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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Electrocatalytic CO 2 reduction on earth abundant 2D Mo 2 C and Ti 3 C 2 MXenes

Mo 2 C and Ti 3 C 2 MXenes were investigated as earth-abundant electrocatalyts for the CO 2 reduction reaction (CO 2 RR). Mo 2 C and Ti 3 C 2 exhibited faradaic efficiencies of 90% (250 mV overpotential) and 65% (650 mV overpotential), respectively, for the reduction of CO 2 to CO in acetonitrile using an ionic liquid electrolyte. The use of ionic liquid 1-ethyl-2-methylimidazolium tetrafluoroborate as an electrolyte in organic solvent suppressed the competing hydrogen evolution reaction. Density functional theory (DFT) calculations suggested that the catalytic active sites are oxygen vacancy sites on both MXene surfaces. Also, a spontaneous dissociation of adsorbed COOH species to a water molecule and adsorbed CO on Mo 2 C promote the CO 2 RR.

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

Elucidating the geometric and electronic structure of a fully sulfided analog of an Anderson polyoxomolybdate cluster

The catalytic activity of transition metal sulfide (TMS) clusters in small molecule activation, redox transformations, and charge transfer has inspired the design of novel TMS-based materials for energy-related catalysis and chemical applications. Polyoxometalates (POMs), known for their structural diversity, can in principle be transformed into TMS clusters; however, fully sulfided analogs are rarely isolated, likely due to the strong tendency of uncapped TMS clusters to agglomerate. Here, we report the geometric and electronic structure of a capping ligand-free fully sulfided analog of heptamolybdate Anderson POM [Mo VI 7 O 24 ] 6− , synthesized through the sulfidation of a nanoconfined POM secured within a porous Zr-metal organic framework (NU-1000). A combined computational and experimental analysis indicates that the sulfided counterpart of the Anderson POM is geometrically and electronically more sophisticated than the parent POM. Comparison of experimental pair distribution function (PDF) data with computational simulations confirms that, unlike the oxygen-only [Mo VI 7 O 24 ] 6− cluster, the [Mo IV 7 (μ 3 -S) 6 (μ 2 -SH) 6 (S 2 ) 6 ] 2− polythiometalate (PTM) exhibits diverse sulfur anions (S 2− , HS − , S 2 2− ). DFT calculations indicate that H 2 S acts as a reducing agent, and together with terminal disulfide (S 2 2− ) ligands in the PTM structure, facilitates the complete reduction of all seven Mo VI centers in the parent POM to Mo IV . These findings are supported by X-ray photoelectron spectroscopy (XPS), which confirms exclusive Mo IV , and elemental analysis, which shows quantitative sulfur incorporation. Difference envelope density (DED) mapping further reveals that the PTM clusters are spatially confined within the MOF pores, preventing agglomeration and preserving molecular integrity.

Rabbani, S. M. Gulam [The Ohio State University, C↗
DOE OSTI2026

Electron temperature relations and the direct N, O, Ne, S, and Ar abundances of 49 959 star-forming galaxies in DESI data release 2

We present the largest direct-method abundance catalogue of galaxies to date, containing measurements of 49 959 star-forming galaxies at z<0.96 from DESI (Dark Energy Spectroscopic Instrument) data release 2. By directly measuring electron temperatures across multiple ionization zones, we provide constraints on a number of electron temperature relations. Using the temperature measurements, we derive reliable abundances for N, O, Ne, S, and Ar, and measure the evolution of abundances and abundance ratios of as a function of metallicity and other galaxy properties. Our measurements include direct oxygen abundances for 49 507 galaxies, leading to the discovery of the two most metal-poor galaxies in the nearby Universe, with oxygen abundances of 12+log⁡(O/H)=6.77−0.03+0.03 dex (1.2 per cent Z⊙⁠) and 12+log⁡(O/H)=6.81−0.04+0.04 dex (1.3 per cent Z⊙⁠). We identify a rare outlier population of 24 galaxies with high-N/O ratios at low metallicity, reminiscent of galaxy abundances observed in the early Universe. We find the Ne/O ratio is constant at low metallicity but increases gradually at 12+log(O/H)>8.105±0.004 dex. We show that the S/O and Ar/O abundance ratios are strongly correlated, consistent with the expected additional Type Ia enrichment channel for S and Ar. In this work, we present an initial survey of the key properties of the sample, with this data set serving as a foundation for extensive future work on galaxy abundances at low redshift.

