Density functional theory modeling of cation diffusion in tetragonal bulk Zr O 2 : Effects of humidity and hydrogen defect complexes on cation transport
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The nickel-plated zircaloy-4 is used as a tritium ( 3 H) getter in the tritium-producing burnable absorber rods (TPBARs) to capture 3 H produced in the 6 Li-riched annular γ-LiAlO 2 pellet under neutron irradiation. The experimental data and our previous theoretical results showed that the 3 H species produced from the γ-LiAlO 2 pellet were mainly 3 H 2 and 3 H 2 O. These 3 H species diffuse from the surface of the LiAlO 2 pellet across vacuum to the nickel-plated zircaloy-4 getter and then further diffuse into the getter to chemically form metal hydrides. While a number of studies show that oxygen binds strongly as compared to 3 H on the nickel (Ni) layer, the detailed mechanism of 3 H species absorption and diffusion across the Ni plate and Ni/Zr interface are still unclear. By employing density functional theory calculations, here we explored the 3 H 2 and 3 H 2 O species adsorption and dissociation on the Ni(111) surface and diffusion into the Ni sublayer. Our results indicated that the 3 H 2 and 3 H 2 O dissociate on the Ni(111) surface. The NiO x and Ni(O 3 H) x could be formed in the Ni layer due to the higher oxygen (O) diffusion energy barrier and formation of Ni vacancy defects. The oxygen was found to be retained in the Ni layer from diffusing across the Ni–Zr interface. This was revealed by comparing the diffusion barriers for 3 H with O. 3 H was found to have nearly three times smaller diffusion barrier than for O, making 3 H comparatively easier to diffuse through the Ni layer. In conclusion, the obtained results provide guidelines for experimental measurements on 3 H retention behavior in TPBARs and may open further avenues to explore the impurity effects on 3 H diffusion and storage at the Ni/zircaloy interfaces.
To improve the performance of Cu(In,Ga)Se 2 thin-film photovoltaic devices, a robust understanding of the dominant diffusion pathways of the alloy species In and Ga is needed. Here, the most probable defect complexes and mechanisms for In and Ga diffusion are identified with the aid of density functional theory. The binding energies and migration barriers for these complexes are calculated in bulk CuInSe 2 and CuGaSe 2 . Analytic models and kinetic lattice Monte Carlo simulations are employed to predict the diffusivity of In and Ga under variations in composition and temperature. Here, we find that a model based on coulombic interactions between group III antisites and vacancies on the Cu-sublattice produces results that match well with experiment.
The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. While conventional KMC methods based on transition state theory naturally yield reversible dynamics that exactly obey the detailed balance criterion, providing strong guarantees on the properties of the stationary distribution, many recently-proposed ML-based approaches to barrier predictions provide no such guarantees. In this study, we derive conditions under which physics-informed ML architectures exactly enforce the detailed balance condition by construction, even when relying on non-extensive descriptions of states in terms of local environments around mobile defects. In conclusion, using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost.
Li–Mg alloys are important because of their beneficial role in fostering uniform plating and stripping of lithium in all-solid-state batteries. The alloy Li x Mg 1–x forms a solid solution on the BCC crystal structure when the lithium content is greater than x ≈ 0.3. The activation barriers of lithium and magnesium exchanges with a vacancy, crucial for substitutional diffusion, are predicted to be exceptionally low and almost identical, with negligible dependence on the alloy composition. The equilibrium vacancy concentration at room temperature is predicted to be very low, and it also remains almost constant with no dependence on Mg content in the alloy (for x Li ≥ 0.5). Nevertheless, both experiments and kinetic Monte Carlo simulations indicate that the tracer diffusion coefficients decrease by almost an order of magnitude with the addition of Mg to the alloy. In this contribution, the crucial role that chemical short-range order plays in affecting the diffusion coefficients is studied. Chemical short-range order is found to increase the effective activation barrier for lithium and magnesium diffusion by making successive atomic hops with vacancies correlated.
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The effects of ionizing radiation on materials often reduce to "bad news". Radiation damage usually leads to detrimental effects such as embrittlement, accelerated creep, phase instability, and radiation-altered corrosion. Here we report that proton irradiation decelerates intergranular corrosion of Ni-Cr alloys in molten fluoride salt at 650 °C. We demonstrate this by showing that the depth of intergranular voids resulting from Cr leaching into the salt is reduced by proton irradiation alone. Interstitial defects generated from irradiation enhance diffusion, more rapidly replenishing corrosion-injected vacancies with alloy constituents, thus playing the crucial role in decelerating corrosion. Our results show that irradiation can have a positive impact on materials performance, challenging our view that radiation damage usually results in negative effects.
