The effect of physical aging on the embrittlement of steam-sterilized polycarbonate

Polycarbonate is known to suffer from dramatic reductions in ductility upon exposure to hot, humid environments, such as during steam sterilization. Two phenomena have been proposed to be the main causes of this embrittlement: hydrolysis and microcavity formation. The present study focuses on a third phenomenon, whose contribution to the embrittlement has until now been considered insignificant: (physical) aging. By studying the influence of steam sterilization on the tensile deformation behavior of polycarbonate, it is shown that aging actually is one of the dominant factors in the embrittlement.     

Energy and technology lessons since Rio

The 1992 Framework Convention on Climate Change created the basic international architecture for addressing climate change. That treaty was negotiated at a time when the research literature examining emissions mitigation and the role of energy technology was relatively limited. In the two subsequent decades a great deal has been learned. The problem of stabilizing the concentration of greenhouse gases in the atmosphere has proved far more difficult than envisioned in 1992 and the role of technology appears even more important when emissions mitigation strategies are co-developed in the context of multiple competing ends.     

The Natural Course of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent form of chronic liver disease in the world, paralleling the epidemic of obesity and Type 2 diabetes mellitus (T2DM). NAFLD exhibits a histological spectrum, ranging from “bland steatosis” to the more aggressive necro-inflammatory form, non-alcoholic steatohepatitis (NASH) which may accumulate fibrosis to result in cirrhosis. Emerging data suggests fibrosis, rather than NASH per se, to be the most important histological predictor of liver and non-liver related death. Nevertheless, only a small proportion of individuals develop cirrhosis, however the large proportion of the population affected by NAFLD has led to predictions that NAFLD will become a leading cause of end stage liver disease, hepatocellular carcinoma (HCC), and indication for liver transplantation. HCC may arise in non-cirrhotic liver in the setting of NAFLD and is associated with the presence of the metabolic syndrome (MetS) and male gender. The MetS and its components also play a key role in the histological progression of NAFLD, however other genetic and environmental factors may also influence the natural history. The importance of NAFLD in terms of overall survival extends beyond the liver where cardiovascular disease and malignancy represents additional important causes of death.     

Study of the Effects of LAS and Zein Concentrations on Protein Solubilization

The effects of both linear alkylbenzene sulfonate (LAS) and zein protein concentrations on protein solubilization were investigated to better understand the influence of increasing concentration of the surfactant on protein denaturation and skin irritation. Different methods have been used for the determination of the amount of protein dissolved. A wide range of LAS concentrations and zein amounts were used. The results confirm that protein solubilization increases with LAS concentration increase and demonstrate that a minimum LAS/Zein ratio exists for all the zein to be dissolved.     

Modularisierung von Gaswäschern für die CO2‐Entfernung aus Biogas

Modularization has been identified as one of the research fields of the ?50 % idea”?. A development methodology for modules must consider both the economies of scale for investment costs and costs of operation and maintenance. In this paper, the impact of an absorber module, which is offered as discretized diameter scaling, on the total process is investigated at the example of the CO₂ separation from biogas. The simulation shows the effect of this approach to the stripper diameter and the energy demand of the process. The calculations form the basis for applying cost models.     

Silica Colloidal Membranes with Enantioselective Permeability

Robust mesoporous membranes composed of silica spheres were surface-modified with chiral selector moieties, including small molecules, macrocycles, and polymers. Diffusion rates of enantiomers of a chiral dye through the resulting asymmetrically modified colloidal membranes were measured and the corresponding permselectivities were calculated. The membranes showed enantioselectivities in the range of 1.2–1.8, which were not significantly affected by the structure of the surface-immobilized chiral electors. This selectivity is on par with most reported polymer-based solid membranes and bulk liquid membranes. The enantioselectivity results from the surface-facilitated mechanism of transport of enantiomers through the mesopores.     

Prediction of corrosion-induced cover cracking in reinforced concrete structures

Usually, the time of repair/replacement of reinforced concrete structures due to corrosion is controlled by cracking of the concrete cover. Thus, it is important to be able to predict with sufficient accuracy the time from corrosion initiation to crack formation in the concrete cover. The paper presents a critical overview of existing empirical, analytical and numerical models for predicting the time to corrosion-induced cover cracking. A simple nonlinear finite element (FE) is then formulated and validated. The FE model is employed to investigate inaccuracies of analytical models arising due to simplified formulation of the nonlinear behaviour of concrete and inability to account for actual location of corroding reinforcing bars. Recommendations on the applicability of analytical models are provided. Finally, the FE model is used to estimate the amount of corrosion products penetrating into concrete pores and microcracks since this amount is an essential parameter for analytical and numerical models.     

A method for rapid vulnerability assessment of structures loaded by outside blasts

The blast loads have in most cases not been assumed as design basis loads of nuclear power plant buildings and structures. Recent developments however stimulated a number of analyses quantifying the potential effects of such loads.An effort was therefore made by the authors to revisit simple and robust structural analysis methods and to propose their use in the vulnerability assessment of blast-loaded structures. The leading idea is to break the structure into a set of typical structural elements, for which the response is estimated by the use of slightly modified handbook formulas. The proposed method includes provisions to predict the inelastic response and failure. Simplicity and versatility of the method facilitate its use in structural reliability calculations.The most important aspects of the proposed method are presented along with illustrative sample applications demonstrating:

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results comparable to full scale dynamic simulations using explicit finite element codes and

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the performance of the method in screening the existing structures and providing the structural reliability information for the vulnerability analysis.

     

Sample sizes required to detect interactions between two binary fixed-effects in a mixed-effects linear regression model

Mixed-effects linear regression models have become more widely used for analysis of repeatedly measured outcomes in clinical trials over the past decade. There are formulae and tables for estimating sample sizes required to detect the main effects of treatment and the treatment by time interactions for those models. A formula is proposed to estimate the sample size required to detect an interaction between two binary variables in a factorial design with repeated measures of a continuous outcome. The formula is based, in part, on the fact that the variance of an interaction is fourfold that of the main effect. A simulation study examines the statistical power associated with the resulting sample sizes in a mixed-effects linear regression model with a random intercept. The simulation varies the magnitude (Δ) of the standardized main effects and interactions, the intraclass correlation coefficient (ρ), and the number (k) of repeated measures within-subject. The results of the simulation study verify that the sample size required to detect a 2×2 interaction in a mixed-effects linear regression model is fourfold that to detect a main effect of the same magnitude.