Mechanical breathing in organic electrochromics.

The repetitive size change of the electrode over cycles, termed as mechanical breathing, is a crucial issue limiting the quality and lifetime of organic electrochromic devices. The mechanical deformation originates from the electron transport and ion intercalation in the redox active material. The dynamics of the state of charge induces drastic changes of the microstructure and properties of the host, and ultimately leads to structural disintegration at the interfaces.

Laser Shock Peening on Zr-based Bulk Metallic Glass and Its Effect on Plasticity: Experiment and Modeling.

The Zr-based bulk metallic glasses (BMGs) are a new family of attractive materials with good glass-forming ability and excellent mechanical properties, such as high strength and good wear resistance, which make them candidates for structural and biomedical materials. Although the mechanical behavior of BMGs has been widely investigated, their deformation mechanisms are still poorly understood. In particular, their poor ductility significantly impedes their industrial application.

Laser deposited coatings of Co-Cr-Mo onto Ti-6Al-4V and SS316L substrates for biomedical applications.

Functionally gradient bio-coating material was built by laser deposition. Co-Cr-Mo material was deposited on a Ti-6Al-4V substrate transitioning from 0% to 100%. Control over the cooling rate is shown to be a key to reduce the effects of thermal expansion differences of the materials.

Investigation of early plasma evolution induced by ultrashort laser pulses.

Early plasma is generated owing to high intensity laser irradiation of target and the subsequent target material ionization. Its dynamics plays a significant role in laser-material interaction, especially in the air environment(1-11). Early plasma evolution has been captured through pump-probe shadowgraphy(1-3) and interferometry(1,4-7).

Sparse multiple kernel learning for signal processing applications.

In many signal processing applications, grouping of features during model development and the selection of a small number of relevant groups can be useful to improve the interpretability of the learned parameters. While a lot of work based on linear models has been reported to solve this problem, in the last few years, multiple kernel learning has come up as a candidate to solve this problem in nonlinear models. Since all of the multiple kernel learning algorithms to date use convex primal problem formulations, the kernel weights selected by these algorithms are not strictly the sparsest possible solution.