Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids.

Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers. The characterization of transport properties, including diffusion and leaching of these molecules, is largely hampered by the long timescales involved in shelf life experiments. In this work, we employ atomistic molecular simulation techniques to explore the main mechanisms involved in the bulk and interfacial transport of monomer molecules from three polymers commonly employed as packaging materials: polyamide-6, polycarbonate, and poly(methyl methacrylate). Our simulations showed that both hopping and continuous diffusion play important roles in inbound monomer diffusion and that solvent-polymer compatibility significantly affects monomer leaching. These results provide rationalization for monomer leaching in model food formulations as well as bulky industry-relevant molecules. Through this molecular-scale characterization, we offer insights to aid in the design of polymer/consumer product interfaces with reduced risk of contamination and longer shelf life.

Advancing material property prediction: using physics-informed machine learning models for viscosity.

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

Molecular-Scale Exploration of Mechanical Properties and Interactions of Poly(lactic acid) with Cellulose and Chitin.

Poly(lactic acid) (PLA), one of the pillars of the current overarching displacement trend switching from fossil- to natural-based polymers, is often used in association with polysaccharides to increase its mechanical properties. However, the use of PLA/polysaccharide composites is greatly hampered by their poor miscibility, whose underlying nature is still vastly unexplored. This work aims to shed light on the interactions of PLA and two representative polysaccharide molecules (cellulose and chitin) and reveal structure-property relationships from a fundamental perspective using atomistic molecular dynamics. Our computational strategy was able to reproduce key experimental mechanical properties of pure and/or composite materials, reveal a decrease in immiscibility in PLA/chitin compared to PLA/cellulose associations, assert PLA-oriented polysaccharide reorientations, and explore how less effective PLA-polysaccharide hydrogen bonds are related to the poor PLA/polysaccharide miscibility. The connection between the detailed chemical interactions and the composite behavior found in this work is beneficial to the discovery of new biodegradable and natural polymer composite mixtures that can provide needed performance characteristics.

Development of scalable and generalizable machine learned force field for polymers.

Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training approach that uses partial charges from a semi-empirical method. Our developed QRNN model for polymers produces stable molecular dynamics (MD) simulation trajectory and captures the dynamics of polymer chains as indicated by the striking agreement with experimental values. Our model allows working on much larger systems than allowed by DFT simulations, at the same time providing a more accurate force field than classical force fields which provides a promising avenue for large-scale molecular simulations of polymeric systems.

Exploring the Effects of Wetting and Free Fatty Acid Deposition on an Atomistic Hair Fiber Surface Model Incorporating Keratin-Associated Protein 5-1.

The complex development of cosmetic and medical formulations relies on an ever-growing accuracy of predictive models of hair surfaces. Hitherto, modeling efforts have focused on the description of 18-methyl eicosanoic acid (18-MEA), the primary fatty acid covalently attached to the hair surface, without explicit modeling of the protein layer. Herein, the molecular details of the outermost surface of the human hair fiber surface, also called the F-layer, were studied using molecular dynamics (MD) simulations. The F-layer is composed primarily of keratin-associated proteins KAP5 and KAP10, which are decorated with 18-MEA on the outer surface of a hair fiber. In our molecular model, we incorporated KAP5-1 and evaluated the surface properties of 18-MEA through MD simulations, resulting in 18-MEA surface density, layer thickness, and tilt angles in agreement with previous experimental and computational studies. Subsequent models with reduced 18-MEA surface density were also generated to mimic damaged hair surfaces. Response to wetting of virgin and damaged hair showed rearrangement of 18-MEA on the surface, allowing for water penetration into the protein layer. To demonstrate a potential use case for these atomistic models, we deposited naturally occurring fatty acids and measured 18-MEA's response in both dry and wet conditions. As fatty acids are often incorporated in shampoo formulations, this work demonstrates the ability to model the adsorption of ingredients on hair surfaces. This study illustrates, for the first time, the complex behavior of a realistic F-layer at the molecular level and opens up the possibility of studying the adsorption behavior of larger, more complex molecules and formulations.

Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations.

