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Issue
36 — February 2004 |
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In
situ Particle Sizing of Concentrated Dispersions |
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Rolf
Daniels |
1 Introduction
The knowledge of particle size is one of the key issues in characterizing disperse
systems. A lot of analytical techniques are known for this purpose [1]. A major
draw back of most of the common techniques, e.g. laser diffraction or electrical
zone sensing, is that extensive dilution of the samples is required before measurement.
This forces the particle size analyst to determine particle size off line. Moreover,
it bears the risk that the physico-chemical equilibrium is disturbed dramatically.
In turn, particle size can change significantly due to aggregation or coalescence.
In this case a technique is required which allows size measurements on concentrated
samples. Such a method is often referred to as in process, in line, or in situ
method. Figure 1 shows the major steps which are required
for an in situ method in comparison to a conventional off line technique. From
this diagram it is evident that the most straightforward approach is using an
in situ method to measure particle size in line.
Figure 1: Comparison of the required steps
for particle size analysis.
(A) conventional method, off line
(B) in situ method, off line
(C) in situ method, in line
Our primary goal in determining particle sizes is to characterize emulsion systems
which are stabilized with a suitable polymer. Although many of them were stable
on storage, we cannot assume that extensive dilution would not have an effect
on their particle size. Therefore, in 1989 we started this work with a rather
simple in situ particle sizing technology, which was called „Scanning Laser
Microscopy (SLM)“. Though the results looked promising, we encountered several
restrictions concerning size range and resolution [2,3].
This encouraged us, in cooperation with Meßtechnik Schwartz, Duesseldorf,
to further develop the in situ technology. Our combined efforts helped to improve
the method substantially during the past years and resulted, in 2001, in a completely
new instrument [4,5]. To distinguish this technology clearly from the previous
one, we named it 3D ORM (Optical Reflectance Measurement) technology.
Although this technology seems to be rather helpful where conventional methods
fail, it is so far scarcely described in literature. My intention in writing this
article is therefore to provide a brief introduction to the principle of operation,
the theory of data processing, and of course, the practical use of this innovative
particle sizer.
2 Principle of operation
The complete set up of an 3D ORM instrument is illustrated in Figure
2. The main components are: sensor, signal processing unit, and a personal
computer running the MTS/ LT software for data analysis.
Figure
2: A schematic diagram of the principle components required for an 3D
ORM instrument [7].
The basic principle of the instrument is very simple and is illustrated in Figure
3. The light source is a laser diode which is built in the signal processing
unit. The lasers used for 3D ORM instruments vary depending on the particular
application. We use a 10 mW semiconductor laser (wavelength: 780 nm) for the analysis
of emulsions with particles as small as 100 nm and dispersed phase volume fractions
as large as 0.8.
The light is transmitted to the sensor using optical fibers. Within the sensor
the laser beam passes consecutively a collimator lens, a wedge prism, a focusing
lens and two sapphire windows. During operation the focusing lens rotates eccentrically.
This makes the laser beam rotate and pass the wedge prism at sites with different
path length. Consequently, the focal point of the laser beam moves in normal direction
to the sensor window. Combined with the circular movement of the laser beam, the
focal point scans across the sample on an elliptical orbit under a certain angle
to the sensor window. This movement of the focal point in a three dimensional
space in front of the sensor is the origin of the expression “3D ORM”
for this technology.
Figure
3: A schematic representation of the basic principle of 3D ORM. The optical
system makes the focal point scan across the sample on an elliptical orbit in
front of the sensor.
If the vertical and radial moving focal point hits a particle, light is reflected.
This light is then captured by the optical system inside of the instrument, and
is passed to the detector. Its task is to transform the reflected light into an
electrical signal which then can be further processed.
Figure 4 shows how the electrical pulses generated by the
detector are related to the particles which are actually scanned by the laser
beam.
Figure
4: Schematic diagram of the relation between scanned particles, corresponding
raw signal, and filtered time-of-flight signal. The chord length of a scanned
particle is proportional to the time of flight for a pulse.
In the first approximation, the chord length of a scanned particle lc can be accounted
for by
where delta tS is the time of flight for a pulse. The proportionality
constant in this equation is vS, the velocity of the scanning
focal point.
However, a systematic error occurs when the length of a pulse is translated as
the corresponding chord length by using this simple approach. This error depends
on the relation of the diameter of the scanned circle and the chord length of
the particle (Figure 5).
