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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Synthetic Control of Exciton Dynamics in Bioinspired Cofacial Porphyrin Dimers

Understanding how the complex interplay among excitonic interactions, vibronic couplings, and reorganization energy determines coherence-enabled transport mechanisms is a grand challenge with both foundational implications and potential payoffs for energy science. We use a combined experimental and theoretical approach to show how a modest change in structure may be used to modify the exciton delocalization, tune electronic and vibrational coherences, and alter the mechanism of exciton transfer in covalently linked cofacial Zn-porphyrin dimers (meso-beta linked AB m-β and meso-meso linked AA m-m ). While both AB m-β and AA m-m feature zinc porphyrins linked by a 1,2-phenylene bridge, differences in the interporphyrin connectivity set the lateral shift between macrocycles, reducing electronic coupling in AB m-β and resulting in a localized exciton. Pump-probe experiments show that the exciton dynamics is faster by almost an order of magnitude in the strongly coupled AA m-m dimer, and two-dimensional electronic spectroscopy (2DES) identifies a vibronic coherence that is absent in AB m-β . Theoretical studies indicate how the interchromophore interactions in these structures, and their system-bath couplings, influence excitonic delocalization and vibronic coherence-enabled rapid exciton transport dynamics. Real-time path integral calculations reproduce the exciton transfer kinetics observed experimentally and find that the linking-modulated exciton delocalization strongly enhances the contribution of vibronic coherences to the exciton transfer mechanism, and that this coherence accelerates the exciton transfer dynamics. These benchmark molecular design, 2DES, and theoretical studies provide a foundation for directed explorations of nonclassical effects on exciton dynamics in multiporphyrin assemblies.

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

Tandem bulk oxygen diffusion and surface reactions in reducible metal oxides control redox cycle dynamics

The interplay between bulk oxygen diffusion and surface reactions in reducible metal oxides is key in heterogeneous catalysts, but direct measurements of oxygen mobility, transient kinetics, and in situ spectroscopies have been lacking. Here, we reveal complex dynamic behavior of ceria-zirconia by H 2 using transient kinetics via mass spectrometry and in situ Raman and near-ambient pressure x-ray photoelectron spectroscopies. Molecular dynamics simulations with a machine learning potential delineate competitive oxygen diffusion mechanisms, with an optimal mobility at intermediate reductions. We expose a compensation between vacancy availability and lattice distortion at intermediate to high reductions and Frenkel defects at low reductions, underscoring a potential deficiency of 16 O/ 18 O exchange experiments in deducing oxygen mobility. Vacancies in proximity require electron localization on Ce atoms further away. The continuous replenishment of surface oxygen results in a varying reduction rate, with H 2 dissociation being the rate-limiting step. Multiscale transient simulations, consistent with experiments, indicate catalysts of potentially spatially varying oxidation states. The approach is broadly applicable to reducible oxide materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interplay of coulomb and exciton–phonon coupling controls singlet fission dynamics in two pentacene polymorphs

Pentacene is an important model organic semiconductor in both the singlet exciton fission (SF) and organic electronics communities. We have investigated the effect of changing crystal structure on the SF process, generating multiple triplet excitons from an initial singlet exciton, and subsequent triplet recombination. Unlike for similar organic semiconductors that have strong SF sensitive to polymorphism, we find almost no quantitative difference between the kinetics of triplet pair (TT) formation in the two dominant polymorphs of pentacene. Both pairwise dimer coupling and momentum-space crystal models predict much faster TT formation from the bright singlet excited state of the Bulk vs ThinFilm polymorph, contrasting with the experiment. GW and Bethe–Salpeter equation calculations, including exciton–phonon coupling, reveal that ultrafast phonon-driven transitions in the ThinFilm polymorph compensate the intrinsically slower purely Coulomb-mediated TT formation channel, rationalizing the similarity in observed rates. Taking into account the influence of subtle structural distinctions on both the direct and phonon-mediated SF pathways reveals a predictive capability to these methods, expected to be applicable to a wide variety of molecular crystals.

