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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Collection of More than 900 Gas Mixture Adsorption Experiments in Porous Materials from Literature Meta-Analysis

Information on mixture adsorption equilibrium is vital in developing adsorption-based separation processes. Because measuring mixture adsorption is more difficult than measuring single-component adsorption, far more data of the latter kind are available. Previous efforts to compile experimental mixture adsorption data for gases have given data sets with at most a few dozen examples. Here, we report the results of systematic literature meta-analysis that produced a data set of more than 900 gas mixture adsorption experiments. This collection includes data from 125 different binary mixtures including 60 different molecular species and information from 333 different adsorbents. We refer to this data set as the Binary adsorption ISOtherm ExperimeNtal 2020 (BISON-20) Database. Because the BISON-20 data set enormously expands the number and variety of experimental results for binary gas adsorption that are readily available, it will be a useful resource for future efforts in developing new materials or processes for gas separations. As initial applications of the BISON-20 data set, we show how identifying replicate measurements can be used to assess the reliability of binary adsorption data, how the accuracy of Ideal Adsorbed Solution Theory (IAST) can be systematically tested using experimental data, and how trends in selectivity for gas separations across many materials can be examined.

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

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

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

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables

The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. Here, we demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C 45 H 92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.

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

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2021

Localized and Collective Dynamics in Liquid-like Polyethylenimine-Based Nanoparticle Organic Hybrid Materials

In this work, broadband dielectric spectroscopy, rheology, and nuclear magnetic resonance spectroscopy are employed to study molecular dynamics in a nanoparticle organic hybrid materials(NOHMs)system comprising 20 wt% silica nanoparticles ionically-bonded to a polyethylenimine canopy. By comparing the neat polymer (used as a canopy) to the derivative NOHMs, we find that timescales characterizing segmental dynamics in the NOHM are identical to those for the neat polymer. Detailed analysis of the carbon-spin lattice relaxation times yields mechanistic insights into localized and collective dynamics, in quantitative agreement with dielectric results. Interestingly, the NOHMs retain liquid-like characteristics unlike conventional polymer nanocomposites but exhibit higher viscosity due to additional contributions from tethered polymer chains, and mesoscopic structuring. These findings demonstrate the potential of achieving unique and desired material properties via NOHMs by an informed choice of the canopy material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal–Organic Frameworks

Herein, a collection of more than 20,000 experimentally derived crystal structures for metal–organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals. The band gaps of more than 12,000 1D MOFs have been computed at the density functional theory-generalized gradient approximation level, finding more than 2000 materials that have a zero band gap. Molecular simulations of CH4 adsorption in a small number of 1D MOFs indicated that adsorbate-induced deformation plays a significant role in determining adsorption isotherms in these materials. As a result, methods that have been used previously for high-throughput predictions of molecular adsorption in 3D MOFs are not suitable for 1D MOFs.

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

Tunable Collective Excitations in Epitaxial Perovskite Nickelates

The formation of plasmons through the collective excitation of charge density has generated intense discussions, offering insights to fundamental sciences and potential applications. While the underlying physical principles have been well-established, the effects of multibody interactions and orbital hybridization on plasmonic dynamics remain understudied. Here, in this work, we present the observation of conventional metallic and correlated plasmons in epitaxial La 1-x Sr x NiO 3 (LSNO) films with varying Sr doping concentrations (x = 0, 0.125, 0.25), unveiling their intriguing evolution. Unlike samples at other doping concentrations, the x = 0.125 intermediate doping sample does not exhibit the correlated plasmons despite showing high optical conductivity. Through experimental investigation using spectroscopic ellipsometry and X-ray absorption spectroscopy, that is further supported by theoretical calculations, the O2p-Ni3d orbital hybridization for x = 0.125 is found to be significantly enhanced, alongside a considerable weakening of its effective interaction comprising long-range Coulomb and variable interaction, U*. These factors account for the absence of correlated plasmons and the high optical conductivity observed in LSNO(0.125). Our findings highlight the significant impact of orbital hybridization on the electronic structures and the formation of quasiparticles in strongly correlated systems, opening new paths for plasmonic-based engineering research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Polaritonic Bright and Dark States Collectively Affect the Reactivity of a Hydrolysis Reaction

