Characterisation of impact response of metallic foam/ceramic laminates

The impact response of laminated composites consisting of alternate layers of AI ahoy foam and Al2O3 was studied experimentally in low and intermediate velocity regimes. Low velocity impacts (1.2-2.8 m s(-1)) were conducted using an instrumented falling weight apparatus and were compared with static indentation tests (0.2 x 10(-4) m s(-1)). Intermediate velocity impacts were carried out by means of both Hopkinson bar (60 m s(-1)) and gas gun (200 m s(-1)) tests, Post-impact damage was assessed using X-ray radiography and microscopy, It was found that there is good correlation between low velocity impact and quasi-static responses. In both cases, penetration of the layered targets resulted in the formation of a distinctive plug. Increasing impact velocity (intermediate velocity range) snitched the penetration mode from plugging to fragmentation, giving rise to an increase in the absorbed energy. In this range, impacts led to localisation of damage in the region under the projectile, Furthermore, a comparison has been made between the penetration response of foam laminates and dense metal laminates of equivalent areal density. Preliminary results suggest that the dense metal laminates are superseded by the foam laminates on an energy absorption basis.

Density control of ZnO nanowires grown using Au-PMMA nanoparticles and their growth behavior.

Au nanoparticles stabilized by poly(methyl methacrylate) (PMMA) were used as a catalyst to grow vertically aligned ZnO nanowires (NWs). The density of ZnO NWs with very uniform diameter was controlled by changing the concentration of Au-PMMA nanoparticles (NPs). The density was in direct proportion to the concentration of Au-PMMA NPs. Furthermore, the growth process of ZnO NWs using Au-PMMA NPs was systematically investigated through comparison with that using Au thin film as a catalyst. Au-PMMA NPs induced polyhedral-shaped bases of ZnO NWs separated from each other, while Au thin film formed a continuous network of bases of ZnO NWs. This approach provides a facile and cost-effective catalyst density control method, allowing us to grow high-quality vertically aligned ZnO NWs suitable for many viable applications.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.