Molecular Dynamics Simulation on the Heat Transfer in the Cross-Linked Poly(dimethylsiloxane)

In this work, the effect of cross-linking degree and stretching on the thermal conductivity of poly(dimethylsiloxane) (PDMS) is explored by performing a molecular dynamics simulation. Our results demonstrate that the thermal conductivity of PDMS exhibits a monotonous rise with an increase in the cross-linking degree. By decomposing the total heat flux into three microscopic heat transfer modes, the high cross-linking degree improves the contribution from bonding interactions to the heat transfer more than that from the nonbonding interactions. An analysis of the vibrational density of states shows a blue-shift of the vibrational modes at low frequencies, indicating a large phonon group velocity due to the strong interchain bonding interaction. From the spectral distribution of heat flux, the spectral contributions are shifted toward the higher frequencies with the increasing cross-linking degree, which reflects more contribution from the high-frequency modes to the heat transfer. Stretching can improve the thermal conductivity parallel to the tensile direction with the increase in strain. This is mainly due to the further improved contribution of bonding interactions or high-frequency modes to heat transfer. Interestingly, the anisotropy of the thermal conductivity first decreases and then increases with the increasing cross-linking degree. Our study conducts a detailed investigation of the thermal conductivity of cross-linked PDMS, providing guidance on the application of thermal interface materials.

Heat Transfer Enhancement in Tree-Structured Polymer Linked Gold Nanoparticle Networks

Human brains use a tree-like neuron network for information processing at high efficiency and low energy consumption. Tree-like structures have also been engineered to enhance mass and heat transfer in various applications. In this work, we reveal the heat transfer mechanism in tree-structured polymer linked gold nanoparticle (AuNP) networks using atomistic simulations. We report both upward and downward heat fluxes between root and leaf nodes in tree-structured polyethylene (PE) and poly(p-phenylene) (PPP) linked AuNP networks at tree levels from 1 to 5. We found that the heat conductance increases with an increasing polymer tree level. The heat transfer enhancement is due to the resulting increase in the low-frequency vibrational modes. This and other thermal properties are affected by the location of the AuNPs in the tree. Moreover, complex tree structures with at least five levels were found to be robust in the sense that disabling half of the leaves did not change the overall heat conductance.

Transforming the Petroleum Industry through Catalytic Oxidation Reactions vis-à-vis Preceramic Polymer Catalyst Supports

Preceramic polymers, for instance, are used in a variety of chemical processing industries and applications. In this contribution, we report on the catalytic oxidation reactions generated using preceramic polymer catalyst supports. Also, we report the full knowledge of the use of the remarkable catalytic oxidation, and the excellent structures of these preceramic polymer catalyst supports are revealed. This finding, on the other hand, focuses on the functionality and efficacy of future applications of catalytic oxidation of preceramic polymer nanocrystals for energy and environmental treatment. The aim is to design future implementations that can address potential environmental impacts associated with fuel production, particularly in downstream petroleum industry processes. As a result, these materials are being considered as viable candidates for environmentally friendly applications such as refined fuel production and related environmental treatment.

Analysis of the Structure and the Thermal Conductivity of Semi-Crystalline Polyetheretherketone/Boron Nitride Sheet Composites Using All-Atom Molecular Dynamics Simulation

Thermal transport simulations were performed to investigate the important factors affecting the thermal conductivity based on the structure of semi-crystalline polyetheretherketone (PEEK), and the addition of boron nitride (BN) sheets. The molecular-level structural analysis facilitated the prediction of the thermal conductivity of the optimal structure of PEEK reflecting the best parameter value of the length of amorphous chains, and the ratio of linkage conformations, such as loops, tails, and bridges. It was found that the long heat transfer paths of polymer chains were induced by the addition of BN sheets, which led to the improvement of the thermal conductivities of the PEEK/BN composites. In addition, the convergence of the thermal conductivities of the PEEK/BN composites in relation to BN sheet size was verified by the disconnection of the heat transfer path due to aggregation of the BN sheets.

