Reversibly crosslinked self‐healing PCL‐based networks

Poly(ε‐caprolactone) (PCL)‐based thermoreversible networks with self‐healing properties were prepared through Diels–Alder (DA) and retro‐DA reactions. Bis‐ or Tris‐maleimide compounds and a series of copolymer(caprolactone‐diene) PCL_XF_Y (X: degree of polymerization and Y: furan‐average functionality) with Y between 2.4 and 4.9 were used. The successive sequences of formation and dissociation of polycaprolactone networks via DA and retro‐DA reactions were observed repeatedly by dynamic mechanical analyses (DMA) and their gel‐temperatures determined. The cross‐linking densities, thermal properties, and thermal reversibility of the PCL_XF_Y/multimaleimide polymers have been modulated by the structure and functionalities of the used diene and dienophile moieties. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Mining tag-clouds to improve social media recommendation

Multimedia Tools and Applications - Massive amounts of data are available on social websites, therefore finding the suitable item is a challenging issue. According to recent social statistics, we...     

DMSO Chemically Alters Cell Membranes to Slow Exocytosis and Increase the Fraction of Partial Transmitter Released

Dimethyl sulfoxide (DMSO) is frequently used as a solvent in biological studies and as a vehicle for drug therapy; but the side effects of DMSO, especially on the cell environment, are not well understood, and controls with DMSO are not neutral at higher concentrations. Herein, electrochemical measurement techniques are applied to show that DMSO increases exocytotic neurotransmitter release, while leaving vesicular contents unchanged. In addition, the kinetics of release from DMSO‐treated cells are faster than that of untreated ones. The results suggest that DMSO has a significant influence on the chemistry of the cell membrane, leading to alteration of exocytosis. A speculative chemical mechanism of the effect on the fusion pore during exocytosis is presented.     

Efficient analysis of parametric sensitivity and uncertainty of fuel cell models with application to SOFC

Sensitivity analysis (SA) and uncertainty quantification (UQ) are used to assess and improve engineering models. SA of fuel cell models can be challenging because of the large number of design parameters to optimize. Following the regular approach of manually testing the fuel cell outputs under changing each design parameter one at a time can be tedious. In this work, a framework of SA and UQ methods is applied with a purpose of efficiently analysing fuel cell models. The SA and UQ methods are also compared to increase the confidence in the results. In this methodology, all design parameters and their effects can be analysed in an integrated form, where model variance, sensitivity, linearity/nonlinearity, parameter interactions, and importance ranking can be assessed. This paper highlights and compares between local SA (one-at-a-time linear perturbation), parameter screening (Morris screening), variance decomposition (Sobol indices), and regression-based SA. For UQ, stochastic methods (Monte Carlo sampling) and deterministic methods (using SA profiles) are used. All methods are applied to solid oxide fuel cell (SOFC) to analyse the power output and system efficiency under 21 uncertain design parameters. Using different methods, the uncertainty in the SOFC responses (maximum power and efficiency) is about 11%, when the current density is about 13 200 A m⁻². In addition, analysis shows that operating temperature (T), length of grain contact (X), grain size (D_s), porosity (ϵ), and electrolyte thickness (L_e) contribute to more than 97% of the SOFC's maximum power variance, with individual contributions as follows: 63.3%, 13.1%, 12.5%, 5.4%, and 3.6%, respectively. The previous conclusions are subjective to the simplified SOFC model used in this study to demonstrate the methods. Benchmarking the UQ methods in capturing the response uncertainty and the SA methods in raking the design parameters demonstrate very good agreement between them. The methods applied in this paper can be used to achieve a comprehensive mathematical understanding of more advanced fuel cell or energy models, which can lead to better performance.