Recent progress in developing and implementing Pt-alloy cathode catalysts and thin (10-15 micron) low resistance membranes has enabled high performance state of art (SOA) membrane electrode assembly (MEA) with low Pt loading. However, these high performing MEAs do not meet durability requirements, especially at peak power, because of complex degradation mechanisms that are sensitive to the materials, MEA design, and fuel cell operating strategy. Specifically, power degradation of the cathode occurs via Pt and Co dissolution as well as deterioration of O 2 transport properties. Additionally, thin membranes are subject to failure due to manufacturing defects in the adjacent gas diffusion media and electrodes and the formation of membrane-attacking radical species caused by high gas crossover. In this project led by General Motors LLC (GM), the objective was to enhance the durability of SOA MEA through optimization of operating conditions, instead of new materials development. Along with our project partners, we have mapped the impact of operating conditions on the durability of SOA MEA. Output of the project include a low Pt loading SOA MEA that exceeds Department of Energy (DOE) 2020 target of >1 W/cm 2 at rated power and pathway to achieve >5000 h of durability. Durability studies in the project provide a detailed understanding of failure modes and operating condition sensitivity on cathode and membrane failure, critical for defining operating conditions and hybridization strategies that can guide system controls to maximize low-Pt MEA life. The project also generated and validated degradation models that will provide future research direction, critical for guiding future cycles of automotive MEA development.
Niobium metal occupies nearly 100% of the volume of a typical 2D transmon device. While the aluminum Josephson junction is of utmost importance, maintaining quantum coherence across the entire device means that pair-breaking in Nb leads, capacitive pads, and readout resonators can be a major source of decoherence. The established contributors are surface oxides and hydroxides, as well as absorbed hydrogen and oxygen. Metal encapsulation of freshly grown surfaces with non-oxidizing metals, preferably without breaking the vacuum, is a successful strategy to mitigate these issues. While the positive effects of encapsulation are undeniable, it is important to understand its impact on the macroscopic behavior of niobium films. We present a comprehensive study of the bulk superconducting properties of Nb thin films encapsulated with gold and palladium/gold, and compare them to those of bare Nb films. Magneto-optical imaging, magnetization, resistivity, and London and Campbell penetration depth measurements reveal significant differences in encapsulated samples. Both sputtered, and epitaxial Au-capped films exhibit the highest residual resistivity ratio and superconducting transition temperature, as well as the lowest upper critical field, London penetration depth, and critical current. These results are in good agreement with the microscopic theory of anisotropic normal and superconducting states of Nb. We conclude that pair-breaking in the bulk of niobium films, driven by disorder throughout the film rather than just at the surface, is a significant source of quantum decoherence in transmons. We also conclude that gold capping not only passivates the surface but also affects the properties of the entire film, significantly reducing the scattering rate due to defects likely induced by surface diffusion if the film is not protected immediately after fabrication.
The aggregation of irradiation-induced defect clusters in UO 2 leads to the formation of dislocation loops and cavities which contribute swelling and ultimately produce deleterious effects on the fuel. Despite their fundamental role in fuel evolution, the kinetics of these defect clusters are not currently well understood. In this work, we investigate the diffusive behavior of interstitial clusters via Molecular Dynamics (MD) simulations. In this work, our investigation considers a range of defect cluster sizes, each composed of N UO 2 units or anti-Schottky defects. We report a complex cluster size - mobility relation; increasing cluster size corresponds to an increase in mobility up until a critical size (N=4) where diffusivity reaches a maximum, after which the trend reverts and further increasing size corresponds to a decrease in mobility. The rapid migration observed correlates well with the very low barriers reported in a few historic experimental studies. Further analysis of the cluster shape and orientation reveals a strong structural preference for near-planar configurations oriented normal to the $\langle$100$\rangle$ direction. Rotational energy barriers are found to be similar in both magnitude and trend to the observed diffusion barriers. Lastly, connections are drawn to the behavior of large defect clusters including the formation of (1/3) $\langle$111$\rangle$ Frank dislocation loops.
