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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Quantifying Membrane Structure and Dynamics during Bioproduct Production in Zymomonas mobilis by Molecular Simulation

The conversion of lignocellulosic biomass into biofuels and bioproducts by microbial biorefineries is central to a sustainable chemical industry. Zymomonas mobilis is one such biorefinery chassis and is resistant to ethanol stress, leading to its use in biomass conversion to biofuels and bioproducts. However, Z. mobilis growth is often inhibited by organic acids, aldehydes, alcohols, ketones, and amides found in biomass hydrolysate. The resulting slow growth inhibits production and as a result drives up the price for the resulting products. One hypothesis is that these molecules interact with or disrupt the bacterial membrane, triggering stress responses and hindering growth. To test this hypothesis at the molecular level, we employ all-atom molecular dynamics (MD) simulations to investigate lignocellulose-derived small molecules and their impact on a biologically relevant Z. mobilis membrane model. Simulations were conducted across a range of inhibitor concentrations from 0 to 2.5 mol %, analyzing key membrane properties such as area per lipid (APL), membrane thickness, lipid-order parameter (−S CH ), lateral diffusion coefficient (D xy ), and permeability coefficient (Pm). From simulation, we observed altered membrane structure and dynamics at these modest small molecule concentrations commonly found in hydrolysates. Generally, the membranes become thinner, with a higher area per lipid and lower-order parameter as the small molecule concentration increases. These trends are stronger for more hydrophobic molecules with greater hydrophobic bulk, as isobutanol, propanol, and propanoic acid showed greater membrane perturbations as the concentration increased compared to other small molecules. Tracking small molecule distributions directly in our equilibrium simulations allows us to determine concentration-dependent free energy profiles for these molecules. While the trends are noisy, generally the barriers to crossing the membrane decrease as the concentration increases, indicating that the membranes become leakier as small molecule concentrations rise. Comparing between native Z. mobilis membranes with hopanoids and membranes sharing the same phospholipid composition but without hopanoids, hopanoids stabilize and order the membrane for smaller molecules to maintain membrane structure but appear insufficient for larger hydrophobic molecules like isobutanol. These findings provide a mechanistic understanding of how small molecules found in biomass degradation streams interact with the Z. mobilis membrane, offering valuable insights for future strain engineering efforts to optimize biofuel and bioproduct synthesis from biomass feedstocks by highlighting limits to small molecule tolerance. This knowledge can guide the modification of membrane composition to develop more robust microbes, thereby improving microbial survival and yields in industrial contexts.

Singh, Nitin Kumar [Michigan State Univ., East Lan↗
DOE OSTI2021

Understanding Methanol Synthesis on Inverse ZnO/CuO x /Cu Catalysts: Stability of CH 3 O Species and Dynamic Nature of the Surface

Inverse ZnO/Cu catalysts are key systems in the conversion of CO 2 , a common atmospheric pollutant, into methanol, a high-value chemical and fuel. The chemistry of methanol and methoxy groups over inverse ZnO/Cu 2 O/Cu(111) catalysts was investigated employing Ambient Pressure X-ray Photoelectron Spectroscopy (AP-XPS), Scanning Tunneling Microscopy (STM) and calculations based on Density Functional Theory (DFT). The results of AP-XPS show that the adsorption of methanol on the binary oxide substrate at 300 K leads to formation of *CH 3 O and *HCOO species with a minor amount of *CH x . Furthermore, most of the methoxy groups disappeared from the surface after heating to 450 K, the onset temperature for the formation of methanol during the hydrogenation of CO 2 . The results of AP-XPS, STM and DFT point to preferential adsorption of methoxy on the ZnO regions of the binary oxide. On the supported ZnO or on a ZnO-Cu 2 O interface, the breaking of the O-H bond in methanol is an exothermic process with a negligible (1-2 kcal/mol) or non-existent energy barrier depending on the size and shape of the ZnO islands. STM

