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Attapulgite (ATT) has never been used as a barrier additive in polypropylene (PP). As a filler, ATT should be added in high content to PP. However, that would result in increased costs. Moreover, the compatibility between ATT and the PP matrix is poor due to the lack of functional groups in PP. In this study, carboxylic groups were introduced to PP to form a modified polypropylene (MPP). ATT was purified, and a low content of it was added to MPP to prepare MPP/ATT nanocomposites. The analysis from FTIR indicated that ATT could react with MPP. According to the results of oxygen and water permeability tests, the barrier performance of the nanocomposite was optimal when the ATT content was 0.4%. This great improvement in barrier performance might be ascribed to the following three reasons: (1) The existence of ATT extended the penetration path of O2 or H2O molecules; (2) O2 or H2O molecules may be adsorbed and stored in the porous structure of ATT; (3) Most importantly, –COOH of MPP reacted with –OH on the surface of ATT, thereby the inner structure of the nanocomposite was denser, and it was less permeable to molecules. Therefore, nanocomposites prepared by adding ATT to MPP have excellent properties and low cost. They can be used as food packaging materials and for other related applications.

Recurrent event data are common in clinical studies when participants are followed longitudinally, and are often subject to a terminal event. With the increasing popularity of large pragmatic trials and a heterogeneous source population, participants are often nested in clinics and can be either susceptible or structurally unsusceptible to the recurrent process. These complications require new modeling strategies to accommodate potential zero-event inflation as well as hierarchical data structures in both the terminal and non-terminal event processes. In this paper, we develop a Bayesian semiparametric model to jointly characterize the zero-inflated recurrent event process and the terminal event process. We use a point mass mixture of non-homogeneous Poisson processes to describe the recurrent intensity and introduce shared random effects from different sources to bridge the non-terminal and terminal event processes. To achieve robustness, we consider nonparametric Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific frailty distribution, and develop a Markov Chain Monte Carlo algorithm for posterior inference. We demonstrate the superiority of our proposed model compared with competing models via simulations and apply our method to a pragmatic cluster-randomized trial for fall injury prevention among the elderly.