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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Memory-dictated dynamics of single-atom Pt on CeO2 for CO oxidation

Abstract Single atoms of platinum group metals on CeO 2 represent a potential approach to lower precious metal requirements for automobile exhaust treatment catalysts. Here we show the dynamic evolution of two types of single-atom Pt (Pt 1 ) on CeO 2 , i.e., adsorbed Pt 1 in Pt/CeO 2 and square planar Pt 1 in Pt AT CeO 2 , fabricated at 500 °C and by atom-trapping method at 800 °C, respectively. Adsorbed Pt 1 in Pt/CeO 2 is mobile with the in situ formation of few-atom Pt clusters during CO oxidation, contributing to high reactivity with near-zero reaction order in CO. In contrast, square planar Pt 1 in Pt AT CeO 2 is strongly anchored to the support during CO oxidation leading to relatively low reactivity with a positive reaction order in CO. Reduction of both Pt/CeO 2 and Pt AT CeO 2 in CO transforms Pt 1 to Pt nanoparticles. However, both catalysts retain the memory of their initial Pt 1 state after reoxidative treatments, which illustrates the importance of the initial single-atom structure in practical applications.

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

Characterization, Structure, and Reactivity of Hydroxyl Groups on Metal‐Oxide Cluster Nodes of Metal–Organic Frameworks: Structural Diversity and Keys to Reactivity and Catalysis

Among the most stable metal–organic frameworks (MOFs) are those incorporating nodes that are metal oxide clusters with frames such as Zr 6 O 8 . This review is a summary of the structure, bonding, and reactivity of MOF node hydroxyl groups, emphasizing those bonded to nodes containing aluminum and zirconium ions. Hydroxyl groups are often present on these nodes, sometimes balancing the charges of the metal ions. They arise during MOF syntheses in aqueous media or in post-synthesis treatments. They are identified with infrared and 1 H nuclear magnetic resonance spectroscopies and characterized by their reactivities with polar compounds such as alcohols. Terminal OH, paired µ 2 -OH, and aqua groups on nodes are catalytic sites in numerous reactions. Relatively unreactive hydroxyl groups (such as isolated µ 2 -OH groups) may replace reactive groups and inhibit catalysis; some node hydroxyl groups (e.g., µ 3 -OH) are mere spectators in catalysis. There are similarities between MOF node hydroxyl groups and those on the surfaces of bulk metal oxides, zeolites, and enzymes, but the comparisons are mostly inexact, and much remains to be understood about MOF node hydroxyl group chemistry. It is posited that understanding and controlling this chemistry will lead to tailored MOFs and improved adsorbents and catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast Proton Transfer and Hydrogen Evolution Reactivity Mediated by [Co 13 C 2 (CO) 24 ] 4–

A common approach to speeding up proton transfer (PT) by molecular catalysts is manipulation of the secondary coordina-tion sphere with proton relays and these enhance overall reaction rates by orders of magnitude. In contrast, heterogenous electrocatalysts have band structures that promote facile PT concerted with electron transfer (ET), known as the Volmer mechanism. Here in this paper, we show that [Co 13 C 2 (CO) 24 ] 4– , containing multiple Co-Co bonds to statistically enhance observed rates of PT, promotes PT on the order of 2.3 × 10 9 M -1 s -1 which suggests a diffusion-limited rate. The fast ET and PT chemistry is at-tributed to the delocalized electronic structure of [Co 13 C 2 (CO) 24 ] 4– . Electrochemical characterization of [Co 13 C 2 (CO) 24 ] 4– in the presence and absence of protons reveals ET kinetics and diffusion behavior similar to other small clusters such as nano-materials and fullerenes.