36 MATERIALS SCIENCE↗
DOE OSTI2021

Surface-sensitive photon avalanche behavior revealed by single-avalanching-nanoparticle imaging

Avalanching nanoparticles (ANPs) are a new class of lanthanide-based upconverting material demonstrating steep optical nonlinearities with the potential to advance applications ranging from subwavelength bioimaging to neuromorphic computing, nanothermometry, and pressure transduction. Here, we use single-nanocrystal imaging to uncover design-dependent heterogeneity in ANP threshold intensity (I th ). Quantitative comparisons between distributions of I th and ANP shell properties reveal correlations between mean I th values, histogram widths, and nanocrystal shell thickness. Evaluating avalanching behaviors using an established model of shell-dependent surface energy transfer shows that variations in shell thickness-and the resultant energy transfer through the shell to the surface and environment-are likely the primary contributor to ANP-to-ANP I th heterogeneity. Further, nanocrystals with an ~6 nm average shell thickness show I th heterogeneity beyond the extent expected from statistical measurements of shell size and variability using transmission electron microscopy (TEM). Here these results provide a principal guide for the design and application of ANPs to environmental sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Insights from Computational Studies on the Anisotropic Volume Change of Li x NiO 2 at High States of Charge (x < 0.25)

The need for high-capacity Li-ion battery cathodes has favored the increase of Ni content in commercial battery cells. However, at high states of charge (SOCs), Ni-rich materials undergo a phase transition and volume collapse with deleterious effects on battery performance. It is uncertain whether this drastic volume change is caused by the phase transition or not. To provide more insight into the volume-phase transition relationship in the high Ni cathode LixNiO 2 , we performed density functional theory calculations, along with molecular dynamics simulations using machine learning potentials to calculate the temperature- and composition-dependent free energy differences between the suspected phases at high SOCs (x < 0.25). Herein, we find that the calculated free energy difference between the suspected phases containing different oxygen stacking sequences is small at room temperature. Furthermore, we find that the collapse of the layered LiNiO 2 c-lattice parameter at high SOCs is mainly due to the electronic depletion of the oxygen sublattice and the lack of screening from positive Li ions. The interactions between adjacent oxygen ions across an empty Li layer (NiO 2 ) are largely controlled by van der Waals interactions and are in fact similar regardless of the oxygen stacking, which explains the negligible free energy differences between O1 and O3 stacking in NiO 2 .

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

Structural Gating Enhances Long-Distance Light-Driven Interfacial Electron Transfer

Structural gating provides a molecular means to transfer electrons preferentially in one desired vectorial direction, a behavior needed for applications in artificial photosynthesis. At the interfaces utilized herein, visible-light absorption by a transition metal complex opens a “structural gate” by planarization of otherwise rotating phenyl rings in p-phenylene ethynylene (PE) bridge units. Planarization provides a conjugated pathway for electron flow toward a conductive oxide surface. Interfacial electron transfer to the oxide restores rotation and closes the gate to the unwanted recombination reaction. This structural gating results in nearly quantitative long-distance (>20 Å) interfacial electron transfer that occurs ~1000 times faster than transfer in the opposite direction. A comparative kinetic study of these complexes with those that contain ionic bridge units, without gating function, as a function of the applied potential and hence –ΔG° provided a physical basis for the structural gating. A small distance-dependent reorganization energy with weak electronic coupling underlies the success of this gate that enables efficient long-distance electron transfer and slow recombination.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Distinguishing Models for Mixed Halide Lead Perovskite Photosegregation via Terminal Halide Stoichiometry

Photoinduced halide segregation in mixed halide hybrid perovskites [i.e., APb(I 1–x Br x ) 3 ] represents an intrinsic instability that impedes their commercialization. Resolving this issue requires developing a microscopic understanding of the phenomenon. Key to this is distinguishing existing models of halide photosegregation by comparing their corresponding predictions to experiment. In this work, we test the temperature dependency of predicted perovskite terminal stoichiometries (x terminal ), following photosegregation, in single and double cation mixed halide perovskites. Our results show a general temperature invariance of x terminal . This largely supports the idea that band gap difference between parent and halide-segregated perovskite phases drives photosegregation. Beyond this, careful examination of temperature- and halide composition-dependent emission energies suggests an alternative explanation for the enhanced photostability of mixed cation/mixed halide perovskites, linked to band gap energy differences between parent and phase-segregated perovskite phases. Together, our results make inroads in clarifying the microscopic origin of mixed halide perovskite photosegregation and pave the way toward approaches that stabilize their use in applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport

MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.