Figure
5: Schematic representation of the arc length and chord length of a particle.
(The size of the particle has been emphasised for clarity.)
In general, the chord length lC and the arc length lA
are related by:
The actually occurring error can be estimated by:
The raw signal from the detector is amplified and transmitted to an electronic
filter system. This unit separates the pulses into two main fractions, namely
the "good" pulses which are further considered and the "bad"
ones which are discarded. Criteria for the selection include the symmetry of a
pulse and the slope of the flanks of the signal.
Special electronic circuits convert the „good“ pulses into a rectangular
shaped signal which then contains the time of flight delta tS
as a characteristic measure. These delta tS values are divided
into appropriate size channels which are ordered in a geometric scale. This register
typically consists of 250 channels. Since the scanning velocity of the laser beam
is known, these channels correspond to distinct size bands [6,7].
At the end, the signal processing unit passes a chord length distribution to a
computer, which performs the appropriate data analysis.
3 Data processing
If the laser beam randomly and vertically scans a single particle, a characteristic
chord length distribution is produced. The origin of the chord length distribution
can be understood by reference to Figure 6.
Figure
6: Derivation of the normalised chord length distribution of a singular
cubic particle.
(A) Map of scanned chord lengths from the projected area of the particle
(B) Transformation into a two-dimensional chord length distribution
(C) Ordering the chord lengths due to their frequency
(D) Normalisation of the frequency distribution function
When the laser beam scans across a particle, it generates an ensemble of chord
lengths which fit into the projected outline of the particle. The chord length
lC of each scan is given by the difference of the corresponding
upper (xu) and lower (xl) boundary
where the beam enters or leaves the projected area. This map of chord lengths
can be transformed in order to express the chord length as a function of the normal
direction y, which is perpendicular to the scanning direction. Arranging these
values in relation to the chord length gives the corresponding frequency plot.
The final step is to normalize the x-values for the maximum chord length lc,max
and the y-values for the maximum frequency delta ymax which then gives the normalized
frequency distribution.
Using this principle, a characteristic chord length distribution can be generated
for any particle shape [6]. This is the most simple example when we are dealing
with spherical particles, because an analytical solution of the problem exists.
The normalized chord length distribution function of a singular sphere f’(x)
is given by
The resulting incremental and cumulative chord length distribution for a singular
sphere is shown in Figure 7.
Figure
7: Plot of the incremental and cumulative chord length distribution of
a singular sphere
For more complex shapes the chord length distribution has to be determined by
Monte Carlo simulations. Figure 8 shows the resulting distribution
function for a few relatively simple particle shapes. From this it is evident
that the proper selection of the appropriate shape factor dramatically influences
the result of the particle size analysis.
Figure
8: Plot of the incremental chord length distribution of singular particles
of varying shape.
In a real experiment with perhaps as many as 10 to 12 particles/ml, the total
chord length distribution will be the sum of the scans of all particles present.
For a mixture of equally distributed spheres this is illustrated in
Figure 9.
Figure
9: Plot of the incremental q(x) and cumulative Q(x) chord length distribution
of a equally distributed sample of spheres. The green line shows the corresponding
particle size distribution by number h(x).
Unfortunately, we also have to solve the opposite problem: A measured chord length
distribution has to be transformed into an appropriate particle size distribution.
Mathematically, this is called an inverse problem. This means that we need to
find a particle size distribution which could have generated the data we measured.
The general correlation function is given by:
where:
h0 is the number distribution of chord
lengths,
q0 is the number distribution of particles
as a function of the particle size,
p describes the probability of measuring a chord length lC when
scanning a particle of size lC,max,
lC is the chord length,
lc,max is the maximum chord length of an individual particle,
and
lC,MAX is the maximum
chord length of the largest particle.
This equation is called a FREDHOLM integral equation. It is valid for all convex
shaped particles and uses the maximum chord length lc,max as a reference value.
There are several numerical approaches to solve the FREDHOLM integral equation.
The most recent version of the MTS / LT© software uses the CHAHINE method
for extracting the size distribution. Mathematically, it is rather complex and
I will not discuss this in further detail. For further information see reference
[9].
The resulting particle size distribution can be presented in many different forms,
e.g. volume distribution, number distribution, RRSB distribution, or a trend signal
for in process applications. Figure 10 shows an example of
a screen shot of the Win ORM software.
