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Saidi, Wissam A.

Publications and source records attributed to Saidi, Wissam A..

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Computational descriptor for electrochemical currents of carbon dioxide reduction on Cu facets

Computation screening is crucial for designing efficient electrochemical catalysts for carbon dioxide (CO 2 R) reduction that produce valuable hydrocarbons and oxygenates. In this work, leveraging density functional theory calculations for the CO adsorption energy ΔE CO on seventeen Cu terminations, we discover a strong linear correlation between ΔE CO and the experimentally measured CO 2 R electrochemical currents (ACS Catal. 2022, 12, 11, 6578–6588). Examining ab initio thermodynamics of early critical intermediates CO*, COH*, and CHO*, we find that CO* → CHO* is the thermodynamically controlling step. Beyond the general CO adsorption energy that only shows a linear trend with CO 2 R activity, we show that the reaction free energy of CO* → CHO* is the descriptor for the overall CO 2 R activity for Cu facets, as it displays a volcano relationship with the experimental current. Importantly, we show that high step and kink density of the Cu terminations not only enhances CO adsorption strength but also modulates the CO* → CHO* pathway, as respectively exemplified in the (941) and (741) facets. In addition, we explain that the high activity of (741) is due to its relatively low hydrogen evolution reaction activity compared with the other Cu surfaces.

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

Cryptate binding energies towards high throughput chelator design: metadynamics ensembles with cluster–continuum solvation

A tiered forcefield/semiempirical/meta-GGA pipeline together with a thermodynamic scheme designed with error cancellation in mind was developed to calculate binding energies of [2.2.2] cryptate complexes of mono- and divalent cations. Stable complexes of Na, K, Rb, Ca, Zn and Pb were generated, revealing consistent cation–N lengths but highly variable cation–O lengths and an amine stacking mechanism potentially augmenting the cation size selectivity. Metadynamics, used for searching the high-dimensional potential energy surface, together with a cluster–continuum model for affordable – yet accurate – solvation modeling, enabled the discovery of more stable geometries than those previously reported. Similar solvation energy curve shapes for lone vs. coordinated ions enabled rapid solvation convergence via the cancellation of errors stemming from finite cluster sizes. In conclusion, an R 2 of 0.850 vs. experimental aqueous binding energies was obtained, validating this scheme as the backbone of a high-throughput workflow for chelator design.

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

Temperature Dependent Early-Stage Oxidation Dynamics of Cu(100) Film with Faceted Holes

Fundamental understanding of surface oxidation dynamics is critical for rational corrosion protection and advanced manufacturing of nanostructured oxides. In situ environmental TEM (ETEM) provides high spatial (nano- to atomic- scale) and temporal (< 0.1 s) resolution to investigate the early-stage oxidation/corrosion dynamics of metals and alloys. Thin samples with facets are widely used to enable cross-sectional observation of the oxidation dynamics in ETEM. However, how different facet orientations oxidize under the same conditions, and how these facets change the oxidation process, has not been investigated before. Here, using in situ ETEM, we systematically compare the oxidation dynamics of Cu(001) thin films, with faceted holes exposing {100} and {110} facets at temperatures ranging from 250–600 °C under 0.03 Pa O 2 . Oxidation preference is observed to change, from Cu(110) facets at lower temperatures to Cu(100) facets at ~ 500 °C. Oxide growth mechanisms change from outward growth on Cu 2 O surfaces at low temperatures, to inward growth on Cu-Cu 2 O interfaces at high temperatures. At high temperatures (500–600 °C), a rod-like Cu 2 O morphology is observed, with side facets of ~ {024} and top facets of {100} on Cu(100). This differs from the square-shaped Cu 2 O exposing {110} facets formed on Cu(001) surfaces. Rod-like oxides exhibit directional growth along their lengths with linear growth rates, regardless of rod length and width. This suggests that O from Cu(001) surfaces, rather than Cu(100) facets, serves as an O source for oxide growth. These results show a direct comparison of oxidation at different orientations with temperature, underscoring the temperature dependence of oxidation preference. Our results also suggest future in situ ETEM experiments viewing oxidation corrosion cross-sectionally should be cautious when oxide size is comparable with sample thickness, as the oxidizing mechanism may change due to sample thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ni coarsening under humid atmosphere in the electrode of solid oxide cells: A combined study of density-functional theory and phase-field modeling

