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Online monitoring of nanoparticle suspensions using dynamic light scattering, ultrasound spectroscopy and process tomography
19th European Symposium on Computer Aided Process Engineering – ESCAPE19 J. JeĪowski and J. Thullie (Editors) © 2009 Elsevier B.V. All rights reserved.
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Online monitoring of nanoparticle suspensions using dynamic light scattering, ultrasound spectroscopy and process tomography Xue Z Wanga, Lande Liua, Ruifa Lia, Richard J Tweedieb, Ken Primroseb, Jason Corbettc and Fraser McNeil-Watsonc a
Institute of Particle Science and Engineering, University of Leeds, Leeds LS2 9JT, UK, E-mail: [email protected] b Industrial Tomography Systems Limited, 39 Deansgate, Manchester M3 2BA UK c Malvern Instruments Ltd, Enigma Business Park, Worcestershire WR14 1XZ, UK
Abstract Dynamic light scattering, ultrasound spectroscopy and electrical resistance tomography were investigated for online monitoring of nanoparticle suspensions. This integrated system provides real time information about particle size distribution, zeta potential and particle concentration and visualises the mixing quality between particles and liquids. As particle size distribution is an indicator of the quality of particulate products, zeta potential measures the stability of colloidal particles and tomography shows particle concentration and the mixing quality between particles and liquids, this integrated multiple sensor system can be applied to nanoparticle manufacturing processes for online process and product quality control. Keywords: dynamic light scattering, ultrasound spectroscopy, process tomography, nanoparticle suspension, process analytical technology 1. Introduction Nanoparticle manufacturing in solid suspensions is becoming increasingly important to pharmaceutical, agrochemical and speciality chemical industries. For instance, nanonization is now used in the pharmaceutical industry to address the low solubility issue of hydrophobic pharmaceutical solids. Nano-processing in industry however faces major challenges compared to the production of larger particles. It is much more difficult to scale-up a process of nanomaterials from laboratory to industrial scale and to achieve consistency and reproducibility in product quality from batch to batch runs. An enabling technique to address the scale-up and manufacturing challenges is online sensing. The principle of using online sensing for process scale-up is that by being able to measure and understand in real-time the evolution of the product quality variables and process conditions as well as their interactions, and subsequently exercise control, product quality can be assured. However, despite the availability of various sensing techniques for measuring the size and size distribution of submicron to nano-scale particles, few of them can be applied online especially at industrial scale conditions.
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Online measurement not only creates practical challenges to the design of an instrument, it also often pushes the instrument to be used beyond its limits such as of solid concentrations. While laboratory research favours low solid concentration, to be commercially viable industrial scale operation is usually required to be at high concentration. High solid concentration can result in particle-particle interactions and multiple scattering, leading to measurement errors.
Figure 1 The experimental rig. DLS – dynamic light scattering, USS – ultrasound spectroscopy, ERT – Electrical resistance tomography
The purpose of this study is to investigate the use of two of the most promising techniques for online sensing, dynamic light scattering (DLS) and ultrasound spectroscopy (USS), for real-time measurements of particle size distribution (PSD) and zeta potential during processing of nanoparticle slurries. Electrical resistance tomography (ERT) is also integrated into this multiple sensor system for the characterisation of mixing conditions and solid concentration, the later is required by DLS and USS in PSD measurement. The focus is on studying the various variables that impact the size measurement results, including solid concentration, mixing condition and zeta potential. The experimental system is shown in Figure 1. Ultrasound spectroscopy1-3 is a sensing technique highly suitable for online measurement, in particular for dense nano sized particle systems, where the system stability may be sensitive to changes in concentration. The principle of ultrasound spectroscopy measures the attenuation (energy loss) of sound waves due to sound absorption by media when a sound wave is transmitted and propagating into the media; the attenuation is then inverted into PSD according to the theory of ECAH 4, 5 or the coupled phase model3. Laser scattering technique often provides the benchmark laboratory-based method for PSD measurement. Since light is a type of electromagnetic wave, it can be described by the wave properties e.g. amplitude, frequency, and wavelength. When the light beam is shot through the suspension, the particles in the suspension scatter the light, resulting in change in phase of the light from that of the original beam. If the particles in suspension do not move, this phenomenon is called static light scattering. While in real suspension where particles move, it is called dynamic light scattering (DLS). This phenomenon is applied to particle size measurement. The particles in suspension move as a Brownian motion which means that large particles have low velocity while the small particles