Vibrational strong coupling (VSC) has emerged as a means for modifying chemical reactivity. Despite the intriguing discoveries and progresses in the field, the precise mechanisms that govern polaritonic chemistry still deserve further interrogation. Herein, we use the hydrolysis of ammonia borane in D 2 O as an exemplary reaction and systematically investigate the influence of VSC on its reactivity. Experimental evidence of the coexistence of a resonant effect and reaction acceleration is observed in this system. In particular, we find that when the O-D stretching mode of D 2 O is strongly coupled to a cavity mode, reaction acceleration is observed. The reaction rate acceleration factor, mu, is consistently observed to be dependent on the coupling conditions between the vibrational and cavity modes, and reaches a minimum at zero mode detuning, suggesting that a resonant effect is likely in play. In addition, we find that mu decreases with an increasing Rabi splitting. Based on these experimental findings, we propose that the overall influence of VSC on this reaction is likely determined collectively by the polaritonic bright and dark states. In conclusion, these findings could help shed new light on the intricate effects of VSC on ground-state reaction landscapes.

Rabi splitting↗
DOE OSTI2024

Collective dynamics and long-range order in thermal neuristor networks

Abstract In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Science & Technology - Other Topics↗
DOE OSTI2024

Tuning collective anion motion enables superionic conductivity in solid-state halide electrolytes

Halides of the family Li 3 MX 6 (M = Y, In, Sc and so on, X = halogen) are emerging solid electrolyte materials for all-solid-state Li-ion batteries. They show greater chemical stability and wider electrochemical stability windows than existing sulfide solid electrolytes, but have lower room-temperature ionic conductivities. Here, in this work, we report the discovery that the superionic transition in Li 3 YCl 6 is triggered by the collective motion of anions, as evidenced by synchrotron X-ray and neutron scattering characterizations and ab initio molecular dynamics simulations. Based on this finding, we used a rational design strategy to lower the transition temperature and thus improve the room-temperature ionic conductivity of this family of compounds. We accordingly synthesized Li 3 YCl x Br 6-x and Li 3 GdCl 3 Br 3 and achieved very high room-temperature conductivities of 6.1 and 11 mS cm -1 for Li 3 YCl 4.5 Br 1.5 and Li 3 GdCl 3 Br 3 , respectively. These findings open new routes to the design of room-temperature superionic conductors for high-performance solid batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

Electric Fields at Breast Cancer and Cancer Cell Collective Galvanotaxis

Cancer growth interferes with local ionic environments, membrane potentials, and transepithelial potentials, resulting in small electrical changes in the tumor microenvironment. Electrical fields (EFs) have significant effects on cancer cell migration (galvanotaxis/electrotaxis), however, their role as a regulator of cancer progression and metastasis is poorly understood. Here, we employed unique probe systems to characterize the electrical properties of cancer cells and their migratory ability under an EF. Subcutaneous tumors were established from a triple-negative murine breast cancer cell line (4T1), electric currents and potentials of tumors were measured using vibrating probe and glass microelectrodes, respectively. Steady outward and inward currents could be detected at different positions on the tumor surface and magnitudes of the electric currents on the tumor surface strongly correlated with tumor weights. Potential measurements also showed the non-homogeneous intratumor electric potentials. Cancer cell migration was then surveyed in the presence of EFs in vitro. Parental 4T1 cells and metastatic sublines in isolation showed random migration in EFs of physiological strength, whereas cells in monolayer migrated collectively to the anode. Our data contribute to an improved understanding of breast cancer metastasis, providing new evidence in support of an electrical mechanism that promotes this phenomenon.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hidden order across online extremist movements can be disrupted by nudging collective chemistry

