Understanding the balance between additives’ miscibility and plasticisation effect in polymer composites: a computational study

Giunta, G.; Smith, Lois E H; Bartha, Kristof; Karimi–Varzaneh, Hossein Ali; Carbone, Paola

doi:10.1039/d2sm01642g

Cited by 2 publications

(18 citation statements)

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“…and N PL and N PI are the numbers of PL and PI beads, respectively. We elected to only study one PI chain length based on our previous work using the same model, 33 in which PI lengths of 1, 10, 150, and 300 were investigated. It was found that, for each PL molecule studied, the miscibility was consistent regardless of PI length, with the exception of the PI solvent (i.e., a monomer solution), for which all PLs studied were miscible.…”

Section: ■ Methodologymentioning

confidence: 99%

“…We further simulated rigid, rod-like PLs, for which the aforementioned bending potential was replaced by a harmonic potential U normalr normalo normald ( θ ) = 1 2 k h ( θ − θ 0 ) 2 where k h = 100 kJ mol –1 rad –2 is a force constant and θ 0 is an equilibrium bond angle, which is fixed according to the geometry of each PL molecule (see Figure ). This model has been used in previous studies of polymer composites. ,, All simulations in this work were performed using the GROMACS 5.0 MD simulation package…”

Section: Methodsmentioning

confidence: 99%

“…30,32,34 These models have been successfully employed in polymer simulations to study structural features and plasticization effects 30,35−37 as well as to study polymer blends. 38 Recently, using a chemically specific bead and spring model, 33 we studied how the addition of small diluents affects a polymer/filler interface and which structural features improve their chances of adsorption onto a graphitic filler surface. There, we showed that just changing the backbone rigidity of the additives affects not only their degree of dispersion in the polymer matrix but also their adsorption on the surface of the filler.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Over the recent years, computer simulations have become a powerful and efficient tool to identify relevant PL properties, in part because they allow the study of model features systematically and in isolation. Several recent computational studies have attempted to link PL rigidity, chemistry, molecular weight, and interaction strength with the polymer matrix to their miscibility. − Atomistic simulations are particularly useful when specific directional interactions, such as hydrogen bonds, play a role in the plasticization effect; however, due to their high computational cost, they cannot be used for screening large families of molecules. To overcome this problem, coarse-grained models can be used as less computationally intensive alternatives. ,, These models have been successfully employed in polymer simulations to study structural features and plasticization effects ,− as well as to study polymer blends …”

Section: Introductionmentioning

confidence: 99%

“…Such descriptors should be quickly computable from simplified simulation setups, such as in a vacuum, and ideally experimentally measurable. As in the work by Jablonka et al, we employ a coarse-grained model for both the polymers and the medium-sized molecules following our previous work. , For polyolefins, the major contributor to the solubility is related to the geometry of the chain and local density . Thus, the chosen coarse-grained model, whose parameters have been developed fitting the experimental packing length among other geometrical chain properties, should provide results (in terms of solubility) comparable with an all-atom model.…”

Section: Introductionmentioning

confidence: 99%

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Framework for a High-Throughput Screening Method to Assess Polymer/Plasticizer Miscibility: The Case of Hydrocarbons in Polyolefins

Smith,

Karimi-Varzaneh,

Finger

et al. 2024

Macromolecules

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Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers (PLs), which are small organic molecules, are added to the polymer matrix. The miscibility of these PLs has a large impact on their effectiveness and, therefore, their interactions with the polymer matrix must be carefully considered. Many PL characteristics, including their size, topology, and flexibility, can impact their miscibility and, because of the exponentially large number of PLs, the current trial-and-error approach is very ineffective. In this work, we show that using coarse-grained molecular simulations of a small dataset of 48 PLs, it is possible to identify topological and thermodynamic descriptors that are proxy for their miscibility. Using ad-hoc molecular dynamics simulation setups that are relatively computationally inexpensive, we establish correlations between the PLs’ topology, internal flexibility, thermodynamics of aggregation, and degree of miscibility, and use these descriptors to classify the molecules as miscible or immiscible. With all available data, we also construct a decision tree model, which achieves a F1 score of 0.86 ± 0.01 with repeated, stratified 5-fold cross-validation, indicating that this machine learning method can be a promising route to fully automate the screening. By evaluating the individual performance of the descriptors, we show this procedure enables a 10-fold reduction of the test space and provides the basis for the development of workflows that can efficiently screen PLs with a variety of topological features. The approach is used here to screen for apolar PLs in polyisoprene melts, but similar proxies would be valid for other polyolefins, while, in cases where polar interactions drive the miscibility, other descriptors are likely to be needed.

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Section: ■ Methodologymentioning

confidence: 99%