Why particle size and shape measurement matters!
Crystallisation is a critical step in the isolation of active ingredients (AI) for agrochemical production and has major impacts on downstream processing and ultimately product quality. Key performance indicators of the crystallization process include particle size and morphology and a detailed understanding of the system kinetics is necessary if desirable particle characteristics are to be obtained. Crystallization phenomena are made of several competing processes including particle growth, nucleation, and agglomeration. Population balance models (PBM) can be used to capture the contributions from each of these mechanisms, although obtaining accurate parameters is typically a major challenge in the development of a high-fidelity model.
As with most models, the quality of the output is dependent on the quality of the input. There are numerous sources of error associated with the measurement. These inputs directly impact the estimation of crystallization rate parameters, and hence the predictive capability of the model. One major source of error is the use of particle size distribution (PSD) measurements to characterise seed material or final sample properties. When using laser diffraction techniques, the PSD of the sample can be subtly changed based on the optical model used (i.e., Fraunhofer or Mie). This is unlikely to raise concern for bulk properties, but the impact on kinetic properties, such as crystal growth, can be significant. Using PSD as an input to a mechanistic model for crystal growth, we explore the impact of the optical model selection on the ability to predict the outcome. By doing so, the method for PSD measurement is now more relevant to the crystal growth process and an improved accuracy of the model is achieved.
In addition, we also explore the commonly less utilised 2-dimensional morphological crystallizer models which allow the study of particle shape in addition to particle size. By leveraging a combination of single crystal growth studies and offline microscopy imaging we study the effects of crystal growth along different axes and simulate changes in particle aspect ratio under different crystallization conditions. This allows us to study factors that have an impact on aspect ratio to design crystallization processes with improved particle properties for downstream processing.
What this webinar covers
- Introduction to Malvern Panalytical and particle size analysis tools
- Recommendations for SOPs/workflows when performing particle size analysis for the purpose of mechanistic model calibration and application
- Impact of optical model used for laser diffraction measurements on mechanistic modelling activities and the predictive capacity of the resulting model
- Application of single crystal growth studies and offline image analysis to calibrate morphological crystallization models for the prediction of particle size and shape
- Exploration of resulting design space and scope for improving particle properties for downstream processing via modification of the crystallization process parameters
5th October 2022, 10:00 EDT/ 15:00 BST/ 16:00 CEST
45 minutes plus Q&A
Jennifer Webb, Syngenta
Jennifer Webb is a Team Leader in the Particle Science Group at Syngenta UK based at the Jealott’s Hill R&D site. Her team works on early-stage solid-state and crystallisation support to active ingredient and formulation development with a focus on driving fundamental understanding and innovation for process improvements. She has a PhD in physical chemistry, and her areas of interest include multiscale modelling, crystallization kinetics, process monitoring and control, and solid-state characterisation. Her work involves exploring new methodologies and technologies, working closely with industrial and academic collaborators around the world.
David Bowskill, Syngenta
David Bowskill joined Syngenta in 2021 as a senior particle scientist working in the process studies group at the Jealott’s Hill Research Centre in Bracknell. He graduated with a PhD in crystal structure prediction with a background in computational modelling, physical property prediction, and optimisation. Current work focuses on the use of PAT technologies and automation to investigate the crystallization behaviour of new active ingredients and the integration of experimental and modelling techniques.
Rob Taylor, Malvern Panalytical
Rob Taylor joined Malvern Panalytical as a technical specialist in Laser Diffraction and Analytical Imaging to support their applications in characterizing particle size and shape. He has a PhD in Physical Chemistry and takes particular interest in linking materials properties to product and process performance. Now with an MBA in Technology Management, Rob joined the Strategic Marketing team where he takes great interest in collaborating with other scientific groups to develop applications and understanding in Materials science.