Topological Programmability of Isomerizable Polymers.

Topology isomerizable networks (TINs) can be programmed into numerous polymers exhibiting unique and spatially defined (thermo-) mechanical properties. However, capturing the dynamics in topological transformations and revealing the intrinsic mechanisms of mechanical property modulation at the microscopic level is a significant challenge. Here, we use a combination of coarse-grained molecular dynamics simulations and reaction kinetic theory to reveal the impact of dynamic bond exchange reactions on the topology of branched chains. We find that, the grafted units follow a geometric distribution with a converged uniformity, which depends solely on the average grafted units of branched chains. Furthermore, we demonstrate that the topological structure can lead to spontaneous modulation of mechanical properties. The theoretical framework provides a research paradigm for studying the topology and mechanical properties of TINs.

Impact of Sacrificial Hydrogen Bonds on the Structure and Properties of Rubber Materials: Insights from All-Atom Molecular Dynamics Simulations.

The inclusion of sacrificial hydrogen bonds is crucial for advancing high-performance rubber materials. However, the molecular mechanisms governing the impact of these bonds on material properties remain unclear, hindering progress in advanced rubber material research. This study employed all-atom molecular dynamics simulations to thoroughly investigate how hydrogen bonds affect the structure, dynamics, mechanics, and linear viscoelasticity of rubber materials. As the modified repeating unit ratio (ß) increased, both interchain and intrachain hydrogen bond content rose, with interchain bonds playing a predominant role. This physical cross-linking network formed through interchain hydrogen bonds restricts molecular chain movement and relaxation and raises the glass transition temperature of rubber. Within a certain content of hydrogen bonds, the mechanical strength increases with increasing ß. However, further increasing ß leads to a subsequent decrease in the mechanical performance. Optimal mechanical properties were observed at ß = 6%. On the other hand, a higher ß value yields an elevated stress relaxation modulus and an extended stress relaxation plateau, signifying a more complex hydrogen-bond cross-linking network. Additionally, higher ß increases the storage modulus, loss modulus, and complex viscosity while reducing the loss factor. In summary, this study successfully established the relationship between the structure and properties of natural rubber containing hydrogen bonds, providing a scientific foundation for the design of high-performance rubber materials.

Predicting Natural Rubber Crystallinity by a Novel Machine Learning Algorithm Based on Molecular Dynamics Simulation Data.

Natural rubber (NR) with excellent mechanical properties, mainly attributed to its strain-induced crystallization (SIC), has garnered significant scientific and technological interest. With the aid of molecular dynamics (MD) simulations, we can investigate the impacts of crucial structural elements on SIC on the molecular scale. Nonetheless, the computational complexity and time-consuming nature of this high-precision method constrain its widespread application. The integration of machine learning with MD represents a promising avenue for enhancing the speed of simulations while maintaining accuracy. Herein, we developed a crystallinity algorithm tailored to the SIC properties of natural rubber materials. With the data enhancement algorithm, the high evaluation value of the prediction model ensures the accuracy of the computational simulation results. In contrast to the direct utilization of small sample prediction algorithms, we propose a novel concept grounded in feature engineering. The proposed machine learning (ML) methodology consists of (1) An eXtreme Gradient Boosting (XGB) model to predict the crystallinity of NR; (2) a generative adversarial network (GAN) data augmentation algorithm to optimize the utilization of the limited training data, which is utilized to construct the XGB prediction model; (3) an elaboration of the effects induced by phospholipid and protein percentage (ω), hydrogen bond strength (εH), and non-hydrogen bond strength (εNH) of natural rubber materials with crystallinity prediction under dynamic conditions are analyzed by employing weight integration with feature importance analysis. Eventually, we succeeded in concluding that εH has the most significant effect on the strain-induced crystallinity, followed by ω and finally εNH.

Land use management recommendations for reducing the risk of downstream flooding based on a land use change analysis and the concept of ecosystem-based disaster risk reduction.

