Daniel Edwin Hooks

Electrochemical polishing of metallic surfaces is a scalable, cost-effective technique to smooth surfaces for functional and/or decorative purposes. In direct current electrochemical polishing increasing electrolyte viscosity is used to mediate current density by controlling diffusion. This approach has significant limitations with the uniquely rough surfaces yielded in additive manufacturing which can lead to an inability to smooth very rough surfaces, lack of shape retention, and inducing corrosion in extreme cases. Pulse electropolishing offers the promise of additional control variables for targeted roughness scales. In this work, we explore the underlying electrochemistry that governs pulse electropolishing. We directly correlate pulse parameters (on/off time and current) with specific scales of asperity dissolution. We demonstrate that designing the electrolyte to be less hazardous and less chemically aggressive shifts the control parameters from electrolyte viscosity to pulse waveform parameters. This shift allows for smoothing surface roughness to typical engineering specifications of ∼ 1 μm, affecting the achievable part dimensions, polishing effectiveness, and corrosion resistance, while enhancing operational safety. The pulse electropolishing effectiveness over very large roughness scales with selective removal of partially melted particles, addressing major challenges related to additively manufactured parts.

Cobalt electrodeposition has been studied for various applications ranging from catalysis materials to alloys to low mean-free-path conductors for decreased scale electronic interconnects. Though all of these applications are important from a commercial perspective, it is of general scientific interest in particular because it has been demonstrated that deposition parameters can influence coating shape, morphology, and microstructure; what is unique about cobalt among many more common materials is that the deposition parameters can additionally select between the isomorphic hcp and fcc phases. The ability to select phase is itself of interest to understand, but this feature also allows for an interrelated parameter study that includes phase and microstructure and morphology. Is it possible to select all of these properties at the same time? If so, it may be possible to create microstructure and phase mixtures and gradients and to achieve unique properties not possible to achieve in other manufacturing methods.

Electrodeposition has been used for centuries as a relatively simple, robust, and cost-effective method for applying functional and aesthetic coatings to materials. Fundamental, time-dependent microstructural control remains elusive because systematic relationships between process parameters and resulting coating performance are lacking, due to scattered empirical studies and post-mortem correlations. Here, we aim to use modern techniques in-situ, such as x-ray phase contrast imaging (XPCI) and small angle x-ray scattering (SAXS) to control process-structure relationships of electrodeposited coatings. We will provide results from a range of deposition experiments designed to measure nucleation or growth phenomena directly, allowing connection between physical processes to classical nucleation theory. Complementary SEM and SAXS results will be presented as a means of evaluating utility for high resolution imaging at required time and length scales. Cyclic voltammetry and chronoamperometry will be applied to inferentially approximate relevant nucleation mechanisms. Comparisons to conventional measurements by scanning electron microscopy (SEM) with energy-dispersive x-ray spectroscopy (EDS) are also described. The objective of this work is ultimately to tailor the structure and properties of electrodeposited coatings through real-time parameter variations.

When considering applications of graded compositional coatings, shock physics is not one that readily comes to mind. However, developing processes to fabricate materials with graded compositions/densities has been a long-standing challenge in the shock physics community to understand material behavior under extreme conditions. Ideally, samples for testing must be compositionally graded over a thickness of a few millimeters with grain sizes on the micron scale. Electrodeposition is a promising fabrication technique to reliably produce graded coatings that meet these requirements.

Stress distribution maps in polycrystalline materials are needed to reveal stress pathways caused by short‐and long‐range interactions of crystallites. Stress induced changes to wavenumbers in Raman spectroscopy is a well‐known phenomenon that occurs when a crystalline material undergoes elastic strain. Using spatially resolved Raman spectroscopy, we have developed a technique to experimentally measure grain‐scale residual stresses across a sample of polycrystal quartz Tiger's Eye. The results of this technique are validated using multiple criteria, including evaluation of the residual stress map itself and the accuracy of the measurements taken. The residual map of Tiger's Eye shows striking similarity to observed characteristics of other polycrystalline residual stress distributions, revealing heterogenous areas of high and low magnitude stresses. The stress magnitudes are consistent with residual stress magnitudes previously measured from polycrystalline quartz. Characterization of Tiger's Eye quartz also revealed the geometric nature of the iron oxide inclusions, not previously observed in Tiger's Eye quartz. With this technique, we present a residual stress map of Tiger's Eye quartz that shows with accuracy a heterogeneous stress state with inter‐ and intra‐granular detail.

A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.

Rhenium (Re) electrodeposition is a fascinating but challenging system to develop a robust processing procedure that can be implemented at scale. Beginning with the perrhenate anion (ReO ₄ ^- ), reduction from the +7-oxidation state must occur to deposit metallic Re. Traditionally, Re has been difficult to deposit from aqueous solutions due to the over potential for hydrogen evolution so significant efforts have focused on developing alternative electrolytes. A water-in-salt electrolyte using 5M lithium chloride (LiCl) emerged as a cost-effective, viable option. Successful electrodeposition of metallic Re was only observed with the inclusion of citric acid to locally control the pH at the cathodic interface. Electrolyte engineering is only one aspect of the electrodeposition process that needs to be optimized. Early studies indicated that thick coatings (>10 µm) could not be obtained via DC electroplating. Optimization greatly expands the design space to include cathodic peak current (I _{c, peak} ), cathodic on-time (t _{c, on} ), anodic peak current (I _{a, peak} ), anodic on-time (t _{a, on} ), and off-time (t _{c, off} ). A high-throughput electrodeposition platform was designed, and parameters were systematically investigated using a 3-factor, circumscribed central composite (CCC) design with α = ± 1.68. Crack free deposits were only observed when both a cathodic and anodic pulse were included. However, small scale experiments masked the impact of chlorine evolution on the stability of the electrolyte. In the 5 M LiCl electrolyte chlorine evolution is both kinetically and thermodynamically more favorable than oxygen evolution, limiting the lifespan of the electrolyte. A divided cell with a Nafion membrane can effectively mitigate this issue but the anolyte must be appropriately selected to balance pH and ionic strength to limit unwanted ion diffusion across the membrane. Finally, substrate selection and preparation must also be considered to enable scalable electrodeposition. Iron group metals are known to induce co-deposition of Re, presenting an obvious selection of surface material to deposit on. However, trials using aluminum treated with a zincate and thin electroless nickel layer resulted in selective deposition of Re with substantial pitting and dissolution of the aluminum. Our results indicate that the iron-group metal surface must be optimized to enable scalable electrodeposition of Re. Overall, this work presents broad considerations for the scale-up of difficult-to-electroplate metals that are often overlooked when designing new electrodeposition processes.

Electrodeposition involves many control parameters. Choices of solvent, electrolytes, complexing agents, adsorbents, temperature, and power settings introduce myriad choices in processing conditions. Though many individual chemical and physical processes that influence choices in these processing conditions are understood at some level, the detailed interdependencies are difficult to unravel. Using experience and wealth of past studies, researchers can hypothesize parameters to use when developing deposition methods for new systems. However, trial and error remain integral to the development process, wherein many parameters are varied, and resulting coatings are characterized. Recent revolutionary advances in pulse and pulse-reverse power and electrolyte developments only add to the complexity; controlled current or voltage, on/off/reverse heights and durations alone add many variables. High throughput methods are being developed to more rapidly screen deposition parameters, in which deposition variables are varied and resulting films are characterized by methods including optical microscopy. In some cases in situ characterization methods have been demonstrated and remain in development, but even so, data is sparse given how much uncertainty there is in the time-dependent processing variables. Deep learning methods of various kinds have demonstrated impressive predictions in several fields in recent years. Examples include (1) real-time control of beamline dynamics, in which input parameters from injectors and magnetic lens controls result in downstream images of beam shape, with limited data on intermediate processes and (2) forward prediction of diffraction or microstructure data, among others. In many ways, these examples are very similar to the electrodeposition problem we have outlined: definitive chemistry and physics processes along with input parameters are known, dependencies and time evolution are not, and outcomes are measured downstream in the form of some characterization of output. Previously, we used high throughput trial and error approaches to optimize parameters for rhenium deposition, and input waveforms can be compared to microscopy characterization of the resulting film. Here, we will describe an approach for deep learning using pulse/pulse-reverse deposition parameters and resulting images for this system. Such approaches may enable much more rapid parameter refinement for electrodeposition of many materials.