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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Fullerene Negative Ions: Formation and Catalysis

We first explore negative-ion formation in fullerenes C 44 to C 136 through low-energy electron elastic scattering total cross sections calculations using our Regge-pole methodology. Then, the formed negative ions C 44 – to C 136 – are used to investigate the catalysis of water oxidation to peroxide and water synthesis from H 2 and O 2 . The exploited fundamental mechanism underlying negative-ion catalysis involves hydrogen bond strength-weakening/breaking in the transition state. Density Functional Theory transition state calculations found C 60 – optimal for both water and peroxide synthesis, C 100 – increases the energy barrier the most, and C 136 – the most effective catalyst in both water synthesis and oxidation to H 2 O 2 .

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

Theoretical Multinary Perovskite Oxides Dataset

Perovskite oxides (ternary chemical formula ABO3) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, the AA'BB'O6 compositions were generated in the a-b+a- Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with DFT using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔHf) and decomposition enthalpies (ΔHd) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated AA'BB'O6 perovskite structures using MP-compatible DFT calculations

Bare, Zachary↗
DOE OSTI2023

Dataset of theoretical multinary perovskite oxides

Perovskite oxides (ternary chemical formula ABO 3 ) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula $A_xA^{'}_{1-x}B_yB^{'}_{1-y}O_3$) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed $A_xA^{'}_{1-x}B_yB^{'}_{1-y}O_3$ compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 $A_{0.5}A^{'}_{0.5}B_{0.5}B^{'}_{0.5}O_3$ compositions were generated in the a - b + a - Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔH f ) and decomposition enthalpies (ΔH d ) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated $A_{0.5}A^{'}_{0.5}B_{0.5}B^{'}_{0.5}O_3$ perovskite structures using MP-compatible DFT calculations.

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

How Interface Evolution at the Nanoscale Dictates Protonic Ceramic Cell Behavior

Understanding the effects of heterointerfaces in nanodevices remains a challenge in interface engineering for systems applied in solid oxide fuel cells, catalysis, sensors, and memristors [1]. Previous studies have shown that the presence of interfaces in ceramic oxide nanodevices affects their impedance properties compared to bulk systems [2]. In this work, we analyze a thin-film model system designed to study the interfaces between representative electrode and electrolyte materials employed in protonic ceramic electrochemical cells for the reversible conversion of hydrogen o electricity. The investigated device features a 21-layer architecture that alternates between an electrode material PBSCF (PrBa 0.5 Sr 0.5 Co 1.5 Fe 0.5 O 5+ϵ ) and a proton-conducting electrolyte BZY (BaZr 0.8 Y 0.2 O 3-δ ), with uniform 20 nm thickness for each layer. Here, the device was fabricated using Pulsed Laser Deposition (PLD) and supported on a conductive Nb-doped STO (Nb-SrTiO 3 ) substrate.

dos Santos, Gabriel T. [Northwestern University, E↗
DOE OSTI2022

The Interaction of K and O 2 on Au(111): Multiple Growth Modes of Potassium Oxide and Their Catalytic Activity for CO Oxidation

Abstract In industrial catalysis, alkali cations are frequently used to promote activity or selectivity. Scanning tunneling microscopy, ambient‐pressure X‐ray photoelectron spectroscopy, and density‐functional calculations were used to study the structure and reactivity of potassium oxides in contact with the Au(111) surface. Three different types of oxides (K 2 O 2 , K 2 O and KO y with y <0.5) were observed on top of the gold substrate at 300–525 K. Initially, small aggregates of K 2 O 2 /K 2 O (1–2 nm in size) were seen at the elbows of the herringbone structure. After increasing the K coverage (>0.15 ML), large islands of the oxide (20–40 nm in size) appeared. These islands contained a mixture of K 2 O and KO y ( y <0.5). A key correlation was found involving the structure, oxidation state, and chemical activity of the alkali oxide. The small aggregates of potassium oxide had a very high catalytic activity for the oxidation of CO, being much more than plain promoters.

Shi, Rui↗
DOE OSTI2022

The Interaction of K and O 2 on Au(111): Multiple Growth Modes of Potassium Oxide and Their Catalytic Activity for CO Oxidation

Abstract In industrial catalysis, alkali cations are frequently used to promote activity or selectivity. Scanning tunneling microscopy, ambient‐pressure X‐ray photoelectron spectroscopy, and density‐functional calculations were used to study the structure and reactivity of potassium oxides in contact with the Au(111) surface. Three different types of oxides (K 2 O 2 , K 2 O and KO y with y <0.5) were observed on top of the gold substrate at 300–525 K. Initially, small aggregates of K 2 O 2 /K 2 O (1–2 nm in size) were seen at the elbows of the herringbone structure. After increasing the K coverage (>0.15 ML), large islands of the oxide (20–40 nm in size) appeared. These islands contained a mixture of K 2 O and KO y ( y <0.5). A key correlation was found involving the structure, oxidation state, and chemical activity of the alkali oxide. The small aggregates of potassium oxide had a very high catalytic activity for the oxidation of CO, being much more than plain promoters.