Thermal Transport through Polymer-Linked Gold Nanoparticles

Polymer-nanoparticle networks have potential applications in molecular electronics and nanophononics. In this work, we use all-atom molecular dynamics to reveal the fundamental mechanisms of thermal transport in polymer-linked gold nanoparticle (AuNP) dimers at the molecular level. Attachment of the polymers to AuNPs of varying sizes allows the determination of effects from the flexibility of the chains when their ends are not held fixed. We report heat conductance (G) values for six polymers-viz. polyethylene, poly(p-phenylene), polyacene, polyacetylene, polythiophene, and poly(3,4-ethylenedioxythiophene)-that represent a broad range of stiffness. We address the multimode effects of polymer type, AuNP size, polymer chain length, polymer conformation, system temperature, and number of linking polymers on G. The combination of the mechanisms for phonon boundary scattering and intrinsic phonon scattering has a strong effect on G. We find that the values of G are larger for conjugated polymers because of the stiffness in their backbones. They are also larger in the low-temperature region for all polymers owing to the quenching of segmental rotations at low temperature. Our simulations also suggest that the total G is additive as the number of linking polymers in the AuNP dimer increases from 1 to 2 to 3.

Tuning the polymer thermal conductivity through structural modification induced by MoS2 bilayers

The present work refers to a physical and structural study of nanoconfined polymers in polymer-MoS₂ nanocomposites as a function of MoS₂-MoS₂ interlayer distance. We applied reverse nonequilibrium molecular dynamics (RNEMD) simulations to investigate the thermal conductivity (λ) of polyamide oligomers confined by MoS₂ bilayers. The results of this study indicate that thermal conductivity of polymer can be considerably enhanced when polymer chains are confined by MoS₂ sheets, this behavior is more pronounced by charged surfaces. The presence of MoS₂ surfaces leads to elongation as well as preferential alignment of polymer chains parallel to the MoS₂ surfaces, which in turn results in higher order and denser packing of polymer content and hence larger thermal conductivity in comparison to the bulk polymer. Additionally, the analysis of the number of hydrogen bonds (HBs) in confined polymer chains suggests that a combined effect of the mentioned structural modification and enlarged values of HBs may cooperatively contribute to high polymer thermal conductivity, facilitating phonon transport. The results reported here suggest a significant way to design confined polymer-MoS₂ composites for significantly improving thermal conductivity for a wide variety of applications.

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning

Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.400 W/m·K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all generated polymers with thermal conductivity ≥0.400 W/m·K, we have evaluated their synthesizability by calculating the synthetic accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has an MD-calculated thermal conductivity of 0.693 W/m·K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other material development tasks for different applications.

Effect of Side-Chain π-π Stacking on the Thermal Conductivity Switching in Azobenzene Polymers: A Molecular Dynamics Simulation Study

The light switchable thermal conductivity displayed by some polymers makes them promising for applications like data storage, temperature regulation and light switchable devices. In this study, the mechanism of thermal...

Sequence-Engineering Polyethylene-Polypropylene Copolymers with High Thermal Conductivity Using a Molecular-Dynamics-Based Genetic Algorithm

Polymer sequence engineering is emerging as a potential tool to modulate material properties. Here, we employ a combination of a genetic algorithm (GA) and atomistic molecular dynamics (MD) simulation to design polyethylene-polypropylene (PE-PP) copolymers with the aim of identifying a specific sequence with high thermal conductivity. PE-PP copolymers with various sequences at the same monomer ratio are found to have a broad distribution of thermal conductivities. This indicates that the monomer sequence has a crucial effect on thermal energy transport of the copolymers. A non-periodic and non-intuitive optimal sequence is indeed identified by the GA, which gives the highest thermal conductivity compared with any regular block copolymers, for example, diblock, triblock, and hexablock. In comparison to the bulk density, chain conformations, and vibrational density of states, the monomer sequence has the strongest impact on the efficiency of thermal energy transport via inter- and intra-molecular interactions. Our work highlights polymer sequence engineering as a promising approach for tuning the thermal conductivity of copolymers, and it provides an example application of integrating atomistic MD modeling with the GA for computational material design.