14 SOLAR ENERGY↗
DOE OSTI2020

Control of spin dynamics in artificial honeycomb spin-ice-based nanodisks

In this paper, we report the experimental and theoretical characterization of the angular-dependent spin dynamics in arrays of ferromagnetic nanodisks arranged on a honeycomb lattice. The magnetic field and microwave frequency dependence, measured by broadband ferromagnetic resonance, reveal a rich spectrum of modes that is strongly affected by the microstate of the network. Based on symmetry arguments with respect to the external field, we show that certain parts of the ferromagnetic network contribute to the detected signal. A comparison of the experimental data with micromagnetic simulations reveals that different subsections of the lattice predominantly contribute to the high-frequency response of the array. This is confirmed by optical characterizations using microfocused Brillouin light scattering. Furthermore, we find indications that nucleation and annihilation of vortexlike magnetization configurations in the low-field range affect the dynamics, which is different from clusters of ferromagnetic nanoellipses. Our work opens up new perspectives for designing magnonic devices that combine geometric frustration in gyrotropic vortex crystals at low frequencies with magnonic crystals at high frequencies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Twist again: Dynamically and reversibly controllable chirality in liquid crystalline elastomer microposts

Photoresponsive liquid crystalline elastomers (LCEs) constitute ideal actuators for soft robots because their light-induced macroscopic shape changes can be harnessed to perform specific articulated motions. Conventional LCEs, however, do not typically exhibit complex modes of bending and twisting necessary to perform sophisticated maneuvers. Here, we model LCE microposts encompassing side-chain mesogens oriented along a magnetically programmed nematic director, and azobenzene cross-linkers, which determine the deformations of illuminated posts. On altering the nematic director orientation from vertical to horizontal, the post’s bending respectively changes from light-seeking to light-avoiding. Moreover, both modeling and subsequent experiments show that with the director tilted at 45°, the initially achiral post reversibly twists into a right- or left-handed chiral structure, controlled by the angle of incident light. We exploit this photoinduced chirality to design “chimera” posts (encompassing two regions with distinct director orientations) that exhibit simultaneous bending and twisting, mimicking motions exhibited by the human musculoskeletal system.

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

Dynamic_Syringe_Pump_Controller

A LabVIEW based programmable user interface for operating ISCO D-series and SyriXus-series syringe pumps that enables advanced user operations and integration with auxiliary data sources.

Frash, Luke↗
DOE OSTI2022

Towards Digital and Performance-Based Supervisory HVAC Control Delivery

Upgrading supervisory HVAC control in commercial buildings is one of the most attractive decarbonization tools at our disposal. Modern controls are software programs and can in theory be deployed at scale and with a low up-front carbon "pulse". In practice, however, control delivery is a disjointed and inefficient process, dominated by manual handoffs of imprecise English language documents. A particularly high barrier exists between control implementation and building energy modeling (BEM) which results in control sequences typically not being tested for correctness or performance before implementation. Together with industry partners, DOE and the national labs are developing an ecosystem of tools and standards that can support fully digital performance-based control delivery workflows. This paper describes this ecosystem, which consists of three mutually supportive efforts. Semantic models of buildings and their systems enable automatic configuration and installation of control software. Platform-neutral control descriptions separate control algorithms from control platforms and enable the creation of libraries of reference control implementations. Dynamic whole-building energy-control simulation that can execute physically realistic control sequences makes it possible to test and evaluate the performance of control sequences and then directly compile them for installation and execution in control systems. In addition to digitizing and streamlining project-level control delivery, these standards and related software support benchmarking of control algorithms, both rule-based and optimization-based, and help to both advance the state of the art and to implement ratings and programs that encourage the adoption of high-performance control.

building controls↗
DOE OSTI2025

Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) - Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud (Final Scientific/Technical Report)

This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Local Chain Dynamics in Sequence-Controlled Polymers as a Tunable Handle for Rare Earth Sequestration

Chain dynamics govern the intricate behaviors of proteins, underpinning functions such as catalysis, recognition, and stimulus response, and are an increasingly appreciated aspect of structure–function relationships. Analogously, manipulating chain dynamics and structure in abiotic polymers via sequence control is an exciting, yet underexplored, strategy for improving material functions. In this work, we report a systematic study relating the sequence of polymeric sequestrants to their structure and dynamics, as well as to their binding affinity and selectivity for model substrates, rare earth elements (REEs). A series of sequence-controlled polymers with metal chelating, solubilizing, and structure forming monomers was synthesized via multiblock polymerization, yielding compositionally identical polymers with spectroscopically resolved domains and distinct morphologies. Using a combination of small-angle X-ray scattering and 19 F NMR relaxometry measurements, we connected differences in polymer structure and dynamics to polymer sequence variables such as the patchiness (density) of the structure forming monomer and the location of the chelating monomer. Furthermore, we found that, relative to calcium, all polymers in the series collapse more and have slower dynamics when binding REEs (lanthanum and lutetium) , though the extent of these effects were sequence-dependent and localized to specific domains within the polymer. Notably, sequence-controlled polymers that exhibited the largest conformational and dynamic changes upon binding REEs also bound REEs with the greatest affinity and modest selectivity. Collectively, these results correlate monomer patterning with dynamics, morphology, and REE binding performance en route to the development of efficient and selective macromolecular chelators.

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

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