Shi, Rui↗
DOE OSTI2020

A Perspective on the Electrochemical Oxidation of Methane to Methanol in Membrane Electrode Assemblies

Direct conversion of methane to methanol has been a long-sought objective. Partial oxidation by thermal catalysis is possible but suffers from a rapid loss in methanol selectivity with increasing methane conversion. More recently, the electrochemical oxidation of methane, using water, rather than oxygen, as the oxidizing agent has been considered in both aqueous electrolyte systems and membrane-electrode assemblies (MEAs). While promising results have been demonstrated using MEAs, the absence of key metrics of system performance make it hard to compare and contrast the results of different investigators. Overall, this Perspective examines why MEAs are well-suited for the electrochemical oxidation of methane, defines the metrics for assessing MEA performance, and reviews the progress in the field. An analysis of the challenges to finding suitable electrocatalysts is included with the aim of guiding the search for electrocatalysts that would be both active and selective for the conversion of methane to methanol.

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

Electron Inversion and Tunneling at Silicon Thermal Oxide Interfaces for Solar-Driven Molecular Catalysis to Syngas

Semiconductor photoelectrodes are regularly coupled to solid-state heterogeneous catalysts to perform solar-driven reduction of CO 2 . Less frequently, molecular catalysts are employed to better control the reactivity toward desired products, yet the development of robust semiconductor/molecule interfaces has proven challenging. Here, we demonstrate that a 2–3 nm thermal oxide layer on Si exhibits stability in aqueous solution, high photovoltage, and a photocurrent density of ∼10 mA/cm 2 for the solar-driven photoelectrochemical reduction of a homogeneous molecular catalyst, producing syngas with an ∼2:1 H 2 to CO ratio. Because of a low defect density, the oxide interface forms an electron inversion layer with metal-like electron density at cathodic potentials. This inversion layer facilitates electron transfer to redox-active molecules via tunneling even if the molecule’s reduction potential is beyond the semiconductor’s conduction band edge. Using an electrolyte solution composed of a homogeneous cobalt bis(terpyridine) catalyst in a water/organic solvent mixture, stable photoelectrochemistry was observed under 1-sun illumination, exhibiting an ∼30% Faradaic efficiency for CO that was similar to a glassy carbon electrode under comparable conditions. Furthermore, the results demonstrate that an ultrathin thermal oxide interface is a robust platform for development of aqueous-stable, molecule-driven photoelectrocatalysis.

Catalysts↗
DOE OSTI2023

Biphasic Janus Particles Explain Self-Healing in Pt–Pd Diesel Oxidation Catalysts

The addition of Pd to Pt-based diesel oxidation catalysts is known to enhance performance and restrict the anomalous growth of Pt nanoparticles when subjected to aging at high temperatures in oxidative environments. To gain a mechanistic understanding, we studied the transport of the mobile Pt and Pd species to the vapor phase, since vapor phase transport is the primary route for sintering in these catalysts. The results are surprising: there is a 30-fold drop in the effective vapor pressure of Pt in the Pt-Pd catalysts compared to monometallic Pt. At the same time, there is a significant enhancement in the vapor pressure of Pd, compared to PdO, which otherwise has a negligible vapor pressure at the aging temperature. Such behavior cannot be explained simply by alloying Pt and Pd in the metallic phase, or a core-shell morphology where a PdO shell covers a Pt core. Transmission electron microscopic examination of catalysts aged up to 50 h in air at 800 °C shows that the particles exhibit a biphasic “Janus”-like structure. The metal and oxide phases are conjoined, exposing a metal and an oxide face to the gas phase. The high mobility of the Pt and Pd allows them to be partitioned into the metal and oxide phases, in apparent thermodynamic equilibrium. The PdO helps to trap mobile PtO2 and as a result contains high concentrations of Pt oxide, consistent with its role in mitigating the transport of Pt to the vapor phase and preventing the growth of anomalously large particles. In turn, Pt allows Pd to remain metallic, allowing the catalyst to retain both metal and oxide functionality for catalysis. The regeneration of deactivated catalysts typically requires an external input, such as a change in the working environment from reducing to oxidizing or vice-versa. Here, we show that the mobile species, which are primary contributors to catalyst sintering are effectively returned to the active site, hence our use of the term “selfhealing”. The detailed insights into the inner workings of the Pt-Pd diesel oxidation catalysts can help provide clues to the design of robust and durable heterogeneous catalysts.

