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. We quantify the breathing strain and the evolution of the mechanical properties of poly(3,4-propylenedioxythiophene) thin films in-situ using customized environmental nanoindentation. Upon oxidation, the film expands nearly 30% in volume, and the elastic modulus and hardness decrease by a factor of two. We perform theoretical modeling to understand thin film delamination from an indium tin oxide (ITO) current collector under cyclic load. We show that toughening the interface with roughened or silica-nanoparticle coated ITO surface significantly improves the cyclic performance.

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. In the present work, we show that the ductility of Zr-based BMGs with nearly zero plasticity is improved by a laser shock peening technique. Moreover, we map the distribution of laser-induced residual stresses via the micro-slot cutting method, and then predict them using a three-dimensional finite-element method coupled with a confined plasma model. Reasonable agreement is achieved between the experimental and modeling results. The analyses of serrated flows reveal plentiful and useful information of the underlying deformation process. Our work provides an easy and effective way to extend the ductility of intrinsically-brittle BMGs, opening up wider applications of these materials.

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. The microstructures and composition of the functionally gradient material (FGM) were characterized using an optical microscope, SEM, EDS, and XRD. EDS results showed a gradual transition to 50% Co-Cr-Mo and ∼100% Co-Cr-Mo on the top layer. XRD analysis showed the absence of a brittle intermetallic phase that forms between Titanium and Cobalt. As the amount of Co-Cr-Mo increased, the microhardness of the FGM samples significantly increased. A comparison was made between Co-Cr-Mo deposited on SS316L substrates as well as Ti-6Al-4V. The bonding strength of the coatings on both substrates was tested and found to meet the ASTM standard requirement.

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). However, the studied time frames and applied laser parameter ranges are limited. For example, direct examinations of plasma front locations and electron number densities within a delay time of 100 picosecond (ps) with respect to the laser pulse peak are still very few, especially for the ultrashort pulse of a duration around 100 femtosecond (fs) and a low power density around 10(14) W/cm(2). Early plasma generated under these conditions has only been captured recently with high temporal and spatial resolutions(12). The detailed setup strategy and procedures of this high precision measurement will be illustrated in this paper. The rationale of the measurement is optical pump-probe shadowgraphy: one ultrashort laser pulse is split to a pump pulse and a probe pulse, while the delay time between them can be adjusted by changing their beam path lengths. The pump pulse ablates the target and generates the early plasma, and the probe pulse propagates through the plasma region and detects the non-uniformity of electron number density. In addition, animations are generated using the calculated results from the simulation model of Ref. (12) to illustrate the plasma formation and evolution with a very high resolution (0.04 ~ 1 ps). Both the experimental method and the simulation method can be applied to a broad range of time frames and laser parameters. These methods can be used to examine the early plasma generated not only from metals, but also from semiconductors and insulators.

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. The main reason for using a convex primal formulation is that efficient implementations of kernel-based methods invariably rely on solving the dual problem. This work proposes the use of an additional log-based concave penalty term in the primal problem to induce sparsity in terms of groups of parameters. A generalized iterative learning algorithm, which can be used with a linear combination of this concave penalty term with other penalty terms, is given for model parameter estimation in the primal space. It is then shown that a natural extension of the method to nonlinear models using the "kernel trick" results in a new algorithm, called Sparse Multiple Kernel Learning (SMKL), which generalizes group-feature selection to kernel selection. SMKL is capable of exploiting existing efficient single kernel algorithms while providing a sparser solution in terms of the number of kernels used as compared to the existing multiple kernel learning framework. A number of signal processing examples based on the use of mass spectra for cancer detection, hyperspectral imagery for land cover classification, and NIR spectra from wheat, fescue grass, and diesel are given to highlight the ability of SMKL to achieve a very high accuracy with a very few kernels.