Scholte, D. [Edinburgh U., Inst. Astron.] (ORCID:0↗
DOE OSTI2025

Impact of structural distortions on the correlated electronic structure of orbital-selective Mott insulating Na 3 ⁢Co 2 ⁢SbO 6 under strain

Na 3 Co 2 SbO 6 is a promising candidate to realize the Kitaev spin liquid phase since the large Kitaev spin exchange interaction is tunable via the change in electronic structure, such as the trigonal crystal field splitting (Δ TCF ). Here, in this study, we show that the uncorrelated electronic structure of Na 3 Co 2 SbO 6 is rather insensitive to the strain effect due to the low crystal symmetry accompanied by oxygen displacements and the presence of Sb s orbitals. Using density functional theory plus dynamical mean field theory, we find that the correlated electronic structure of Na 3 Co 2 SbO 6 is an orbital-selective Mott insulating state where the trigonal a 1g orbital is insulating due to the nearly full occupation, while other d orbitals behave as typical Mott insulators, resulting in the effective tunability of Δ TCF under the strain effect. The sign change of Δ TCF can occur as the in-plane tensile strain is applied, and the Kitaev spin liquid phase could possibly be realized due to the strongly suppressed Δ TCF under tensile strain. Our results show that the local Co-site symmetry and dynamical correlation effects will play an important role in engineering the novel magnetic phase in this and related materials.

Nguyen, Nam [Univ. of Illinois, Chicago, IL (Unite↗
DOE OSTI2024

Computational design of high entropy alloy coating for hydrogen turbine applications

This project aims to develop novel high entropy alloy (HEA)-based coatings to protect critical components in hydrogen-fueled turbine power system. The HEA-coatings will demonstrate superior performance in hydrogen combustion environment to commercial NiCoCrAlY coating in current natural gas turbine system. The HEA coating facilitates the formation of a protective scale of alpha-alumina to slow down the inward diffusion of oxidizing species and the outward diffusion of metal elements, and possesses ultrahigh corrosion and spallation resistance to prolong the service lifetime of critical components in hydrogen turbine power system. Aimed to accelerate the discovery of novel HEA coating compositions, high throughput computational modeling including CALPHAD and density functional theory and machine learning are performed to predict phase stability, oxygen permeability, oxidation rate constant, coefficient of thermal expansion, and mechanical properties. Based on the modeling and machine learning prediction, experimental validation is performed. Preliminary results will be presented and approaches to minimize oxidation will be discussed.

alloy design↗
DOE OSTI2023

Kinetic model-based group contribution method for derived cetane number prediction of oxygenated fuel components and blends

A four-step autoignition model was used to derive an expression for the ignition delay (ID) measured in ignition quality testers (IQT) with the derived cetane number (DCN) determined from this ID using the ASTM D6890 standard correlation. The model predicts DCN for individual compounds and blends as a function of each compound's global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in each compound. Measurement data for 125 compounds and 94 binary and ternary blends (including oxygenates: alcohols, aldehydes, esters, ethers, and ketones), from literature and from measurements performed in our lab, were used to obtain the dependence of the measured ignition delay on each functional group. The new kinetic model-based group contribution method was able to predict the ignition delay of both pure compounds and blends, with an average DCN error of 4.4 (19%) and 2.8 (11%), respectively. Here, the blend model was also used to develop an ID mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule gave an average DCN error of 3.6 (16%) for blends. Both the blend model and mixing rule were found to be superior compared to standard linear by volume fraction or mole fraction mixing rules commonly used to estimate the DCN of mixtures.

