Search NASA⌕ Search

SEARCH · Search NASA

Results for “tuning technique”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Solid-source metal-organic MBE for elemental Ir and Ru films

Thin films of elemental metals play a very important role in modern electronic nano-devices as conduction pathways, spacer layers, spin-current generators/detectors, and many other important functionalities. In this work, by exploiting the chemistry of solid metal-organic source precursors, we demonstrate the molecular beam epitaxy synthesis of elemental Ir and Ru metal thin films. The synthesis of these metals is enabled by thermodynamic and kinetic selection of the metal phase as the metal-organic precursor decomposes on the substrate surface. Film growth under different conditions was studied using a combination of in situ and ex situ structural and compositional characterization techniques. The critical role of substrate temperature, oxygen reactivity, and precursor flux in tuning film composition and quality is discussed in the context of precursor adsorption, decomposition, and crystal growth. Computed thermodynamics quantifies the driving force for metal or oxide formation as a function of synthesis conditions and changes in chemical potential. These results indicate that bulk thermodynamics are a plausible origin for the formation of Ir metal at low temperatures, while Ru metal formation is likely mediated by kinetics.

Materials Science↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Real-time dynamic wavelength tuning and intensity modulation of metal-clad nanolasers

To realize ubiquitously used photonic integrated circuits, on-chip nanoscale sources are essential components. Subwavelength nanolasers, especially those based on a metal-clad design, already possess many desirable attributes for an on-chip source such as low thresholds, room-temperature operation and ultra-small footprints accompanied by electromagnetic isolation at pitch sizes down to ∼50 nm. Another valuable characteristic for a source would be control over its emission wavelength and intensity in real-time. Most efforts on tuning/modulation thus far report static changes based on irreversible techniques not suited for high-speed operation. In this study, we demonstrate in-situ dynamical tuning of the emission wavelength of a metallo-dielectric nanolaser at room temperature by applying an external DC electric field. Using an AC electric field, we show that it is also possible to modulate the output intensity of the nanolaser at high speeds. The nanolaser’s emission wavelength in the telecom band can be altered by as much as 8.35 nm with a tuning sensitivity of ∼1.01 nm/V. Additionally, the output intensity can be attenuated by up to 89%, a contrast sufficient for digital data communication purposes. Finally, we achieve an intensity modulation speed up to 400 MHz, limited only by the photodetector bandwidth used in this study, which underlines the capability of high-speed operation via this method. This is the first demonstration of a telecom band nanolaser source with dynamic spectral tuning and intensity modulation based on an external E-field to the best of our knowledge.

Deka, Suruj S. (ORCID:0000000297812317)↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2021

Amorphous dispersions of flubendazole in hydroxypropyl methylcellulose: Formulation stability assisted by pair distribution function analysis

Here, we use X-ray pair distribution function (PDF) analysis applied to high-energy synchrotron X-ray powder diffraction data to evaluate the amorphous solid dispersions interactions and their aging stability. The obtained systems are based on hydroxypropyl methylcellulose (hypromellose) derivatives and flubendazole (FBZ) drug dispersions prepared using a spray-dryer technique. We carry out stability studies under aging parameters (40 °C/75% relative humidity) to tune the systems’ recrystallization. The results reveal that ion-base interactions between the drug-polymer matrix are responsible for reducing clustering processes yielding slower recrystallization and different ordering in the hypromellose phthalate (HPMCP/FBZ) and hypromellose acetate succinate (HPMC-AS/FBZ) systems and complete drug clustering in hypromellose (HPMC-E3/FBZ). The structural ordering was accessed using differential X-ray PDFs that revealed the region between 3.5 Å and 5.0 Å could be related to FBZ intermolecular interactions and is more ordered for the least stable system (HPMC-E3/FBZ) and less ordered for the most stable system (HPMCP/FBZ). These results show that the ion-base interactions between drug and matrix occur at these intermolecular distances.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Challenges and opportunities in plasma-activated reactions of CO 2 with light alkanes

We present upgrading CO 2 with surplus light alkanes to value-added chemicals using one-step plasma-activated processes under mild conditions, where opportunities and challenges for improving product selectivity focus on understanding reaction pathways, decoupling and benchmarking plasma and catalyst roles in reactions, tuning catalyst structural and electronic properties under plasma activation, and developing in situ characterization techniques.

