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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Charge Transport in Solvated Donor–Acceptor Functionalized Peptoids: Molecular Dynamics and Rate Theory

Scalable solar-energy conversion requires photoactive materials that combine the efficiency of natural photosynthetic systems with the stability and processability needed for practical applications. Achieving reliable charge transport in soft, self-assembled organic materials remains challenging, as structural fluctuations and environmental effects strongly influence charge-transfer (CT) rates. Here, we present a broadly applicable computational framework for evaluating CT rates in the condensed phase, combining Fermi’s golden rule rate theory with inputs from all-atom molecular dynamics (MD) simulations and first-principles electronic-structure calculations. The approach does not rely on system-specific parametrization and is applicable to a wide range of soft and disordered materials. We demonstrate the applicability and usefulness of the framework on redox-active peptoids functionalized with iron–porphyrin (Fe–P) complexes, a bioinspired platform with programmable donor–acceptor units and tunable three-dimensional organization. The calculated CT rates exhibit strong sensitivity to molecular conformation, with variations spanning several orders of magnitude. This dependence is shown to arise from the pronounced variation in diabatic electronic coupling with the relative orientations and separations of the Fe–P complexes across the conformational ensemble. The framework provides a consistent route for connecting atomistic structure to CT kinetics in the condensed phase and enables analysis of structure–rate relations in organic semiconducting systems.

Charge transfer↗
DOE OSTI2025

Inverse Thermodynamics: Designing Interactions for Targeted Phase Behavior

The traditional goal of inverse self-assembly is to design interactions that drive particles toward a desired target structure. However, achieving successful self-assembly also requires tuning the thermodynamic conditions under which the structure is stable. In this work, we extend the inverse design paradigm to explicitly address this challenge by developing a framework for inverse thermodynamics, i.e., the design of interaction potentials that realize specific thermodynamic behavior. As a step in this direction, using patchy particle mixtures as a model system, we demonstrate how precise control over both bonding topology and bond energetics enables the programming of targeted phase behavior. In particular, we establish design principles for azeotropic demixing and show how to create mixtures that exhibit azeotropy at any prescribed composition. Our predictions are validated through Gibbs-ensemble simulations [Panagiotopoulos, Mol. Phys. 1987, 61, 813−826]. These results highlight the necessity of coupling structural design with thermodynamic engineering, and provide a blueprint for controlling complex phase behavior in multicomponent systems.

Azeotropes↗
DOE OSTI2025

Key Intermediate Nanostructures in the Self-Assembly of Amphiphilic Polypeptoids Revealed by Cryo-TEM

Amphiphilic copolypeptoids are known to form a variety of nanostructures (fibers, tubes, sheets, etc.), but the assembly mechanisms and key intermediates remain underexplored. This study investigates the intermediate structures formed during the early stages of self-assembly in diblock copolypeptoids using cryo-transmission electron microscopy (cryo-TEM). Here we focused on two diblock copolypeptoids, one with a free N-terminus and the other with a capped N-terminus, which ultimately form less-ordered nanofibers and well-ordered nanosheets, respectively. Through cryo-TEM imaging of vitrified solutions at various time points during the self-assembly process, the study identified micelles and vesicles as key intermediate structures. Notably, the formation of vesicles as intermediates is unusual in crystallization-driven self-assembly and suggests a unique pathway in polypeptoid self-assembly. The study provides direct imaging evidence of key intermediates in polypeptoid self-assembly, advancing the understanding of their self-assembly mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Hierarchical Self-Assembly of Carbon Dots into High-Aspect-Ratio Nanowires

Here, we report a spontaneous and hierarchical self-assembly mechanism of carbon dots prepared from citric acid and urea into nanowire structures with large aspect ratios (>50). Scattering-type scanning near-field optical microscopy (s-SNOM) with broadly tunable mid-IR excitation was used to interrogate details of the self-assembly process by generating nanoscopic chemical maps of local wire morphology and composition. s-SNOM images capture the evolution of wire formation and the complex interplay between different chemical constituents directing assembly over the nano- to microscopic length scales. We propose that residual citrate promotes tautomerization of melamine surface functionalities to produce supramolecular shape synthons comprised of melamine-cyanurate adducts capable of forming long-range and highly directional hydrogen-bonding networks. This intrinsic, heterogeneity-driven self-assembly mechanism reflects synergistic combinations of high chemical specificity and long-range cooperativity that may be harnessed to reproducibly fabricate functional structures on arbitrary surfaces.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Patternable Mesoporous Thin Film Quantum Materials via Block Copolymer Self-Assembly: An Emergent Technology?