Figure
10: Screen shot of the Win ORM© software
4 Range of particle sizes and resolution
3D ORM instruments from MTS measure particles in a nominal range from 100 nm to
a few millimeters. In order to increase resolution, a less wide spread range should
be preferred. We use an instrument qualified for pharmaceutical emulsions (ECA
817 and LabScan 2007). It analyses droplets from 0.1 to 125 µm divided into
1024 bins. Some of the results which are presented in the applications chapter
are from an earlier model (ECA 01015), which measures in the range from 0.4 to
125 µm.
5 Experimental aspects
The experimental set-up of a particle size analysis may have a considerable impact
on the overall performance of a measurement. Although the following issues were
selected more arbitrarily than systematically, they represent most of the aspects
which we believe have to be worth mentioning from our experience with the 3D ORM
technology.
5.1 Sampling considerations
The 3D ORM technology enables the determination of the particle size in the disperse
systems in situ. This, however, does not mean that we encounter no problems with
proper sample selection. When we use an in situ method, the sampling procedure
is where we install our sensor (Figure 11, Figure
12), e.g. adjacent to a homogenizer or somewhere in a storing tank.
Near the homogenizer the particles move around the sensor very rapidly in a turbulent
stream, whereas in a storing tank they stay almost stagnant. For laboratory uses,
we place a sample of at least 200 ml in a glass beaker, where baffles are included,
and mix it with a stirrer which produces a stream directed opposite the sensor
head (Figure 13). This allows us to present a representative
sample, when using the 3D ORM scanning laser beam. In principal, it is also possible
to measure smaller samples, i.e. in a test tube, when the smallest MTS sensor
with 7.85 mm diameter is used (Figure 14).
Figure
11: Sensor installation directly in the mixing area of a pilot scale
homogenizing unit.
Figure
12 : In line installation of the MTS 3D ORM Fingerprint Sensor
Technology.
Figure
13 : LabScan sensor immersed in a glass beaker for off line
measurement in the laboratory. The stirrer produces a stream directed opposite
the sensor head.
Figure
14: 3D ORM sensors designed for varied applications.
Another issue which should be addressed in this context is where the focal zone
is located in relation to the traveling distance to the vertical focus movement.
In general, it should be preferred to have the focus deep (= traveling distance)
within the sample in order to allow particles of all sizes to approach the focus
at random. However, this might be limited to a too low intensity of reflected
light, due to scattering effects. Moreover, the traveling distance to the 3D ORM
focal point from the sapphire lens should not be less than half the diameter of
the biggest particles in order to make sure that – at least theoretically
- all possible chord lengths of these particles can be scanned.
In principal, there is maximal optimal distance to the focus for every sample.
However, for practical reasons it might be straightforward to always use a 3D
ORM instrument with a long traveling distance to the focal point to ensure uniform
value. From our experience, it turns out that we obtain reliable results on most
pharmaceutical emulsions when the traveling distance to the focus was 500 micron
for the ECA 817 and 250 micron for the LabScan 2007 moving from the sensor window
to the sensor head.
5.2 Calibration
Instruments using the 3D ORM technology do not need a calibration on an operation.
If calibration is required this can be done using standard particles, e.g. BCR
standard. However, the user must be aware that there might be differences in the
optical behavior (reflectivity) of this standard and the sample of interest. If
necessary, it is possible to make a software calibration to such standards. In
the same manner it is possible to calibrate the instrument to already existing
results from other particle size analysis. This enables the user to switch the
instrument in an ongoing project. Such a special calibration can help to decrease
the error for a specific sample but might also bring about a severe malfunction
of the instrument with all other samples. For this reason, it seems to be more
appropriate to use 3D ORM data in a comparative sense, and bear in mind that there
is likely to be a significant discrepancy between the measured size and the so
called „true particle size“. However, this is a rather common issue
because each method for particle size analysis can only provide its particular
equivalent diameter which is normally not identical to the so called „true
size“ (or whatever is meant by this expression).
5.3 Reproducibility
To check the reproducibility of the measurement, we divided an emulsion sample
consisting of 2.5 kg into ten equal portions. The measurement was then replicated
ten times using a standard operation procedure consisting of the following steps:
• cleaning sensor head
• filling sample into the beaker
• starting stirrer at 400 rpm
• starting measuring cycle
• storing data
• stopping stirrer and removing sample
• cleaning sensor
Figure 15 shows the results of these experiments. Obviously,
the reproducibility of the ECA 817 was fairly good and we calculated a standard
deviation for the mean volume diameter of 0.64 % [10].