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

The Surface Chemistry of Methanol on TiO 2 (110): Effects of Pressure and Temperature on the Stability of C–O and C–H Bonds

Synchrotron-based ambient pressure X-ray photoelectron spectroscopy (AP-XPS) was used to study the surface chemistry of methanol on TiO 2 (110), examining the effects of methanol pressure, oxide temperature, and coadsorption with H 2 . At 300 K, the adsorption of methanol on TiO 2 (110) leads to the formation of CH 3 O on the surface with a minor amount of CH 3 OH present. The easy cleavage of the O–H bond in the alcohol agrees with the predictions of theoretical calculations, and most of the adsorbed CH 3 O was not associated with the presence of Ti 3+ sites in the oxide substrate. The adsorbed CH 3 O was removed from the TiO 2 (110) surface by heating to 600 K without the deposition of CH x fragments or C on the oxide. The results of temperature-programmed desorption (TPD) showed the evolution of methanol and formaldehyde at 360 and 490 K as a result of a disproportionation reaction: 2CH 3 O → CH 3 OH + CH 2 O. This surface chemistry, where there is no rupture of the C–O bond, and only selective cleavage of O–H and C–H bonds, is very different from that found on metals used in catalysts for methanol reforming, where massive conversion of the alcohol into CO, CH x and C species is seen. Furthermore, the TPD data indicate that any CH 3 O formed on the oxide surface can be hydrogenated and desorbed as CH 3 OH at temperatures below 550 K. In this respect, titania is an ideal support for catalysts employed to achieve methanol synthesis through CO 2 hydrogenation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2020

Growth and structural studies of In/Au(111) alloys and InO x /Au(111) inverse oxide/metal model catalysts

Indium oxide has received attention as an exciting candidate for catalyzing the CO 2 hydrogenation to methanol due to its high selectivity (>80%). Compared to the extent of research on the activity of indium oxide-based powder catalysts, very little is known about the phenomena associated with the formation of surface alloys involving indium or the growth mechanism for indium oxide nanoparticles. In this report, scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS) were employed to elucidate the growth mode, structure, and chemical state of In/Au(111) alloys and InO x /Au(111) inverse model catalysts. Here, our study reveals distinct morphological differences between In/Au(111) and InO x /Au(111), and the InO x structure also depends strongly on the preparation conditions. In/Au surface alloy systems with extremely low coverage (0.02 ML) form islands preferentially on the elbow sites of reconstructed Au(111) herringbone, regardless of hcp and fcc stacking. At higher coverage (0.1 ML), the In islands expand over the herringbone in the <110> direction and create two dimensional domain structures over the entire surfaces. Moreover, this 2D domain structure is disturbed by temperature with high dispersion of indium atoms observed during the annealing process. Oxidation of the In/Au(111) surface alloys with O 2 at 550 K produces InO x /Au(111) systems which contain various sizes of InO x aggregates (from 0.7 to 10 nm). On the other hand, InO x /Au(111) surfaces prepared by vapor deposition of In at 550 K in an O 2 background exhibit highly dispersed and uniformly small InO x particles (~1 nm). Both InO x systems were confirmed to be partially oxidized by XPS.

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

Hydrocarbon, Oxidation, Dehydrogenation and Coupling Over Model Metal Oxide Surfaces

Final report for a 24.5 year single investigator project aimed at understanding structure/function relationships in adsorption and reaction on metal oxide surfaces for understanding heterogeneous catalysis. An experimental surface science approach was taken using single crystal surfaces as model catalysts and supplemented by density functional theory (DFT) calculations. Materials studied experimentally and computationally include the base metal oxide SnO 2 and transition metal oxides α-Cr 2 O 3 , α-Fe 2 O 3 and MnO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Metallic Pd–Cu Alloy Phases Drive Selective Heterogeneous Electrochemical Ketonization of 1-Butene

Electrification of 2-butanone synthesis via ketonization of 1-butene offers a viable pathway to reduce emissions associated with its production as a commodity chemical and enhance its prospects as a clean carbon-based synthetic fuel. However, the direct electrochemical ketonization of alkenes remains underexplored, with previous studies largely limited to epoxides and glycols. Herein, we report an electrochemical heterogeneous system optimized for 1-butene ketonization, converting 1-butene to 2- butanone using a bimetallic PdCu catalyst in aqueous electrolytes. The system achieves a Faradaic efficiency of 20% and a partial current density of 0.6 mA/cm 2 at 1.8 V RHE . In comparison to monometallic Pd and oxidized PdCu analogs, the PdCu catalyst doubles the ketonization Faradaic efficiency and quadruples the production rate. Postelectrolysis characterization reveals that PdCu preserves the surface metallic alloy phase under anodic polarization, which likely accounts for the enhanced ketonization activity. This work demonstrates the significance of the Pd−Cu speciation dynamics and provides a framework for designing selective electrocatalysts for alkene ketonization.