Sustainable management of ecosystems can provide various socio-ecological benefits, including disaster risk reduction. Through their regulating services and by providing natural protection, ecosystems can reduce physical exposure to common natural hazards. Ecosystems can also minimize disaster risk by reducing social and economic vulnerability and enhancing livelihood resilience. To showcase the importance and usefulness of ecosystem-based disaster risk reduction (Eco-DRR), this study (1) analyzed the land use change in a watershed in central Japan, (2) applied the concept of Eco-DRR, and made land use management recommendations regarding the watershed scale for reducing the risk of downstream flooding. The recommendations that emerged from the application, based on the land use change analysis, are: the use of hard infrastructure and vegetation to store and retain/detain stormwater and promote evapotranspiration is recommended for downstream, urban areas; the sustainable management of upland forest ecosystems and secondary forest-paddy land-human systems, and proactive land use planning in the lowland delta, where built land is concentrated, are key to the watershed-scale landscape planning and management to reduce downstream flooding risks.

Protection against Klebsiella pneumoniae using lithium chloride in an intragastric infection model.

Intragastric Klebsiella pneumoniae infections of mice can cause liver abscesses, necrosis of liver tissues, and bacteremia. Lithium chloride, a widely prescribed drug for bipolar mood disorder, has been reported to possess anti-inflammatory properties. Using an intragastric infection model, the effects of LiCl on K. pneumoniae infections were examined. Providing mice with drinking water containing LiCl immediately after infection protected them from K. pneumoniae-induced death and liver injuries, such as necrosis of liver tissues, as well as increasing blood levels of aspartate aminotransferase and alanine aminotransferase, in a dose-dependent manner. LiCl administered as late as 24 h postinfection still provided protection. Monitoring of the LiCl concentrations in the sera of K. pneumoniae-infected mice showed that approximately 0.33 mM LiCl was the most effective dose for protecting mice against infections, which is lower than the clinically toxic dose of LiCl. Surveys of bacterial counts and cytokine expression levels in LiCl-treated mice revealed that both were effectively inhibited in blood and liver tissues. Using in vitro assays, we found that LiCl (5 µM to 1 mM) did not directly interfere with the growth of K. pneumoniae but made K. pneumoniae cells lose the mucoid phenotype and become more susceptible to macrophage killing. Furthermore, low doses of LiCl also partially enhanced the bactericidal activity of macrophages. Taken together, these data suggest that LiCl is an alternative therapeutic agent for K. pneumoniae-induced liver infections.

Dispersion and Diffusion Mechanism of Nanofillers with Different Geometries in Bottlebrush Polymers: Insights from Molecular Dynamics Simulation.

The dispersion and diffusion mechanism of nanofillers in polymer nanocomposites (PNCs) are crucial for understanding the properties of PNCs, which is of great significance for the design of novel materials. Herein, we investigate the dispersion and diffusion behavior of two geometries of nanofillers, namely, spherical nanoparticles (SNPs) and nanorods (NRs), in bottlebrush polymers by utilizing coarse-grained molecular dynamics simulations. With the increase of the interaction strength between the nanofiller and polymer (εnp), both the SNPs and NRs experience a typical "aggregated phase-dispersed phase-bridged phase" state transition in the bottlebrush polymer matrix. We evaluate the validity of the Stokes-Einstein (SE) equation for predicting the diffusion coefficient of nanofillers in bottlebrush polymers. The results demonstrate that the SE predictions are slightly larger than the simulated values for small SNP sizes because the local viscosity that is felt by small SNPs in the densely grafted bottlebrush polymer does not differ much from the macroscopic viscosity. The relative size of the length of the NRs (L) and the radius of gyration (Rg) of the bottlebrush polymer play a key role in the diffusion of NRs. In addition, we characterize the anisotropic diffusion of NRs to analyze their translational and rotational diffusion. The motion of NRs in the direction perpendicular to the end-to-end vector is more hindered, indicating that there is a strong coupling between the rotation of NRs and the motion of the polymer. The NR motion shows stronger anisotropic diffusion at short time scales because of the steric effects generated by side chains of the bottlebrush polymer. In general, our results provide a fundamental understanding of the dispersion of nanofillers and the microscopic mechanism of nanofiller diffusion in bottlebrush polymers.