Figure
15: ECA 817 results of replicated measurements on a 20 % o/w emulsion
demonstrate that in situ particle size analysis highly reproducible.
6 Applications
We use 3D Optical Reflectance Measurements mainly to characterize oil-in-water
emulsions which are stabilized with polymeric emulsifiers, e.g. hydroxypropyl
methylcellulose (HPMC). A lot of experiences have been accomplished during this
work. In order to illustrate the possibilities as well as the limitations of this
technology, I will stress two examples where we have also compared the data to
other methods like Transmission Electron Microscopy (TEM) or laser diffraction.
6.1 Particle size measurement on highly concentrated o/w emulsions [11]
For this study we used o/w emulsions with varying oil volume fractions, i.e. 20,
40, 60 and 80 %. The aqueous phase consisted of a 2.5 % solution of hydroxypropyl
methylcellulose (HPMC). Thus a decreasing amount of polymer had to stabilize an
increasing amount of oil.
After the preparation, all emulsions containing up to 60 % oil appeared macroscopically
homogenous. The microscopic image showed the typical features of a partly flocculated
system. Emulsions containing 80 % oil were still of the oil-in-water type and
behaved like a mayonnaise. However, these samples could not be sufficiently homogenized
due to their high viscosity. The consistency of the other emulsions varied from
liquid like with 20 % oil to soft cream like with 60 % oil.
Particle size was measured the with an ECA 01015. In parallel, we prepared freeze
fractured replicas of the samples and examined the specimens by TEM. The results
are depicted in Figure 16.
Figure
16: Volume distribution and corresponding TEM micrographs of o/w emulsions
with 20, 40, 60, and 80 % oil content; bar: 5µm.
Emulsions with 20 % oil phase revealed the most shallow size distribution. By
increasing the oil content the spread broadens and the mode of the distribution
moves towards smaller values. 80 % emulsions gave almost the same particle size
distribution as the 60 % emulsions. Only a small but reproducible peak in the
size range from 50 to 80 µm indicated the inhomogeneity of this preparation.
However, this is not sufficient for a quantitative characterization of the system.
The TEM micrographs confirm the results of the particle size analysis qualitatively.
Emulsions up to 60 % oil contain almost spherical droplets in the size range from
1 to 10 µm. The internal phase is formed of mainly singular droplets and
some few aggregates of two or more spherical particles. 80 % emulsions consist
of drops which differ greatly in size and are in close contact. As is typical
for a mayonnaise, the drops are polyhedral.
In addition, emulsions with up to 60 % oil content were monitored for more than
2 years while stored at room temperature (Figure 17). After
6 months storage, the macroscopic and microscopic appearance, as well as their
consistency, did not change. Accordingly, particle size analysis revealed only
minor changes. In detail, 20 % emulsions show a ripening process during the first
month of storage. This is a typical feature for this type of emulsion. Beyond
that time, particle size did not change significantly. Emulsions with 40 % oil
behaved in a similar way. Owing to their higher viscosity, the effect is less
pronounced and takes place over a longer period of time. In contrast, the 60 %
emulsions were not sufficiently stable. This is indicated by a second peak in
the particle size distribution which developed during storage.
Figure
17: Particle size of the model emulsions after preparation,
6 months, and 27 months storage.
(A) emulsions containing 20 % oil, (B) emulsions containing 60 % oil.
The measurement enables to monitor coalescence in 60 % emulsions as well as Ostwaldt
ripening which occurs in 20 % emulsions.
From these results, we conclude that 3D ORM technology is able to in situ characterize
emulsions with up to 60 % oil content. The method is sufficiently sensitive to
monitor severe changes due to coalescence as well as to trace small changes produced
by Ostwaldt ripening. Emulsions with 80 % oil could not quantitatively be characterized.
However, we must bear in mind that these emulsions are susceptible to dilution.
Thus, reliable results can only be expected when the samples can be analyzed without
extensive dilution. Thus, this challenging problem cannot be solved by currently
existing methods. The results of the ORM-Measurement, however, can be taken as
a fingerprint which is characteristic for this sample and allows to identify changes
which occur during manufacturing or storing.