Ni coarsening in solid oxide cell (SOC) electrodes is known to be significantly faster under a humid atmosphere. The underlying mechanisms, though, have not been fully understood. In this work, we examine the surface diffusion of Ni(OH) x by a combination of density-functional theory and phase-field modeling. Here, density-functional theory is used to evaluate the adsorption and diffusion of the Ni(OH) x species on Ni (111) surface, and the results are used to obtain the effective surface diffusivity of Ni versus steam and hydrogen gas partial pressures. This diffusivity is used as an input for a phase-field model to investigate the Ni coarsening in SOC electrodes. It is found that Ni(OH) x formed through chemical reaction cannot accelerate coarsening unless an extremely high steam to hydrogen ratio is reached. However, if Ni(OH) x is formed through electrochemical reactions near triple phase boundaries (TPBs), surface diffusion of Ni(OH) x may cause faster Ni coarsening in fuel cell mode under a large overpotential. Specifically, surface diffusion of Ni(OH) may be comparable to or faster than that of Ni under an overpotential that is large but still possible under fuel cell operating conditions.

25 ENERGY STORAGE↗
DOE OSTI2024

The Bell-Evans-Polanyi relation for hydrogen evolution reaction from first-principles

The versatile Bell-Evans-Polanyi (BEP) relation stipulates the kinetics of a reaction in terms of thermodynamics. Herein, we establish the BEP relation for the hydrogen evolution reaction (HER) from fundamental electrochemical principles leveraging the Butler-Volmer relation for a one-step, one-electron process and the transition state theory. Based on first-principles investigations of HER mechanisms on fourteen metal electrodes, we firmly justify the BEP relation solely using an easy-to compute hydrogen adsorption free energy and universal electrochemical constants.

42 ENGINEERING↗
DOE OSTI2024

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Determination of the vacancy distribution over Al cation sites in γ-Al 2 O 3

Although gamma-alumina (γ-Al 2 O 3 ) is an extensively used material with wide-ranging applications due to its inherently high surface area and acidity, its atomic structure is still not fully understood. γ-Al 2 O 3 is described as having a spinel-like structure, where the O sublattice has a face-centered cubic (FCC) arrangement and Al cations are placed in the spinel tetrahedral and octahedral interstitial sites. Achieving the correct stoichiometry of Al 2 O 3 , however, requires the introduction of Al vacancies into some of the interstitial sites. Despite the importance of accurately describing the structure of γ-Al 2 O 3 , the distribution of vacancies between tetrahedral and octahedral sites remains unclear, in part because of the usually poor crystalline quality of γ-Al 2 O 3 that has often been used in previous studies. To determine the actual cation distribution in γ-Al 2 O 3 , single-crystalline γ-Al 2 O 3 was investigated using a correlative approach of experimental and simulated selected-area electron diffraction (SAED) and high-resolution electron energy-loss spectroscopy (EELS). Comparison of the reflection intensities in single-crystal SAED to simulated SAED from models with varied vacancy distributions revealed that vacancies exist primarily on tetrahedral sites, contrary to the placement of vacancies on octahedral sites proposed in several common models. Comparison of EELS spectra—acquired with the highest energy resolution reported so far for γ-Al 2 O 3 —with ab initio multiple scattering EELS simulations confirmed the distribution of vacancies on tetrahedral sites. These results enable more accurate modeling of γ-Al 2 O 3 to better predict its properties in existing and future applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. Here, in this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level. Nanoparticles were produced using a state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive X-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations were employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedra and decahedra which is consistent with previous results on mono-metallic nanoparticles. However, we observed that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally showed that the underpinning of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Abstract for CRADA between National Energy Technology Laboratory and Mattiq, Inc.

To decarbonize the chemicals and fuels industry, we require new and innovative technologies that can leverage renewable feedstocks including renewable electricity and renewable carbon sources, such as CO 2 . State-of-the-art (SOTA) CO 2 utilization technologies that can produce renewable chemicals and fuels, such as the electrochemical reduction of CO 2 , are still significantly lacking in efficiency, and are therefore not cost-competitive with existing infrastructure. The key bottleneck to cost-competitive chemicals and fuels sourced from CO 2 and renewable electricity is the lack of any efficient catalyst material. The National Energy Technology Laboratory (NETL) and Mattiq will collaborate in the development of an efficient catalyst material that can effectively convert CO 2 into products of interest. Specifically, NETL will utilize high-performance computing to greatly reduce the number of potential materials to be investigated by Mattiq ultrahigh-throughput experimental catalyst discovery framework. This work is poised to rapidly accelerate the development of novel and efficient CO 2 reduction electrocatalysts that can bring CO 2 utilization closer to commercial viability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Atomic Dynamics of Multi‐Interfacial Migration and Transformations

