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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Insights into the interaction of nitrobenzene and the Ag(111) surface: A DFT study

Here, this study explores the potential of nitrobenzene as an anolyte material for nonaqueous redox flow batteries (RFBs) by theoretically examining its low-coverage adsorption behavior on neutral and charged Ag(111) model electrode surfaces. At the low coverage limit, DFT calculations show a preference for nitrobenzene to adsorb parallel to the surface, with the benzene ring and nitro group centered over HCP sites. Interactions between nitrobenzene and the surface were analyzed using induced charge density analysis, Bader charge analysis, and projected density of states (PDOS). It was found that nitrobenzene adsorbs primarily through van der Waals interactions with the surface. As nitrobenzene accumulates negative charge, the strength of adsorption diminishes. Understanding the electrode-electrolyte interface is crucial for enhancing RFB electrochemical performance, and this study sheds light on nitrobenzene's interaction with a model Ag electrode.

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

Power-Law Entanglement and Hilbert Space Fragmentation in Nonreciprocal Quantum Circuits

Quantum circuits utilizing measurement to evolve a quantum wave function offer a new and rich playground to engineer unconventional entanglement dynamics. Here, in this work, we introduce a hybrid, nonreciprocal setup featuring a quantum circuit, whose updates are conditioned on the state of a classical dynamical agent. In our example the circuit is represented by a Majorana quantum chain controlled by a classical N-state Potts chain undergoing pair flips. The local orientation of the classical spins controls whether randomly drawn local measurements on the quantum chain are allowed or not. This imposes a dynamical kinetic constraint on the entanglement growth, described by the transfer matrix of an N-colored loop model. It yields an equivalent description of the circuit by an SU(N)-symmetric Temperley-Lieb Hamiltonian or by a kinetically constrained surface growth model for an N-component height field. For N = 2, we find a diffusive growth of the half-chain entanglement toward a stationary profile S(L) ~ L 1/2 for L sites. For N ≥ 3, the kinetic constraints impose Hilbert space fragmentation, yielding subdiffusive growth toward S(L) ~ L 0.57 . This showcases how the control by a classical dynamical agent can enrich the entanglement dynamics in quantum circuits, paving a route toward novel entanglement dynamics in nonreciprocal hybrid circuit architectures.

1-dimensional spin chains↗
DOE OSTI2024

The influence of cloud cover on the reliability of satellite-based solar resource data

Satellite-based solar resource data are often developed and validated by using binary cloudiness categories: clear sky or overcast cloudy sky. To investigate the reliability of solar resource data in partially cloudy conditions, we estimate cloud fraction using two distinct algorithms: a physical retrieval model using surface observed global horizontal irradiance (GHI) and direct normal irradiance (DNI) and a temporal average of cloud mask data estimated by the observed DNI. Our analysis reveals a significant presence of scattered clouds, broken clouds, and mismatches between satellite- and surface-based cloud data at 17 surface sites across the contiguous United States, though confidently clear and cloudy conditions collectively account for more than 70 % of the data. Solar radiation is computed using the National Solar Radiation Database (NSRDB) algorithm and validated using surface observations. Here, our findings suggest that, in the presence of scattered clouds, NSRDB data for clear-sky conditions can be subject to significant overestimation. In cloudy-sky conditions classified by satellite data, DNI computed by the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) can be underestimated when limited clouds are detected by surface observations. The bias observed in several cloudiness categories indicates that the NSRDB is exceptionally accurate in confidently clear conditions. However, clear-sky conditions with scattered clouds and mismatched cloud data contribute significantly to the overall uncertainties in the NSRDB. Therefore, future improvements in solar resource data should involve development and implementation of satellite-derived cloud fraction and should consider a novel radiative transfer model accounting for amplified cloud reflection. The evaluation within cloudiness categories also provides a physical rationale for the superior performance of FARMS-DNI compared to the Direct Insolation Simulation Code (DISC) in both cloudy-sky and all-sky conditions.