08 HYDROGEN↗
DOE OSTI2026

Water-enhanced bifunctional metal-acid catalyst for C=C bond hydrogenation

Water-assisted proton shuttling can promote hydrogenation of polar functional groups, and it is generally believed that such an effect can be hardly applied to hydrogenation of C═C bonds due to the latter's weak interaction with water. Here, we report density functional theory calculations and metadynamics simulations, through which we show a dynamic bifunctional metal–acid site that can be transformed, when interacting with water, into an active configuration for unexpected water-enhanced proton shuttling to C═C bonds. In particular, we investigated B(OH)3 anchored to a Ni catalyst for hydrogenation of cyclohexene in an organic solvent, which showed in experiments an increased rate by 100 times when adding a small amount of water. Metadynamics simulations suggest that a B(OH)3–H2O cluster can form on Ni(111), which promotes the proton transfer in the first hydrogenation step, while the second hydrogenation is still driven by metal-mediated direct H-transfer. The recovery process of B(OH)3–H2O also involves a proton shuttling step. We find that the boric species on the surface serves as an electron reservoir and carries the negative charge to balance the positive charge in the proton transfer steps. This work thus provides fundamental insights into this dynamic transformation process of the metal–acid interface, which can in principle be applied to many other bifunctional systems for hydrogenating non-polar unsaturated groups by engineering the interfacial charge separation.

Sun, Shoutian↗
DOE OSTI2025

Tuning Catalytic Reactivity via Wetting Control through Oxygen Vacancies: Ru Clusters on Anatase TiO 2 and CeO 2 Supports

The shape of supported metal particles regulates their catalytic reactivity and is determined by the degree of wetting between the metal particle and the support surface. Flattened particles that wet support surfaces were reported in various catalytic systems, particularly in the subnanometer size regime. Such consequential metal–support wetting phenomena are poorly understood, and methods to study them on powder catalysts under realistic conditions are lacking. Here, we investigate the size-dependent wetting behaviors of Ru particles on two reducible-oxide supports, anatase TiO 2 (TiO 2 -A) and CeO 2 , under reducing catalytic conditions. X-ray absorption spectroscopy (XAS), low-energy ion scattering (LEIS), and density functional theory (DFT) are combined to determine the shape of Ru particles. Ru particles remain three-dimensional without wetting the TiO 2 -A support within the coverage range studied (0.06–0.98 Ru nm –2 ). In contrast, at low coverages (<0.25 Ru nm –2 ), Ru wets the CeO 2 support to form flat, disordered structures. The higher wettability of CeO 2 than TiO 2 -A is attributed to oxygen vacancies in the near-surface region. The shape difference between small Ru particles or clusters on the two supports leads to drastically contrasting catalytic reactivities in polyolefin hydrogenolysis, despite similar diameters. As a result, this work highlights the implications of metal–support wetting, or cluster shape, on catalytic behaviors of small metal clusters, while establishing the foundation for future systematic studies of such a phenomenon in realistic systems, by delivering a multitechnique methodology and revealing governing fundamental principles.

42 ENGINEERING↗
DOE OSTI2021

Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate–adsorbate lateral interactions. Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. Here, the mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Elucidating the role of $\mathrm{Fe}$-$\mathrm{Mo}$ interactions in the metal oxide precursors for Fe promoted $\mathrm{Mo/ZSM}$-5 catalysts in non-oxidative methane dehydroaromatization

Literature shows that adding Fe as a separate phase to MoO 3 /ZSM-5 catalysts can improve benzene selectivity in methane dehydroaromatization (MDA), but only when added in small quantities, making it difficult to characterize the state of Fe in the catalyst and understand the role of Fe-Mo interactions on the catalytic properties. We explore how the nature of the Mo-Fe interactions in the catalyst precursor can influence the stability and product selectivity in MDA, by employing for the first time Fe 2 (MoO 4 ) 3 /ZSM-5 as a catalyst precursor in MDA. We have compared the activity of Fe 2 (MoO 4 ) 3 /ZSM-5 with monometallic MoO 3 /ZSM-5 and mixed MoO 3 + Fe 2 O 3 /ZSM-5 containing equivalent Mo and Fe loadings and found that Fe 2 (MoO 4 ) 3 /ZSM-5 shows higher benzene selectivity than the mixed MoO 3 + Fe 2 O 3 /ZSM-5 catalyst and exhibits higher stability in reaction compared to the monometallic MoO3/ZSM-5 catalyst. Structural characterization suggests that Fe 2 (MoO 4 ) 3 partially segregates to Fe 2 O 3 and amorphous MoO x during thermal pretreatment. The MoO x species migrate into the zeolite channels during pretreatment, while Fe oxides remain on the external surface of the zeolite. Gas adsorption/desorption techniques and density functional theory calculations demonstrate that the preexisting Fe 2 O 3 phases on the external surface of the zeolite in the mixed MoO 3 + Fe 2 O 3 /ZSM-5 precursor trap (MoO 3 ) 3 clusters preventing them from migrating into the zeolite channels during pretreatment, whereas gradual formation of amorphous MoOx together with the segregation of the Fe 2 O 3 phase when using the Fe 2 (MoO 4 ) 3 precursor diminishes trapping of (MoO 3 ) 3 and consequently enhances migration and anchoring of the MoO x species in the zeolite channels, boosting selectivity to benzene. Characterization of used catalysts suggests that the presence of Fe promotes formation of structured carbon nanofibers which reduce the rate of catalyst deactivation.