The substitution of natural, bio-based and/or biodegradable polymers for those of petrochemical origin in consumer formulations has become an active area of research and development as the sourcing and destiny of material components becomes a more critical factor in product design. These polymers often differ from their petroleum-based counterparts in topology, raw material composition and solution behaviour. Effective and efficient reformulation that maintains comparable cosmetic performance to existing products requires a deep understanding of the differences in frictional behaviour between polymers as a function of their molecular structure. In this work, we simulate the tribological behaviour of three topologically distinct polymers in solution with surfactants and in contact with hair-biomimetic patterned surfaces. We compare a generic functionalized polysaccharide to two performant polymers used in shampoo formulations: a strongly positively charged polyelectrolyte and a zwitterionic copolymer. Topological differences are expected to affect rheological properties, as well as their direct interaction with structured biological substrates. Using a refined Martini-style coarse-grained model we describe the polymer-dependent differences in aggregation behaviour as well as selective interactions with a biomimetic model hair surface. Additionally, we introduce a formalism to characterize the response of the solution to shear as an initial study on lubrication properties, which define the sensorial performance of these systems in cosmetics (i.e., manageability, touch, etc.). The tools and techniques presented in this work illustrate the strength of molecular simulation in eco-design of formulation as a complement to experiment. These efforts help advance our understanding of how we can relate complex atomic-scale solution behaviour to relevant macroscopic properties. We expect these techniques to play an increasingly important role in advancing strategies for green polymer formulation design by providing an understanding for how new polymers could reach and even exceed the level of performance of existing polymers.

A Molecular Dynamics Study of Monomer Melt Properties of Cyanate Ester Monomer Melt Properties.

The objective of this work was to computationally predict the melting temperature and melt properties of thermosetting monomers used in aerospace applications. In this study, we applied an existing voids method by Solca. to examine four cyanate ester monomers with a wide range of melting temperatures. Voids were introduced into some simulations by removal of molecules from lattice positions to lower the free-energy barrier to melting to directly simulate the transition from a stable crystal to amorphous solid and capture the melting temperature. We validated model predictions by comparing melting temperature against previously reported literature values. Additionally, the torsion and orientational order parameters were used to examine the monomers' freedom of motion to investigate structure-property relationships. Ultimately, the voids method provided reasonable estimates of melting temperature while the torsion and order parameter analysis provided insight into sources of the differing melt properties between the thermosetting monomers. As a whole, the results shed light on how freedom of molecular motions in the monomer melt state may affect melting temperature and can be utilized to inspire the development of thermosetting monomers with optimal monomer melt properties for demanding applications.

Molecular-Level Examination of Amorphous Solid Dispersion Dissolution.

Amorphous solid dispersions (ASDs) are commonly used to orally deliver small-molecule drugs that are poorly water-soluble. ASDs consist of drug molecules in the amorphous form which are dispersed in a hydrophilic polymer matrix. Producing a high-performance ASD is critical for effective drug delivery and depends on many factors such as solubility of the drug in the matrix and the rate of drug release in aqueous medium (dissolution), which is linked to bioperformance. Often, researchers perform a large number of design iterations to achieve this objective. A detailed molecular-level understanding of the mechanisms behind ASD dissolution behavior would aid in the screening, designing, and optimization of ASD formulations and would minimize the need for testing a wide variety of prototype formulations. Molecular dynamics and related types of simulations, which model the collective behavior of molecules in condensed phase systems, can provide unique insights into these mechanisms. To study the effectiveness of these simulation techniques in ASD formulation dissolution, we carried out dissipative particle dynamics simulations, which are particularly an efficient form of molecular dynamics calculations. We studied two stages of the dissolution process: the early-stage of the dissolution process, which focuses on the dissolution at the ASD/water interface, and the late-stage of the dissolution process, where significant drug release would have occurred and there would be a mixture of drug and polymer molecules in a predominantly aqueous environment. Experimentally, we used Fourier transform infrared spectroscopy to study the interactions between drugs, polymers, and water in the dry and wet states and the chromatographic technique to study the rate of drug and polymer release. Both experiments and simulations provided evidence of polymer microstructures and drug-polymer interactions as important factors for the dissolution behavior of the investigated ASDs, consistent with previous work by Pudlas et al. (Eur. J. Pharm. Sci.2015, 67, 21-31). As experimental and simulation results are consistent and complementary, it is clear that there is significant potential for combined experimental and computational research for a detailed understanding of ASD formulations and, hence, formulation optimization.