6.2 Influence of formulations variables on the size distribution of o/w emulsions
[12]
Laser diffraction (LD) has become one of the most popular particle sizing techniques
and whenever any method is discussed, corresponding data from LD measurements
are requested. For this reason, the following example compares 3D ORM data to
LD measurements. The results originate from a study where the influence of ethanol
on the properties of o/w emulsions was evaluated (Table 1).
Such an addition of ethanol to o/w emulsions is frequently used in order avoid
other preservatives.
Table 1: Composition of model o/w emulsions
containing different amounts of ethanol.
Apart from improving the microbiological quality, adding ethanol changes the physico-chemical
properties and might have a substantial impact on the appearance of the emulsions.
Accordingly, particle size analysis can help to identify the changes that are
likely to occur.
The 3D ORM measurements were performed on a Meßtechik Schwartz ECA model
817 (0.1 - 125 µm; 18 bins). For LD a Malvern Mastersizer was used and data
were analyzed according to either Fraunhofer and Mie theory. The presented results
are based on Fraunhofer analysis, however, Mie analysis produced distribution
curves which were rather similar, except there was a slight shift towards smaller
particle sizes.
The results of particle size measurement on the model o/w emulsions are shown
in Figure 18. Both methods reveal a decreasing mean volume
diameter with increasing amount of ethanol. This is consistent with images from
light microscopy. The absolute values for D10%, D50%, and D90% are close together
with always slightly increased values with the 3D ORM measurement. Both methods
deliver highly reproducible results. However, on closer inspection there are remarkable
deviations between both methods. 3D ORM indicates that D10% decreases when the
amount of ethanol added increases, whereas this value remains unchanged with LD.
Consequently, the span, which is defined as D10% and D90% range divided by the
mean, decreases with increasing ethanol content, when calculated from LD. In contrast,
the span from 3D ORM measurement appears to be almost invariable with the ethanol
content. In addition, LD suggests bimodal distributions for emulsions without
and with 5 % ethanol, while the corresponding 3D ORM data show monomodality. These
differences can be explained by the underestimated smaller particles which are
a typical feature of LD instruments and will appear predominantly when the particle
sizes vary greatly [1]. Hence, in this example 3D ORM data seem to be somewhat
closer to reality than the results from LD are.
Figure
18: Comparison of volume distribution and span of model emulsions containing
different amounts of ethanol obtained from ORM measurement or laser diffraction.
6.3 Ethylcellulose-stabilized O/W- and W/O- emulsions [13]
Ethylcellulose is used for its ability as an oil soluble polymer to stabilize
surfactant-free o/w emulsions. However, the consistency of these emulsions varied
enormously depending on the processing temperature. Emulsification at 30°
C produced liquid-like systems. Preparations at 15° C, however, delivered
a semisolid cream. Further investigations revealed that the fluid lotions are
of the w/o type whereas the semisolid systems represent o/w emulsions. Photomicrographs
illustrate the structural differences. The fluid lotion is characterized by numerous
small individual droplets, each surrounded by the coherent phase. In contrast,
the semisolid cream exhibits an extremely wide particle size distribution. The
corresponding results from particle size analysis are shown in Figure
19. The droplets of the lotion are measured to be smaller than 4 µm
whereas the cream consists of droplets as large as 15 µm. Furthermore the
particle size of the cream is extremely wide spread. Thus the measurement with
the ORM-System as able to quantify what can be seen from photomicrographs only
in a qualitative manner.
Figure
19: Volume distribution of a w/o type lotion and a o/w cream which have
identical composition and use both ethylcellulose as polymeric emulsifier but
they were prepared at different temperatures (15 or 30 °C).
6.4 Suspensions and Nanoemulsions
Finally, it should be demonstrated that the 3D ORM-Technology is not only able
to measure particle size in polymer-stabilized emulsions. The method can also
be used to characterize other dispersed systems, e.g. tooth pastes, as illustrated
in Figure 20.
Figure 20: Volume distribution of two commercial samples
of tooth pastes.
Figure 21 gives a look into the future. The measurement of
a silicon-in-water nano-emulsion shows that this technology has also a potential
in characterizing sub-micron sized dispersions. Further conclusions need , however,
additional systematic studies.
