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In the earlier theoretical research, impact of entanglement on folding during crystallization was minimized. The combination of 13C isotope labeling and NMR spectroscopy allows us to quantitatively determine stem to stem distance as well as chain folding distance, hence, we are able to probe chain-level structure. Our recent work indicated that polymer chains are possible to fold prior to crystallization. In this poster, we would like to investigate the folding structure of a semi-crystalline polymer in melt-grown crystals (mgc) by using solid-state NMR spectroscopy and SAXS measurement. First, various 13C enriched poly(L-lactic acid) (PLLA) samples with different molecular weights (Mw = 2.5k – 300k g/mol) across critical entanglement length (Mc = 16k g/mol) were prepared in order to observe the molecular weight dependence of folding structure of PLLA. We revealed that entanglements influence the folding number during crystallization. Second, we attempt to observe the entanglement effect through diluting entanglement density, i.e., blending the PLLA above and below the Mc with different ratio and molecular weight. Based on the experimental results, we would like to highlight the impact of entanglements on folding of semicrystalline polymer in the melt-grown crystal. 

Recycling different plastics post-consumers causes downgraded performance due to the physical and chemical property differences conflicting with one another. These properties stem from the incompatibility of the blends to crystallize and blend. As there are millions of tons of waste every year, the ability to effectively blend two plastics such as polyethylene and polypropylene becomes crucial. In this poster, a molecular-level study of polyolefin blend co-crystallization will be explored by utilizing solid-state NMR spectroscopy. It is through NMR spectroscopic techniques and the use of selectively activating various parts of the blend through isotopes that aspects of the arrangement can be made. We will conduct studies into the co-crystallization of the blends utilizing deuterated polymers to access the chain-to-chain interface differences. This will give us the ability to see the relative extent of interaction as well as providing overall system kinetics. From these experiments, a diagram of the co-crystallization structure can be made as well as a defined system to analyze crystallization 

Analyzing the causal impact of different policies in reducing the spread of COVID-19 is of critical importance. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents) which influence both the presence of policies and the spread of COVID-19. Besides, as the confounders may be time-varying, it is even more difficult to capture them. Fortunately, the increasing prevalence of web data from various online applications provides an important resource of time-varying observational data, and enhances the opportunity to capture the confounders from them, e.g., the vigilance of residents over time can be reflected by the popularity of Google searches about COVID-19 at different time periods. In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. To this end, we integrate COVID-19 related observational data covering different U.S. counties over time, and then develop a neural network based causal effect estimation framework which learns the representations of time-varying (unobserved) confounders from the observational data. Experimental results indicate the effectiveness of our proposed framework in quantifying the causal impact of policies at different granularities, ranging from a category of policies with a certain goal to a specific policy type. Compared with baseline methods, our assessment of policies is more consistent with existing epidemiological studies of COVID-19. Besides, our assessment also provides insights for future policy-making.