Nanoparticle Suspensions Using Dynamic Light Scattering, Ultrasound Spectroscopy and Process Tomography
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have high velocity. The properties of scattered light change with respect to the particle velocity. Fast changing of wave of scattered light means small particles while slow change of wave is due to large particles. For concentrated slurry systems, a phenomenon called “multiple scattering” occurs. Mie theory is a methodology to address the issue. It describes the scattered intensity of spherical particle as a series of the product of Mie scattering coefficient with Legendre polynomial.6 An alternative method is to avoid multiple scattering using the back scattering technique.7 By installing a receiver close to the laser gun, the scattered light will not have to travel through the entire sample therefore reduce the multiple scattering effect. It backscatters the light via an angle, instead of passing through the samples, minimising the effect of multiple scattering. The Malvern online DLS system used in this work is based on this principle. ERT is used for cases that the continuous phase is a conductive fluid and the second phase can be either conductive or nor-conductive. Voltage is measured from a number of paired electrodes that are fitted around the inner wall of a pipe cross-section using tomographic sensors. The conductively distribution is then reconstructed to reflect the distribution of the second phase in the flow 8, 9 Frequency=20.MHz
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2. Experiments Experiments were carried out on the rig shown in Figure 1. The process was designed to dilute using local tap water (conductivity 407μS/cm) a concentrated silica colloid (Nissan Corporation America, 24.0%vol with conductivity 4.12mS/cm, mean size of 70-100nm in diameter). ERT measurement was carried out through the whole process. USS and DLS measurements were taken when each dilution process was completed. A simple algorithm is developed to derive the concentration from the ERT conductivities.
2 E ( E 1)I 0 I 0 8EI 0 (I 0 1)(I 0 2) [2 E I 0 (I 0 E 1)] 2 2
(1) 2 E (I 0 2) where I is the volume concentration of the nonconductive phase, I0 is the initial concentration of the suspension and ȕ is the ratio of the initial conductivity to the measured conductivity.
I
USS particle sizing The relation between the acoustic attenuation and concentration for different frequencies is shown in Figure 2, the straight lines are the linear fittings of the attenuation data. It is clearly that the relationship becomes nonlinear as particle concentration is greater than 8.0% vol., and as expected3, that estimated PSD shifted to smaller mean size. PSDs for each concentration calculated by ECAH model and their mean sizes are shown in Figure 3. 120
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Figure 3. PSD and volume mean sizes of silica particles for each concentration. (a) the plots of PSD (the horizontal axial is particle size and the vertical axial is the corresponding volume fraction of the particles. The right arrow on I denotes the decrease of particle concentration). (b) volume mean size plot.
It is seen from (a) of Figure 3 that PSD gradually shifted to smaller particle size region with the increase of particle concentration and eventually became incorrect when concentration is greater than 8.0%vol. Concentration plot and ultrasound spectra The PSDs and their corresponding mean sizes measured by DLS are shown in (a) and (b) of Figure 4. (c) of Figure 4 is the comparison of the mean sizes between USS and DLS and (d) is the zeta potential measurements.
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Figure 4. Size distributions and volume means of silica particles measured by DLS. (a) size distributions (The right arrow on I denotes the decrease of particle concentration). (b) volume mean sizes. (c) comparison of mean sizes. (d) Zeta potential
From Figure 4, it is clear that for higher concentrations, the measured PSDs have slightly larger particle sizes; while the PSDs in lower concentrations as shown in (a) indicate that the dilution might have caused some changes to the system resulting in wider distributions. As seen in (c), a divergence between the two results does occur in higher concentration region (> 5.0%vol). This is due to the difference between the measurement principles. However, as can still be seen, the mean sizes in low concentration region are quite comparable. (d) indicates that with dilution going on the suspension gets more stable as the magnitude of zeta potential increases. In higher concentration region (here > 8.0%vol), the value of zeta potential indicates that the suspension is unstable. ERT results Figure 5 shows how the dispersed phase distributed in the continuous phase when flowing. As can be seen from the images in Figure 5, there was a slightly higher nonconductive area in the up left region of the images. This might be due to vortices generated in this area during the flow of the suspension resulting in particles slightly more concentrated than that in other areas.
