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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

HfTa_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys hafnium-tantalum (Hf-Ta). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Hf and Ta. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect BCC lattice sites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,040 randomized atomic structures over 31 chemical compositions. Further methodological and structural information is contained in the dataset README.txt file.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Origin of Unusual Acidity and Li+ Diffusivity in a Series of Water-in-Salt Electrolytes.

Superconcentrated aqueous electrolytes ("water-in-salt" electrolytes, or WiSEs) enable various aqueous battery chemistries beyond the voltage limits imposed by the Pourbaix diagram of water. However, their detailed structural and transport properties remain unexplored and could be better understood through added studies. Here, we report on our observations of strong acidity (pH 2.4) induced by lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) at superconcentration (at 20 mol/kg). Multiple nuclear magnetic resonance (NMR) and pulsed-field gradient (PFG) diffusion NMR experiments, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations reveal that such acidity originates from the formation of nanometric ion-rich structures. The experimental and simulation results indicate the separation of water-rich and ion-rich domains at salt concentrations >= 5 m and the acidity arising therefrom is due to deprotonation of water molecules in the ion-rich domains. As such, the ion-rich domain is composed of hydrophobic -CF3 (of TFSI-) and hydrophilic hydroxyl (OH-) groups. At 20 m concentration, the tortuosity and radius of water diffusion channels are estimated to be similar to 10 and similar to 1 nm, respectively, which are close to values obtained from hydrated Nafion membranes that also have hydrophobic polytetrafluoroethylene (PTFE) backbones and hydrophilic channels consisting of SO3- ion cluster networks providing for the transport of ions and water. Thus, we have discovered the structural similarity between WiSE and hydrated Nafion membranes on the nanometer scale.

Han, Kee Sung↗
DOE OSTI2022

FINETUNA: fine-tuning accelerated molecular simulations

Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Hydrated Metal and Metal-Nitrate Complexes in Water: Full Lanthanide(III) Series plus Miscellaneous Metal Ions

This is a dataset of hydrated metal complexes and metal–nitrate hydrated complexes intended for public use, reproducibility, and downstream structural analysis. A key feature is coverage across the full lanthanide(III) series (La–Lu), enabling systematic comparisons of coordination motifs and bonding trends across the entire lanthanide sequence. In addition to the lanthanides, the dataset also includes other metal ions such as UO2(VI), Fe(II), and Fe(III). The dataset provides optimized geometries for hydrated and nitrate-containing hydrated complexes, together with representative ab initio molecular dynamics (AIMD) trajectories saved in standard XYZ formats. The accompanying NWChem input decks enable reproduction of the reported calculations and provide a starting point for extending the simulations to related coordination environments. Computationally, DFT calculations employ the B3LYP functional with DFT-D3BJ dispersion corrections and a COSMO continuum solvent model (dielectric constant 78.4) to represent solvation beyond the explicitly treated first hydration shell. AIMD simulations are performed with the NWChem qmd module at 298 K, integrating nuclear motion with the velocity-Verlet algorithm and controlling temperature using a Nosé–Hoover thermostat. Trajectories are approximately 4.8 ps in length and are used primarily to assess short-time stability of candidate coordination motifs, including (for lanthanides) differences between 8- versus 9-water coordination and comparisons between nitrate-bound and nitrate-free hydrated complexes.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2021

CO adsorption on MnO(100): Experimental benchmarks compared to DFT

In this study, CO adsorption on the MnO(100) surface was studied using temperature programmed desorption (TPD) and density functional theory (DFT). TPD results show that CO is weakly-bound on MnO(100), with an experimental adsorption energy of -35.6 ± 2.1 kJ/mol at terrace sites in the low coverage limit. PBE simulations suggest that CO adsorption causes an implausible (2 × 2) surface reconstruction. PBE+U simulations show no signs of surface reconstruction, and provide an accurate estimate of the adsorption energy (-36.4 kJ/mol) when combined with the DFT-D3 method with Becke-Jonson damping to correct for van der Waals interactions. This simulation also shows that CO adsorbs C-down onto the Mn 2+ terrace site in a tilted geometry, which is also observed experimentally and computationally on the similarly-structured NiO(100) transition metal oxide surface. TPD results for large doses show a plateauing of the coverage at about 0.4 monolayers of CO at 85 K, with a defect coverage equivalent to 0.08 monolayers. Adsorption associated with defect sites is indicated by a high-temperature desorption tail which is not satisfactorily explained by DFT simulations of simple step or oxygen vacancy defects.