The hydrogen-bond configuration modulates the energy transfer efficiency in helical protein nanotubes

Energy transport in proteins is critical to a variety of physical, chemical, and biological processes in living organisms. While strenuous efforts have been made to study vibrational energy transport in proteins, thermal transport processes across the most fundamental building blocks of proteins, i.e. helices, are not well understood. This work studies energy transport in a group of "isomer" helices. The π-helix is shown to have the highest thermal conductivity, 110% higher than that of the α-helix and 207% higher than that of the 310-helix. The H-bond connectivity is found to govern thermal transport mechanisms including the phonon spectral energy density, dispersion, mode-specific transport, group velocity, and relaxation time. The energy transport is strongly correlated with the H-bond strength which is also modulated by the H-bond connectivity. These fundamental insights provide a novel perspective for understanding energy transfer in proteins and guiding a rational molecule-level design of novel materials with configurable H-bonds.

Spectral attributes of sub-amorphous thermal conductivity in cross-linked organic-inorganic hybrids

Organic-inorganic hybrids have found increasing applications for thermal management across various disciplines. Such materials can achieve thermal conductivities below the so-called "amorphous limit" of their constituents' thermal conductivity. Despite their technological significance, a complete understanding of the origins of this thermal conductivity reduction remains elusive in these materials. In this paper, we develop a prototypical cross-linked organic-inorganic layered system, to investigate the spectral origins of its sub-amorphous thermal conductivity. Initially, we study the atomic structure of the model and find that besides polymer chain length, the relative drift of the layers governs the reduction in computed basal spacing, in agreement with experimental measurements. We, subsequently, find that organic cross-linking results in up to 40% reduction in thermal conductivity compared to inorganic samples. An in-depth investigation of vibrational modes reveals that this reduction is the result of reduced mode diffusivities, which in turn is a consequence of a vibrational mismatch between the organic and inorganic constituents. We also show that the contribution of propagating modes to the total thermal conductivity is not affected by organic cross-linking. Our approach paves the path toward a physics-informed analysis and design of a wide range of multifunctional hybrid nanomaterials for thermal management applications among others.

Enhancing the Thermo-Mechanical Property of Polymer by Weaving and Mixing High Length-Diameter Ratio Filler

Improving thermo-mechanical characteristics of polymers can efficiently promote their applications in heat exchangers and thermal management. However, a feasible way to enhance the thermo-mechanical property of bulk polymers at low filler content still remains to be explored. Here, we propose mixing high length-diameter ratio filler such as carbon nanotube (CNT), boron nitride (BN) nanotube, and copper (Cu) nanowire, in the woven polymer matrix to meet the purpose. Through molecular dynamics (MD) simulation, the thermal properties of three woven polymers including woven polyethylene (PE), woven poly (p-phenylene) (PPP), and woven polyacetylene (PA) are investigated. Besides, using woven PE as a polymer matrix, three polymer nanocomposites, namely PE-CNT, PE-BN, and PE-Cu, are constructed by mixing CNT, BN nanotube, and Cu nanowire respectively, whose thermo-mechanical characteristics are compared via MD simulation. Morphology and phonons spectra analysis are conducted to reveal the underlying mechanisms. Furthermore, impacts of electron-phonon coupling and electrical field on the thermal conductivity of PE-Cu are uncovered via two temperature model MD simulation. Classical theoretical models are modified to predict the effects of filler and matrix on the thermal conductivity of polymer nanocomposites. This work can provide useful guidelines for designing thermally conductive bulk polymers and polymer nanocomposites.