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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

In Situ Analysis of Solvent and Additive Effects on Film Morphology Evolution in Spin-Cast Small-Molecule and Polymer Photovoltaic Materials

To elucidate the details of film morphology/order evolution during spin-coating, solvent and additive effects are systematically investigated for three representative organic solar cell (OSC) active layer materials using combined in situ grazing incidence wide angle x-ray scattering (GIWAXS) and optical reflectance. Two archetypical semiconducting donor (p-type) polymers, P3HT and PTB7, and semiconducting donor small-molecule, p-DTS(FBTTh 2 ) 2 are studied using three neat solvents (chloroform, chlorobenzene, 1,2-dichlorobenzene) and four processing additives (1-chloronaphthalene, diphenyl ether, 1,8-diiodooctane, and 1,6-diiodohexane). In situ GIWAXS identifies several trends: 1) for neat solvents, rapid crystallization occurs that risks kinetically locking the material into multiple crystal structures or crystalline orientations; and 2) for solvent + additive processed films, morphology evolution involves sequential transformations on timescales ranging from seconds to hours, with key divergences dependent on additive/semiconductor molecular interactions. When π-planes dominate the additive/semiconductor interactions, both polymers and small molecule films follow similar evolutions, completing in 1–5 min. When side chains dominate the additive/semiconductor interactions, polymer film maturation times are up to 9 h, while initial crystallization times <10 s are observed for small-molecule films. In conclusion, this study offers guiding information on OSC donor intermediate morphologies, evolution timescales, and divergent evolutions that can inform OSC manufacture.

25 ENERGY STORAGE↗
DOE OSTI2026

Complex and Unusual Excited-State Relaxation Dynamics of 9,9′-Bifluorenylidene Revealed by Comprehensive Time-Resolved Spectroscopy and MRSF-TDDFT Calculations

Design of novel photochemical molecular motors often requires molecular building blocks that exhibit rather unusual photoactivity, for which conventional analyses of spectroscopic data can lead to conflicting interpretations. We here systematically investigated the excited-state relaxation dynamics of one such molecule, 9,9′-bifluorenylidene (BF), through comprehensive and complementary integration of ultrafast transient absorption (TA) and femtosecond stimulated Raman (FSRS) spectroscopies and first-principles mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT). TA and FSRS identified two sequentially formed transients following photoexcitation. The decay kinetics of the two intermediates differ in response to excitation wavelengths and the viscosity/polarity of solvents. MRSF-TDDFT calculations reveal a direct, barrierless internal-conversion pathway from the bright Franck−Condon state to a dark S1 minimum, where the excited-state population is transiently trapped, accounting for the first transient species observed in spectroscopic experiments. Further tracking down along the PES with MRSF-TDDFT mapped out two nonradiative relaxation pathways via conical intersections that connect the dark S 1 state to three configurations in the ground-state manifolds, within which a ring structure with a C8−C8′ bond and the vibrationally excited ground-state BF were identified from spectroscopic and kinetic data. The complexity of relaxation kinetics was attributed to the flexible torsional and twisting motions about the C9−C9′ bridge bond enabled by the diradical character of the S 1 state. These findings clarify unusual photoactive relaxation dynamics stemming from a novel correlation between structural flexibility and shifting electronic characteristics, and they demonstrate the importance of integrating spectroscopic and advanced electronic structure calculation studies for judicious clarification of complex, competing relaxation pathways of excited states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Solvent effects on the absorption spectrum and first hyperpolarizability of keto-enol tautomeric forms of anil derivatives: A Monte Carlo/quantum mechanics study

Theoretical results for the absorption spectrum and electric properties of the enol and keto tautomeric forms of anil derivatives in the gas-phase and in solution are presented. The electronic properties in chloroform, acetonitrile, methanol, and water were determined by carrying out sequential Monte Carlo simulations and quantum mechanics calculations based on the time dependent density functional theory and on the second-order Møller–Plesset perturbation theory method. The results illustrate the role played by electrostatic interactions in the electronic properties of anil derivatives in a liquid environment. There is a significant increase of the dipole moment in solution (20%-100%) relative to the gas-phase value. Solvent effects are mild for the absorption spectrum and linear polarizability but they can be particularly important for first hyperpolarizability. A large first hyperpolarizability contrast between the enol and keto forms is observed when absorption spectra present intense lowest-energy absorption bands. Dynamic results for the first hyperpolarizability are in qualitative agreement with the available experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Primary processes in the bacterial reaction center probed by two-dimensional electronic spectroscopy

