A fully biobased and supertough thermoplastic vulcanizate (TPV) consisting of polylactide (PLA) and a biobased vulcanized unsaturated aliphatic polyester elastomer (UPE) was fabricated via peroxide-induced dynamic vulcanization. Interfacial compatibilization between PLA and UPE took place during dynamic vulcanization, which was confirmed by gel measurement and NMR analysis. After vulcanization, the TPV exhibited a quasi cocontinuous morphology with vulcanized UPE compactly dispersed in PLA matrix, which was different from the pristine PLA/UPE blend, exhibiting typically phase-separated morphology with unvulcanized UPE droplets discretely dispersed in matrix. The TPV showed significantly improved tensile and impact toughness with values up to about 99.3 MJ/m(3) and 586.6 J/m, respectively, compared to those of 3.2 MJ/m(3) and 16.8 J/m for neat PLA, respectively. The toughening mechanisms under tensile and impact tests were investigated and deduced as massive shear yielding of the PLA matrix triggered by internal cavitation of VUPE. The fully biobased supertough PLA vulcanizate could serve as a promising alternative to traditional commodity plastics.

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Super-tough poly(l-lactide)/crosslinked polyurethane (PLLA/CPU) blends with a CPU phase dispersed in the PLLA matrix were prepared by reactive blending of PLLA with poly(ethylene glycol) (PEG), glycerol, and 4,4′-methylenediphenyl diisocyanate (MDI).

High-resolution fetal electrocardiogram (FECG) plays an important role in assisting physicians to detect fetal changes in the womb and to make clinical decisions. However, in real situations, clear FECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander, high-frequency noise. In this paper, we proposed a novel integrated adaptive algorithm based on independent component analysis (ICA), ensemble empirical mode decomposition (EEMD), and wavelet shrinkage (WS) denoising, denoted as ICA-EEMD-WS, for FECG separation and noise reduction. First, ICA algorithm was used to separate the mixed abdominal ECG signal and to obtain the noisy FECG. Second, the noise in FECG was reduced by a three-step integrated algorithm comprised of EEMD, useful subcomponents statistical inference and WS processing, and partial reconstruction for baseline wander reduction. Finally, we evaluate the proposed algorithm using simulated data sets. The results indicated that the proposed ICA-EEMD-WS outperformed the conventional algorithms in signal denoising.