3. Conclusions A system combining multiple sensors including dynamic light scattering (DLS), ultrasound spectroscopy (USS) and electrical resistance tomography (ERT) has been described for online characterisation of nanoparticle suspensions. ERT provides realtime information about the mixing quality of the dispersed phase in a continuous phase, and importantly solid concentration and conductivity which are required by DLS and USS for measurement of particle size distributions (PSD). The DLS sensor also measures zeta potential which is also important because its value is an indicator of stability of the suspension. Both instruments, DLS and USS, provide measurements about PSD10. Preliminary comparison was made for the two sets of data. It must be pointed out that due to the difference in measurement principles, the two sets of size
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distributions are unlikely to be identical for this specially designed experiment in high concentration region. However, they are comparable especially for the results of low concentrations in terms of their mean sizes as they appeared to be consistent. The knowledge obtained from experiments is useful for applying the instruments for online characterization of nanoparticle suspensions.
1)
2)
3)
4)
5)
6)
7)
8)
Figure 5. Constructed conductive images for each concentration of suspensions (the numbers are referring to the reference number of measurements)
4. Acknowledgements UK Technology Strategy Board (Grant Reference: TP/2/SC/6/I/10097) and EPSRC (Grant Reference: EP/E040624/1) are thanked for their financial support.
5. References 1. McClements DJ, Adv Colloid Interf Sci, 1991, 37: 33-72. 2. Povey MJW, Pharma Sci Tech Today, 2000, 3: 373-380. 3. Dukhin AS, Goetz PJ, Ultrasound for characterizing colloids. Particle sizing, Zeta potential, Rheology. Elsevier: Amsterdam-New York-Tokyo, 2002. 4. Allegra JR, Hawley SA, J. Acoust. Soc. Amer., 1972, 51: 1545-1564. 5. Estein PS, Carhart RR, J. Acoust. Soc. Amer., 1953, 25: 553-565. 6. Flesia C, Schwendimann P., Applied Physic B, 1993, 56: 157-163. 7. Malvern Instruments, www.malvern.co.uk/common/downloads/campaign/MRK65601.pdf 8. Wang M, 7th World Cong Chem Eng, Glasgow, UK, 2005. 9. Williams RA, 7th World Congr Chem Eng, Glasgow, UK, 2005. 10. Wang XZ, Liu LD, Li RF, Tweedie RJ, Primrose K, Corbett J, McNeil-Watson FK, 2009, Chem Eng Res Des, in press, doi:10.1016/j.cherd.2008.12.014.
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Online monitoring of nanoparticle suspensions using dynamic light scattering, ultrasound spectroscopy and process tomography Xue Z Wanga, Lande Liua, Ruifa Lia, Richard J Tweedieb, Ken Primroseb, Jason Corbettc and Fraser McNeil-Watsonc a
Institute of Particle Science and Engineering, University of Leeds, Leeds LS2 9JT, UK, E-mail: [email protected] b Industrial Tomography Systems Limited, 39 Deansgate, Manchester M3 2BA UK c Malvern Instruments Ltd, Enigma Business Park, Worcestershire WR14 1XZ, UK
Abstract Dynamic light scattering, ultrasound spectroscopy and electrical resistance tomography were investigated for online monitoring of nanoparticle suspensions. This integrated system provides real time information about particle size distribution, zeta potential and particle concentration and visualises the mixing quality between particles and liquids. As particle size distribution is an indicator of the quality of particulate products, zeta potential measures the stability of colloidal particles and tomography shows particle concentration and the mixing quality between particles and liquids, this integrated multiple sensor system can be applied to nanoparticle manufacturing processes for online process and product quality control. Keywords: dynamic light scattering, ultrasound spectroscopy, process tomography, nanoparticle suspension, process analytical technology 1. Introduction Nanoparticle manufacturing in solid suspensions is becoming increasingly important to pharmaceutical, agrochemical and speciality chemical industries. For instance, nanonization is now used in the pharmaceutical industry to address the low solubility issue of hydrophobic pharmaceutical solids. Nano-processing in industry however faces major challenges compared to the production of larger particles. It is much more difficult to scale-up a process of nanomaterials from laboratory to industrial scale and to achieve consistency and reproducibility in product quality from batch to batch runs. An enabling technique to address the scale-up and manufacturing challenges is online sensing. The principle of using online sensing for process scale-up is that by being able to measure and understand in real-time the evolution of the product quality variables and process conditions as well as their interactions, and subsequently exercise control, product quality can be assured. However, despite the availability of various sensing techniques for measuring the size and size distribution of submicron to nano-scale particles, few of them can be applied online especially at industrial scale conditions.
