Development of a coarse-grained molecular dynamics model for poly(dimethyl-co-diphenyl)siloxane.

Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, e.g., as in a poly(dimethyl-co-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. To overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-co-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, etc.

Investigating structure and dynamics of unentangled poly(dimethyl-co-diphenyl)siloxane via molecular dynamics simulation.

Polysiloxane is one of the most important polymeric materials in technological use. Polydimethylsiloxane displays glass-like mechanical properties at low temperatures. Incorporation of phenyl siloxane, via copolymerization for example, improves not only the low-temperature elasticity but also enhances its performance over a wide range of temperatures. Copolymerization with the phenyl component can significantly change the microscopic properties of polysiloxanes, such as chain dynamics and relaxation. However, despite much work in the literature, the influence of such changes is still not clearly understood. In this work, we systematically study the structure and dynamics of random poly(dimethyl-co-diphenyl)siloxane via atomistic molecular dynamics simulations. As the molar ratio ϕ of the diphenyl component increases, we find that the size of the linear copolymer chain expands. At the same time, the chain-diffusivity slows down by over an order of magnitudes. The reduced diffusivity appears to be a result of a complex interplay between the structural and dynamic changes induced by phenyl substitution.

Transparency Change Mechanochromism Based on a Robust PDMS-Hydrogel Bilayer Structure.

Hydrogels and polydimethylsiloxane (PDMS) are complementary to each other, since the hydrophobic PDMS provides a more stable and rigid substrate, while the water-rich hydrogel possesses remarkable hydrophilicity, biocompatibility, and similarity to biological tissues. Herein a transparent and stretchable covalently bonded PDMS-hydrogel bilayer (PHB) structure is prepared via in situ free radical copolymerization of acrylamide and allylamine-exfoliated-ZrP (AA-e-ZrP) on a functionalized PDMS surface. The AA-e-ZrP serves as cross-linking nano-patches in the polymer gel network. The covalently bonded structure is constructed through the addition reaction of vinyl groups of PDMS surface and monomers, obtaining a strong interfacial adhesion between the PDMS and the hydrogel. A mechanical-responsive wrinkle surface, which exhibs transparency change mechanochromism, is created via introducing a cross-linked polyvinyl alcohol film atop the PHB structure. A finite element model is implemented to simulate the wrinkle formation process. The implication of the present finding for the interfacial design of the PHB and PDMS-hydrogel-PVA trilayer (PHPT) structures is discussed.

Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials.

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer-particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer-particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

Molecular self-assembled monolayers anomalously enhance thermal conductance across polymer-semiconductor interfaces.

Thermal issues have become increasingly important for the performance and lifetime of highly miniaturized and integrated devices. However, the high thermal resistance across the polymer/semiconductor interface greatly weakens the fast heat dissipation. In this study, applying the self-assembled monolayer (SAM) technique, organic molecules are employed as heat regulators to mediate interfacial thermal conductance (ITC) between semiconductors (silicon or Si) and polymers (polystyrene or PS). Silane-based SAM molecules with unique functional groups, such as -NH2, -CH3, -SH, and -Cl, are orderly assembled into Si-PS interfaces. Their roles in ITC and the heat transfer mechanism were systematically investigated. Molecular simulations demonstrate that the Si-PS interface decorated with SAM molecules can significantly facilitate heat transfer in varying degrees. Such a difference is primarily due to the different non-bonded interactions and compatibility between SAMs and PS. Compared with the pristine Si-PS interface, the interface incorporated with 3-chloropropyl trimethoxysilane shows the greatest improvement in ITC, about 507.02%. Such improvements are largely attributed to the SAM molecules, as the thermal bridges straighten the molecular SAM chains, develop strong non-bonded interactions with PS, provide the covalent bonding between Si and PS, exhibit a strong coupling effect between two materials' vibrational modes, and eliminate the discontinuities in the temperature field. Eventually, these demonstrations are expected to offer molecular insights to enable effective thermal management through surface engineering for critical-heat transfer materials and microelectronic devices.

Unstructured Self-Assembled Molecular Lamella Induces Ultrafast Thermal Transfer through a Cathode/Separator Interphase in Lithium-Ion Batteries.

Thermal issues associated with lithium-ion batteries (LIBs) can dramatically affect their life cycle and overall performance. However, the effective heat transfer is deeply restrained by the high thermal resistance across the cathode (lithium cobalt oxide, LCO)-separator (polyethylene, PE) interface. This work presents a new approach to tailoring the interfacial thermal resistance, namely, unstructured self-assembled lamella (USAL). Compared to the popular self-assembled monolayers, although the USAL gives a redundant interface and amorphous molecule patterns, it can also provide many benefits, including easy assembly, more thermal bridges, and ready pressurization. Three small organic molecules (SOMs) were assembled into an LCO-PE interface, providing unique functional groups, -NH2, -SH, and -CH3, to illustrate its energy conversion efficiency. Through molecular dynamics simulations, our results show that the USAL can facilitate interfacial heat transfer remarkably. A 3-aminopropyl trimethoxysilane (APTMS)-coated LCO-PE system with 11.4 Å thickness demonstrates the maximum enhancement of thermal conductance, about 320% of the pristine system. Such enhancement is attributed to the developed double heat passages by strong non-bonded interactions across LCO-SOM and PE-SOM interfaces, a tuned temperature field, and high compatibility between SOMs and PE. Importantly, due to SOMs' amorphous morphology, the pressure can be imposed and further enhance the interfacial heat transfer. Results show the improved thermal conductance rises the most for the APTMS-coated LCO-PE system with 11.4 Å thickness at 10 GPa, almost 685% higher than that of the pristine system. The high efficiency of heat transfer comes as a result of the enhanced binding strength across the LCO-SOM and SOM-PE interface, the reduced phonon scattering in PE and SOMs, and the high LCO stiffness. These investigations are expected to provide a new perspective for modulating the heat transfer across the interphase of LIBs and achieve more effective thermal management for the multi-material system.

Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges.

Machine learning (ML) has emerged as one of the most powerful tools transforming all areas of science and engineering. The nature of molecular dynamics (MD) simulations, complex and time-consuming calculations, makes them particularly suitable for ML research. This review article focuses on recent advancements in developing efficient and accurate coarse-grained (CG) models using various ML methods, in terms of regulating the coarse-graining process, constructing adequate descriptors/features, generating representative training data sets, and optimization of the loss function. Two classes of the CG models are introduced: bottom-up and top-down CG methods. To illustrate these methods and demonstrate the open methodological questions, we survey several important principles in constructing CG models and how these are incorporated into ML methods and improved with specific learning techniques. Finally, we discuss some key aspects of developing machine-learned CG models with high accuracy and efficiency. Besides, we describe how these aspects are tackled in state-of-the-art methods and which remain to be addressed in the near future. We expect that these machine-learned CG models can address thermodynamic consistent, transferable, and representative issues in classical CG models.