Deliverables
This result allows models for the prediction of physical properties to be derived with less experimental data than was previously possible. This has wide-ranging applications in any industry, which manufactures chemicals, from the polymer industry to the pharmaceutical industry. The ability to predict properties with less data means that predictions and design become less expensive and less time-consuming. It also means that more innovation is possible than before and it allows companies to achieve a competitive advantage. This in turn will help sustain economic competitiveness and growth within the EU.
The result will be disseminated widely in terms of method. A software implementation will also be developed. Further experience with a wider range of compounds than those studied for PMILS is needed before commercialisation is possible and this is the subject of an application for funding with the UK Royal Society. Currently, the result is being used by the two institutions, which initiated it.
We have investigated with computational chemistry techniques the visible spectra of substituted anthraquinones. A wide panel of theoretical methods has been used, with various basis sets and density functional theory (DFT) functionals, in order to assess a level of theory that would lead to converged excitation energies. It turns out that the hybrid Becke-Lee-Yang-Parr and Perdew-Burke-Erzenrhof functionals with the 6-31G (d,p) atomic basis set provide reliable lambda max when the solvent effects are included in the model. Combining the results of both DFT schemes allows the prediction of lambda max with a standard deviation limited to 13nm.
Description of the result
The result is a general methodology to predict values of macroscopic properties of polymeric systems starting from lower level descriptions of the material. It can been applied to span two or more different levels depending on the property under consideration and under the calculation techniques available.
In the course of the project its applicability to the following properties (among others) has been demonstrated:
- Prediction of complexation energy / time of dye molecules to high polymers (polyamides), spanning quantum chemical and atomistic modelling methods.
- Prediction of linear elastic properties of polymers (polyethylene), spanning Monte Carlo for structure generation and Molecular Dynamics for calculation of Lamé constants
- Prediction of solubility of small molecules in polymeric matrices of varying molecular architecture, spanning Gibbs ensemble simulation at the atomistic level and group contribution techniques.
- Prediction of diffusivity of small molecules in polymeric matrices of varying molecular architecture, spanning Transition State Theory for penetrant jumps between available sites and group contribution techniques.
- Prediction of mechanical properties for anisotropic polymers (as oriented by processing), spanning GENERIC-Monte Carlo for structure generation under an externally imposed deformation velocity field and Molecular Dynamics for elastic moduli.
Quantified data on the result and visibility for the general public
The result has an impact on the general public through the possibility to tailor the molecular architecture of polymers and their additives to specific applications, resulting in improved materials and products in the areas of textiles, piping for communication lines and energy distribution, packaging materials, etc.
Further collaboration, dissemination and use of the result
Collaboration is foreseen with
- Large polymer producers, mainly interested in raw polymer improvement through control of the molecular architecture.
- Small and medium enterprises of the polymer processing industrial sector, mainly interested in processability and optimization of properties of the final (processed) product. Regarding dissemination, results will be published in several top-level scientific journals in the field of polymer and materials science (e.g. Macromolecules, Journal of non-Newtonian Fluid Mechanics, Polymer, Modeling and Simulation in Materials Science and Engineering).
- Small and medium enterprises of the polymer processing industrial sector, mainly interested in processability and optimization of properties of the final (processed) product. Regarding dissemination, results will be published in several top-level scientific journals in the field of polymer and materials science (e.g. Macromolecules, Journal of non-Newtonian Fluid Mechanics, Polymer, Modeling and Simulation in Materials Science and Engineering).
The result is a state-of-the-art Monte Carlo algorithm, based on the use of an advanced set of chain-connectivity altering moves, for the simulation of long-chain, polyethylene (PE) melts, with a linear and a non-linear molecular architecture. The code can handle both monodisperse and polydisperse systems.
In the course of the project, the code was successfully applied in the simulation of model H-shaped PE systems. These consist of PE chains possessing a main backbone (a crossbar) trapped between two branch points each of which is linked to two dangling arms.
In addition to providing rigorous estimates of the thermodynamic and conformational properties of H-polymers, the code provided a large number of uncorrelated and fully equilibrated (at all length scales) configurations for subsequent Molecular Dynamics (MD) studies.
This allowed us to successfully predict the following properties (among others) of H-shaped PE melts:
- Branch point friction,
- Diffusivity of chain center-of-mass,
- Spectrum of relaxation times,
- Zero shear rate viscosity
The developed software has opened up the way toward the simulation of the viscoelastic properties of polymers bearing long or short branches along their backbone. In connection with groups specializing in non-equilibrium molecular dynamics (NEMD) simulations, it can be used to explain the unique strain-hardening properties of branched PE melts and their performance superiority in fluid-flow processing operations over linear polymer melts.
The Computer Aided Polymer Design (CAPD) is a tool that assists the user in designing polymers with specific target properties. The output is in the form of suggested polymers described as repeat units (linear polymer structures) and/or for the identified polymer repeat units, the structural information (in terms of number of branches, branch length and molecular weight that satisfy the property targets. In this way, it serves as a first screening, which is very fast, easy and reliable. It provides a list of candidate polymers that can be further tested with laboratory experiments to identify the final product. Thereby, CAPD contributes in product development and analysis by minimizing cost and time by helping the designer to focus on candidates that are most likely to lead to a successful product.
At the current state, a tool for generating property prediction methods for polymer structures has been developed and a tool for generating the molecular structures of chemicals has also been developed. The next stage is to modify the structure generation tool to generate only the incomplete structure (which is a polymer repeat unit). The tool is Windows-based and comes with an easy to use interface.
Test problems generating the structures of polymer repeat units are available. Also, property estimation of polymer repeat units as well as repeat unit structures is possible in the current version. For property estimation of polymer repeat units, the developed model uses a group contributionplus concept where the contribution of any unavailable group is predicted through a topology index based methodology.
This result is a generic approach for stochastic differential equations (SDEs); here it is applied to the SDE model of polymer melt dynamics developed by Öttinger and coworkers, which predicts polymer viscosity based on a set of input parameters including plateau modulus and reptation time. The approach is an optimisation scheme in which, by comparing experimental viscosities with viscosities predicted by the model (over wide ranges of values of the input parameters), optimal parameters for the polymers can be deduced; there is currently no practical alternative method available to obtain this information.
By deducing parameters for many experimental structures, and obtaining parameters from mesoscale simulation, these parameters may be linked to structural characteristics of the polymers, thus enabling the prediction of parameters for possible new structures under consideration, and thence their viscosities, without the need for costly and time-consuming synthesis.
This can have significant implications in the area of polymer design; it would result not only in significant time savings in the prediction of visco-elastic properties for possible new structures, but also in an increase in the reliability of such predictions.
Hence the approach has great potential in the design of polymers with particular customer-specified rheological properties.
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