Ga 2 O 3 is emerging as an excellent potential semiconductor for high power and optoelectronic devices. However, the successful development of Ga 2 O 3 in a wide range of applications requires a full understanding of the role and nature of its point and extended defects. Here, in this work, high quality epitaxial Ga 2 O 3 films were grown on sapphire substrates by metal-organic chemical vapor deposition and fully characterized in terms of structural, optical, and electrical properties. Then defects in the films were investigated by a combination of depth-resolved Doppler broadening and lifetime of positron annihilation spectroscopies and thermally stimulated emission (TSE). Positron annihilation techniques can provide information about the nature and concentration of defects in the films, while TSE reveals the energy level of defects in the bandgap. Despite very good structural properties, the films exhibit short positron diffusion length, which is an indication of high defect density and long positron lifetime, a sign for the formation of Ga vacancy related defects and large vacancy clusters. These defects act as deep and shallow traps for charge carriers as revealed from TSE, which explains the reason behind the difficulty of developing conductive Ga 2 O 3 films on non-native substrates. Positron lifetime measurements also show nonuniform distribution of vacancy clusters throughout the film depth. Further, the work investigates the modification of defect nature and properties through thermal treatment in various environments. It demonstrates the sensitivity of Ga 2 O 3 microstructures to the growth and thermal treatment environments and the significant effect of modifying defect structure on the bandgap and optical and electrical properties of Ga 2 O 3 .
Functional properties of transition-metal oxides strongly depend on crystallographic defects; crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Yet, localized lattice distortions remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here, in this study, we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SrIrO 3 films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO 3 , and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments.
Self-diffusion is a fundamental physical process that, in solid materials, is intimately correlated with both microstructure and functional properties. In this work, a universal approach is presented to precisely characterize self-diffusion in ionic solids by isotopically enriching anions and/or cations at specific locations within an epitaxial film stack, and characterize their redistribution at high spatial resolution with atom probe tomography. Nanoscale anion diffusivity is quantified in epitaxial α-Fe 2 O 3 thin films deposited by molecular beam epitaxy with a thin (10 nm) buried layer highly enriched in 18 O. The isotopic sensitivity of the atom probe allows precise measurement of 18 O distribution across the sharp interfaces between this layer and the surrounding Fe 2 O 3 after annealing. Short-circuit anion diffusion through 1D and 2D structural defects in Fe 2 O 3 are also directly visualized in 3D. This versatile approach to study precisely tailored thin film samples at high spatial and mass fidelity will facilitate a deeper understanding of atomic-scale diffusion phenomena.
Transition metal borides, which are three-dimensional (3D) layered materials containing covalently bonded B networks, have shown a number of excellent properties, such as radiation resistance and the ability to act as a diffusion barrier in integrated circuits. However, defect behavior, which controls many of the materials’ properties, has remained unknown in these materials. In this work, we investigate the effects of the B networks on the defect chemistry in both binary borides (CrB, Cr 3 B 4 , Cr 2 B 3 ) and ternary MAB phases (Cr 2 AlB 2 , Cr 3 AlB 4 , Cr 4 AlB 6 ) using first-principles calculations. We find that increasing the number of B rings in the structure leads to lower formation energies and higher concentrations of Frenkel pairs. The results can be explained by the fact that the strongest Cr-B bond is weakened when borides have more B rings, leading to a reduction in the formation energy of Cr and B vacancies. Also, the bonds associated with Cr atoms bonded within B rings are softer in structures containing more B rings, which allows Cr interstitials to form with a lower energy cost and contributes to an increase in the concentration of Cr interstitials.
α-MnO 2 type materials have been studied as electrode materials in rechargeable batteries and electrocatalysts due to their 2 × 2 tunneled crystal structures capable of accommodating cations and their tunable physiochemical properties. In this study, we deliberately synthesized K + containing α-MnO 2 (K 0.9 Mn 8 O 16 ) hollow nanotubes varying the dimensions of the hollow regions and level of surface defects. The K 0.9 Mn 8 O 16 nanotube material samples have similar crystallinity, thermal stability, and average Mn oxidation state. Oxygen surface defects in the hollow regions were revealed through detailed studies using electron energy loss spectroscopy. The impact of the hollow regions and associated surface defects on the electrochemistry of K x Mn 8 O 16 were investigated using cyclic voltammetry, galvanostatic intermittent titration technique, and galvanostatic cycling. The K 0.9 Mn 8 O 16 nanotubes with a large hollow region (~30 nm) and higher level of surface defects show higher apparent lithium ion diffusion coefficients and lower polarization compared to the nanotubes with a small hollow region (~10 nm). In-situ lithiation demonstrated that the dimensions of the nanotube walls expanded, but the hollow region did not change in size as result of lithiation. Furthermore, this research demonstrates that tuning particle architecture and surface defects can positively impact functional behavior of electrochemical storage materials.