Recent developments in quantum materials hold promise for revolutionizing energy and information technologies. The use of soft matter self-assembly, for example, by employing block copolymers (BCPs) as structure directing or templating agents, offers facile pathways toward quantum metamaterials with highly tunable mesostructures via scalable solution processing. Here, we report the preparation of patternable mesoporous niobium carbonitride-type thin film superconductors through spin-coating of a hybrid solution containing an amphiphilic BCP swollen by niobia sol precursors and subsequent thermal processing in combination with photolithography. Spin-coated as-made BCP-niobia hybrid thin films on silicon substrates after optional photolithographic definition are heated in air to produce a porous oxide, and subsequently converted in a multistep process to carbonitrides via treatment with high temperatures in reactive gases including ammonia. Grazing incidence small-angle X-ray scattering suggests the presence of ordered mesostructures in as-made BCP-niobia films without further annealing, consistent with a distorted alternating gyroid morphology that is retained upon thermal treatments. Wide-angle X-ray scattering confirms the synthesis of phase-pure niobium carbonitride nanocrystals with rock-salt lattices within the mesoscale networks. Electrical transport measurements of unpatterned thin films show initial exponential rise in resistivity characteristic of thermal activation in granular systems down to 12.8 K, at which point resistivity drops to zero into a superconducting state. Magnetoresistance measurements determine the superconducting upper critical field to be over 16 T, demonstrating material quality on par with niobium carbonitrides obtained from traditional solid-state synthesis methods. Here. we discuss how such cost-effective and scalable solution-based quantum materials fabrication approaches may be integrated into existing microelectronics processing, promising the emergence of a technology with tremendous academic and industrial potential by combining the capabilities of soft matter self-assembly with quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modulating the Molecular Geometry and Solution Self-Assembly of Amphiphilic Polypeptoid Block Copolymers by Side Chain Branching Pattern

Solution self-assembly of coil-crystalline diblock copolypeptoids has attracted increasing attention due to its capability to form hierarchical nanostructures with tailorable morphologies and functionalities. While the N-substituent (or side chain) structures are known to affect the crystallization of polypeptoids, their roles in dictating the hierarchical solution self-assembly of diblock copolypeptoids are not fully understood. Herein, we designed and synthesized two types of diblock copolypeptoids, i.e., poly(N-methylglycine)-b-poly(N-octylglycine) (PNMG-b-PNOG) and poly(N-methylglycine)-b-poly(N-2-ethyl-1-hexylglycine) (PNMG-b-PNEHG), to investigate the influence of N-substituent structure on the crystalline packing and hierarchical self-assembly of diblock copolypeptoids in methanol. With a linear aliphatic N-substituent, the PNOG blocks pack into a highly ordered crystalline structure with a board-like molecular geometry, resulting in the self-assembly of PNMG-b-PNOG molecules into a hierarchical microflower morphology composed of radially arranged nanoribbon subunits. By contrast, the PNEHG blocks bearing bulky branched aliphatic N-substituents are rod-like and prefer to stack into a columnar hexagonal liquid crystalline mesophase, which drives PNMG-b-PNEHG molecules to self-assemble into symmetrical hexagonal nanosheets in solution. A combination of time-dependent small/wide-angle X-ray scattering and microscopic imaging analysis further revealed the self-assembly mechanisms for the formation of these microflowers and hexagonal nanosheets. These results highlight the significant impact of the N-substituent architecture (i.e., linear versus branched) on the supramolecular self-assembly of diblock copolypeptoids in solution, which can serve as an effective strategy to tune the geometry and hierarchical structure of polypeptoid-based nanomaterials.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Two-temperature effects in Hall-MHD simulations of the HIT-SI experiment