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

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

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

Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of medium-entripy alloys (easily extendable to multi-principal-element alloys), such as Ti–Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co–Ni-X (X = Al, Mg, V). Ti–Nb–Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40–50 %. We attributed results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti 5 Mo 95 alloys. Density functional theory (DFT) and molecular dynamics (MD) simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. Here, to aid material selection, we present two periodic tables illustrating elemental effects on (a) H 2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

08 HYDROGEN↗
DOE OSTI2025

Origin of Stabilization of Ligand-Centered Mixed Valence Ruthenium Azopyridine Complexes: DFT Insights for Neuromorphic Applications

Redox-driven conductance changes are critical processes in molecular- and coordination-complex-based memristive thin films and devices that are envisioned for neuromorphic technologies, but fundamental mechanisms of conductance switching are not fully understood. Here, we explore charge disproportionation (CD) processes in [Ru II L 2 ](PF 6 ) 2 molecular systems that intrinsically involve interfragment charge transfer (IFCT). Using a combination of ab initio molecular dynamics simulation (AIMD), time-dependent density functional theory (TD-DFT), and density functional theory (DFT) calculations, we investigate the electron transfer mechanisms and the roles of temperature and cell volumetric expansion in facilitating the counterion movements and electronic transitions required for low-cost IFCT and charge redistribution. A detailed analysis of the density of states and TD-DFT calculations highlights that unpaired electrons play a crucial role in low-energy transitions, with the azo (N=N) groups of the ligand serving as the primary sites for electronic transport between molecular fragments, further stabilizing the asymmetric state. Localization of added electrons on azo ligands occurs with negligible change at the Ru centers, supported by atomic volume expansions up to +4.74 bohr 3 , and goes along with a progressive reduction of the HOMO−LUMO gap across redox states, suggesting enhanced conductivity. The TD-DFT analysis reveals a dominant IFCT excitation at 2082.76 nm in the doubly reduced (22) state, while a stabilization energy of 1.20 eV of the asymmetric (13) state relative to the symmetric (22) state is predicted by constrained DFT. Periodic DFT and AIMD simulations emulating a molecular film show that the stabilization of the asymmetric state, relative to a symmetric one, translates in net charge separation values (order of ∼0.33 e) that are strongly linked to increased counterion mobility (average counterion displacements exceeding 0.7 Å per atom during CD events) and the involvement of azo groups in electron redistribution. These findings, which align with previously reported experimental and computational data, provide key insights into the IFCT mechanisms and electronic transport facilitated by azo groups, with important implications for redox-driven memristive and neuromorphic technologies.

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

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Density functional theory↗
DOE OSTI2021

Experimental and Theoretical Study of the Electronic Structures of Lanthanide Indium Perovskites LnInO 3

Ternary lanthanide indium oxides LnInO 3 (Ln = La, Pr, Nd, Sm) were synthesized by high-temperature solid-state reaction and characterized by Xray powder diffraction. Rietveld refinement of the powder patterns showed the LnInO 3 materials to be orthorhombic perovskites belonging to the space group Pnma, based on almost-regular InO 6 octahedra and highly distorted LnO 12 polyhedra. Experimental structural data were compared with results from density functional theory (DFT) calculations employing a hybrid Hamiltonian. Valence region X-ray photoelectron and K-shell X-ray emission and absorption spectra of the LnInO 3 compounds were simulated with the aid of the DFT calculations. Photoionization of lanthanide 4f orbitals gives rise to a complex final-state multiplet structure in the valence region for the 4f n compounds PrInO 3 , NdInO 3 , and SmInO 3 , and the overall photoemission spectral profiles were shown to be a superposition of final-state 4f n-1 terms onto the cross-section weighted partial densities of states from the other orbitals. The occupied 4f states are stabilized in moving across the series Pr-Nd-Sm. Band gaps were measured using diffuse reflectance spectroscopy. These results demonstrated that the band gap of LaInO 3 is 4.32 eV, in agreement with DFT calculations. This is significantly larger than a band gap of 2.2 eV first proposed in 1967 and based on the idea that In 4d states lie above the top of the O 2p valence band. However, both DFT and X-ray spectroscopy show that In 4d is a shallow core level located well below the bottom of the valence band. Band gaps greater than 4 eV were observed for NdInO 3 and SmInO 3 , but a lower gap of 3.6 eV for PrInO 3 was shown to arise from the occupied Pr 4f states lying above the main O 2p valence band.

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

Exploring the Vibrational Side of Spin-Phonon Coupling in Single-Molecule Magnets via 161 Dy Nuclear Resonance Vibrational Spectroscopy

Synchrotron-based nuclear resonance vibrational spectroscopy (NRVS) using the Mossbauer isotope 161 Dy has been employed for the first time to study the vibrational properties of a single-molecule magnet (SMM) incorporating Dy III , namely [Dy(Cy3PO)(2)(H2O)(5)]Br-3•2 (Cy3PO)•2 H2O•2 EtOH. The experimental partial phonon density of states (pDOS), which includes all vibrational modes involving a displacement of the Dy III ion, was reproduced by means of simulations using density functional theory (DFT), enabling the assignment of all intramolecular vibrational modes. This study proves that 161 Dy NRVS is a powerful experimental tool with significant potential to help to clarify the role of phonons in SMMs.