42 ENGINEERING↗
DOE OSTI2021

Exemption of lattice collapse in Ni–MnO 2 birnessite regulated by the structural water mobility

Lattice collapse and associated mechanical fracture frequently occur in Li-intercalated metal oxide cathodes at the deep charge state upon Li-ion removal, governed by chemical compositions and the resulting electron density of the oxygen atoms. However, similar lattice collapse for metal oxide electrodes in aqueous storage and its mitigation have not been well studied. In this work, we reported the lattice collapse of MnO 2 layered birnessite during the aqueous de-sodiation process at high voltage due to the structural water motion, as evidenced by in situ XRD. Unlike non-aqueous Li-intercalated electrodes, Ni-dopants mitigated the lattice collapse of birnessite at deep charge states. Moreover, density functional theory (DFT) calculations showed that Ni doping induces charge depletion of lattice oxygen due to its higher electronegativity than Mn. This charge reduction, in turn, yields a significant decrease in electrostatic repulsion between the oxygens belonging to lattice and structural water. Classical molecular dynamics simulations based on atomic charges obtained from DFT elucidate that Ni-doping facilitates immobilization of structural water in (Ni)MnO 2 and prevents lattice collapse upon Na-ion removal. (Ni)MnO 2 exempted from lattice collapse shows an improved storage capacity relative to MnO 2 while maintaining similar cycling stability.

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

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Material Discovery and Design Principles of Perovskite Oxides for Reversible Solid Oxide Cells (R-SOC)

Reversible solid oxide cells (R-SOCs) are highly efficient devices for energy conversion and storage, capable of operating for both hydrogen utilization and production. In fuel cell mode, an R-SOC consumes hydrogen or natural gas to generate electricity, while in electrolysis mode, it produces hydrogen from steam. The discover of new materials with rapid oxygen surface exchange kinetics and enduring stability is crucial for the economically viable commercialization of R-SOCs. To facilitate this pursuit, we conducted extensive Density Functional Theory (DFT) calculations and developed Machine Learning (ML) models to predict critical catalytic properties essential for R-SOCs, such as oxygen surface exchange/diffusivity, and area-specific resistance (ASR). BaCoxFeyZrzO3-d(BFCZ)(x+y+z=1) emerged as a promising family of electrode materials with high activity and stability, validated through systematic experimental study. Moreover, a robust numerical multiphysics model was developed to optimize materials and microstructure parameters, providing the ability to predict the performance of functional R-SOCs.

Liu, Jian↗
DOE OSTI2023

GUN4 appeared early in cyanobacterial evolution

Photosynthesis relies on chlorophylls, which are synthesized via a common tetrapyrrole trunk pathway also leading to heme, vitamin B12, and other pigmented cofactors. The first committed step for chlorophyll biosynthesis is insertion of magnesium into protoporphyrin IX by magnesium chelatase. Magnesium chelatase is composed of H-, I-, and D-subunits, with the tetrapyrrole substrate binding to the H-subunit. This subunit is rapidly inactivated in the presence of substrate, light, and oxygen, so oxygenic photosynthetic organisms require mechanisms to protect magnesium chelatase from similar loss of function. An additional protein, GUN4, binds to the H-subunit and to tetrapyrroles. GUN4 has been proposed to serve this protective role via its ability to bind linear tetrapyrroles (bilins). In the current work, we probe the origins of bilin binding by GUN4 via comparative phylogenetic analysis and biochemical validation of a conserved bilin-binding motif. Based on our results, we propose that bilin-binding GUN4 proteins arose early in cyanobacterial evolution and that this early acquisition represents an ancient adaptation for maintaining chlorophyll biosynthesis in the presence of light and oxygen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Electronic Structure of the Au Nanoparticle-TiO 2 Heterojunction: Influence of Nanoparticle Size, Shape, Oxygen Vacancies, and Temperature