Surface coatings of steels used in extreme conditions and corrosive environments generally aim to provide protection and increased durability. In the case of tritium-producing burnable absorber rods (TPBARs) used in nuclear reactors, a 316 stainless steel has been coated with Al. Scanning transmission electron microscopy (STEM) characterization of the coating found three Al-rich (>60 atom % Al) iron aluminide alloys identified as hexagonal FeNiAl 5 , monoclinic Fe 4 Al 13 , and orthorhombic Fe 2 Al 5 . Density functional theory simulations using nudged elastic band have been performed to investigate the diffusion of interstitial tritium in each Al-rich iron aluminide phase. While FeNiAl 5 and Fe 4 Al 13 can be viewed as the stacking of two layers, the structural peculiarity of Fe 2 Al 5 is that channels of variable Al vacancy content are present along the c-axis. Therefore, three stoichiometries for Fe 2 Al x phase, namely, Fe 2 Al 4 , Fe 2 Al 5 , and Fe 2 Al 6 , have been considered to evaluate the impact of Al vacancy concentration on tritium diffusion behavior. Altogether, we found that at 600 K, tritium diffusion decreases from a faster rate in the channels of Fe 2 Al x phases (D T ≤ 10 –11 m 2 ·s –1 ) to Fe 4 Al 13 (D T ≈ 10–12 m2·s–1), and finally in FeNiAl5 (D T ≈ 10 –13 m 2 ·s –1 ). Here, we also find that interstitial tritium generally diffuses faster in Fe–Al coating phases than in the tritium breeding material γ-LiAlO 2 (D T ≈ 10 –14 m 2 ·s –1 ) but slightly slower than in 316 stainless steel (D T ≈ 10 –10 m 2 ·s –1 ).
Non-equilibrium defects often dictate the macroscopic properties of materials. They largely define the reversibility and kinetics of processes in intercalation hosts in rechargeable batteries. Recently, imaging methods have demonstrated that transient dislocations briefly appear in intercalation hosts during ion diffusion. Despite new discoveries, the understanding of impact, formation and self-healing mechanisms of transient defects, including and beyond dislocations, is lacking. Here, operando X-ray Bragg Coherent Diffractive Imaging (BCDI) and diffraction peak analysis capture the stages of formation of a unique metastable domain boundary, defect self-healing, and resolve the local impact of defects on ionic diffusion in Na x Ni 1-y MnyO 2 intercalation hosts in a charging sodium-ion battery. Results, applicable to a wide range of layered intercalation materials due to the shared nature of framework layers, elucidate new dynamics of transient defects and their connection to macroscopic properties, and suggest how to control the nanostructure dynamics.
Rechargeable batteries based on multivalent working ions are promising candidates for next-generation high-energy-density batteries. Development of these technologies, however, is largely limited by the low diffusion rate of multivalent ions in solid-state materials, thereby necessitating a better understanding of the design principles that control multivalent-ion mobility. We report Ca 1.5 Ba 0.5 Si 5 O 3 N 6 as a potential calcium solid-state conductor and investigate its Ca migration mechanism by means of ab initio computations and neutron diffraction. This compound contains partially occupied Ca sites in close proximity to each other, providing a unique mechanism for Ca migration. Nuclear density maps obtained with the maximum entropy method from neutron powder diffraction data provide strong evidence for low-energy percolating one-dimensional pathways for Ca-ion migration. Ab initio molecular dynamics simulations further support a low Ca-ion migration barrier of ~400 meV when Ca vacancies are present and reveal a unique "vacancy-adjacent"concerted ion migration mechanism. This work provides a new understanding of solid-state Ca-ion diffusion and insights into the future design of novel cation configurations that utilize the interactions between mobile ions to enable fast multivalent-ion conduction in solid-state materials.