14 SOLAR ENERGY↗
DOE OSTI2026

Aperture and roughness govern iron oxide passivation in olivine fractures during carbon mineralization

Efficient carbon dioxide mineralization in silicate-rich formations depends on reaction rates within fracture networks, yet the role of fracture microstructure remains poorly constrained. We investigated how fracture aperture and surface roughness influence mineralization by reacting synthetic forsteritic olivine fractures with controlled apertures (0.5–1.6 millimeters) and roughness (6–16 micrometers) with supercritical carbon dioxide at 13.7 megapascals and 185 degrees Celsius. Optical microscopy and Raman spectroscopy showed that rougher fracture surfaces promoted iron oxide precipitation, which passivated olivine surfaces, limiting magnesium availability for carbonate formation while removing iron as a reactant. These effects were more pronounced in smaller-aperture fractures. A geochemical model incorporating surface topography and passivation reproduced the observed trends, confirming that increased roughness can reduce reactivity despite greater surface area. Our results demonstrate that fracture microstructure strongly influences iron oxide passivation and carbon dioxide mineralization, with important implications for predicting long-term performance of engineered carbon dioxide storage systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Preliminary modeling of triply periodic minimal surface (TPMS) structures using RELAP5-3D

With the United States Department of Energy (DOE)’s goal of quadrupling the nation’s nuclear energy supply by 2050, and with the Advanced Fuels Campaign pushing for new types of advanced reactor fuels and geometries, the need has arisen for new nuclear fuel designs. One such design is to swap out current nuclear fuel geometries in exchange for another type of geometry, called a Triply Periodic Minimal Surface (TPMS). TPMSs are self-supporting, infinitely repeating lattices—attributes that lend themselves well to additive manufacturing. These surfaces also possess enhanced heat transfer properties thanks to their internal area changes and large surface-area-to-volume ratios. Their drawback, however, is an increased pressure drop. Given the small amount of correlations and data (Reynolds numbers in the 2,000–8,000 range), and the minimal amount of experience so far obtained by modeling TPMS structures using 1D systems codes such as the Reactor Excursion and Leak Analysis Program (RELAP5-3D), further research into this topic was needed. Using data from the University of Wisconsin - Madison (UW), curve fits were created for both a Heat Transfer Coefficient (HTC) correlation and a Darcy friction factor empirical coefficient correlation. The curves’ coefficients and multipliers were then output and utilized in RELAP5-3D models of two upcoming experiments—Flow Loop for INFLUX Pressure drop (FLIP) and Microreactor Agile Non-nuclear Experimental Test (MAGNET)—aimed at increasing the available data for Reynolds numbers to the 16,000–36,000 range for TPMS structures. The models were run under the conditions utilized by a Computational Fluid Dynamics (CFD) analysis performed by another group at Idaho National Laboratory. Only CFD pressure drop values were obtained from the FLIP test, and those values showed that the RELAP5-3D models had a lower rate of pressure increase in comparison to the CFD values. In addition, there seemed to be a vertical shift upward in the pressure drop for both models whenever the TPMS porosity decreased, and the RELAP5-3D models showed a higher vertical shift in comparison to the CFD values. The MAGNET results did not correspond to any CFD or experimental results against which they could be compared, so they were instead compared against the proposed CFD input conditions. These values were then compared with each other to make sure the model seemed to be performing as expected, paving the way for future tests that can be run for the purpose of further analyses and comparisons. The pressure drop increased with temperature and mass flow rate independently. The temperature change would decrease with increasing mass flow rate and temperature, which was just as we expected based on the fact that the lower viscosity and decreased density would result in higher friction and churning losses. The last metric that was assessed was the enthalpy flow change, which increased with increasing mass flow rate and decreasing temperature.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Pathways to Electrochemical Ironmaking at Scale Via the Direct Reduction of Fe 2 O 3