The electronic structure of the gold nanoparticle-titanium dioxide (AuNP-TiO 2 ) heterojunction plays a critical role for charge transfer and recombination dynamics that underpin its photocatalytic function. However, building a representative model to capture the key physics remains a significant challenge. Here, we investigate the influence of the AuNP size and shape, as well as oxygen vacancy (V O ) defects at the anatase-phase TiO 2 (101) surface and the temperature of the heterojunction, on its interfacial electronic properties. Using density functional theory (DFT), we compare the closed-shell Au 20 and open-shell Au 19 clusters interfaced with pristine and V O defect TiO 2 surfaces. We find that the presence of a V O defect transforms pure TiO 2 from a p-type to an n-type semiconductor, reversing the interfacial band bending from downward to upward. For the heterosystem, density of states (DOS) analysis shows that V O minimally affects the open-shell Au 19 -TiO 2 system, but it significantly alters the closed-shell Au 20 -TiO 2 system, converting it from p-type to n-type at the Γ-point at 0 K. Furthermore, ab initio molecular dynamics (AIMD) simulations at 300 K reveal significant thermal fluctuations in AuNP positions relative to the TiO 2 surface. These fluctuations result in dynamic variations in the gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) in all systems studied: Au 19 NP-Pristine TiO 2 , Au 20 NP-Pristine TiO 2 , Au 19 NP-V O defect TiO 2 , and Au 20 NP-V O defect TiO 2 . Complementary Bader charge analysis performed at both 0 K and finite-temperature AIMD snapshots supports the emergence of an upward band bending and the formation of a Schottky barrier at the V O containing heterojunctions. Notably, we find that at finite temperature, an Au atom can dynamically passivate the V O , leading to a pronounced widening of the HOMO-LUMO gap in the AuNP-V O defect TiO 2 heterojunctions. Our computational findings underscore the pivotal role of the V O defect and thermal effects in modulating interfacial band alignment, electronic states, and HOMO-LUMO gaps, providing insights for designing Au-TiO 2 heterojunctions with tailored electronic properties.

Mora Perez, Carlos [Lawrence Berkeley National Lab↗
DOE OSTI2022

Overcoming significant challenges in extracting off-stoichiometric thermodynamics using the compound energy formalism through complementary use of experimental and first principles data: A case study of Ba 1-x Sr x FeO 3-δ

The compound energy formalism (CEF) is a powerful framework to describe the thermodynamics of metal oxides as a function of off-stoichiometry, temperature, and composition. The thermodynamic properties are crucial materials design attributes in metal oxide-based oxygen-exchange chemical processes. Despite the richness of information an accurate CEF model provides, a method to determine a unique and accurate fit for oxygen exchange materials remains elusive. This contribution details a method for fitting the CEF model that overcomes the current fitting challenges through three innovations: 1) the combination of density functional theory calculations with experimental data decorrelates excess terms and delineates the enthalpic/entropic contributions to the Gibbs free energy; 2) a systematic determination of the important CEF model terms, removing thermodynamic predetermining human intervention; 3) a self-consistent solution of the starting oxygen offstoichiometry (δ 0 ) of thermogravimetric measurements. Thus, our method enables the reliable extraction of off-stoichiometric metal oxide thermodynamic properties and facilitates rapid materials compositional screening, and reliable process design of systems dependent on off-stoichiometric redox-active metal oxides. We apply this method to a Ba x Sr 1-x FeO 3-δ test case. We find by systematically examining the performance of the CEF model fit with and without each innovation that all three innovations are necessary for an accurate fit. We determined that reduction enthalpy is higher and more sensitive to off-stoichiometry when the Sr fraction is large (139.5 and 185.3 kJ/mol O 2 for SrFeO 3 at δ = 0 and δ = 0.5, respectively vs. nearly constant 83 kJ/mol O 2 for BaFeO 3 ). However, the reduction entropy is mostly insensitive to Sr fraction, but highly dependent on δ suggesting larger contributions in the non-configurational entropy parameters.

