Modelling & Simulation in Formulations 2022


  • 1) The role of computational fluid dynamics in processing of complex fluids. Prof. Panagiota Angeli, ThAMeS Multiphase, Department of Chemical Engineering, UCL
  • Abstract.   The increased level of sophistication in modern formulated products, along with the need to achieve consistent product quality and meet customer expectations, pose major product and process development challenges. Uncertainties on the manufacturing process because of lack of understanding of the effect of process conditions on the formulation properties and complex rheology, means that companies produce based on forecast. Computational fluid dynamics simulations emerge as a powerful tool to simulate real conditions in industrial unit operations and, validated against experiments at appropriate scales, offer cost effective ways for process design and scale up. In the talk, we will discuss the development and application of computational fluid dynamics simulations in processes involving non-Newtonian fluids and dispersions. Examples from both batch and continuous processes will be presented, together with relevant experimental studies. We will show that a synergistic approach including modelling and experiments is very important not only for the validation of the computational tools but also for building fundamental process knowledge. Manufacturing challenges will be highlighted for the case studies.
  • 2) Molecular Simulation of Polymer Nanocomposite. Dr Alessandro Patti, Department of Applied Physics, University of Granada
  • Abstract.  Polymer nanocomposites (PNCs) are hybrid materials comprising a polymer matrix that hosts organic or inorganic nanoparticles (NPs). The incorporation of NPs, usually between 1 and 5 wt%, allows one to improve the macroscopic response of a polymer, including its thermal and mechanical resistance and viscoelastic behaviour. However, formulating PNCs that are more suitable to target applications than their engendering polymers is not trivial as it depends on an intricate network of correlated factors that pivot around the  interactions established at the polymer/NP interface. In this talk, we will discuss recent molecular simulation results that try to clarify the role of some of these factors and how they can be employed to control the behaviour of NPs and polymer chains at the small scale and influence the PNC’s response at the macroscopic scale. Starting from simple models, where NPs are represented as soft spheres, we will show how manipulating the NP surface chemistry and shape anisotropy, one can better control their distribution and orientation in the polymer matrix. We will discuss how coupling NPs’ functionalisation to an accurate choice of the polymer architecture can provide a powerful tool to fine-tune the material properties, including its viscosity.  Finally, we will see how the use of block copolymers, able to self-assemble into ordered mesophases, offers an opportunity to drive the arrangement of functional NPs into precise nanodomains, where specific functions can be activated.
  •  3) “In Silico Skin Penetration Modelling : A Cosmetic Europe Case Study. Results and Update. Dr Sébastien Grégoire
  • Abstract.In 2019, Cosmetic Europe published results of the skin penetration of 56 cosmetic relevant chemicals. The performance of 6 in silico models to predict skin penetration on this data set was published in 2021.
    Since then, the status of In Silico Skin Penetration Modelling has been updated. Indeed, Simulation Plus, in the last version of the TCAT model of GastroPlus, added a component describing chemical volatility. Furthermore, some papers have used the published data to assess the vehicle volatility and the deposition layer in skin. More recently, the impact of vehicle evaporation on the degree of saturation and its consequence on skin absorption has been modelled.
    These results show that in silico models have to face several difficulties. In the existing in silico models, the vehicle is applied at finite dose to be consistent with the real condition of use. The variation in the concentration of the chemical in the vehicle should therefore have been taken into account. Some tested chemicals are volatile. It means that the skin absorption would be limited by the remaining amount of chemical at the skin surface. Some other chemicals show peptide reactivity. As the chemical penetrates into and through the skin, it can react modifying the skin’s diffusion properties.
    In this presentation the results of the evaluated In silico models will be presented. Given the constraints of the experimental protocol, the lessons learned and the opportunities for further development of the in silico model will be discussed.
  • 4) Digitalization of formulation R&D via science-based web-apps. Sander van Loon, Van Loon Chemical Innovations B.V.  Amsterdam, The Netherlands.
  • Abstract. The applied predictive formulation sciences, Hansen Solubility Parameters (HSP) and Hydrophilic Lipophilic Difference (HLD) are very powerful to find matching ingredients, replace them and make them compatible, resulting in improved overall performance of end-products. These models are applicable to solutions, dispersions and emulsions, and give a sustainable predictive parameter to all kinds of ingredients: you can use them repeatedly, allowing it for digitalization in product developments to drastically reduce experimental work, complexity, time and cost. Our central database with these predictive ingredient parameters, is linked to our web-apps, allowing for science-based formulation of end-products in a sustainable and collaborative manner. The sciences will be explained and examples from the HSP based web-app will be demonstrated to discuss how this can boost formulation R&D. 
  • 5) Developing sustainable detergents through integration of computational modeling and protein engineering.   Dr Mehdi D. Davari, Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Germany.
  • Abstract. Modern laundry detergent formulations comprise many ingredients, including enzymes, surfactants, builders, bleaching agents, fillers, and other minor additives, which have a synergistic effect in removing stains from the fabric surfaces. Proteases are widely used in complex detergent systems to remove protein-based stains (e.g., egg, milk, blood, grass) from textiles. Understanding the molecular interactions of main detergent components is crucial in improving the formulations and performance of detergent industry products.  In this talk, I will highlight enzyme engineering activities in HICAST (Henkel Innovation Campus for Advanced and Sustainable Technologies) research cluster. HICAST aimed to develop novel and sustainable laundry detergents through a fundamental understanding of interactions among enzymes and detergent components. The main objective of this work was to gain an in-depth molecular understanding of molecular interactions that govern an enzymatic activity boost on a detergent protease in the presence of polymers and surfactants. The integrative approach of computational modeling (atomistic and coarse-grained molecular dynamic simulations), colorimetric analysis, biophysical characterization (CD, FCS, ITC, and DLS), and protein engineering proved to be a promising workflow to gain molecular insights into the interactions that govern the boosting effect and to improve protease performance in detergents [1, 2]. The gained molecular understanding of fundamental principles causing enhanced protease performance is likely applicable to other detergent enzymes. It can be further applied to the engineering of enzymes, polymers, and surfactants compositions in modern laundry detergents.