MXene↗
DOE OSTI2023

Emergent layer stacking arrangements in c-axis confined MoTe 2

The layer stacking order in 2D materials strongly affects functional properties and holds promise for next-generation electronic devices. In bulk, octahedral MoTe 2 possesses two stacking arrangements, the ferroelectric Weyl semimetal T d phase and the higher-order topological insulator 1T' phase. However, in thin flakes of MoTe 2 , it is unclear if the layer stacking follows the T d , 1T', or an alternative stacking sequence. Here, we use atomic-resolution scanning transmission electron microscopy to directly visualize the MoTe 2 layer stacking. In thin flakes, we observe highly disordered stacking, with nanoscale 1T' and T d domains, as well as alternative stacking arrangements not found in the bulk. We attribute these findings to intrinsic confinement effects on the MoTe 2 stacking-dependent free energy. Our results are important for the understanding of exotic physics displayed in MoTe 2 flakes. More broadly, this work suggests c-axis confinement as a method to influence layer stacking in other 2D materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Quantum simulation of charge and exciton transfer in multi-mode models using engineered reservoirs

Quantum simulation enables studies of open-system dynamics in non-perturbative regimes by programming electronic, vibrational, and environmental interactions on comparable energy scales. Trapped ions offer this capability, combining spins, phonons, and tunable dissipation on one platform. We demonstrate an open-system quantum simulation of charge and exciton transfer in a multi-mode linear vibronic coupling model. Using tailored spin-phonon interactions with reservoir engineering, we emulate a system with two dissipative vibrational modes coupled to donor and acceptor sites and track its non-equilibrium dynamics. We continuously tune the system from the charge transfer regime to the vibrationally assisted exciton transfer regime and find that degenerate modes enhance transfer rates at large energy gaps, while non-degenerate modes activate pathways that reduce the energy-gap dependence. Thus, the presence of one additional vibration introduces interfering pathways and reshapes non-perturbative excitation transfer. Our results establish a scalable, hardware-efficient route to simulate vibronic processes with engineered environments.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Strain-tuned incompatible magnetic exchange-interaction in La2NiO4

Abstract Magnetic frustration is a route for novel ground states, including spin liquids and spin ices. Such frustration can be introduced through either lattice geometry or incompatible exchange interactions. Here, we find that epitaxial strain is an effective tool for tuning antiferromagnetic exchange interactions in a square-lattice system. By studying the magnon excitations in La 2 NiO 4 films using resonant inelastic x-ray scattering, we show that the magnon displays substantial dispersion along the antiferromagnetic zone boundary, at energies that depend on the lattice of the film’s substrate. Using first principles simulations and an effective spin model, we demonstrate that the antiferromagnetic next-nearest neighbour coupling is a consequence of the two-orbital nature of La 2 NiO 4 . Altogether, we illustrate that compressive epitaxial strain enhances this coupling and, as a result, increases the level of incompatibility between exchange interactions within a model square-lattice system.

