A Two-Step Sol–Gel Method for Synthesis of Nanoporous TiO2

This article reports a study of the effects of synthesis parameters on the preparation and formation of mesoporous titania nanopowders by employing a two-step sol-gel method. These materials displayed crystalline domains characteristic of anatase. The first step of the process involved the hydrolysis of titanium isopropoxide in a basic aqueous solution mediated by neutral surfactant. The solid product obtained from step 1 was then treated in an acidified ethanol solution containing the same titanium precursor to thicken the pore walls. Low pH and higher loading of the Ti precursor in step 2 produced better mesoporosity and crystallinity of titanium dioxide polymorphs. The resultant powder exhibited a high surface area (73.8 m(2)/g) and large pore volume (0.17 cm(3)/g) with uniform mesopores. These materials are envisaged to be used as precursors for mesoporous titania films as a wide band gap semiconductor in dye-sensitized nanocrystalline TiO2 solar cells.

A method for screening the temperature dependence of three-dimensional crystal formation

Temperature is an important parameter controlling protein crystal growth. A new temperature-screening system (Thermo-screen) is described consisting of a gradient thermocycler fitted with a special crystallization-plate adapter onto which a 192-well sitting-drop crystallization plate can be mounted (temperature range 277-372 K; maximum temperature gradient 20 K; interval precision 0.3 K). The system allows 16 different conditions to be monitored simultaneously over a range of 12 temperatures and is well suited to conduct wide (similar to 20 K) and fine (similar to 3 K) temperature-optimization screens. It can potentially aid in the determination of temperature phase diagrams and run more complex temperature-cycling experiments for seeding and crystal growth.

A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.