Redox-induced interconversions of metal oxidation states typically result in multiple phase boundaries that separate chemically and structurally distinct oxides and suboxides. Directly probing such multi-interfacial reactions is challenging because of the difficulty in simultaneously resolving the multiple reaction fronts at the atomic scale. Using the example of CuO reduction in H 2 gas, a reaction pathway of CuO → monoclinic m-Cu 4 O 3 → Cu 2 O is demonstrated and identifies interfacial reaction fronts at the atomic scale, where the Cu 2 O/m-Cu 4 O 3 interface shows a diffuse-type interfacial transformation; while the lateral flow of interfacial ledges appears to control the m-Cu 4 O 3 /CuO transformation. Together with atomistic modeling, it is shown that such a multi-interface transformation results from the surface-reaction-induced formation of oxygen vacancies that diffuse into deeper atomic layers, thereby resulting in the formation of the lower oxides of Cu 2 O and m-Cu 4 O 3 , and activate the interfacial transformations. In conclusion, these results demonstrate the lively dynamics at the reaction fronts of the multiple interfaces and have substantial implications for controlling the microstructure and interphase boundaries by coupling the interplay between the surface reaction dynamics and the resulting mass transport and phase evolution in the subsurface and bulk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Influence of cation species on thermal expansion of Y 2 Si 2 O 7 –Gd 2 Si 2 O 7 solid solutions

Mixtures of Y 2 Si 2 O 7 and Gd 2 Si 2 O 7 were synthesized by solid-state reaction at 1600°C and characterized via in situ x-ray diffraction (XRD) to determine their coefficients of thermal expansion (CTE). All solid solutions within the system exhibited the orthorhombic δ-RE 2 Si 2 O 7 (Pna2 1 ) structure. Thermal expansion measurements of Y 2 Si 2 O 7 and Gd 2 Si 2 O 7 correlated well with reported values in literature, and all synthesized solid solutions exhibited CTEs between Y 2 Si 2 O 7 and Gd 2 Si 2 O 7 . Generally, there was a slight decrease in CTE exhibited by the materials with increasing Gd 2 Si 2 O 7 content, with Gd 2 Si 2 O 7 having the lowest CTEs and Y 2 Si 2 O 7 the highest CTEs. Here, the decrease in CTE was attributed to stronger bonds of Gd-O over Y-O, as determined by calculated crystal orbital Hamilton populations using density functional theory. However, such differences were very small and crystal structure was the dominating factor in CTE trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Machine learning atomistic potentials trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost. The curation of the DFT datasets can be extensive in size and time-consuming to train and refine. In this study, we focus on addressing these barriers by developing minimalistic and flexible datasets for many elements in the periodic table regardless of their mass, electronic configuration, and ground state lattice. These DFT datasets have on average, ~4000 different structures and 27 atoms per structure, which we found sufficient to maintain the predictive accuracy of DFT properties and notably with high transferability. We envision these highly curated training sets as starting points for the community to expand, modify, or use with other machine learning atomistic potential models, whatever may suit individual needs, further accelerating the utilization of machine learning as a tool for material design and discovery.

42 ENGINEERING↗
DOE OSTI2023

Study of Tritium Diffusivity in Pure and Sn-Defective Zr: A First-Principles Density Functional Theory Approach

Zirconium alloys (e.g., zircaloy-4) are used as tritium ( 3 H) getter materials in tritium-producing burnable absorber rods (TPBARs) owing to their ability to capture 3 H and chemically convert them into metal hydrides. Understanding of 3 H diffusion mechanisms in zircaloy is crucial for the optimal design of material performance in nuclear technology. Here, for this work, we perform first-principles density functional theory calculations to study the 3 H diffusion mechanism in pure and impure Zr with a low concentration of tin (Sn) atoms to determine the impact of the presence of Sn on the movement of 3 H atoms through the material. First, we calculated the diffusion barriers for 3 H in pure Zr by taking different migration pathways. We then introduced a low concentration of Sn impurity and systematically explored the impurity effect on the diffusion barriers for 3 H. Using our calculated diffusion energy barriers, we further obtained the diffusion coefficients and analyzed the results by comparing them with the experimental and previously calculated values. A diffusion coefficient of the order of 10 –8 m 2 /s is predicted. We also found that the presence of a Sn impurity could reduce the diffusivity up to 4 orders of magnitude. In conclusion, our results could serve as guidelines for further experimental investigations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