Disrupting the emergence and evolution of potentially violent online extremist movements is a crucial challenge. Extremism research has analyzed such movements in detail, focusing on individual- and movement-level characteristics. But are there system-level commonalities in the ways these movements emerge and grow? Here we compare the growth of the Boogaloos, a new and increasingly prominent U.S. extremist movement, to the growth of online support for ISIS, a militant, terrorist organization based in the Middle East that follows a radical version of Islam. We show that the early dynamics of these two online movements follow the same mathematical order despite their stark ideological, geographical, and cultural differences. The evolution of both movements, across scales, follows a single shockwave equation that accounts for heterogeneity in online interactions. These scientific properties suggest specific policies to address online extremism and radicalization. We show how actions by social media platforms could disrupt the onset and ‘flatten the curve’ of such online extremism by nudging its collective chemistry. Our results provide a system-level understanding of the emergence of extremist movements that yields fresh insight into their evolution and possible interventions to limit their growth.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Single-photoelectron collection efficiency in 4D ultrafast electron microscopy

In femtosecond (fs) 4D ultrafast electron microscopy (UEM), a tradeoff is made between photoelectrons per packet and time resolution. One consequence of this can be longer-than-desirable acquisition times for low-density packets, and particularly for low repetition rates when complete photothermal dissipation is required. Thus, gaining an understanding of photoelectron trajectories in the gun region is important for identifying factors that limit collection efficiency (CE; fraction of photoelectrons that enter the illumination system). Here, we continue our work on the systematic study of photoelectron trajectories in the gun region of a Thermo Fisher/FEI Tecnai Femto UEM, focusing specifically on CE in the single-electron regime. Using General Particle Tracer, calculated field maps, and the exact architecture of the Tecnai Femto UEM, we simulated the effects of fs laser parameters and key gun elements on CE. The results indicate CE strongly depends upon the laser spot size on the source, the (unbiased) Wehnelt aperture diameter, and the incident photon energy. The CE dispersion with laser spot size is found to be strongly dependent on aperture diameter, being nearly dispersionless for the largest apertures. A gun crossover is also observed, with the beam-waist position being dependent on the aperture diameter, further illustrating that the Wehnelt aperture acts as a simple, fixed electrostatic lens in UEM mode. This work provides further insights into the operational aspects of fs 4D UEM.

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

Softening by charging: how collective modes of ionic association in concentrated redoxmer/electrolyte solutions define the structural and dynamic properties in different states of charge

Understanding the physical and chemical processes occurring in concentrated electrolyte solutions is required to achieve redox flow batteries with high energy density. Highly concentrated electrolyte solutions are often studied in which collective crowded interactions between molecules and ions become predominant. In this work, experimental and computational methods were used to examine non-aqueous electrolyte solutions in two different states of charge as a function of redoxmer concentration. As the latter increases and the ionic association strengthens, the electric conductivity passes through a maximum and the solution increasingly gels, which is seen through a rapid non-linear increase in viscosity. We establish that the structural rigidity of ionic networks is closely connected with this loss of fluidity and show that charging generally yields softer ionic assemblies with weaker attractive forces and improved dynamical properties.

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

Strain-affected ferroelastic domain walls in RbMnFe charge-transfer materials undergoing collective Jahn–Teller distortion

Many rubidium manganese hexacyanoferrate materials, with the general formula Rb x Mn[Fe(CN) 6 ] (x+2)/3 ·zH 2 O, exhibit diverse charge-transfer-based functionalities due to the bistability between a high temperature Mn II (S = 5/2)Fe III (S = 1/2) cubic phase and a low-temperature Mn III (S = 2)Fe II (S = 0) tetragonal phase. The collective Jahn–Teller distortion on the Mn sites is responsible for the cubic-to-tetragonal ferroelastic phase transition, which is associated with the appearance of ferroelastic domains. In this study, we use X-ray diffraction to reveal the coexistence of 3 types of ferroelastic tetragonal domains and estimate the spatial extension of the strain around the domain walls, which represents about 30% of the volume of the crystal.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