In the initial steps of photosynthesis, reaction centers convert solar energy to stable charge-separated states with near-unity quantum efficiency. The reaction center from purple bacteria remains an important model system for probing the structure–function relationship and understanding mechanisms of photosynthetic charge separation. Here we perform 2D electronic spectroscopy (2DES) on bacterial reaction centers (BRCs) from two mutants of the purple bacterium Rhodobacter capsulatus , spanning the Q y absorption bands of the BRC. We analyze the 2DES data using a multiexcitation global-fitting approach that employs a common set of basis spectra for all excitation frequencies, incorporating inputs from the linear absorption spectrum and the BRC structure. We extract the exciton energies, resolving the previously hidden upper exciton state of the special pair. We show that the time-dependent 2DES data are well-represented by a two-step sequential reaction scheme in which charge separation proceeds from the excited state of the special pair (P*) to P + H A − via the intermediate P + B A − . When inhomogeneous broadening and Stark shifts of the B* band are taken into account we can adequately describe the 2DES data without the need to introduce a second charge-separation pathway originating from the excited state of the monomeric bacteriochlorophyll B A *.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
NASA NTRS1992

More About Architecture For Intelligent Robotic Control

Boolean neural networks proposed to implement part of intermediate level of hierarchical architecture of control system for artificially intelligent control of robot hand. Concept described in "Architecture for Intelligent Control of Robotic Tasks" (NPO-17871). Rule level of architecture implemented in two Boolean neural networks operated and updated in alternation. No explicit programming of network. Internal configuration not unique but, depends on initial state and history of previous adaptations. Accepts new rules sequentially presented by external controller.

Fiorini, Paolo↗
NASA NTRSDate not supplied

Understanding Resilience Optimization Architectures With an Optimization Problem Repository

Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.

Resilience↗
NASA NTRSDate not supplied

How the Open Data Policy of the Landsat Program Has Advanced Our Understanding of Environmental Change

A time series is a sequence of observations of a phenomenon taken sequentially in time. A crucial characteristic of a time series is the dependence among adjacent observations – techniques for analyzing this dependence are referred to as time series analysis. This analytical approach enables us to predict or forecast future values of a time series, study the impact of various inputs on the observed phenomenon, and examine interrelationships among related time series variables. Within the geographical sciences, time series analysis has historically been limited to coarse-resolution satellite data, as constructing time series of data suitable for studying land cover and land use dynamics, such as Landsat data, were prohibitively costly. A transformative shift occurred in 2008 when the U.S. Government decided to make free and open all past and future data collected by the Landsat satellite program. The decision brought about a paradigm shift away from analyzing individual images or observations to continuous monitoring in time. Of particular relevance to environmental remote sensing is the ability to forecast observations – if we can predict how future observations should behave, we can infer information about how the land surface is changing. In this presentation, we will examine literature examples that showcase scientific gains enabled by time series analysis of satellite data. We will delve into how the analysis of dense time series of satellite data revealed that overall rate of forest disturbance in the Amazon has increased despite a reduction in deforestation; how different types of forest degradation, previously unquantified, are now being accurately assessed in the Caucasus region; and how we now can study the highly dynamic and intricate patterns of shifting cultivation in Southeast Asia.

Pontus Olofsson↗
DOE OSTI2020

Wavelength- and irradiance-dependent changes in intracellular nitric oxide level

Photobiomodulation (PBM) refers to beneficial effects of low energy light absorption. Although there is a large and growing body of literature describing down-stream physiological benefits of PBM, there is limited understanding of the molecular mechanisms that initiate and mediate these effects at the level of photon absorption. At present, the leading hypothesis is that light absorption induces release of nitric oxide (NO) from the active site of cytochrome c oxidase (COX), allowing it to bind O 2 instead. This is believed to increase mitochondrial respiration, and result in greater overall health of the cell due to increased ATP production. Aim: Although NO itself is a powerful signaling molecule involved in a host of biological responses, less attention has been devoted to the mechanisms of NO release and subsequent effects on cell signaling. The purpose of this work is to investigate wavelength-specific effects on intracellular NO release in living cells. Approach: We have conducted in depth dosimetry analyses of NO production and function in an in vitro retinal model in response to low energy exposure to one or more wavelengths of laser light. Results: We found statistically significant wavelength-dependent elevations (10-30%) in intracellular NO levels following laser exposures at 447, 532, 635 or 808 nm. Sequential or simultaneous exposures to light at two different wavelengths enhanced the NO modulation up to 50% of unexposed controls. Additionally, the immediate increases in cellular NO levels were independent of the function of nitric oxide synthase, depended greatly on the substrate source of electrons entering the electron transport chain, and did not result in increased levels of cGMP. Conclusions: This study concludes the simple model of light-mediated release of NO from COX is unlikely to explain the wide variety of PBM effects reported in the literature. Our multi-wavelength method provides a novel tool for studying immediate and early mechanisms of PBM, as well as exploring intracellular NO signaling networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Sequential slip transfer of mixed-character dislocations across Σ3 coherent twin boundary in FCC metals: a concurrent atomistic-continuum study