352
X.Z. Wang et al.
Online measurement not only creates practical challenges to the design of an instrument, it also often pushes the instrument to be used beyond its limits such as of solid concentrations. While laboratory research favours low solid concentration, to be commercially viable industrial scale operation is usually required to be at high concentration. High solid concentration can result in particle-particle interactions and multiple scattering, leading to measurement errors.
Figure 1 The experimental rig. DLS – dynamic light scattering, USS – ultrasound spectroscopy, ERT – Electrical resistance tomography
The purpose of this study is to investigate the use of two of the most promising techniques for online sensing, dynamic light scattering (DLS) and ultrasound spectroscopy (USS), for real-time measurements of particle size distribution (PSD) and zeta potential during processing of nanoparticle slurries. Electrical resistance tomography (ERT) is also integrated into this multiple sensor system for the characterisation of mixing conditions and solid concentration, the later is required by DLS and USS in PSD measurement. The focus is on studying the various variables that impact the size measurement results, including solid concentration, mixing condition and zeta potential. The experimental system is shown in Figure 1. Ultrasound spectroscopy1-3 is a sensing technique highly suitable for online measurement, in particular for dense nano sized particle systems, where the system stability may be sensitive to changes in concentration. The principle of ultrasound spectroscopy measures the attenuation (energy loss) of sound waves due to sound absorption by media when a sound wave is transmitted and propagating into the media; the attenuation is then inverted into PSD according to the theory of ECAH 4, 5 or the coupled phase model3. Laser scattering technique often provides the benchmark laboratory-based method for PSD measurement. Since light is a type of electromagnetic wave, it can be described by the wave properties e.g. amplitude, frequency, and wavelength. When the light beam is shot through the suspension, the particles in the suspension scatter the light, resulting in change in phase of the light from that of the original beam. If the particles in suspension do not move, this phenomenon is called static light scattering. While in real suspension where particles move, it is called dynamic light scattering (DLS). This phenomenon is applied to particle size measurement. The particles in suspension move as a Brownian motion which means that large particles have low velocity while the small particles
Nanoparticle Suspensions Using Dynamic Light Scattering, Ultrasound Spectroscopy and Process Tomography
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have high velocity. The properties of scattered light change with respect to the particle velocity. Fast changing of wave of scattered light means small particles while slow change of wave is due to large particles. For concentrated slurry systems, a phenomenon called “multiple scattering” occurs. Mie theory is a methodology to address the issue. It describes the scattered intensity of spherical particle as a series of the product of Mie scattering coefficient with Legendre polynomial.6 An alternative method is to avoid multiple scattering using the back scattering technique.7 By installing a receiver close to the laser gun, the scattered light will not have to travel through the entire sample therefore reduce the multiple scattering effect. It backscatters the light via an angle, instead of passing through the samples, minimising the effect of multiple scattering. The Malvern online DLS system used in this work is based on this principle. ERT is used for cases that the continuous phase is a conductive fluid and the second phase can be either conductive or nor-conductive. Voltage is measured from a number of paired electrodes that are fitted around the inner wall of a pipe cross-section using tomographic sensors. The conductively distribution is then reconstructed to reflect the distribution of the second phase in the flow 8, 9 Frequency=20.MHz
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Figure 2. Concentration effect on the acoustic attenuation of silica suspensions (D attenuation; I particle concentration)
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2. Experiments Experiments were carried out on the rig shown in Figure 1. The process was designed to dilute using local tap water (conductivity 407μS/cm) a concentrated silica colloid (Nissan Corporation America, 24.0%vol with conductivity 4.12mS/cm, mean size of 70-100nm in diameter). ERT measurement was carried out through the whole process. USS and DLS measurements were taken when each dilution process was completed. A simple algorithm is developed to derive the concentration from the ERT conductivities.
2 E ( E 1)I 0 I 0 8EI 0 (I 0 1)(I 0 2) [2 E I 0 (I 0 E 1)] 2 2
(1) 2 E (I 0 2) where I is the volume concentration of the nonconductive phase, I0 is the initial concentration of the suspension and ȕ is the ratio of the initial conductivity to the measured conductivity.