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

Low-temperature hydroformylation of ethylene by phosphorous stabilized Rh sites in a one-pot synthesized Rh-(O)-P-MFI zeolite

Zeolites containing Rh single sites stabilized by phosphorous were prepared through a one-pot synthesis method and are shown to have superior activity and selectivity for ethylene hydroformylation at low temperature (50°C). Catalytic activity is ascribed to confined Rh 2 O 3 clusters in the zeolite which evolve under reaction conditions into single Rh 3+ sites. These Rh 3+ sites are effectively stabilized in a Rh-(O)-P structure by using tetraethylphosphonium hydroxide as a template, which generates in situ phosphate species after H 2 activation. In contrast to Rh 2 O 3 , confined Rh0 clusters appear less active in propanal production and ultimately transform into Rh(I)(CO) 2 under similar reaction conditions. As a result, we show that it is possible to reduce the temperature of ethylene hydroformylation with a solid catalyst down to 50°C, with good activity and high selectivity, by controlling the electronic and morphological properties of Rh species and the reaction conditions.

Catalysis↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modulating CO 2 Electrocatalytic Conversion to the Organics Pathway by the Catalytic Site Dimension

Electrochemical reduction of carbon dioxide to organic chemicals provides a value-added route for mitigating greenhouse gas emissions. Here, we report a family of carbon-supported Sn electrocatalysts with the tin size varying from single atom, ultrasmall clusters to nanocrystallites. High single-product Faradaic efficiency (FE) and low onset potential of CO 2 conversion to acetate (FE = 90% @ –0.6 V), ethanol (FE = 92% @ –0.4 V), and formate (FE = 91% @ –0.6 V) were achieved over the catalysts of different active site dimensions. The CO 2 conversion mechanism behind these highly selective, size-modulated p-block element catalysts was elucidated by structural characterization and computational modeling, together with kinetic isotope effect investigation.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Exploring the Impact of Active Site Structure on the Conversion of Methane to Methanol in Cu‐Exchanged Zeolites

Abstract In the past, Cu‐oxo or ‐hydroxy clusters hosted in zeolites have been suggested to enable the selective conversion of methane to methanol, but the impact of the active site's stoichiometry and structure on methanol production is still poorly understood. Herein, we apply theoretical modeling in conjunction with experiments to study the impact of these two factors on partial methane oxidation in the Cu‐exchanged zeolite SSZ‐13. Phase diagrams developed from first‐principles suggest that Cu‐hydroxy or Cu‐oxo dimers are stabilized when O 2 or N 2 O are used to activate the catalyst, respectively. We confirm these predictions experimentally and determine that in a stepwise conversion process, Cu‐oxo dimers can convert twice as much methane to methanol compared to Cu‐hydroxyl dimers. Our theoretical models rationalize how Cu‐di‐oxo dimers can convert up to two methane molecules to methanol, while Cu‐di‐hydroxyl dimers can convert only one methane molecule to methanol per catalytic cycle. These findings imply that in Cu clusters, at least one oxo group or two hydroxyl groups are needed to convert one methane molecule to methanol per cycle. This simple structure–activity relationship allows to intuitively understand the potential of small oxygenated or hydroxylated transition metal clusters to convert methane to methanol.

