Hybrid 3D structures of ZnO nanoflowers and PdO nanoparticles as a highly selective methanol sensor.

The present study concerns the enhancement of methanol selectivity of three dimensional (3D) nanoflowers (NFs) of ZnO by dispersing nickel oxide (NiO) and palladium oxide (PdO) nanoparticles on the surface of the nanoflowers to form localized hybrid nano-junctions. The nanoflowers were fabricated through a liquid phase deposition technique and the modification was achieved by addition of NiCl and PdCl2 solutions. In addition to the detailed structural (like X-ray diffraction (XRD), electron dispersive spectroscopy (EDS), X-ray mapping, XPS) and morphological characterization (by field emission scanning electron microscopy (FESEM)), the existence of different defect states (viz. oxygen vacancy) was also confirmed by photoluminescence (PL) spectroscopy. The sensing properties of the pristine and metal oxide nanoparticle (NiO/PdO)-ZnO NF hybrid sensor structures, towards different alcohol vapors (methanol, ethanol, 2-propanol) were investigated in the concentration range of 0.5-700 ppm at 100-350 °C. Methanol selectivity study against other interfering species, viz. ethanol, 2-propanol, acetone, benzene, xylene and toluene was also investigated. It was found that the PdO-ZnO NF hybrid system offered enhanced selectivity towards methanol at low temperature (150 °C) compared to the NiO-ZnO NF and pristine ZnO NF counterparts. The underlying mechanism for such improvement has been discussed with respective energy band diagram and preferential dissociation of target species on such 3D hybrid structures. The corresponding improvement in transient characteristics has also been co-related with the proposed model.

Imprecise knowledge based design and development of titanium alloys for prosthetic applications.

Imprecise knowledge on the composition-processing-microstructure-property correlation of titanium alloys combined with experimental data are used for developing rule based models for predicting the strength and elastic modulus of titanium alloys. The developed models are used for designing alloys suitable for orthopedic and dental applications. Reduced Space Searching Algorithm is employed for the multi-objective optimization to find composition, processing and microstructure of titanium alloys suitable for orthopedic applications. The conflicting requirements attributes of the alloys for this particular purpose are high strength with low elastic modulus, along with adequate biocompatibility and low costs. The 'Pareto' solutions developed through multi-objective optimization show that the preferred compositions for the fulfilling the above objectives lead to ß or near ß-alloys. The concept of decision making employed on the solutions leads to some compositions, which should provide better combination of the required attributes. The experimental development of some of the alloys has been carried out as guided by the model-based design methodology presented in this research. Primary characterizations of the alloys show encouraging results in terms of the mechanical properties.

Stoichiometry, Length, and Wall Thickness Optimization of TiO2 Nanotube Array for Efficient Alcohol Sensing.

The present study concerns development of an efficient alcohol sensor by controlling the stoichiometry, length, and wall thickness of electrochemically grown TiO2 nanotube array for its use as the sensing layer. Judicious variation of H2O content (0, 2, 10 and 100% by volume) in the mixed electrolyte comprising ethylene glycol and NH4F resulted into the desired variation of stoichiometry. The sensor study was performed within the temperature range of 27 to 250 °C for detecting the alcohols in the concentration range of 10-1000 ppm. The nanotubes grown with the electrolyte containing 2 vol % H2O offered the maximum response magnitude. For this stoichiometry, variation of corresponding length (1.25-2.4 µm) and wall thickness (19.8-9 nm) of the nanotubes was achieved by varying the anodization time (4-16 h) and temperatures (42-87 °C), respectively. While the variation of length influenced the sensing parameters insignificantly, the best response magnitude was achieved for ∼13 nm wall thickness. The underlying sensing mechanism was correlated with the experimental findings on the basis of structural parameters of the nanotubes.