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

Direct interpolative construction of the discrete Fourier transform as a matrix product operator

The quantum Fourier transform (QFT), which can be viewed as a reindexing of the discrete Fourier transform (DFT), has been shown to be compressible as a low-rank matrix product operator (MPO) or quantized tensor train (QTT) operator. However, the original proof of this fact does not furnish a construction of the MPO with a guaranteed error bound. Meanwhile, the existing practical construction of this MPO, based on the compression of a quantum circuit, is not as efficient as possible. We present a simple closed-form construction of the QFT MPO using the interpolative decomposition, with guaranteed near-optimal compression error for a given rank. This construction can speed up the application of the QFT and the DFT, respectively, in quantum circuit simulations and QTT applications. We also connect our interpolative construction to the approximate quantum Fourier transform (AQFT) by demonstrating that the AQFT can be viewed as an MPO constructed using a different interpolation scheme.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Interfacial Hydrogen-Bond Dynamics in Transition Metal Compounds

Understanding how water behaves when confined within atomic layers of active transition-metal carbides, nitrides, and carbonitrides is essential for uncovering the fundamental principles needed to engineer solid–liquid interfaces at the atomic scale. Yet, how lattice element chemistry and surface termination groups collectively regulate the structure and mobility of such interlayer water remains poorly understood. Here, we present a composition-controlled investigation of interlayer water dynamics in layered transition-metal nitride, carbide, and carbonitride systems using a systematic integration of quasi-elastic neutron scattering (QENS), ab initio molecular dynamics (AIMD) simulations, and density functional theory (DFT) calculations. QENS measurements show that nitride-rich systems host mobile, translationally diffusing water with thermally activated self-diffusion coefficients on the order of 10 –10 m 2 s –1 , whereas mixed C/N lattices confine water to localized, nontranslational motion that is insensitive to temperature. AIMD and DFT reveal that lattice C/N chemistry and surface functional group composition reshape the first hydration layer by modulating the surface electronic structure and termination-dependent hydrogen-bond networks, leading to pronounced differences in water ordering and thermal resilience. On the other hand, fully carbide systems exhibit intermediate behavior, highlighting that water mobility is not primarily controlled by the hydration level alone but by the coupling between lattice composition and surface chemistry. Overall, this study establishes how surface chemistry and lattice composition jointly control interfacial hydrogen bond dynamics, offering a mechanistic framework for designing transition-metal layered materials with tailored interfacial transport properties.

Hydration↗
DOE OSTI2025

Understanding Trigger Linkage Dynamics in Energetic Materials Using Mixed Picramide Nitrate Ester Explosives

The ability to predict the handling sensitivity of new organic energetic materials has been a longstanding goal. We report the synthesis and characterization of six new nitropicramide energetic materials with mixed functional groups that mimic known explosives such as nitroglycerin, erythritol tetranitrate (ETN), and pentaerythritol tetranitrate (PETN). The molecules have been studied theoretically using quantum molecular dynamics (QMD) simulations and density functional theory (DFT) calculations to identify the weakest bond in the reactants - the trigger-linkages - which control handling sensitivity, and to quantify their specific enthalpies of explosion. In good accord with the drop weight impact sensitivity data, our calculations predict that the sensitivities of the molecules are very similar owing to the small variations of the energy output and rates of trigger linkage rupture. In addition, both the QMD and DFT calculations point to the nitropicramide N–NO 2 bonds as the trigger linkages rather than the more typical O–NO 2 bonds. We propose that the switch of the trigger linkage from the nitrate esters to the nitramine groups arises from the strongly electron withdrawing character of the adjacent trinitrobenzene groups.