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

Quantitative nanoscale tracking of oxygen vacancy diffusion inside single ceria grains by in situ transmission electron microscopy

Oxygen vacancy formation and migration in ceria is critical to its electrochemical and catalytic properties in systems for chemical and energy transformation, but its quantification is rather challenging especially at atomic-scale because of disordered distribution. Here we report a rational approach to track oxygen vacancy diffusion in single grains of pure and Sm-doped ceria at -20 °C to 160 °C using in situ (scanning) transmission electron microscopy ((S)TEM). To create a gradient in oxygen vacancy concentration, a small region (~30 nm in diameter) inside a ceria grain is reduced to the C-type CeO 1.68 phase by the ionization or radiolysis effect of a high-energy electron beam. The evolution in oxygen vacancy concentration is then mapped through lattice expansion measurement using scanning nano-beam diffraction or 4D STEM at a spatial resolution better than 2 nm; this allows direct determination of local oxygen vacancy diffusion coefficients in a very small domain inside pure and Sm-doped ceria at different temperatures. Further, the activation energies for oxygen transport are determined to be 0.59, 0.66, 1.12, and 1.27 eV for pure CeO 2 , Ce 0.94 Sm 0.06 O 1.97 , Ce 0.89 Sm 0.11 O 1.945 , and Ce 0.8 Sm 0.2 O 1.9 , respectively, implying that activation energy increases due to impurity scattering. The results are qualitatively supported by density functional theory (DFT) calculations. In addition, our in situ TEM investigation reveals that dislocations impede oxygen vacancy diffusion by absorbing oxygen vacancies from the surrounding areas and pinning them locally. With more oxygen vacancies absorbed, dislocations show extended strain fields with local tensile zone sandwiched between the compressed ones. Therefore, dislocation density should be reduced in order to minimize the resistance to oxygen vacancy diffusion at low temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling the Role of Surface Ir-Oxo Species in O 2 Evolution at IrO 2 Electrocatalysts via Embedded Cluster Multireference Calculations

Understanding the mechanisms driving the oxygen evolution reaction (OER) on iridium oxide (IrO 2 )-based catalysts is essential to improving their performance and enabling an actual scale-up of water-splitting photoelectrochemical cells. The mechanistic pathways at IrO 2 interfaces have been extensively investigated computationally using density functional theory (DFT), which predicts a high-energy barrier for the last step of the OER of molecular oxygen detachment and release from the catalyst surface. Nevertheless, surface O 2 over- and under-binding results by standard generalized gradient approximation and hybrid density functionals, respectively, call for further analysis of this crucial step via multireference methods. Aiming at unveiling the nature of such a barrier, we hereby address the formation of O 2 from the most-stable IrO 2 (110) surface with both periodic DFT and an electrostatic embedded cluster approach at the n-electron valence-state perturbation theory. With this multireference approach, we find a value for the aforementioned energy barrier that is much closer to experimental indications than DFT ones. Here, an in-depth analysis of the involved molecular orbitals suggests that the origin of this barrier is related to the breaking of a π interaction between O 2 and Ir surface atom and to a significant additional O 2 interaction with adjacent electrophilic Ir-oxo species, which is present under experimental operating conditions. Besides shedding light on the mechanism of the OER on IrO 2 , these findings point out the importance of multireference methods for dissecting complex reactions at electrocatalytic interfaces and pave the route for further investigations with effective embedding approaches.

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

The optimal co-doping of SrFe 1-x Co x O 3-δ oxygen carriers in redox applications