A two-temperature Hall-magnetohydrodynamic (Hall-MHD) model, which evolves the electron and ion temperatures separately, is implemented in the PSI-Tet 3D MHD code and used to model plasma dynamics in the Helicity Injected Torus–Steady Inductive (HIT-SI) experiment. The two-temperature model is utilized for HIT-SI simulations in both the PSI-Tet and NIMROD codes at a number of different injector frequencies in the 14.5–68.5 kHz range. At all frequencies, the NIMROD two-temperature model results in increased toroidal current, lower chord-averaged density, higher average temperatures, outward radial shift of the current centroid, and axial symmetrization of the current centroid, relative to the single-temperature NIMROD simulations. The two-temperature PSI-Tet model illustrates similar trends, but at high frequency operation, it exhibits lower electron temperature, smaller toroidal current, and decreased axial symmetrization with respect to the single-temperature PSI-Tet model. With all models, average temperatures and toroidal currents increase with the injector frequency. Power balance and heat fluxes to the wall are calculated for the two-temperature PSI-Tet model and illustrate considerable viscous and compressive heating, particularly at high injector frequency. Parameter scans are also presented for artificial diffusivity, wall temperature, and density. Both artificial diffusivity and the density boundary condition significantly modify the plasma density profiles, leading to larger average temperatures, toroidal current, and relative density fluctuations at low densities. As a result, a low density simulation achieves sufficiently high current gain (G > 5) to generate significant volumes of closed flux lasting 1–2 injector periods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Learning how structures form in drift-wave turbulence

Drift-wave turbulence produces anomalous transport via cross-correlations between fluctuations. This transport has profound implications for confinement, structure formation, and virtually all aspects of the non-linear turbulent dynamics. Herein, we use a data-driven method based on deep learning in order to study turbulent transport in the 2D Hasegawa–Wakatani system and infer a reduced mean-field model from numerical solution. In addition to the usual turbulent diffusion, we find an effect which couples the particle flux to the local gradient of vorticity, which tends to modulate the density profile. The direct coupling to the shear is relatively weak. In addition, the deep learning method finds a model for spontaneous zonal flow generation by negative viscosity, stabilized by non-linear and hyperviscous terms. We compare these results to analytic calculations using quasilinear theory and wave kinetics, finding qualitative agreement, though the calculations miss certain higher-order effects. A simplified, 1-D model for the evolution of the profile, flow, and intensity based on the deep learning results is solved numerically and compared to previous models for staircasing based on bistability. We see that the physics uncovered by the deep learning method provided simple explanations for the formation of zonal structures in the density, flow, and turbulence fields. We highlight the important role of symmetry in the deep learning method and speculate on the portability of the method to other applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Staircase formation by resonant and non-resonant transport of potential vorticity

The E × B staircase is a quasi-periodic pattern of pressure profile corrugations. In this work, we present a new mechanism for E × B staircase formation that involves resonant transport versus non-resonant transport. We start from a potential vorticity evolution system and use quasi-linear theory, a model dispersion relation, and a bi-Lorentzian spectrum approximation, to construct the relation between the fluxes and the profiles. With these fluxes, we close the profile evolution equations and the extended turbulence intensity evolution equation, which together constitute a turbulence-profile evolution system. In this system, the Doppler effect from the E × B mean flow can cause resonance between trapped ion precession motion and the trapped ion mode, which drives a resonant transport contribution to the fluxes. The profiles will be flattened where the resonant transport is switched on. In contrast, for the regions of non-resonant transport, profiles are steeper. A quasi-periodic pattern of profile corrugation (the E × B staircase) spontaneously emerges in this system, which is the two states mentioned above, arranged as alternating layers in space. The feedback processes during the staircase pattern formation are identified. An estimate of the critical value of the boundary heat flux is obtained, above which the staircase formation will be triggered. An estimate scaling of the step size in the staircase pattern is obtained. The resonant turbulent transport is also a mechanism for collisionless saturation of zonal flow. Furthermore, this work is related to internal transport barrier formation and suggests some new scenarios, such as an enhanced confined L mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The geometry of signal space: a case study of direct mapping between seismic signals and event distribution

