Insights into nanoparticle shape transformation by energetic ions.

Shape modification of embedded nanoparticles can be achieved by means of swift heavy ion irradiation. During irradiation, the particles elongate and align with the direction of the ion beam, presumably due to nanometer-scale phase transitions induced by individual ion impacts. However, the details of this transformation are not fully understood. The shape of metal nanoparticles embedded in dielectric matrices defines the non-linear optical properties of the composite material. Therefore, understanding the transformation process better is beneficial for producing materials with the desired optical properties. We study the elongation mechanism of gold nanoparticles using atomistic simulations. Here we focus on long-timescale processes and adhesion between the nanoparticle and the matrix. Without the necessity of ad-hoc assumptions used earlier, our simulations show that, due to adhesion with the oxide, the nanoparticles can grow in aspect ratio while in the molten state even after silicon dioxide solidifies. Moreover, they demonstrate the active role of the matrix: Only explicit simulations of ion impacts around the embedded nanoparticle provide the mechanism for continuous elongation up to experimental values of aspect ratio. Experimental transmission electron microscopy micrographs of nanoparticles after high-fluence irradiation support the simulations. The elongated nanoparticles in experiments and their interface structures with silica, as characterized by the micrographs, are consistent with the simulations. These findings bring ion beam technology forward as a precise tool for shaping embedded nanostructures for various optical applications.

Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography.

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues.