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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Empirically Optimized One-Electron Pseudopotential for the Hydrated Electron: A Proof-of-Concept Study

Mixed quantum-classical molecular dynamics simulations have been important tools for studying the hydrated electron. They generally use a one-electron pseudopotential to describe the interactions of an electron with the water molecules. Furthermore, this approximation shows both the strength and weakness of the approach. On the one hand, it enables extensive statistical sampling and large system sizes that are not possible with more accurate ab initio molecular dynamics methods. On the other hand, there has (justifiably) been much debate about the ability of pseudopotentials to accurately and quantitatively describe the hydrated electron properties. These pseudopotentials have largely been derived by fitting them to ab initio calculations of an electron interacting with a single water molecule. In this paper, we present a proof-of-concept demonstration of an alternative approach in which the pseudopotential parameters are determined by optimizing them to reproduce key experimental properties. Specifically, we develop a new pseudopotential, using the existing TBOpt model as a starting point, which correctly describes the hydrated electron vertical detachment energy and radius of gyration. In addition to these properties, this empirically optimized model displays a significantly modified solvation structure, which improves, for example, the prediction of the partial molar volume.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Realization of a high luminosity muon beam at the Jefferson Lab

The proton is the major component of the visible matter of our universe, so being able to determine its fundamental properties, such as its charge radius, is of vital importance. In recent years, several experiments have been performed for the accurate measurement of the proton radius with different experimental techniques. The main and most common are based on two different techniques: ep scattering and Lamb shift spectroscopy of regular hydrogen atoms. The latter can also be performed on muonic hydrogen atoms with a significant improvement in accuracy. Recent measurements that use this method have highlighted a discrepancy between the results obtained with the diffusion of electrons and those obtained with the spectroscopic technique leading to the so-called ?proton radius puzzle?. The explanation of why this difference between the measured values exists is still a matter of discussion. The last attempt to address the puzzle was the PRad Experiment. It was performed with a unique experimental setup that gave the scientists an excellent control on the systematic errors that are usually present in electron-proton scattering experiments. This has significantly increased the precision of the measurement, bringing it in the same area as the spectroscopic ones done on muonic hydrogen atoms, which are considered the most precise. However, to solve the puzzle once and for all, even more precise electron scattering measurements are needed. Alternatively, new methods can be used, such as eg. muon-proton scattering at low transferred momenta in order to lower the systematic errors that comes with the measurement. This thesis tries to address this problem by studying the set up of an intense muon beam at the Jefferson Laboratory (JLab) that can be used to carry out a high-precision m ? p scattering experiment. Using Montecarlo simulations I studied the muon production resulting from the interaction of the primary electron beam with the beam-dump at JLab Hall-A. The simulations were performed using the FLUKA package via the FLAIR graphical interface. In this study, I modified an existing geometry (built for the ?Beam Dump eXperiment?) and performed several studies in order to optimize the muon beam parameters. In order to estimate the beam intensity, I studied the muon generation and transportation through the concrete bunker surrounding the Hall-A beam-dump. Detailed studies on muon attenuation and dispersion at different depth allowed me to track the beam profile in space and momentum as a function of the distance from the beam-dump. Last, but not least, I studied a possible tagging system for the muon beam. The tagging system will allow us to know with good precision (O(1=1000)) the energy of each muon of the beam. Based on the results obtained, I created an event generator that incorporates the detailed description of the muon beam (energy, position, emittance) useful to define and optimize the experimental setup for a future measurement of the proton radius at the JLab.

Fulci, Antonino↗
DOE OSTI2023

Neural network-based control of an ultrafast laser

With the recent advances in machine learning (ML) and data science (DS), the control, modeling, and analysis of these complex systems continues to improve. In this work, we report on the optimization of the intensity of a femtosecond laser using feedforward neural networks (FFNN) that model the input–output relationships of the data. The input parameters of the system were optimized to achieve the required performance of the femtosecond laser. We propose a neural network-based control system to model the relationship between the spectral amplitude and phase of the input laser pulse at the amplifier input and the shape of the output pulse. Low-jitter laser parameter inputs and the resulting laser pulse duration were modeled, and the resulting correlation between the input and output data was used to optimize the laser pulse. Here, we demonstrate improved processing and laser control performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Geometry and size dependent microstructure and crack formation in Rene 41 superalloy fabricated by laser powder bed fusion

This study provides a systematic investigation into the size and geometry-dependent microstructural evolution and cracking susceptibility of LPBF-fabricated Rene 41. By coupling experimental microstructural analysis with thermal modeling, this research uniquely identifies the relationship between geometry-specific thermal histories, carbide coarsening, and liquation cracking. Four different geometries with varying thickness were fabricated with the identical process parameters. It was found that the grain size and morphology are not affected by the part size. However, the thinner parts exhibited coarser sub-grain structures compared to the thicker ones. The crack formation was observed for the parts with cross-sections smaller than 1 mm, whereas thicker parts had high density without any defects. Further, the cracks were observed in the interdendritic regions, suggesting that liquation cracking was the active micro-crack formation mechanism. The detailed microstructural analysis combined with a thermal finite element analysis proved that the heat extraction efficiency was lower for thinner parts causing a lower cooling rate and coarser carbides, making them more susceptible to constitutional liquation. Microhardness measurements were conducted for each geometry and correlated with the observed microstructural variations. The findings highlight the critical need for geometry-specific optimization of LPBF process parameters to mitigate cracking and achieve microstructural uniformity, offering valuable insights into the fabrication of complex, high-performance aerospace components.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding the solubility of metal salts and supporting electrolytes in Deep Eutectic Solvents [Slides]

Our objective is to determine how alterations in these parameters affect solubility in order to identify the optimal conditions for Zirconium electrodeposition. Create a variety of Deep Eutectic Solvents by combining different combinations and molar ratios of both hydrogen bond donors and hydrogen bond acceptors. Investigate the solubility of different Zirconium metal sources and supporting electrolytes individually, as well as in combination of both. Analyze various parameter conditions including temperature, pH, and conductivity. Determine the optimal composition and parameter setup for Zirconium electrodeposition. Obtain Zirconium coating through various methods and parameter adjustments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Effect of Geometry, Spin, and Orbital Optimization in Achieving Accurate, Correlated Results for Iron–Sulfur Cubanes

Iron–sulfur clusters comprise an important functional motif in the catalytic centers of biological systems, capable of enabling important chemical transformations at ambient conditions. This remarkable capability derives from a notoriously complex electronic structure that is characterized by a high density of states that is sensitive to geometric changes. The spectral sensitivity to subtle geometric changes has received little attention from correlated, large active space calculations, owing partly to the exceptional computational complexity for treating these large and correlated systems accurately. Here, to provide insight into this aspect, we report the first Complete Active Space Self Consistent Field (CASSCF) calculations for different geometries of the [Fe(II/III) 4 S 4 (SMe) 4 ] -2 clusters using two complementary, correlated solvers: spin-pure Adaptive Sampling Configuration Interaction (ASCI) and Density Matrix Renormalization Group (DMRG). We find that the previously established picture of a double-exchange driven magnetic structure, with minute energy gaps (<1 mHa) between consecutive spin states, has a weak dependence on the underlying geometry. However, the spin gap between the singlet and the spin state 2S + 1 = 19, corresponding to a maximal number of Fe-d electrons being unpaired and of parallel spin, is strongly geometry dependent, changing by a factor of 3 upon slight deformations that are still within biologically relevant parameters. The CASSCF orbital optimization procedure, using active spaces as large as 86 electrons in 52 orbitals, was found to reduce this gap compared to typical mean-field orbital approaches. Our results show the need for performing large active space calculations to unveil the challenging electronic structure of these complex catalytic centers and should serve as accurate starting points for fully correlated treatments upon inclusion of dynamical correlation outside the active space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multiplexed Anodic Stripping Voltammetry Detection of Heavy Metals in Water Using Nanocomposites Modified Screen-Printed Electrodes Integrated With a 3D-Printed Flow Cell

In this study, we present multiplexed anodic stripping voltammetry (ASV) detection of heavy metal ions (HMIs)—As(III), Cd(II), and Pb(II)—using a homemade electrochemical cell consisting of dual working, reference and counter screen-printed electrodes (SPE) on polyimide substrate integrated with a 3D-printed flow cell. Working and counter electrodes were fabricated by the screen-printing of graphite paste while the Ag/AgCl paste was screen-printed as a reference electrode (Ag/AgCl quasi-reference electrode). The working electrodes were modified with (BiO) 2 CO 3 -reduced graphene oxide (rGO)-Nafion [(BiO) 2 CO 3 -rGO-Nafion] and Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 MNPs) decorated Au nanoparticles (AuNPs)-ionic liquid (IL) (Fe 3 O 4 -Au-IL) nanocomposites separately to enhance HMIs sensing. Electrochemical detection was achieved using square wave ASV technique. The desired structure of the flow electrochemical cell was optimized by the computational fluid dynamic (CFD). Different experimental parameters for stripping analysis of HMIs were optimized including deposition time, deposition potential and flow rate. The linear range of calibration curves with the sensing nanocomposites modified SPE for the three metal ions was from 0–50 μg/L. The limits of detection (S/N = 3) were estimated to be 2.4 μg/L for As(III), 1.2 μg/L for Pb(II) and 0.8 μg/L for Cd(II). Furthermore, the homemade flow anodic stripping sensor platform was used to detect HMIs in simulated river water with a 95–101% recovery, indicating high selectivity and accuracy and great potential for applicability even in complex matrices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Silver Nanowire-Indium Zinc Oxide Composite Flexible Transparent Conducting Electrodes Made by Spin- coating and Photonic Curing

Realizing high-throughput, low-cost perovskite solar cell (PSC) manufacturing is highly sought-after in photovoltaic (PV) research in recent years. To fully achieve roll-to-roll (R2R) manufacturing of PSCs, it is important to consider the flexible transparent electrode (TE). PET/ITO is a commonly used substrate for making flexible PSCs. When optimizing transparent conducting materials, there is a tradeoff between sheet resistance (Rsh) and optical transparency. Because commercial PET/ITO substrates are made with slow (~1 m/min) vacuum deposition processes, they tend to be expensive. Therefore, it would be advantageous to develop a high-throughput, R2R compatible, solution-deposition approach for fabricating the TE on PET substrates. While various solution-deposition processes, such as blade coating or slot-die coating, can achieve the desired web speed of > 10 m/min, there is still a need to improve the post-deposition annealing step. One promising post-deposition processing technique is intense-pulsed-light processing, also known as photonic curing. Photonic curing delivers short (0.01 – 100 ms) pulses of broadband (200 – 1500 nm) light from a xenon flash lamp to the samples. Any materials in the sample stack that absorb light will convert the impinging light pulse into heat within the sample, which drives changes in the sample (calcination, phase change, crystallization, etc.). Photonic curing has three main advantages over thermal annealing: 1. Faster processing speed (milliseconds or seconds). 2. Compatibility with plastic substrates. 3. Smaller physical footprint and less wasted energy. Since the light pulses are on for a short time, the intensity can be high while the total energy delivered to the sample is low, minimizing damages to the plastic substrates. In this work, a hybrid TE material is fabricated on PET substrates using photonic curing. The hybrid TE material contains a layer of silver nanowires (AgNWs) and a layer of metal-oxide (InOx, ITO, IZO, etc.). The AgNWs increase the light absorbed by the film during the photonic curing process, which leads to higher processing temperatures, possibly improving the conversion of the metal-oxide layer. The AgNWs also enhance the electrical conductivity of the final TE layer after photonic curing. A AgNW and metal-oxide bilayer is formed by spin coating each solution onto the PET substrate sequentially followed by a single photonic curing process. We use average optical transmittance (Tavg) from 400 to 700 nm and average Rsh to evaluate the TE performance. The following photonic curing parameters are varied to optimize Tavg (maximize) and Rsh (minimize): Pulse voltage, pulse envelope, number of micro-pulses, duty cycle, number of pulses, and pulse repetition rate. Preliminarily, we also observe a significant impact on the TE properties by the volume of AgNW deposited during the spin coating deposition step. Using dispense volumes of 80 µL and 20 µL, we achieve samples with Tavg = 73%, Rsh = 19 Ω/sq, and roughness = 9 nm, and Tavg = 83%, Rsh = 58 Ω/sq, and roughness = 5.6 nm, respectively, after photonic curing.

14 SOLAR ENERGY↗
DOE OSTI2020

Performance Limits of an Alternating Current Electroluminescent Device

Abstract The use of an alternating current (AC) voltage is a simple, versatile method of producing electroluminescence from generic emissive materials without the need for contact engineering. Recently, it was shown that AC‐driven, capacitive electroluminescent devices with carbon nanotube network contacts can be used to generate and study electroluminescence from a variety of molecular materials emitting in the infrared‐to‐ultraviolet range. Here, performance trade‐offs in these devices are studied through comprehensive device simulations and illustrative experiments, enhancing understanding of the mechanism and capability of electroluminescent devices based on alternating as opposed to direct current (DC) schemes. AC‐driven electroluminescent devices can overcome several limitations of conventional DC‐driven electroluminescent devices, including the requirement for proper alignment of material energy levels and the need to process emitting materials into uniform thin films. By simultaneously optimizing device geometry, driving parameters, and material characteristics, the performance of these devices can be tuned. Importantly, the turn‐on voltage of AC‐driven electroluminescent devices approaches the bandgap of the emitting material as the gate oxide thickness is scaled, and internally efficient electroluminescence can be achieved using low‐mobility single‐layer emitter films with varying thicknesses and energy barrier heights relative to the contact.

Wang, Vivian↗
DOE OSTI2022

High temperature high strength austenitic steel fabricated by laser powder-bed fusion

Extremely fast cooling rates during laser powder-bed fusion (LPBF) can result in materials with unique microstructures. For LPBF 316L stainless steel, the formation of sub-grain cellular structures with high dislocation density has been linked to superior tensile properties at room temperature. This cellular structure offers also a new route for the development of high temperature LPBF steels with the nucleation of nano-size strengthening carbides in the cell walls. HK30Nb steel (Fe-25Cr-20Ni-Nb-C) was, therefore, fabricated by LPBF to evaluate its potential for high temperature applications. Optimization of the fabrication parameters yielded material with density greater than 99.7%, with nano Nb-rich precipitates in the cell walls. Annealing at 800 °C for 5h resulted in the nucleation and growth of additional precipitates mainly in the cell walls and at grain boundaries. Furthermore, the high dislocation density led to yield strength at 20–900 °C two to three times higher than yield strength for cast HK30Nb and the nano carbides in the cell walls significantly improved the cellular structure stability at 800 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Development of an interatomic potential for the W–Ta system

A physics-inspired, data-driven interatomic potential framework for multi-element system is presented. This potential is based on the generalization of the embedded atom method potential and systematically incorporates two-, three- and many-body effects. Different atomic environments are described by atom-centered Gaussian basis sets which provides sufficient flexibility to capture diverse atomic environments and allows for easy optimization of the free parameters. An interatomic potential model for the tungsten–tantalum (W–Ta) system is developed using this framework by training on data from ab initio density functional theory (DFT) calculations. The potential is thoroughly tested at various compositions by comparing bulk and defect properties from experimental and DFT data. It is shown that the potential predicts the elastic constants, defect properties, such as vacancy and interstitial formation energies, core structures of dislocations, and melting points for both pure and tantalum and tungsten with an accuracy comparable to other single element machine learning based potentials. In conclusion, the potential model is used to investigate the formation energies of ordered alloys and vacancy formation energies for chemically random W–Ta alloys.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Perspectives on the relationship between materials chemistry and roll-to-roll electrode manufacturing for high-energy lithium-ion batteries

As lithium-ion battery (LIB) active material and cell manufacturing costs continue to drop with wider adoption of electric vehicles, electrode and cell processing costs remain too high in terms of reaching the ultimate U.S. Department of Energy (DOE) cell cost target of $80/kWh. This paper primarily covers major materials chemistry advancements made over the last 10 years at Oak Ridge National Laboratory (ORNL) in the space of advanced manufacturing science for LIBs with the aim of simultaneously meeting the ultimate cost target, 500 Wh/kg gravimetric energy density, 10-15-min fast charge times, and 1000 deep discharge cycles. Aqeous electrode processing with a variety of active anode and cathode materials is now a standard procedure at the DOE Battery Manufacturing R&D Facility at ORNL (BMF), including the latest processes developed for Ni-rich cathodes. New results on cobalt-free LiNi 0.8 Fe 0.1 Al 0.1 O 2 (NFA 811) are also included and discussed in an electrode processing advantage context. In addition, colloidal processing advancements have been made for Si/C composite anodes for achieving >600 mA h/g capacities. Optimization of electrode coating parameters and drying protocols have been completed, which has elucidated how key processing variables need to be changed when parameters such as slurry solids loading, solvent type, and wet electrode thickness are changed. ORNL has also increased the line speeds at which thick cathodes can be processed using ultra-fast electron beam (EB) curing and substantially decreased formation cycling times to <1 day. Finally, key details of these advancements are discussed in the context of materials chemistry and process-property-performance relationships.

25 ENERGY STORAGE↗
DOE OSTI2024

A comprehensive study on two types of supercapacitor composite electrodes comprising MnO 2 and activated carbon nanofibers: Self-supporting membrane and ground powder

Manganese dioxide (MnO 2 )@carbon composites have been attractively considered as electrode materials for supercapacitors (SCs) due to synergistic effects. This work systematically investigated the structure of MnO 2 @carbon nanofiber (CNF) composite electrodes with the different forms of CNFs and the corresponding electrochemical performance of SCs. In brief, novel activated carbon nanofibers were first fabricated by electrospinning the hydroxyl-containing poly(amic acid) solution, and then the preferred CNF material was decorated by MnO 2 crystals in the form of self-supporting membrane and ground powders, respectively. Additionally, the synthesis parameters were investigated and optimized based on the electrochemical performance of SCs. The results reveal that the powdered composite electrode exhibits a higher specific surface area of 501 m 2 g –1 compared to the self-supporting membrane composite electrode under the same conditions, resulting in a promising specific capacity of 214.1 mAh g –1 (770.8 Fg –1 ) in 6M KOH solution at 0.5 A g –1 . In addition, the capacitance of the symmetrical SC device assembled by G-HMC-1:3–80 reaches 179.8 Fg –1 , coupled with an energy density of 24.86 Whkg –1 at 230 Wkg –1 power density. This work provides valuable hints for designing SC composite electrode materials with outstanding performance.

25 ENERGY STORAGE↗
DOE OSTI2025

Integrated process-structure–property–performance optimization of cold-sprayed zinc coatings on AZ91 magnesium alloy

High-pressure die cast (HPDC) AZ91 magnesium alloy is widely used in automotive components such as transmission housings and brackets for its excellent strength-to-weight ratio. Zinc-based cold spray coatings can be applied selectively to vulnerable areas to enhance corrosion resistance, minimize galvanic coupling with dissimilar metals, and eliminate the need for full-surface oxide coatings, making the process more efficient and targeted. A comprehensive evaluation of 16 combinations of nitrogen carrier gas temperatures and pressures led to the identification of an optimal range of process parameters, yielding Zn coatings with porosity <0.5 % by area, wear rates reduced by a factor of two compared to uncoated AZ91, and adhesion strengths up to 35 MPa. The enhanced mechanical performance of the coating is attributed to the low porosity and the formation of a metallurgical bond at the coating-substrate interface. Corrosion studies using macroscale potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) revealed a significant decrease in corrosion rate and a shift to more noble corrosion potentials (ZCP) for coated substrates. Furthermore, the Zn cold-sprayed samples exhibited significantly lower corrosion-induced evolved hydrogen content compared to the base AZ91 substrate and AZ91 coated with industrial coatings, demonstrating that the Zn layer effectively protects the substrate from the corrosive environment. Overall, cold spray Zn coatings significantly improve the mechanical and corrosion performance of AZ91 Mg alloys, addressing key material challenges and enabling their broader use in automotive applications.

Corrosion performance↗