly taken into
account in our size-distribution histograms.
This is readily visible in Fig 4b although the
effect was not detected in most samples. 
Finally, several remarks can be made when
comparing the mean diameters obtained by
DLS and image analysis. Values corresponding
to four different dispersions with increasing
polydispersity index PD are given in Table 1.
First of all, there is a significant difference
between the values of DN and DZ(DLS) which
increases at higher polydispersity. This could
be expected, since the detection is based on
different properties of the particles. In partic-
ular, DLS gives more statistical 'weight' to the
larger particles which are more highly scatter-
ing objects. Moreover, DLS is sensitive to the
ionic environment of the nanospheres that
cryo-TEM cannot visualise since it has the same
density as the embedding medium. The com-
parison of DZ(TEM) (calculated from the cryo-
TEM data as a D(6,5) statistical mean) with
DZ(DLS) is more relevant. DZ(DLS) always appears to
be larger than DZ(TEM). The values are close
when PD is low, but the deviation becomes
larger with increasing polydispersity. The
fourth case, with PD =1.50, perfectly illustrates
how cautious one has to be to interpret and
compare the data obtained with techniques
sensitive to different properties of the parti-
cles. 
C O N C L U S I O N
In this work, several techniques were used con-
currently to study aqueous dispersions of
polyurethane nanoparticles. Light scattering
was non-destructive and allowed us to charac-
terise rapidly a large number of samples over
a wide range of sizes. However, it showed
some limitations in the case of polydisperse
distributions and with particles smaller than
50 nm. The combined use of cryo-TEM and
image analysis appeared to be complementary
to DLS. In particular, the absence of aggrega-
tion as well as differences in nanoparticle
morphology were important results provided
by this direct visualisation. However, the size
analysis of soft polymer particles only made
sense for diameters below 100 nm. Moreover,
the smaller objects (<10 nm) may not have
been properly detected. These limits would
obviously vary for different polymers, depend-
ing on factors such as particle density, crys-
tallinity and Tg. In addition, the use of zero-
loss energy-filtered imaging would certainly
increase the contrast of cryo-TEM micrographs
and consequently improve particle detection.
R E F E R E N C E S
1. Sudol E.D. and El Aasser M.S. In: Emulsion Polymerization 
and Emulsion Polymers, Lovell P.A., El-Aasser M.S. Eds, 
Wiley-Interscience, New-York, pp 699-722, 1997.
2. Putaux J.L. et al. Ultrastructural aspects of phytoglycogen
from cryo-TEM and quasi-elastic light scattering. Int. 
J. Biol. Macromol. 26, 145-150, 1999.
3. Chalaye S. et al. Synthesis of composite latex particles filled
with silica. Control of the composite particles composition.
Macromol. Symp. 169, 89-96, 2001.
4. Collins E.A. Measurement of Particle Size and Particle Size
Distribution. In: Emulsion Polymerization and Emulsion 
Polymers, Lovell P.A., El-Aasser M.S., Eds, Wiley-Interscience,
New-York, pp 385-436, 1997.
5. Lahtinen M. et al. The chain extension of anionic 
prepolymers in the preparation of aqueous
poly(urethane-urea) dispersions, Polym. Int. 52, 1027-1034,
2003.
6. Dubochet J. et al. Cryo-electron microscopy of vitrified 
specimens. Quart. Rev. Biophys. 128, 219-228, 1988.
7. Harris J.R. Negative Staining and Cryoelectron Microscopy:
the Thin Films Techniques, Bios Scientific Publishers, Oxford,
UK, 1997. 
8. Optimas user guide, Media Cybernetics, www.mediacy.com
.©2004 Rolston Gordon Communications.
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Cryo-TEM and Image Analysis of Polymer Nanoparticle Dispersions

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Cryo-TEM and Image Analysis of Polymer Nanoparticle Dispersions

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