Electrochemical ironmaking can provide an energy efficient, zero-emissions alternative to traditional methods of ironmaking, but the scalability of low-temperature electrochemical cells may be constrained by reactor throughput and the availability of acceptable feedstocks. Electrodes directly converting solid iron-oxide particles to metal circumvent traditional mass-transport limitations but are sensitive to both the particle size and nanoscale morphology of reactants. Furthermore, the effect of these properties on reactor throughput has not been systematically studied at model electrowinning surfaces. Here, we have used size-controlled, homologous α-Fe 2 O 3 particles to study how the nanoscale morphology of oxides influences the obtainable current density toward Fe metal and integrated these results in a technoeconomic model for alkaline iron electrowinning systems. Micron-scale α-Fe 2 O 3 with nanoscale porosity can be used to form Fe at current densities commensurate with industrial water electrolysis (>0.6 A cm –2 ) in the absence of external convection, providing a path to cost-competitive and scalable ironmaking using electrochemistry.

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

Implementation of Surface Tension on a Reacting Flow Solver, PeleLM: Preprint

In liquid rocket engines, the fuel is supplied to the combustion chamber in the liquid state though injectors. Such fuel undergoes atomization, vaporization, and combustion processes. To design reliable and efficient injectors, it is required to understand the full processes. This research is part of an effort to develop a full atomization-vaporization-combustion solver from first principles. As an initial step to tackle the atomization process, a multiphase flow solver is under development. For the development, a library of the volume of fluid scheme for multiphase, IRL is coupled with a reacting Navier-Stokes equation solver, PeleLM. Furthermore, as the surface tension has considerable effects on spray breakup. surface tension is implemented in the momentum equation using the continuum surface force model and the improved height function technique.

height function↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2026

A physics informed model for the prediction of surface energies and the shape of metal particles

Surface energies of metal-based systems are important for determining the Wulff-constructed shapes of metal nanoparticles and understanding the stability. We have developed a physics informed method to predict the total energy of metal-based systems across a wide range of configurations. Our method has been tested against density functional theory (DFT) calculations for late transition metals. This method enables on-the-fly surface energy predictions based on the fundamental of materials property and allows for the Wulff construction of metal particles for a random number of elemental atoms and without the need for DFT calculations. By making a division between atoms in the different layers of the model system, we can improve the accuracy of the model, suggesting a dissimilarity between the electronic structure due to an alternating compression and expansion of atomic layers. We find that our model accurately and effectively provides valuable insights into the distribution and stability of nanoparticles.

Mandal, Shyama Charan (ORCID:0000000245882874)↗
DOE OSTI2025

Scaling High-Resolution Soil Organic Matter Composition to Improve Predictions of Potential Soil Respiration Across the Continental United States

Despite the importance of microbial soil organic matter (SOM) respiration in regulating the flux of carbon between soils and the atmosphere, soil carbon cycling models remain primarily based on climate and soil properties, leading to large uncertainty in predictions. To address this knowledge gap, we analyzed high-resolution water-extractable SOM profiles from soil cores collected across the United States by the 1,000 Soils Pilot of the Molecular Observation Network. Our innovation lies in using machine learning to distill thousands of SOM formula into tractable units; and it enables integrating data from molecular measurements into soil respiration models. In surface soils, SOM chemistry provided better estimates of potential soil respiration than soil physicochemistry, and using them combined yielded the best prediction. Overall, we identify specific subsets of organic molecules that may improve predictions of global soil respiration and create a strong basis for developing new representations in process-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Microstructure Scale Lithium-Ion Battery Modeling: Part III. When and Where Lithium Plating Occurs and its Correlation with the Electrode Microstructure