Figure 21: Measurement on a sub-micron
sized silicon-in-water emulsion demonstrates the possible applications of 3D ORM
technology in the future.
7 Conclusions
3D ORM technology is primarily intended for in situ measurements. Since
in real systems almost none of the other particle sizing methods can be used in
a similar way, this feature can hardly be compared. However, 3D ORM technology
also enables off line measurement. When such results are compared to TEM
and LD measurements it turns out that 3D ORM is an equipotent method. It produces
results which are reliable and highly reproducible when emulsions are analyzed
together with droplets in the range from 0.4 to 40 µm and up to 60 % internal
phase. For more concentrated Systems it is possible to create a fingerprint which
represents the sample. In all cases, 3D ORM has the unique advantage that no sample
preparation is required for an in real time and in situ measurement. This reduces
not only the work load, but also helps to avoid artifacts when particulate systems
are analyzed which are critical to dilution. This includes, of course, a lot of
emulsion systems, and is most vital for suspensions in order to avoid uncontrolled
dissolution of particles. The MTS 3D ORM sensors can also be sterilized under
in situ conditions.
First results on sub-micrometer particulate systems with the nanoScan 2009 look
also promising. However, the final decision over future applications depends on
additional data.
8 References
[1] Washington, C., Particle size analysis in pharmaceutics and other industries,
Ellis Horwood London, 1992
[2] Daniels, R., Barta, A., Erfahrungen beim Einsatz der in situ-Partikelmessung
sowie der Messung der elektrischen Leitfähigkeit bei der Rezepturoptimierung
von Emulsionen, Chemie-Anlagen + Verfahren 8/93, 62 - 66, 1993
[3] Daniels, R., Barta, A., Herstellung, Charakterisierung und Stabilitätsprüfung
von O/W-Emulsionen mit Methylhydroxypropylcellulose als Emulgator, Pharm. Ztg.
Wiss. 4, 177 - 183 1991
[4] Daniels R., Gerber M., Karwoth R., Empt U., Vorrichtung zur Messung von Partikelabmessungen
in Fluiden. Deutsches/Europäisches Patent DE 196 23 095 A1/EP 0 813 052 A3
[5] Schwartz; F.H., Braun; M., Apparatus for measuring particle dimensions in
fluids. United States Patent 5,900,933
[6] Karwoth, R., Handbook of ORM Technology, Meßtechnik Schwartz, D-Düsseldorf,
1996
[7] Lenzing, M; Handbook of 3D ORM Technology, Meßtechnik Schwartz, D-Düsseldorf,
2002
[8] Karwoth, R., Internal documentation, Meßtechnik Schwartz, D-Düsseldorf,
1996
[9] Twomey, S., Introduction to the mathematics of inversion in remote sensing
and indirect measurement, Elsevier Scientific Publishing Amsterdam, 1977
[10] Rimpler, S., Pharmazeutisch-technologische Charakterisierung von O/W-Emulsionen
mit Methylhydroxypropylcellulose als Polymeremulgator, Thesis University of Regensburg,
1996
[11] Rimpler, S., Daniels, R., In situ particle sizing in highly concentrated
oil-in-water emulsions, Pharmaceutical Technology Europe .8(9), 72 - 80, 1996
[12] Wollenweber C., Einfluss von Ethanol auf Methylhydroxypropylcellulose stabilisierte
Öl-in-Wasser Emulsionen. Dissertation Technische Universität Braunschweig
2000. http://opus.tu-bs.de/opus/volltexte/2000/91
[13] Melzer E, Herstellung und physikochemische Charakterisierung von W/O-Emulsionen
unter Verwendung von Ethylcellulose als nichtionischem Polymeremulgator. Dissertation
Technische Universität Braunschweig 2000. http://opus.tu-bs.de/opus/volltexte/2000/146
Author
Prof. Dr. Rolf Daniels
Prof. Dr. Rolf Daniels has a Ph.D. degree in Pharmaceutics. Before continuing
his academic career, he worked for Pfizer in the department of pharmaceutical
development for 2 years. In 1995 he became Professor of Pharmaceutics in the Institute
of Pharmaceutical Technology at the Technical University of Braunschweig. His
main interests are in the field of surfactant-free emulsions, stability assessment
of semi-solids, and controlled delivery of insect repellents. Since 1997 he has
been head of the department Dermocosmetics of the Society of Dermopharmacy (GD).
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