Sequential slip transfer across grain boundaries (GB) has an important role in size-dependent propagation of plastic deformation in polycrystalline metals. For example, the Hall–Petch effect, which states that a smaller average grain size results in a higher yield stress, can be rationalised in terms of dislocation pile-ups against GBs. In spite of extensive studies in modelling individual phases and grains using atomistic simulations, well-accepted criteria of slip transfer across GBs are still lacking, as well as models of predicting irreversible GB structure evolution. Slip transfer is inherently multiscale since both the atomic structure of the boundary and the long-range fields of the dislocation pile-up come into play. In this work, concurrent atomistic-continuum simulations are performed to study sequential slip transfer of a series of curved dislocations from a given pile-up on Σ3 coherent twin boundary (CTB) in Cu and Al, with dominant leading screw character at the site of interaction. A Frank-Read source is employed to nucleate dislocations continuously. It is found that subject to a shear stress of 1.2 GPa, screw dislocations transfer into the twinned grain in Cu, but glide on the twin boundary plane in Al. Moreover, four dislocation/CTB interaction modes are identified in Al, which are affected by (1) applied shear stress, (2) dislocation line length, and (3) dislocation line curvature. Our results elucidate the discrepancies between atomistic simulations and experimental observations of dislocation-GB reactions and highlight the importance of directly modeling sequential dislocation slip transfer reactions using fully 3D models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficiency of charge transfer in changing the dissociation dynamics of OD + transients formed after the photo-fragmentation of D 2 O

Here, we present an investigation of the relaxation dynamics of deuterated water molecules after direct photo-double ionization at 61 eV. We focus on the very rare D + + O + + D reaction channel in which the sequential fragmentation mechanisms were found to dominate the dynamics. Aided by theory, the state-selective formation and breakup of the transient OD + (a 1 Δ, b 1 Σ + ) is traced, and the most likely dissociation path—OD + : a 1 Δ or b 1 Σ + → A 3 Π → X 3 Σ – → B 3 Σ – —involving a combination of spin–orbit and non-adiabatic charge transfer transitions is determined. The multi-step transition probability of this complex transition sequence in the intermediate fragment ion is directly evaluated as a function of the energy of the transient OD + above its lowest dissociation limit from the measured ratio of the D + + O + + D and competing D + + D + + O sequential fragmentation channels, which are measured simultaneously. Our coupled-channel time-dependent dynamics calculations reproduce the general trends of these multi-state relative transition rates toward the three-body fragmentation channels.

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

Microgrid Service Restoration Incorporating Unmonitored Automatic Voltage Controllers and Net Metered Loads

Islanded microgrids may experience voltage and frequency instability due to uncontrolled state changes of voltage regulation devices and inaccurate demand forecasts. Uncontrolled state changes can occur if optimal microgrid restoration and dispatch algorithms, used for generating control commands for distributed energy resources, do not incorporate the behavior of automatic controllers of voltage regulation devices. Inaccurate demand forecasts may be encountered since post-outage demand of behind-the-meter net metered (NM) loads can vary significantly from their historical NM profiles. Here, this paper proposes an optimization formulation which allows optimal control of voltage regulators and capacitor banks without remote control and communication capabilities. A generalized demand model for NM loads is proposed which incorporates the cold load pickup phenomenon and their time varying post-outage demand in accordance with the IEEE 1547 standard. The time dependent optimal control formulation and the NM demand model are integrated in a sequential microgrid restoration algorithm by linearization of the involved logic propositions. A detailed case study on the unbalanced IEEE 123-node test system in OpenDSS validates the effectiveness of the proposed approach.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1975

