In this study, we synthesize silica-core gold-satellite nanoparticles (SGNPs) for the surface-enhanced Raman scattering (SERS) based sensing applications. They consist of gold satellite nanoparticles (AuNPs) fixed on the silica core nanoparticles, which sizes of AuNPs can be tunned by varying the amount of reactants (growth solution and reducing agent). Their surface plasmon resonance (SPR) properties were characterized by using UV-vis spectroscopy, showing that the growth of AuNPs on silica cores leads to the light absorption in the longer wavelength region. Furthermore, the size increase of AuNPs exhibited the dramatic change in SERS activity due to the formation of hot spots. The optimized SGNPs showing enhancement factor ~3.8x106 exhibited a detection limit of rhodamine 6G (R6G) as low as 10-8M. These findings suggest the importance of size control of SGNPs and their SPR properties to develop highly efficient SERS sensors.

The β-transus temperature in titanium alloys plays an important role in the design of thermo-mechanical treatments. It primarily depends on the chemical composition of the alloy and the relationship between them is non-linear and complex. Considering these relationships is difficult using mathematical equations. A feed-forward neural-network model with a back-propagation algorithm was developed to simulate the relationship between the β-transus temperature of titanium alloys, and the alloying elements. The input parameters to the model consisted of the nine alloying elements (i.e., Al, Cr, Fe, Mo, Sn, Si, V, Zr, and O), whereas the model output is the β-transus temperature. The model developed was then used to predict the β-transus temperature for different elemental combinations. Sensitivity analysis was performed on a trained neural-network model to study the effect of alloying elements on the β-transus temperature, keeping other elements constant. Very good performance of the model was achieved with previously unseen experimental data. Some explanation of the predicted results from the metallurgical point of view is given. The graphical-user-interface developed for the model should be very useful to researchers and in industry for designing the thermo-mechanical treatment of titanium alloys.