Although the oxygen carrier SrCoO 3 has higher redox activity than SrFeO 3 , cobalt is both more expensive and scarcer than iron, which would hinder the wide implementation of SrCoO 3 . For these reasons, doping SrFeO 3 with Co is a potential compromise, benefitting the redox properties of SrFeO 3 , while still limiting the overall amount of cobalt being used. In this work, to find the optimal level of Co-doping, density functional theory calculations were performed to investigate the Co-doping effect on the oxygen vacancy formation and oxygen migration in SrFe 1- x Co x O 3- δ ( x = 0, 0.125, 0.25, 0.375, 0.5). Our findings show that the oxygen vacancy formation energies ( E f ) decrease with the increase of Co content resulting from the increased composition of the O-2p band at the Fermi level upon Co doping. In particular, the E f decreases nearly 0.5 eV between the x = 0 and x = 0.25 samples while E f only decreases 0.1 eV further as Co content is increased to x = 0.5. We obtain that x = 0.25 is an optimal cost/benefit ratio for Co doping, which is preserved at both low oxygen vacancy concentrations ( δ = 0.0625 values listed above) and at high concentrations of δ = 0.1875 and 0.375. Kinetically, the oxygen migration barrier has slight change upon Co doping due to the similar size of Co and Fe. Therefore, considering both redox activity and economics in reversible oxygen storage applications, x = 0.25 is suggested as the optimal Co-doping value in SrFe 1- x Co x O 3- δ .

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

Propylamine Functionalized Porous Fiber Sorbents with High Oxidation Stability for Direct Air Capture

Amine-based sorbents are highly effective for direct air capture (DAC) due to their excellent CO2 capture efficiency under ultra-dilute conditions. However, these sorbents can suffer degradation when exposed to combinations of heat, oxygen, and other atmospheric components, indicating a need for improved sorbent stability. In this study, we developed amidoxime functionalized polymer of intrinsic microporosity (PIM-1-AO) fibers with tethered amines, specifically tris(2-aminoethyl)amine (TAEA) and tris(3-aminopropyl)amine (TAPA), denoted as PIM-1-AO-TAEA and PIM-1-AO-TAPA, respectively. In addition, large scale sorbent fiber spinning has been successfully performed by producing meters of fibers in the lab. We demonstrated that substituting TAEA with TAPA, which has propyl chains instead of ethyl chains, reduces oxidative degradation under regeneration conditions. The findings revealed that PIM-1-AO-TAPA not only significantly outperforms PIM-1-AO-TAEA after accelerated aging process, but also outperforms any other sorbent used in DAC aged under similar conditions. Moreover, PIM-1-AO-TAPA sorbent fibers showed one of the highest amine efficiencies (0.25) considering the low amine loading (21%) coupled with robust CO2 uptake performance (1.12 mmol/g) under DAC conditions.

amine-based sorbent↗
DOE OSTI2021

Substituted ALPO-5 Zeolites as Promising O 2 Sorption Pump Materials: A Density Functional Theory Study

Many chemical processes depend on having an environment that is low in oxygen partial pressure (P O2 < 100 Pa); sorption pumps are a promising route to establishing that environment by either oxygen pumping or oxygen separation from an inert gas. Near ambient sorption-based processes rely on either pressure or thermal swings, requiring no moving parts, no electricity, and neither very high nor very low temperatures. In this work, we use ab initio calculations to explore zeolites as a class of materials for sorption based oxygen pumping/separation. Our calculations indicate that while the neat ALPO-5 zeolite does not adsorb O 2 , O 2 does adsorb on zeolites selectively substituted with transition metals and metalloids and hence, can enable separation and pumping. ALPO-5 substituted with Si, Ge, Sn, Pd, Pt, Ti, V, Cr, Mn, Zr, Mo, Hf, W, Ce, and Pr provides adsorption energies ranging from -0.19 to -3.92 eV, (-) indicates exothermic process. Additionally, we provide a comprehensive understanding of what controls the adsorption energy: 1) the substi-tutions must be able to adopt an oxidation state that is more positive than the cation it replaces, and 2) the size of the pore into which the O 2 adsorbs to the wall. Additionally, we conduct a ther-modynamic analysis of a thermal swing cycle to approximate the optimal O 2 binding energy for 2 low energy O 2 pumping/separation. We find that the minimum energy cost likely occurs when the adsorption energy is in the range of 0.75 – 1.00 eV (72 – 97 kJ·mol -1 ), which corresponds to Ge, V, Pt, or Ce substituted ALPO-5.

36 MATERIALS SCIENCE↗