Failure analysis of automotive battery parts

In the injection molding process defective pieces are sometimes detected, showing weak points which break easily. In this work, the possible causes of these failure points are evaluated. Thermal analysis techniques were applied to the material to better understand the degradation that takes place with relation to the transformation process. Simulations were made to determine the possible causes of deterioration and at the same time reproduce the effects of this fragility. The creation of the weak points was simulated and their origins were determined.     

In situ generated O-glycan core 1 structure as substrate for Gal(beta 1-3)GalNAc beta-1,6-GlcNAc transferase

beta-Galactosidase from bovine testes was used in a one pot reaction together with a recombinant beta-1,6-GlcNAc transferase for the synthesis of GlcNAc(beta 1-6)GalNAc(alpha 1-OBn) (core 6-Bn). The galactosidase, which reversibly links galactose via a (beta 1-3) linkage to N-acetylgalactosamine, provides the substrate for the GlcNAc transferase in situ. The synthesis was carried out with a yield > 90%.     

Determination of natural and synthetic colorants in prescreened dairy samples using liquid chromatography-diode array detection

A simple and novel screening method for food colorants was proposed. Synthetic or natural colorants were discriminated as they were selectively adsorbed on cotton or RP-C18 sorbent columns, respectively. After elution, each fraction was monitored at 400, 530, and 610 nm for yellow, red, and green-blue-brown additives, respectively, with a DAD spectrophotometer. The screening method serves as a filter to indicate whether the target colorants are present above or below the detection limit of the method (6-15 or 25-10,000 n/mL for synthetic or natural colorants, respectively). Positive samples were discriminated by LC-DAD, using a flow system similar to that of the screening method. The LC-DAD discrimination/confirmation method is very sensitive; it exhibits a linear range of 0.01-50 microg/mL (excluding curcumin and caramel, which are linear up to 200 and 1,500 microg/mL, respectively). The method was applied to the determination of natural and synthetic colorants in dairy samples with good precision.     

The role of structure on the thermal properties of graphitic foams

A high conductivity graphite foam developed at Oak Ridge National Laboratory (ORNL) owes its unique thermal properties to the highly aligned graphitic structure along the cell walls. The material exhibits a peak in thermal conductivity at temperatures similar to that of highly ordered natural graphite, indicating the foam has an extremely graphitic nature. This paper explores the graphitic structure of the foam and attempts to correlate the morphology of the ligaments with the bulk thermal properties, up to 182 W/m·K. First, the manufacturing process of the foam and the resulting material properties are reported. Then, several models for representing the bulk materials properties are reviewed. Examination by optical image analysis, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) was used to examine the structure of the graphite foam. In addition, crystallographic structure determined by X-ray diffraction is reported. A simple two parameter model of the morphology was developed and then used to predict the overall thermal properties of the foam based on the assumed highly ordered ligament structure. This new model correlated (within 5%) thermal conductivity to density of several foams, provided the average ligament conductivity could be accurately represented. From the new model and the material characterization data, it was determined that the average ligament thermal conductivity of the foam is >1650 W/m·K at room temperature, and increases to more than 2300 W/m·K at liquid nitrogen temperatures.     

Computational analysis of cartilage implants based on an interpenetrated polymer network for tissue repairing

Interpenetrated polymer networks (IPNs), composed by two independent polymeric networks that spatially interpenetrate, are considered as valuable systems to control permeability and mechanical properties of hydrogels for biomedical applications. Specifically, poly(ethyl acrylate) (PEA)–poly(2-hydroxyethyl acrylate) (PHEA) IPNs have been explored as good hydrogels for mimicking articular cartilage. These lattices are proposed as matrix implants in cartilage damaged areas to avoid the discontinuity in flow uptake preventing its deterioration. The permeability of these implants is a key parameter that influences their success, by affecting oxygen and nutrient transport and removing cellular waste products to healthy cartilage. Experimental try-and-error approaches are mostly used to optimize the composition of such structures. However, computational simulation may offer a more exhaustive tool to test and screen out biomaterials mimicking cartilage, avoiding expensive and time-consuming experimental tests. An accurate and efficient prediction of material's permeability and internal directionality and magnitude of the fluid flow could be highly useful when optimizing biomaterials design processes. Here we present a 3D computational model based on Sussman–Bathe hyperelastic material behaviour. A fluid structure analysis is performed with ADINA software, considering these materials as two phases composites where the solid part is saturated by the fluid. The model is able to simulate the behaviour of three non-biodegradable hydrogel compositions, where percentages of PEA and PHEA are varied. Specifically, the aim of this study is (i) to verify the validity of the Sussman–Bathe material model to simulate the response of the PEA–PHEA biomaterials; (ii) to predict the fluid flux and the permeability of the proposed IPN hydrogels and (iii) to study the material domains where the passage of nutrients and cellular waste products is reduced leading to an inadequate flux distribution in healthy cartilage tissue. The obtained results show how the model predicts the permeability of the PEA–PHEA hydrogels and simulates the internal behaviour of the samples and shows the distribution and quantification of fluid flux.