Physics↗
DOE OSTI2022

Dynamics of photosynthetic light harvesting systems interacting with N-photon Fock states

Here, we develop a method to simulate the excitonic dynamics of realistic photosynthetic light harvesting systems, including non-Markovian coupling to phonon degrees of freedom, under excitation by N-photon Fock state pulses. This method combines the input–output and the hierarchical equations of motion formalisms into a double hierarchy of density matrix equations. We show analytically that under weak field excitation relevant to natural photosynthesis conditions, an N-photon Fock state input and a corresponding coherent state input give rise to equal density matrices in the excited manifold. However, an N-photon Fock state input induces no off-diagonal coherence between the ground and excited subspaces, in contrast with the coherences created by a coherent state input. We derive expressions for the probability to absorb a single Fock state photon with or without the influence of phonons. For short pulses (or, equivalently, wide bandwidth pulses), we show that the absorption probability has a universal behavior that depends only upon a system-dependent effective energy spread parameter Δ and an exciton–light coupling constant Γ. This holds for a broad range of chromophore systems and for a variety of pulse shapes. We also analyze the absorption probability in the opposite long pulse (narrow bandwidth) regime. We then derive an expression for the long time emission rate in the presence of phonons and use it to study the difference between collective vs independent emission. Finally, we present a numerical simulation for the LHCII monomer (14-mer) system under single photon excitation that illustrates the use of the double hierarchy equations.

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

Exact constraints and appropriate norms in machine-learned exchange-correlation functionals

Machine learning techniques have received growing attention as an alternative strategy for developing general-purpose density functional approximations, augmenting the historically successful approach of human-designed functionals derived to obey mathematical constraints known for the exact exchange-correlation functional. More recently, efforts have been made to reconcile the two techniques, integrating machine learning and exact-constraint satisfaction. We continue this integrated approach, designing a deep neural network that exploits the exact constraint and appropriate norm philosophy to de-orbitalize the strongly constrained and appropriately normed (SCAN) functional. The deep neural network is trained to replicate the SCAN functional from only electron density and local derivative information, avoiding the use of the orbital-dependent kinetic energy density. The performance and transferability of the machine-learned functional are demonstrated for molecular and periodic systems.

Artificial neural networks↗
DOE OSTI2023

Progress with applications of three-ion ICRF scenarios for fusion research: A review

The viability of magnetic confinement fusion as an energy source depends on achieving the high ion temperatures required for D-T fusion. Among the available techniques, plasma heating with waves in the ion cyclotron range of frequencies (ICRF) is a prominent method for bulk ion heating in fusion plasmas. Furthermore, a detailed understanding of the non-linear physics of alpha heating and the complex impact of MeV-range fast ions on plasma dynamics becomes progressively more important. This paper provides a comprehensive overview of recent developments with the three-ion ICRF scenarios on Alcator C-Mod, ASDEX Upgrade and JET tokamaks. The results demonstrate the flexibility of these novel scenarios for heating bulk ions in D-T ≈ 50%-50% plasmas and efficient generation of MeV-range fast ions in multi-ion species plasmas. In conclusion, several key results relevant for ITER and future fusion reactors are highlighted.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Electron dynamics in radio frequency magnetron sputtering argon discharges with a dielectric target

Abstract We demonstrate a self-consistent and complete description of electron dynamics in a typical electropositive radio frequency magnetron sputtering (RFMS) argon discharge with a dielectric target. The electron dynamics, including the electron power absorption dynamics in one radio frequency (RF) period, is studied via a fully kinetic 2d3v particle-in-cell/Monte Carlo collision (PIC/MCC) electrostatic simulation. The interplay between the fundamental plasma parameters is analyzed through their spatiotemporal dynamics. Due to the influence of magnetic trap on the electron transport, a spatially dependent charging that perturbs the electric potential is observed on the dielectric target surface, resulting in a spatially dependent ion energy distribution along the target surface. The E × B drift-to-discharge current ratio is in approximate agreement with Bohm diffusion. The electron power absorption can be primarily decoupled into the positive Ohmic power absorption in the bulk plasma region and the negative pressure-induced power absorption near the target surface. Ohmic power absorption is the dominant electron power absorption mechanism, mostly contributed by the azimuthal electron current. The power absorption due to electron inertial effects is negligible on time-average. Both the maximum power absorption and dissipation of electrons appear in the bulk plasma region during the second half of the RF period, implying a strong electron trapping in magnetron discharges. The contribution of secondary electrons is negligible under typical RFMS discharge conditions.

