[en] The possibility of using through-mask electrodeposition to fill features with active sidewalls was investigated. Both metal (Ni) and conductive substrates were employed; the demolding of electroformed Ni metal parts from metal substrates was difficult despite the use of various lubricants. Because of damage to the electrodeposited parts during the demolding process, conductive plastic substrates appear more feasible than metal substrates. Direct current was capable of filling features with low aspect ratios ((approx)2) with only minor voiding. For higher aspect ratio features ((approx)7), pulsed deposition and direct current with the leveling agent coumarin appeared to be more effective than pulsed reverse deposition. Since the characteristic diffusion time constant varies with the square of the feature depth, chloride ions are necessary to prevent passivation during the long pulse off-times required for uniform feature filling through a thick mask. It is shown that although thick masks require long pulse off-times, the recommended deposition rate for uniform filling (available in the literature) should not depend on the mask thickness (although the total deposition time will)

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[en] OHVT Mission is to conduct, in collaboration with our heavy vehicle industry partners and their suppliers, a customer-focused national program to research and develop technologies that will enable trucks and other heavy vehicles to be more energy efficient and able to use alternative fuels while simultaneously reducing emissions

[en] Ni and Ni alloys are being developed as baseline materials for LIGA technology and prototyping at Sandia National Laboratories. A conventional, additive-free sulfamate electrolyte has been chosen for pure Ni electrodeposition due to its simplicity and ability to produce ductile, low-stress films. When depositing certain Ni alloys, saccharin is typically employed as an electrolyte bath additive. While saccharin is well known and effective as a stress reliever, it has a significant impact on the microstructure of the deposit and its annealing behavior. The electrodeposition of pure Ni in the presence of saccharin is studied here to understand its effects in the absence of an alloying element (such as Co or Fe). The grain structure and Vickers hardness of Ni deposited with and without saccharin on a rotating disk electrode were all found to be consistent with previous studies available in the literature. The following observations were made: (1) The fine, columnar morphology obtained without saccharin became an equiaxed, nano-sized grain structure with saccharin (from(approx)1.5(micro)m to(approx)40 nm nominal grain size, respectively). The grain refinement resulting from saccharin is not accompanied with an increase in film stress, in contrast to the grain refinement associated with certain Ni alloys. (2) A change in the deposit texture from weak (210) to (111) along the film growth direction with the addition of saccharin. (3) An increase in Vickers hardness by a factor of(approx)2 (from(approx)170 to(approx)320) upon the addition of saccharin. (4) A rapid decrease in hardness with annealing from the high, as-deposited values for films deposited with saccharin to a value lower than that of annealed Ni from an additive-free bath. (5) Accelerated grain growth during annealing for films deposited with saccharin; this has not been observed previously in the literature to the authors' best knowledge

[en] Through an exhaustive process of verification, validation and uncertainty quantification, this dissertation performs sensitivity analysis, uncertainty quantification up to 3^rd-order (including covariance and skewness), and forward and inverse predictive modeling for a dissolver model of interest to nonproliferation activities regarding aqueous reprocessing of spent nuclear fuel. This dissolver model comprises sixteen nonlinear differential equations, which include 1291 model parameters characterizing the underlying physical and chemical processes. The original results presented in this dissertation highlight the effects of uncertainties which necessarily characterize measurements and computations, and the reduction in the predicted uncertainties by combining optimally the experimental and computational information. The uncertainties in the dissolver model parameters are propagated to compute uncertainties in the model responses by using first-order sensitivities (i.e., functional derivatives) of the respective responses to the model parameters. The first-order sensitivities to all model parameters of the time-dependent acid concentrations are computed by applying the adjoint sensitivity analysis method for nonlinear systems with function-valued responses originally conceived by Cacuci (1981a). Furthermore, this work also develops a reduced-order surrogate dissolver model, and extends Cacuci's original adjoint technique to enable the computation of second-order sensitivities. As shown in this work, the second-order sensitivities are essential for computing the skewness (i.e., third-order moment) of the response distribution, highlighting the latter's asymmetrical (non-Gaussian) features. The response sensitivities also serve as the weighting functions for combining experimental and computational information for the dissolver model using the comprehensive predictive modeling methodology originally developed by Cacuci and Ionescu-Bujor (2010b). The only experimental information available in the open literature for this dissolver model are the measurements performed by Lewis and Weber (1980) of the nitric acid in the compartment furthest away from the inlet. Using this experimental information with the forward and inverse predictive modeling formalism is shown to yield optimal predictions throughout the entire dissolver, reducing everywhere the uncertainties in these predicted results. This stems from the fact that the predictive modeling methodology combines and transmits information simultaneously over the entire phase-space, comprising all time steps and spatial locations. Another remarkable original result obtained in this dissertation is the innovative use of the predictive modeling framework of Cacuci and Ionescu-Bujor (2010b) in an inverse prediction mode for inferring unknown model parameters (specifically: the time-dependent inlet boundary condition) from measurements of the acid concentration in the compartment furthest from the inlet. This is particularly useful in applications where inferences on a target of interest can only be made from indirect measurements. In summary, this dissertation presents an efficient mathematical model for a dissolver of spent nuclear fuel of interest to international nuclear safeguards and nonproliferation, and demonstrates the procedure for rigorous uncertainty quantification and validation of this model. The dissertation also introduces an innovative adjoint procedure for computing second-order response sensitivities to model parameters, and highlights the latter's essential role for computing non-Gaussian features of the distributions of model responses of interest. The methodology demonstrated in this dissertation will serve as a role model for rigorous forward and inverse predictive modeling of other nuclear facilities of interest to international nuclear safeguards and nonproliferation, aiming at optimizing predictions for ''signatures'' and ''causes'' of interest while reducing drastically the accompanying uncertainties, thus enabling more accurate risk-informed decision processes.

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