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

Stabilizing LiCoO 2 at 4.6 V by regulating anti-oxidative solvents

For LiCoO 2 (LCO) operating at high voltages (>4.5 V vs. Li/Li + ), the intensive side reactions between LCO and traditional ethylene carbonate (EC)-based electrolytes with LiPF 6 salts can produce plenty of corrosive species (such as HF and HPO 2 F 2 ), causing severe surface degradation. Herein, anti-oxidative fluoroethylene carbonate (FEC) and difluoroethylene carbonate (DFEC) were selected as co-solvents to reduce the generation of corrosive species. Besides, PF 6 − anions enrich the Helmholtz plane of the LCO/electrolyte interface and promote the formation of a robust cathode/electrolyte interphase (CEI) featuring LiF/Li x PO y F z /Li 3 PO 4 inorganics and P-containing organics under the synergy of fluorinated solvents, which significantly inhibits the catalysis of highly oxidative Co 4+ /O n− (0 < n < 2). Benefiting from the reduced corrosive species and reinforced CEI, the layered structure of the LCO surface is well preserved during long-term cycling, with a highly reversible O3/H1-3 phase transition. Consequently, a LCO||graphite pouch cell exhibits a remarkable capacity retention of 85.7% after 500 cycles in 3.0–4.55 V. Furthermore, this work provides a new insight into developing advanced functional electrolytes for high-voltage lithium-ion batteries.

Co/O loss↗
DOE OSTI2023

Heavy Versus Light Lanthanide Selectivity for Graphene Oxide Films is Concentration Dependent

Rare earths are important materials in various technologies such as catalysis and optoelectronics. Graphene oxide (GO) is a promising material for separation applications, including the isolation of lanthanides from complex mixtures. Previous works using fatty acid monolayers have demonstrated preferential heavy versus light lanthanide adsorption, which has been attributed to differences in lanthanide ion size. In this work, we used interfacial X-ray fluorescence measurements to reveal that GO thin films at the air/water interface have no lanthanide selectivity for dilute subphases. However, at high subphase concentrations, ~8 times more Lu is adsorbed than La. By comparing the GO results with an ideal monolayer with a carboxylic acid headgroup, arachidic acid (AA), we demonstrate that the number of Lu ions adsorbed to GO is significantly higher than the number expected to compensate for the surface charge. Vibrational sum frequency generation (SFG) spectroscopy results on both GO thin films and AA monolayers reveal a red-shifted SFG signal in the OH region, which we attribute to partial dehydration of the adsorbed ions and carboxylic acid headgroups. Liquid surface X-ray reflectivity data show that the GO thin film structure does not significantly change between the very dilute and concentrated subphases. We speculate that the functional groups of both GO and AA facilitate cation dehydration, which is essential for ion adsorption. Heavy lanthanide Lu has stronger ion–ion correlations that can overcome the electrostatic repulsion between cations at higher concentrations compared to light lanthanide La, meaning GO and AA can exhibit apparent overcharge with Lu. Lastly, the layered structure of the GO films and reactive chemical nature of GO itself can accommodate ion adsorption.

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

Node-Solution Microenvironment Governs the Selectivity of Thioanisole Oxidation within Catalytic Zr-Based Metal–Organic Framework

Lewis acidic metal oxides, including zirconia (ZrO 2 ), are catalytically active toward oxidative reactions in the presence of sacrificial oxidants like t-butyl hydroperoxide (TBHP). The structural ambiguity and heterogeneity of the ZrO 2 surface impose challenges to chemists in understanding the reaction mechanism down to atomic-level precision. The inorganic, Zr-oxo nodes of many crystalline metal–organic frameworks (MOFs) structurally mimic ZrO 2 . Herein, we report three novel findings: (A) Zr-based MOF, Zr-MOF-808 is catalytically competent in activating TBHP to induce oxygen atom transfer (OAT) reactions to a model substrate, thioanisole, at room temperature, (B) its reaction mechanism can be derived with greater structural precision owing to the crystallinity of the MOF, and (C) the node-binding agent and other reaction conditions significantly impact the selectivity between the singly oxidized methyl phenyl sulfoxide vs the doubly oxidized sulfone. These findings suggest that both the activity and selectivity of OAT reactions within Zr-MOF-808 are governed by the chemistry occurring at the interface of the node and the surrounding reaction medium. Implications of these findings in OAT reactions and other MOF/metal oxide-catalyzed relevant catalysis are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