I
USS particle sizing The relation between the acoustic attenuation and concentration for different frequencies is shown in Figure 2, the straight lines are the linear fittings of the attenuation data. It is clearly that the relationship becomes nonlinear as particle concentration is greater than 8.0% vol., and as expected3, that estimated PSD shifted to smaller mean size. PSDs for each concentration calculated by ECAH model and their mean sizes are shown in Figure 3. 120
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Figure 3. PSD and volume mean sizes of silica particles for each concentration. (a) the plots of PSD (the horizontal axial is particle size and the vertical axial is the corresponding volume fraction of the particles. The right arrow on I denotes the decrease of particle concentration). (b) volume mean size plot.
It is seen from (a) of Figure 3 that PSD gradually shifted to smaller particle size region with the increase of particle concentration and eventually became incorrect when concentration is greater than 8.0%vol. Concentration plot and ultrasound spectra The PSDs and their corresponding mean sizes measured by DLS are shown in (a) and (b) of Figure 4. (c) of Figure 4 is the comparison of the mean sizes between USS and DLS and (d) is the zeta potential measurements.
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Figure 4. Size distributions and volume means of silica particles measured by DLS. (a) size distributions (The right arrow on I denotes the decrease of particle concentration). (b) volume mean sizes. (c) comparison of mean sizes. (d) Zeta potential
From Figure 4, it is clear that for higher concentrations, the measured PSDs have slightly larger particle sizes; while the PSDs in lower concentrations as shown in (a) indicate that the dilution might have caused some changes to the system resulting in wider distributions. As seen in (c), a divergence between the two results does occur in higher concentration region (> 5.0%vol). This is due to the difference between the measurement principles. However, as can still be seen, the mean sizes in low concentration region are quite comparable. (d) indicates that with dilution going on the suspension gets more stable as the magnitude of zeta potential increases. In higher concentration region (here > 8.0%vol), the value of zeta potential indicates that the suspension is unstable. ERT results Figure 5 shows how the dispersed phase distributed in the continuous phase when flowing. As can be seen from the images in Figure 5, there was a slightly higher nonconductive area in the up left region of the images. This might be due to vortices generated in this area during the flow of the suspension resulting in particles slightly more concentrated than that in other areas.
3. Conclusions A system combining multiple sensors including dynamic light scattering (DLS), ultrasound spectroscopy (USS) and electrical resistance tomography (ERT) has been described for online characterisation of nanoparticle suspensions. ERT provides realtime information about the mixing quality of the dispersed phase in a continuous phase, and importantly solid concentration and conductivity which are required by DLS and USS for measurement of particle size distributions (PSD). The DLS sensor also measures zeta potential which is also important because its value is an indicator of stability of the suspension. Both instruments, DLS and USS, provide measurements about PSD10. Preliminary comparison was made for the two sets of data. It must be pointed out that due to the difference in measurement principles, the two sets of size
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distributions are unlikely to be identical for this specially designed experiment in high concentration region. However, they are comparable especially for the results of low concentrations in terms of their mean sizes as they appeared to be consistent. The knowledge obtained from experiments is useful for applying the instruments for online characterization of nanoparticle suspensions.
1)
2)
3)
4)
5)
6)
7)
8)
Figure 5. Constructed conductive images for each concentration of suspensions (the numbers are referring to the reference number of measurements)
4. Acknowledgements UK Technology Strategy Board (Grant Reference: TP/2/SC/6/I/10097) and EPSRC (Grant Reference: EP/E040624/1) are thanked for their financial support.
5. References 1. McClements DJ, Adv Colloid Interf Sci, 1991, 37: 33-72. 2. Povey MJW, Pharma Sci Tech Today, 2000, 3: 373-380. 3. Dukhin AS, Goetz PJ, Ultrasound for characterizing colloids. Particle sizing, Zeta potential, Rheology. Elsevier: Amsterdam-New York-Tokyo, 2002. 4. Allegra JR, Hawley SA, J. Acoust. Soc. Amer., 1972, 51: 1545-1564. 5. Estein PS, Carhart RR, J. Acoust. Soc. Amer., 1953, 25: 553-565. 6. Flesia C, Schwendimann P., Applied Physic B, 1993, 56: 157-163. 7. Malvern Instruments, www.malvern.co.uk/common/downloads/campaign/MRK65601.pdf 8. Wang M, 7th World Cong Chem Eng, Glasgow, UK, 2005. 9. Williams RA, 7th World Congr Chem Eng, Glasgow, UK, 2005. 10. Wang XZ, Liu LD, Li RF, Tweedie RJ, Primrose K, Corbett J, McNeil-Watson FK, 2009, Chem Eng Res Des, in press, doi:10.1016/j.cherd.2008.12.014.