03 NATURAL GAS↗
DOE OSTI2023

Evaluation of the critical behavior near ferromagnetic to paramagnetic phase transition in CrTe 1-x Se x alloys: An experimental study

For this study, we used the conventional solid-state reaction method to prepare stoichiometric samples of CrTe 1-x Se x , where 0 ≤ x ≤ 0.10, and investigated the structural and critical behavior of the prepared samples. Room temperature powder X-ray diffraction, along with Rietveld refinement, revealed the emergence of the NiAs structure with P6 3 /mmc (194) space group with increasing Se substitution. The high-temperature linear fit to inverse of the dc-susceptibility versus temperature for the mother sample resulted in an effective moment of 3.65μ B Cr with Curie-Weiss temperature near 335K, which is slightly higher than the Tc of ~332K obtained from the inflection point of magnetization versus temperature. Magnetization isotherms were employed to investigate the critical behavior of ferromagnetic CrTe 1-x Se x with 0.0 ≤ x ≤ 0.10 near their Curie temperatures (Tc). The magnetic behavior near Tc was found to follow 3D mean-field critical exponents with a second-order phase transition in all samples investigated. We fine-tuned the critical exponents (β, γ, and δ) using a combination of an iteration technique, the Kouvel-Fisher method, and modified Arrott plots. All samples follow a mean field behavior, with Tc ranging from 298 to 340K. The acquired values exhibit self-consistency, as indicated by the results from the Widom scaling relation. Furthermore, the magnetization isotherms exhibit a universal scaling behavior, providing additional credence to the calculated critical exponents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Crystallographic map: A general lattice and basis formalism enabling efficient and discretized exploration of crystallographic phase space

Three-dimensional lattices are fundamental to solid-state physics. The description of a lattice with an atomic basis constitutes the necessary information to predict solid phase properties and evolution. Here, we present an algorithm for systematically exploring crystallographic phase space. Further, coupled with ab initio techniques, such as density functional theory, this algorithm offers an approach for exploring and tuning materials behavior, with a broad range of potential applications: particularly martensitic phase transformations and materials stability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hydrogen density mapping in biomolecular crystals through dynamic nuclear polarization

Many fundamental biological processes, including those in photosynthetic reaction centers and enzyme active sites, involve charge and energy transfer, bond cleavage, protonation and hydrogen bonding. Because H atoms play such central roles in these reactions, accurately determining their positions is essential. Yet, conventional X-ray crystallography primarily resolves the heavy atoms in biological structures and provides limited insight into hydrogen, even at atomic resolution. Neutron macromolecular crystallography (NMC) overcomes this limitation by offering exceptional sensitivity to hydrogen and deuterium. Here, we present a theoretical framework for the development of dynamic nuclear polarization NMC (DNP-NMC) techniques, which exploit the alignment of neutron and proton nuclear spins to enhance and tune the hydrogen signal contribution. The DNP-NMC approach advances the resolution of H atoms within biomolecular crystals, whether bound to protein residues or present in solvent. The method establishes key relationships for the coherent structure factor of polarized neutron scattering from hydrogenous matter. It theoretically achieves full accuracy in phase reconstruction and offers a path to improve neutron structure determination, achieving accuracies exceeding ≳80% by incorporating titration states. Using a variant of the hybrid input/output phase-retrieval algorithm, it allows recovery of the hydrogen density with ≳90% phase accuracy. In conclusion, we further discuss sources of experimental uncertainty for the upcoming DNP-enabled, quasi-Laue IMAGINE-X experiment at Oak Ridge National Laboratory's High Flux Isotope Reactor.

