British Association For Crystal Growth Annual Conference 2017
Comparing methods for induction time probability distribution
measurements
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M.L. Briuglia , J. Sefcik , N. Candoni , S. Veesler , J.H. ter Horst
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) EPSRC Doctoral Training Centre in Continuous Manufacturing and Crystallisation, c/o Department of
Chemical and Process Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street,
Glasgow, G1 1XJ, United Kingdom
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) CNRS, CINaM,
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, Case 913, 13288 Marseille Cedex 09, France
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) EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation, c/o
Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Technology and
Innovation Centre, 99 George Street, Glasgow, G1 1RD, United Kingdom
[email protected]
The control of crystal nucleation kinetics is a challenge that can be overcome by obtaining fundamental
understanding of crystal nucleation. The stochastic nature of nucleation enables a procedure to obtain
reliable nucleation kinetics using the variation in multiple measurements under equal and well-controlled
conditions. Recently such a method, based on the determination of probability distributions of induction times
under equal supersaturated conditions in 1ml stirred solutions was developed by Jiang and ter Horst [1]. An
alternative approach to determine nucleation kinetics from such variations makes use of microfluidics. It is a
fast and easy method studying the nucleation in nanoliter droplets. A large number of experiments with
independent nucleation events are provided within droplets of equal chemical composition [2].
In principle both methods, under the same experimental conditions, should result in the same kinetic
behaviour. However, to our knowledge, this has never been tested in practise. Therefore, the goal of this
work is to compare obtained experimental rates from these nucleation rate measurement methods and
explain any differences.
For the microfluidic method, the optimal conditions to ensure nucleation of isonicotinamide in ethanol within
the droplets were established (Fig.1) and the droplet stability at different temperatures and supersaturation
ratios were tested. Then, using both methods, isothermal induction times were measured at different
supersaturation ratios (Fig.2). Finally, nucleation rate parameters from both methods were determined and
compared.
Fig. 1. Isonicotinamide crystals nucleated within ethanol nanodroplets generated through microfluidics.
British Association For Crystal Growth Annual Conference 2017
Fig. 2. Probability distribution P(t) of 50 induction times for 137 mg/ml of isonicotinamide in ethanol at three
different crystallisation temperatures: Tx=20°C (violet); 25°C (green); 30°C (orange).
The experiments conducted with the microfluidic method had a higher nucleation rate J compared to the
values obtained with the 1ml stirred solutions (Fig.3). Since the nucleation probability in the nanodroplets is
much smaller because the volumes in the microfluidic method is orders of magnitude smaller this has to be
compensated by using higher supersaturations in the microfluidic method. However, this does not fully
explain the difference between results obtained by using the two methods. In addition, the hydrodynamics
are completely different in the two tested methods since nucleation takes place in stagnant nanodroplets in
the microfluidic method, contrary to the 1ml stirred solutions. In addition, the nanodroplet interface between
oil and solution might well influence the heterogeneous nucleation mechanism taking place in the
nanodroplets compared to the 1ml stirred solutions. This work underlines the importance of carrying out a
large number of experiments per condition in order to obtain statistically relevant data enabling the
fundamental study of crystal nucleation.
Fig. 3. Using the equation P(t) = 1-exp[-JV(t-tg)], the nucleation rates J were determined from the probability
distribution of induction times within the nanoliter droplets using microfludics and compared to those obtained
from the 1ml stirred solutions using Crystal 16.
References:
[1] S.Jiang, S, J.H. ter Horst, J Cryst. Growth Des. 2011, 11, 256−261.
[2] M. Ildefonso, N. Candoni, S. Veesler, Cryst. Growth Des. 2011, 11, 1527–1530.