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Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field for efficiently designing and developing polymeric materials. However, the focus of polymer informatics has predominantly centered on single-component polymers, leaving the vast chemical space of polymer blends relatively unexplored. This study employs a high-throughput molecular dynamics (MD) simulation combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, the TC of about 600 amorphous single-component polymers and 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is identified, which involves a weighted sum approach that extends existing polymer representation from single-component polymers to polymer blends. An AL framework, combining MD simulation and ML, is employed to explore the TC of approximately 550,000 unlabeled polymer blends. The AL framework proves highly effective in accelerating the discovery of high-performance polymer blends for thermal transport. Additionally, we delve into the relationship between TC, radius of gyration (R_g), and hydrogen bonding, highlighting the roles of inter- and intra-chain interactions in thermal transport in amorphous polymer blends. A significant positive association between TC andR_gimprovement and an indirect contribution from H-bond interaction to TC enhancement are revealed through a log-linear model and an odds ratio calculation, emphasizing the impact of increasingR_gand H-bond interactions on enhancing polymer blend TC.

Optical manipulation of nanoparticles (NPs) in liquid has garnered increasing interest for various applications, ranging from biological systems to nanofabrication. A plane wave as an optical source has recently been shown to be capable of pushing or pulling an NP when the NP is encapsulated by a nanobubble (NB) in water. However, the lack of an accurate model to describe the optical force on NP-in-NB systems hinders a comprehensive understanding of NP motion mechanisms. In this study, we present an analytical model using vector spherical harmonics to accurately capture the optical force and the resultant trajectory of an NP in an NB. We test the developed model using a solid Au NP as an example. By visualizing the vector field line of the optical force, we reveal the possible moving paths of the NP in the NB. This study can provide valuable insights for designing experiments to manipulate supercaviting NPs using plane waves.