Chemistry↗
DOE OSTI2022

Oxygen vacancy-rich amorphous FeNi hydroxide nanoclusters as an efficient electrocatalyst for water oxidation

Here, a one-pot strategy is presented to directly synthesize amorphous Fe x Ni y hydroxide nanoclusters (denoted as ANC-Fe x Ni y , <2 nm) with oxygen vacancies induced by ionic liquids. The ANC-Fe x Ni y catalyst presents abundant catalytic sites and high intrinsic conductivity. As such, the optimized ANC-Fe 1 Ni 2 exhibits high activity in oxygen evolution reaction (OER) with a Tafel slope of 39 mV dec –1 and an overpotential of 266 mV at 10 mA cm –2 . Notably, the optimized ANC-Fe 1 Ni 2 shows an extraordinarily large mass activity of 3028 A g FeNi –1 at the overpotential of 300 mV, which is ~24-fold of commercial RuO 2 catalyst. The superior activity of these Fe x Ni y hydroxide nanoclusters is ascribed to (i) the amorphous and distorted structure with abundant oxygen vacancies, and (ii) enhanced active site density by downsizing the ANC-Fe x Ni y clusters. This strategy provides a novel route for enhancing OER electrocatalytic performance and highly encouraging for the future application of amorphous metal hydroxides in catalysis.

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

CO oxidation on MgAl 2 O 4 supported Ir n : activation of lattice oxygen in the subnanometer regime and emergence of nuclearity-activity volcano

CO oxidation on Pt group metals is affected by the metal size and reducibility of the oxide support. In this study, we report that Ir supported on MgAl 2 O 4 , traditionally considered non-reducible, exhibits properties similar to reducible oxides when the Ir size is in the subnanometer regime. To show this effect, we synthesized subnanometer Ir clusters and compared their properties to single atoms and nanoparticles (1–1.5 nm). The CO oxidation activity is highest on Ir 0.6–0.8nm while showing distinctly different reaction orders in CO and O 2 (0, +0.4), than single atoms (1, 0) and nanoparticles (–1, +1). Microcalorimetry, in situ X-ray absorption, and infrared spectroscopies show that the CO-saturated Ir 0.6–0.8nm clusters could adsorb and activate O 2 despite binding CO more strongly than nanoparticles. Density functional theory calculations on CO saturated Ir 4 clusters suggest that the increased activity is due to the ability to activate O 2 on oxygen vacancies at the Ir–MgAl 2 O 4 interface. The findings show the important effect of the metal nuclearity on the support and catalyst properties and can guide future design of CO oxidation catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Nature of molybdenum carbide surfaces for catalytic hydrogen dissociation using machine-learned potentials: an ensemble-averaged perspective

Molybdenum carbides with an electronic structure similar to noble metals have gained attention as a promising low-cost catalyst for biomass valorization and the hydrogen evolution reaction. However, our fundamental understanding of the catalyst surface and how different phases of these catalysts behave at varying reaction conditions is limited to ground state density functional theory calculations as ab initio molecular dynamics (AIMD) is computationally prohibitive at relevant length and time scales. Here, in this work, we train a multi-atomic cluster expansion (MACE) machine-learned interatomic potentials (MLIP) to study hydrogen dissociation and dynamics over Mo, δ-MoC, α-Mo 2 C, and β-Mo 2 C surfaces at varying temperatures and hydrogen partial pressures. Our simulations identify unique and different molecular and atomic hydrogen adsorption sites on different surfaces that do not depend on the temperature. At low hydrogen pressures, the surface coverage is monolayer, which transitions to two-layer adsorption at higher pressures. We find that atomic hydrogen diffusion and recombinations are preferred over molybdenum atom hollow sites, while the diffusion over carbon-terminated facets was negligible, signifying particularly strong C–H interactions. In contrast, molecular hydrogen adsorption occurs mostly atop Mo or the bridging sites. At a comparable hydrogen loading, β-Mo 2 C (001) is the most active surface for hydrogen dissociation reaction. This work provides insights into the dynamic nature of the hydrogen dissociation chemistry and the diversity of hydrogen adsorption sites on molybdenum carbides.

08 HYDROGEN↗