Li-ion battery performance and degradation are closely related to the cell’s underlying electrode microstructure. Electrode microstructures are typically characterized with volume-averaged properties that neglect the impact of local heterogeneities. However, local heterogeneities create hot spots that can trigger degradation onset. Herein, a microstructure scale electrochemical model is used to investigate the impact of microstructure heterogeneity on lithium plating. The model predicts lithium plating is not uniform, even when considering a relatively small portion of the electrode (a cross-sectional area of 154×144 µm 2 ), preferring to plate on larger particles as compared to smaller particles. While local heterogeneities control where plating occurs, the model predicts that volume-averaged properties control when plating occurs. Additionally, the model predicts that the active material specific surface area has a linear relationship with the plating onset. However, the linear relationship between increased active material surface area and delayed plating response appears to be sensitive to the microstructure feature used to increase the active interface area. Here, a comparative case-study is explored where the specific surface area is increased by either reducing the active material particle diameter, adding open-porosity cracks, or increasing the active material surface roughness. The model predicts that increasing the specific surface area by reducing the active material particle diameter is the most effective strategy for delaying lithium plating. At 6C, reducing particle size is shown to be 3 and 20 times more effective than, respectively, adding open-porosity cracks and increasing surface roughness. A dual-layer electrode architecture combining gradations both for average properties and uniformities is eventually proposed to improve homogeneous material utilization and reduce degradation at high charge rates.

25 ENERGY STORAGE↗
DOE OSTI2025

Coupled Lake‐Atmosphere‐Land Physics Uncertainties in a Great Lakes Regional Climate Model

Abstract This study develops a surrogate‐based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near‐surface air temperature (T2m). Average physics uncertainties are determined to be 1.5C for LST and T2m over land, and 1.9C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Computational flow modeling of triply periodic minimal surfaces as feed channel spacers in ultra-high pressure reverse osmosis applications

Triply periodic minimal surfaces (TPMS) are a special class of mathematical surfaces characterized by a high surface area-to-volume ratio. They have generated considerable interest in fields such as acoustics, heat transfer, and membrane-based filtration processes. This study evaluates the performance of four different TPMS designs—Schoen Gyroid, Schoen Crossed Layers of Parallels (CLP), Schoen Transverse Crossed Layers of Parallels (tCLP), and Schwarz-Primitive—when used as feed channel spacers under ultra-high pressure reverse osmosis (UHPRO) conditions, at approximately 200 bar. Our experimentally validated computational fluid dynamics model reveal different flow patterns within the feed channels for each of the four TPMS designs, leading to varying hydrodynamic and permeation properties. Under the simulated UHPRO conditions, the Gyroid and tCLP designs yield up to a 23% increase in average permeate velocity and a 14% reduction in average membrane-surface concentration relative to a non-woven spacer of the same porosity. Furthermore, the enhanced performance comes with an increased feed channel pressure drop, although it only constitutes less than 4% of the operating pressure when extrapolated for a meter-long membrane module. Additionally, the study analyzes the effects of varying inlet velocity and spacer porosity on membrane performance. Overall, this research provides valuable insights into the potential use of TPMS spacers in UHPRO applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Benchmarking Density Functional Theory Methods for Efficient Calculations of a Strongly Correlated Li 1– x Ni 1– y O 2−δ System

Transition metal oxides (TMOs), such as LiNiO 2 , are promising candidates for energy storage and electronic devices due to their unique electronic properties, exceptional physical and chemical characteristics, and ability to adopt multiple oxidation states. However, accurately predicting their properties using mean-field density functional theory (DFT) is challenging due to the presence of strongly correlated d-electrons and the complex interplay between their structural, electronic, and magnetic responses. These challenges are further exacerbated by the need to model defects, surfaces, and interfaces, which require computationally efficient, large-scale simulations. To address these issues, we carry out a benchmark study on the Li 1–x NiO 2 system, evaluating the performance of several popular functionals. Our findings demonstrate that combining SCAN functional relaxation with single-step HSE calculations provides a practical and scalable computational strategy. This approach balances accuracy and efficiency, enabling high-throughput simulations of strongly correlated TMOs and improved predictive modeling capability of TMOs for practical applications.