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

Molecular growth upon ionization of van der Waals clusters containing HCCH and HCN is a pathway to prebiotic molecules

The growth mechanisms of organic molecules in an ionizing environment such as the interstellar medium are not completely understood. Here we examine by means of ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) computations the possibility of bond formation and molecular growth upon ionization of van der Waals clusters of pure HCN clusters, and mixed clusters of HCN and HCCH, both of which are widespread in the interstellar medium. Ionization of van der Waals clusters can potentially lead to growth in low temperature and low-density environments. Our results show, that upon ionization of the pure HCN clusters, strongly bound stable structures are formed that contain N∙∙∙H bonds, and growth beyond pairwise HCN molecules is seen only in a small percentage of cases. In contrast, mixed clusters, where HCCH is preferentially ionized over HCN, can grow up to 3 or 4 units long with new carbon–carbon and carbon–nitrogen covalent bonds. Moreover, cyclic molecules formed, such as the radical cation of pyridine, which is a prebiotic molecule. The results presented here are significant as they provide a feasible pathway for molecular growth of small organic molecules containing both carbon and nitrogen in cold and relatively denser environments such as in dense molecular clouds but closer to the photo-dissociation regions, and protoplanetary disks. In the mechanism we propose, first, a neutral van der Waals cluster is formed. Once the cluster is formed it can undergo photoionization which leads to chemical reactivity without any reaction barrier.

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

Data-driven design of novel halide perovskite alloys

The great tunability of the properties of halide perovskites presents new opportunities for optoelectronic applications as well as significant challenges associated with exploring combinatorial chemical spaces. Here, in this work, we develop a framework powered by high-throughput computations and machine learning for the design and prediction of mixed cation halide perovskite alloys. In a chemical space of ABX 3 perovskites with a selected set of options for A, B, and X species, pseudo-cubic structures with B-site mixing are simulated using density functional theory (DFT) and several properties are computed, including stability, lattice constant, band gap, vacancy formation energy, refractive index, and optical absorption spectrum, using both semi-local and hybrid functionals. Neural networks (NN) are used to train predictive models for every property using tabulated elemental properties of A, B, and X site species as descriptors. Starting from a DFT dataset of 229 points, we use the trained NN models to predict the structural, energetic, electronic, and optical properties of an extensive dataset of 17955 compounds, and perform high-throughput screening in terms of stability, band gap, and defect tolerance, to obtain 392 promising compounds that are ranked as potential absorbers according to their photovoltaic figure of merit. Compositional trends in the screened set of attractive mixed cation halide perovskites are revealed and additional computations are performed on selected compounds. The data-driven design framework developed here is promising for designing novel mixed compositions and can be extended to a wider perovskite chemical space in terms of A, B, and X species, different kinds of mixing at the A, B, or X sites, non-cubic phases, and other properties of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Screening binary alloys for electrochemical CO 2 reduction towards multi-carbon products

Electrochemical reduction of CO 2 (eCO 2 R) to high-value chemicals presents an attractive approach for utilizing CO 2 . Copper (Cu) is presently the only electrocatalyst that fulfills this purpose with notable activity, but selectivity remains a problem. To identify catalysts for eCO 2 R with high selectivity towards multicarbon (C 2(+) ) products, we explore binary systems composed of strongly and weakly CO binding metals alloyed with Cu, Fe, Co, Ni, and Pd. A total number of 142 alloys with two commonly studied configurations, L1 2 and L1 0 , are simulated with density functional theory (DFT). We leverage recent progress in the atomistic understanding of the eCO 2 R mechanism and use the binding energies of CO* and C* as descriptors when screening for C 2(+) selectivity. We evaluate the stability of the binary alloys by analyzing the formation energy of the clean alloy surfaces. Our theoretical screening identifies about 16 Cu-based alloys and 18 non-Cu based alloys with optimal C 2(+) selective properties for eCO 2 R. For the non-Cu based binary alloys, the p-block elements play an important role in tuning the C* and CO* adsorption energies. In terms of stability, most of the Cu-based systems alloyed with metals that exhibit strong CO* binding are unstable. Ni-based alloys are more stable than the Co-based alloys followed by the Fe-based alloys, and all the Pd-based alloys are stable. In general, the L1 0 structural Fe, Co, Ni, and Pd-based alloys are more stable than the corresponding L1 2 alloys. Our approach identifies materials known to have good C 2(+) selectivity, but it also proposes several other promising materials that have not previously been tested for eCO 2 R.

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

Warping Functions Design for Long Warped ZT-DFT-s-OFDM

Time-frequency warped waveform is a novel type of well-contained waveform composed of raised cosine (RC) pulses with roll-off factors profile customized for spectrally efficient signal containment. The waveform is suited for massive machine-type communication (mMTC), and Internet-of-Things (IoT) applications. The paper is a follow-up to the original work of time-frequency warped waveforms; here, we are proposing a warping function suited for length-independent symbols, which is missing from the previous work. The paper proposes the design steps starting from a piecewise warping function with parts of straight segments and other curved parts. A maximum Out-of-band leakage is used as a criterion to determine the warping function parameters. The roll-off factors profile determined in the previous study is used as a minimum bound for the leakage limits. Gains are presented over Windowed Zero-tail Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (ZT-DFT-s-OFDM) in the simulations section.

Ibrahim, Mostafa↗