Modern control concepts in hydrology

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Duong, N.↗
DOE OSTI2020

Engineering the Composition of Bimetallic Nanocrystals to Improve Hydrodeoxygenation Selectivity for 2-Acetylfuran

Hydrodeoxygenation (HDO) of 2-acetylfuran (2-AF) was examined at 473 K and 33 bar over carbon-supported, bimetallic catalysts. Monodispersed, Pt-based (PtNi, PtCo, and PtCu) and Ni-based (NiCu) nanocrystals (NCs) with well-defined sizes and compositions were prepared via colloidal synthesis. Steady-state, flow-reactor measurements demonstrate that selectivities for the HDO reaction to 2-ethylfuran (2-EF) are strongly dependent on the alloy compositions. The HDO of 2-AF is shown to be sequential, where 2-EF can react further to form over-hydrogenated products. The NiCu 3 /C nanocatalyst was superior to the other bimetallics; with the optimal residence time, it was possible to achieve a selectivity of 75% 2-EF with 80% conversion of 2-AF, with good stability for up to at least 5 hours time-on-stream. Deactivation due to coking became an issue when the NCs were deposited on oxide supports with weak acidity (SiO 2 and γ-Al 2 O 3 ). Furthermore, this work provides insights into which bimetallic catalytic systems might be useful for HDO of 2-AF reaction.

2-acetylfuran↗
DOE OSTI2022

Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements

We consider the inverse source problem in the parabolic equation, where the unknown source possesses the semi-discrete formulation. Theoretically, we prove that the flux data from any nonempty open subset of the boundary can uniquely determine the semi-discrete source. This means the observed area can be extremely small, and that is the reason we call it sparse boundary data. For the numerical reconstruction, we formulate the problem from the Bayesian sequential prediction perspective and conduct the numerical examples which estimate the space-time-dependent source state by state. To better demonstrate the method’s performance, we solve two common multiscale problems from two models with a long source sequence. The numerical results illustrate that the inversion is accurate and efficient.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamic deuterium retention properties of tungsten measured using laser-induced breakdown spectroscopy

Dynamic deuterium (D) retention properties of tungsten (W) are investigated under steady-state D plasma exposure in the PISCES-A linear plasma device. In contrast to static retention, dynamic retention is quickly released from the material at the termination of the incident plasma flux, and thus in-operando laser-induced breakdown spectroscopy (LIBS) measurements have been conducted during steady-state D plasma exposure. A procedure is, first, established to extract the dynamic retention component from the in-operando LIBS D I 656.1 nm line intensity, which can contain contributions from dynamic and static retention as well as background D/D 2 gas, excited by both steady-state and laser-induced plasmas. Using the developed procedure, the dynamic D retention in W is systematically examined while scanning the following plasma exposure parameters: incident ion energy, E i , sample temperature, T s , incident ion flux, Γ i . No clear E i dependence is seen in the range of E i ~ 45–175 eV, as expected from a small variation in the average implantation depth (~3-6 nm) of D in W. The dynamic retention is found to monotonously decrease with increasing T s from 348 to 573 K, while the total static retention is reported to peak at T s ~ 500–600 K. It is revealed that the dynamic retention linearly increases with increasing Γ i , and then saturates at Γ i ≥ 0.75 × 10 21 m –2 s –1 . Possible physical mechanisms for the observed dependence of the dynamic retention on T s and Γ i are discussed. Furthermore, sequential pure He, followed by pure D, plasma exposures show that the dynamic D retention is not strongly affected by He bubbles in the near-surface region.

super-saturated surface layer↗
DOE OSTI2019

Sequential Learning with Active Partial Labeling for Building Metadata

Modern buildings are instrumented with thousands of sensing and control points. The ability to automatically extract the physical context of each point, e.g., the type, location, and relationship with other points, is the key to enabling building analytics at scale. However, this process is costly as it usually requires domain expertise with a deep understanding of the building system and its point naming scheme. In this study, we aim to reduce the human effort required for mapping sensors to their context, i.e., metadata mapping. We formulate the problem as a sequential labeling process and use the conditional random field to exploit the regular and dependent structures observed in the metadata. We develop a suite of active learning strategies to adaptively select the most informative subsequences in point names for human labeling, which significantly reduces the inputs from domain experts.We evaluated our approach on three different buildings and observed encouraging performance in metadata mapping from the proposed solution.

99 GENERAL AND MISCELLANEOUS↗