25 ENERGY STORAGE↗
DOE OSTI2024

Efficient learning of accurate surrogates for simulations of complex systems

Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse or dynamic data. Here, we introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data, and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation-of-state model can be reliably autogenerated from expensive calculations using few model evaluations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

CHESS 2025: Crown polygons and extracted reflectance for field sampling sites

This dataset contains (1) crown polygons for each tree, meadow, and shrub site sampled in the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign (in geojson format, .geojson) and (2) extracted reflectance, uncertainty, and shade estimates for each crown polygon from the 2018 National Ecological Observatory Network (NEON) and 2025 CHESS campaigns. (in CSV format, .csv). Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). Crown polygons were manually delineated for each site in the 2025 campaign using a combination of field-collected GPS data (doi:10.15485/3022418), RGB (red, green, blue) and false color reflectance mosaics (doi:10.15485/3013535), and LiDAR-derived (Light Detection and Ranging) canopy height (CHM) and digital surface (DSM) models (DOI and citation to be added upon publication). Where there was misalignment between the spectrometer- and LiDAR-derived data products, polygons prioritized alignment with the spectrometer-derived data products. Polygons were delineated conservatively to only select pixels representative of vegetation samples collected in the field. Crown polygons for 2018 are published at (doi:10.15485/1618130) and were developed using the same protocol. For each polygon, all pixels from all flightlines were extracted where the pixel centroid was contained within the polygon. For each pixel, we extracted the surface reflectance, uncertainty, and shade estimates. Details on the extracted datasets are available at (doi:10.15485/3013527, doi:10.15485/3013535). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Vertical z-axis discontinuous carbon fibers for improved lightning strike performance of continuous fiber-reinforced polymer composites

Effective lightning strike protection for critical aerospace and wind applications requires high electrical conductivity to dissipate current efficiently. However, polymer matrix composites face a challenge due to their inherently insulating nature. While conventional carbon fiber-reinforced composites (CFRP) exhibit electrical conductivity in the planar direction, achieving through-thickness conductivity remains an ongoing challenge. In this work, we have undertaken the fabrication of CFRP interleaved with vertically oriented carbon fibers (Z-fiber) to impart higher electrical conductivity along the thickness direction. Two Z-fiber composite variations are prepared: Z-1 with a single layer of Z-fiber and Z-5 with five interleaved layers and compared with no Z-fiber layer (Z-0) composite. The composite panels were subjected to lab-scale lightning strike tests with a current magnitude of 100 kA. To emulate real-world service conditions, an aerospace-grade paint coating was applied to the composite laminates. Comparative analysis shows Z-1 reduces damage diameter to ∼22 mm compared to Z-0 (∼26 mm), while Z-5 exhibits the least damage (∼16.7 mm), confirmed by optical microscopy. Z-5 demonstrates nine times higher through-thickness electrical conductivity than Z-0, reducing electrical anisotropy substantially. Thermal-electric finite element damage modeling predicts surface damage within 6% of experimental values for both Z-0 and Z-5 composites. Flexural tests post-lightning reveal Z-5 retains 66% flexural strength and 86% modulus, significantly better than Z-0, which retains less than 40% for both properties. This study highlights the efficacy of Z-fiber composites in lightning strike protection, offering improved through-thickness conductivity and mechanical property retention.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Understanding the Unique Reactivity of Cu for Electrochemical CO 2 Reduction with a 3-Site Model

Cu-based catalysts for the electrochemical reduction of CO 2 and CO exhibit a perplexingly unique reactivity toward multicarbon based products compared to other studied electrocatalysts. Here, in this study, we use insights gained from a recent phenomenological 3-site microkinetic model and grand-canonical density functional theory calculations to clarify the importance of an underemphasized aspect critical to Cu’s unique reactivity: a population of so-called “reservoir” sites. Using model Cu surface motifs, we discuss how these types can be represented by undercoordinated structural defects like step edges and grain boundaries which form a network of highly anisotropic migration channels. These pathways are found to be amenable for feeding *CO over time to reactive sites like Cu adatoms more active toward C–C coupling. These results highlight an often overlooked aspect of catalyst optimization: reservoir site engineering, which exploits surface mobility and presents an equally important avenue for electrocatalyst engineering oversimply maximizing active site densities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