  • 1. M.J. Thiele#, M.D. Davari#, M. König, I. Hoffman, N. Junker, T. Mirzaei Garakani, L. Vojcic, J. Fitter, U. Schwaneberg, Enzyme-Polyelectolytes Complexes Boost the Performance of Enzymes, ACS Catalysis, 8 (11), (2018) 10876-10887. #shared first authors
  • 2. M.J. Thiele#, M.D. Davari#, I. Hofmann, M. König, C.G. Lopez, L. Vojcic, W. Richtering, U. Schwaneberg, L. A. Tsarkova. Enzyme‐Compatible Dynamic Nanoreactors from Electrostatically Bridged Like-Charged Surfactants and Polyelectrolytes. Angewandte Chemie International Edition. (2018), doi: 10.1002/anie.201805021. #Shared first authors
  • 6) Energetic Material Formulations: The quest to bridge the gap between atomistic and macro-scale by simulation. Prof Ken Lewtas1,2,3, Dr Rick Anderson4, Dr David Bay4.  1Lewtas Science & Technology Ltd,2 The Falcon Project Ltd, 3 The University of Warwick, 4 Hartee Centre, Science and Technology Facilities Council.
  • Abstract:Solid rocket propellants have been used for many decades both commercially and militarily and this talk will give a brief introduction to the field. Recently, we have been looking into these systems in order to solve existing problems and improve both safety and performance aspects. To this end, we have formed a collaborative team with the Falcon Project, the Hartree Centre and several universities. This collaboration covers R&D through to real-world rocket firings and we have tried to use simulation techniques across all length-scales to aid the understanding and with an ultimate goal of becoming predictive. This short talk will focus mainly on the atomistic molecular simulations on the components of these rocket formulations and build into new molecules which promise to improve processing, safety and performance. Commercial developments have meant that little has been published on this work. However a reference to a recent paper (below) gives an idea of the coarse-grained simulations we employ to test against experiment.
  • Wax Formation in Linear and Branched Alkanes with Dissipative Particle Dynamics, J Chem Theory Comput., 2020 Nov 10;16(11):7109-7122
  • 7) Population Balances for Full-Chain Constitutive Models of Living Polymers.Dr Joe Peterson, University of Cambridge, Department of Applied Maths and Theoretical Physics.
  • Abstract. Wormlike micelles (WLM) are long, polymer-like macromolecules that self-assemble from small molecule surfactants. In formulation science, WLM are a useful materials platform for tuning viscoelastic properties in complex fluid that already contain surfactants – current applications of WLM range from enhanced oil recovery to ordinary consumer products like shampoo.  Because the value of WLM lies in their ability to tune a material’s viscoelastic properties, it is important to develop models that describe (and eventually predict) the rheology of such materials and what makes them distinct from covalently bonded polymers. Because WLM are self-assembled structures, polymerization reactions like scission and recombination are reversible and non-terminating, making WLM a classical “living polymer” material. In living polymer systems, there is a complex relationship between reversible polymerization dynamics and stress relaxation processes, as reactions can reshape how stress is distributed across structures in a system. The interplay between reactions and stress relaxation is perhaps best understood for small deformations (linear rheology) of well-entangled and “fast breaking” linear-chain polymers undergoing reversible scission. There, the classical work by Cates describes how reactions move slow-relaxing interior chain segments to end positions where stress relaxation is faster. Unfortunately, practical applications of living polymers often involve non-linear rheology and/or slow-breaking systems, where a more general modeling framework has remained incomplete and/or intractable.  In this talk, we share our recent work on the use of population balances in full-chain constitutive models of living polymers. We present a systematic method for constructing living polymer models by appending population balance equations to the stress relaxation dynamics as described by existing non-linear differential constitutive models of stress relaxation phenomena in polymeric materials. We consider results for reptation, contour length fluctuations (CLF), and chain retraction. Finally, when considering a ‘fast breaking’ limit of the full non-linear model, we combine all of the preceding results together and show that the resultant leading-order equations (which we call the STARM model) simplify dramatically and are suitable for computational fluid dynamics applications.
  • 8) Pareto active learning for in silico polymer design.   Dr Brian Yoo, Ferolabs.
  • Abstract. Active learning techniques are being increasingly adopted into materials design workflows. While most active learning approaches help address small data challenges, they often optimize for a single (overall) objective. In most applications, however, there are competing objectives where the goal is to find the set of Pareto optimal materials. We present an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with tunable confidence and showcase how the algorithm can be leveraged for in silico polymer design with complex combinatorial search spaces.
Organising committee
  • Prof Flor Siperstein, University of Manchester
  • Prof Paola Carbone, University of Manchester
  • Dr Charlie Wand, University of Exeter / RSC-SMT
  • Dr. Stephan Köhler, BASF SE
  • Dr Annalaura Del Regno, BASF SE
  • Dr Philip P. Gill, Roxel (UK Rocket Motors) Ltd / RSC-FST


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