Physics↗
DOE OSTI2023

Conceptual study of a two-layer silicon pixel detector to tag the passage of muons from cosmic sources through quantum processors

Abstract Recent studies in quantum computing have shown that quantum error correction with large numbers of physical qubits are limited by ionizing radiation from high-energy particles. Depending on the physical setup of the quantum processor, the contribution of muons from cosmic sources can constitute a significant fraction of these interactions. As most of these muons are difficult to stop, we perform a conceptual study of a two-layer silicon pixel detector to tag their hits on a solid-state quantum processor instead. With a typical dilution refrigerator geometry model, we find that efficiencies greater than 50% are most likely to be achieved if at least one of the layers is operated at the deep-cryogenic (<1 K) flanges of the refrigerator. Following this finding, we further propose a novel research program that could allow the development of silicon pixel detectors that are fast enough to provide input to quantum error correction algorithms, can operate at deep-cryogenic temperatures, and have very low power consumption.

Instruments & Instrumentation↗
DOE OSTI2021

Energy consumption and charging load profiles from long-haul truck electrification in the United States

Abstract The urgent need to decarbonize the transportation sector combined with falling battery prices has spurred industry and policy interest in long-haul truck electrification. The charging behavior and resulting loads from electrified long-haul freight trucks are crucial for the smooth operation of the electric grid and have far-reaching environmental impacts (e.g., greenhouse gas and other air pollutant emissions). However, the aggregate energy impact of a fleetwide shift to electrified long-haul freight trucking has not been explored. This study combines electric truck design scenarios, bottom-up truck weight modeling, vehicle energy modeling, large-scale truck traffic data, and simulation of likely operation and charging behaviors to estimate end-use energy consumption and location-specific hourly charging loads for a national fleet of long-haul electric trucks. Relative to a fleet of future diesel trucks, electrification would reduce direct end-use energy consumption by 0.9 × 10 18 J (0.9 quadrillion BTU), but electrification might increase life cycle energy consumption depending on the electricity source. The electricity required to charge long-haul electric trucks is equivalent to five percent of annual electricity consumption in the United States (US). The simulated truck charging loads peak during the day across the US grid regions, but the charging peaks’ exact timing is sensitive to when trucks are dispatched for operation. The load shapes suggest that electric trucks’ charging loads can coincide with peaks in solar power generation, and planning could enable on- or off-site integration between truck charging stations and renewable electricity generation.

Tong, Fan (ORCID:0000000346613956)↗
DOE OSTI2025

Low Carbon Inducible2/Fatty Acid Desaturase4 locus in C. reinhardtii directs plastid peroxidase location and trans fatty acid production

Light capture and photosynthetic energy conversion depend on photosynthetic complexes that are embedded within lipid membranes. Components of these complexes are vulnerable to damage by reactive oxygen species, byproducts of photosynthesis that accumulate under environmental stress. Here we explore the basis for a lipid-based sensing mechanism allowing plants or algae to assess and respond to damage to the photosynthetic membranes. In Chlamydomonas reinhardtii, Low Carbon Inducible2 (LCI2) and Fatty Acid Desaturase4 (FAD4) are two proteins derived from the same locus by a differential splicing event, sharing an N-terminus encoded by the first two exons. FAD4 produces a 16-carbon, trans double bond-containing fatty acid found exclusively in phosphatidylglycerol of chloroplast membranes, while LCI2 recruits peroxidase activity to the membrane. The unique organization and transcriptional regulation of the LCI2/FAD4 locus represents a regulatory interface that allows cells to initiate the biosynthesis of a fatty acid unique to the photosynthetic membranes while also linking it to the production of an enzyme involved in the mitigation of reactive oxygen species.

Nicodemus, Timothy J [Michigan State University, E↗
DOE OSTI2026

Machine learning approach for vibronically renormalized electronic band structures

Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