PIM moulding of post consumer mixed plastics

Post consumer plastics from household are highly mixed and contaminated and are thus particularly difficult to recycle. Although advances in sorting and cleaning technologies for waste plastics have enable the relatively pure and clean streams such as bottles to be recycled, there is a increasing need for processing technologies that can utilise the low grade and mixed plastic residues from the plastics recovery facilities (PRF). In this work, potentials of utilisation of such feedstock in Powder Impression Moulding (PIM), a process capable of fabricating lightweight sandwich structures, are investigated in terms effects of loading and size of flakes from PE-rich mixed plastics in the formulations of the core on flexural properties of the sandwich panels. It was demonstrated that sandwich panels can be made by incorporating about 75 wt% of coarse flakes of a low-grade mixed plastics material directly obtained from a PRF

Influences of impurities in recycled plastics on properties of PIM sandwich panels

Powder impression moulding (PIM) is a novel technology for manufacturing lightweight sandwich panels from plastics in powder form. The process is featured by its high tolerant to impurities or contaminants in the feedstock and thus requires much less materials segregation and cleaning operations when use recycled plastics. This paper investigate the influences of polymer impurities and soil contamination on structure and properties of PIM sandwich panels using compositions that simulate a PE-rich recycled plastic feedstock. It is demonstrated that the PIM process can accommodate considerable impurities (rPET residues or soil contamination) in a core dominated by LDPE/HDPE blendes. The variation of flexural properties can be predicted or controlled through monitoring of impurities. There exist significant scopes for reduction of the degree of sorting and cleaning in recycling systems by using lower grades of recyclates and for reduction of the associated costs and energy consumption

Critical curvature of large-NN nonlinear O(N)O(N) sigma model on S2S^2

We study the nonlinear $O(N)$ sigma model on $S^2$ with the gravitational coupling term, by evaluating the effective potential in the large-$N$ limit. It is shown that there is a critical curvature $R_c$ of $S^2$ for any positive gravitational coupling constant $\xi$, and the dynamical mass generation takes place only when $R<R_c$. The critical curvature is analytically found as a function of $\xi$ $(>0)$, which leads us to define a function looking like a natural generalization of Euler-Mascheroni constant.Comment: 7 pages, LaTe

Real-time imaging of pulvinus bending in Mimosa pudica

Mimosa pudica is a plant that rapidly shrinks its body in response to external stimuli. M. pudica does not perform merely simple movements, but exhibits a variety of movements that quickly change depending on the type of stimuli. Previous studies have investigated the motile mechanism of the plants from a biochemical perspective. However, an interdisciplinary study on the structural characteristics of M. pudica should be accompanied by biophysical research to explain the principles underlying such movements. In this study, the structural characteristics and seismonastic reactions of M. pudica were experimentally investigated using advanced bio-imaging techniques. The results show that the key factors for the flexible movements by the pulvinus are the following: bendable xylem bundle, expandable/shrinkable epidermis, tiny wrinkles for surface modification, and a xylem vessel network for efficient water transport. This study provides new insight for better understanding the M. pudica motile mechanism through structural modification.open1111Nsciescopu

State space mixed models for longitudinal obsservations with binary and binomial responses

We propose a new class of state space models for longitudinal discrete response data where the observation equation is specified in an additive form involving both deterministic and random linear predictors. These models allow us to explicitly address the effects of trend, seaonal or other time-varying covariates while preserving the power of state space models in modeling serial dependence in the data. We develop a Markov Chain Monte Carlo algorithm to carry out statistical inferene for models with binary and binomial responses, in which we invoke de Jong and Shephard's (1995) simulaton smoother to establish an efficent sampling procedure for the state variables. To quantify and control the sensitivity of posteriors on the priors of variance parameters, we add a signal-to-noise ratio type parmeter in the specification of these priors. Finally, we ilustrate the applicability of the proposed state space mixed models for longitudinal binomial response data in both simulation studies and data examples