SUMMARY Under favourable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal-space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection and scaling, with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning and possible application to studies of repeating events in induced, volcanic and glacial seismicity. Our results place limits on the widely held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗
DOE OSTI2025

Microscopic Imprints of Learned Solutions in Tunable Networks

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Compositional patterning in irradiated alloys: Effective potentials and effective interfacial energy

Compositional patterning (CP) in binary alloys during energetic particle irradiation is studied using a kinetic model that considers two competing kinetic processes, a thermally activated one promoting macroscopic phase separation (MPS) of the concentration field c(r,t) and a forced one resulting in finite-range random atomic mixing. The forced mixing is modeled by a Gaussian relocation distribution with a characteristic distance R. A series of approximate kinetic models are introduced by expanding the mixing function into a series of n terms, thus replacing the non-local evaluations of the concentration field c(r'-r,t) by local derivatives of c(r,t). This approach makes it possible to obtain exact effective potentials and build steady-state diagrams for each order-n model. Phase field (PF) simulations using these order-n models reveal that near the onset of patterning, phase evolution is accurately described using an order-3 model, which changes smoothly from an extended Cahn-Hilliard free energy in the MPS regime to a one-mode Swift-Hohenberg functional in the CP regime. Deeper into the patterning regime, higher-order models are required to achieve convergence, yielding square-like concentration profiles characteristic of a strong segregation regime. These higher-order effective free energies are analogous to multimodal Swift-Hohenberg functionals. Here, a new definition for the effective interfacial energy is proposed in the CP regime, since the interfacial area is no longer an excess quantity in that regime, precluding the use of the standard thermodynamic definition of interfacial energy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Universality class for loopless invasion percolation models and a percolation avalanche burst model for hydraulic fracturing

Invasion percolation is a model that was originally proposed to describe growing networks of fractures. In this work, we describe a loopless algorithm on random lattices, coupled with an avalanche-based model for bursts. The model reproduces the characteristic b-value seismicity and spatial distribution of bursts consistent with earthquakes resulting from hydraulic fracturing (“fracking”). We test models for both site invasion percolation and bond invasion percolation. These have differences on the scale of site and bond lengths l. But since the networks are characterized by their large-scale behavior, l << L, we find small differences between scaling exponents. Though data may not differentiate between models, our results suggest that both models belong to different universality classes.

58 GEOSCIENCES↗
DOE OSTI2021

Entropy production fluctuations encode collective behavior in active matter

We derive a general lower bound on distributions of entropy production in interacting active matter systems. The bound is tight in the limit that interparticle correlations are small and short-ranged, which we explore in four canonical active matter models. In all models studied, the bound is weak where collective fluctuations result in long-ranged correlations, which subsequently links the locations of phase transitions to enhanced entropy production fluctuations. We develop a theory for the onset of enhanced fluctuations and relate it to specific phase transitions in active Brownian particles. Furthermore, we also derive optimal control forces that realize the dynamics necessary to tune dissipation and manipulate the system between phases. In so doing, we uncover a general relationship between entropy production and pattern formation in active matter, as well as ways of controlling it.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Observations of heteroclinic bifurcations in resistive magnetohydrodynamic simulations of the plasma response to resonant magnetic perturbations

A class of topological magnetic island bifurcations that has not previously been observed in toroidal plasmas is described. Increasing an externally applied three-dimensional magnetic field in resistive magnetohydrodynamic simulations results in the asymmetric elongation of resonant island flux surfaces followed by a sequence of heteroclinic bifurcations. Here, these bifurcations produce new sets of hyperbolic-elliptic fixed points as predicted by the Poincaré-Birkoff fixed point theorem. Field line calculations verify that the new fixed points do not connect to those of the prebifurcated islands as required for heteroclinic bifurcations on a torus with winding numbers composed of common integer factors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