dynamic nuclear polarization↗
DOE OSTI2023

Uncertainty quantification for equations of state: copper as an example

Equations of state are essential for providing a fundamental description of materials properties in thermodynamic equilibrium and are used to provide closure relations for hydrodynamics simulations. Generally, equations of state rely on simple physics-based parameterized materials models to inform on the free energy of a material through out a given thermodynamic state space. Historically the parameters of these models have been tuned by hand to fit various experimental data. However, modern optimization and uncertainty quantification techniques allow us to quickly test thousands of parameter combinations and obtain meaningful uncertainty estimates on the parameters, opening opportunities for assessing systematic uncertainties in experiments, assessing model adequacy, and more. In this report, we use Bayesian inference to fit the solid (fcc) equation of state of copper. We focus on fitting five different experimental datasets, including the isobaric density, isobaric heat capacity, room temperature isotherm, principal isentrope, and principal Hugoniot. We fit all five data types simultaneously, and then explore the extent to which combinations of 2 subsets of the 5 datasets can constrain the EOS parameters, as compared to the fit to all 5. This information is useful for investigating the extent to which different datasets can con strain EOS models and thereby help guide experimental investigations in order to best constrain the EOS. We also discuss ways that the methodologies can be used to investigate systematic discrepancies between experiments, as well as how the methods can be used to assess model uncertainty. The framework we develop is general, in that it can be used with a variety of optimization or uncertainty quantification techniques and with a variety of data sources, including both experimental and ab-inito data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Reaction Mechanism for Carbon Soot in Post-Detonation Flows

This report documents the generation of a mechanism to predict the inclusion of carbon soot particles in a high explosive flow. The mechanism includes gasification and oxidation reactions, formation, sublimation, radiation, and agglomeration. Each part of the mechanism is derived from properties in the literature. The influence of each part of the mechanism is explored using simple, example simulations consisting of a 12 mm diameter 2,4,6-Trinitrotoluene charge detonated in ambient air. The mechanism has not been quantitatively compared to experiments. Additional efforts will be required to tune and validate it, which will require continued advancements in experimental diagnostics and simulation techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Electron Cloud Measurements in Fermilab Booster

Fermilab Booster synchrotron requires an intensity upgrade from 4.5x1012 to 6.5x1012 protons per pulse as a part of Fermilabs Proton Improvement Plan-II (PIP-II). One of the factors which may limit the high-intensity performance is the fast transverse instabilities caused by electron cloud effects. According to the experience in the Recycler, the electron cloud gradually builds up over multiple turns in the combined function magnets and can reach final intensities orders of magnitude greater than in a pure dipole. Since the Booster synchrotron also incorporates combined function magnets, it is essential to discover any existence of an electron cloud. And if it does, its effects on the PIP-II era Booster and its mitigating techniques. As the first step, the presence or absence of the electron cloud was investigated using the clearing bunch technique. This paper presents experimental details and observations of the bunch-by-bunch tune shifts of beams with various bunch train structures at low and high intensities and simulation results conducted using PyECLOUD.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Quantification of [MTBD][beti] loading and its effects on Pt/HSC electrode performance in hydrogen fuel cells

High surface area carbon-supported platinum catalysts (Pt/HSC) are widely used in polymer electrolyte membrane fuel cells but often suffer from limited proton and oxygen transport within porous domains. To address these challenges, we integrate the ionic liquid [MTBD][beti] into Pt/HSC catalyst layers using two deposition methods-one-pot and sequential deposition-to tune IL distribution within micropores and mesopores. A combination of ex situ and in operando techniques were employed to elucidate electrode structure-performance relationships across a range of IL loadings. Sequential deposition achieved more efficient pore filling and higher IL retention at lower IL:C ratios, enabling enhanced proton conductivity and protection of active sites. Compared to the IL-free Pt/HSC, IL-modified electrodes demonstrated up to 28% improvement in mass activity and enhanced high-current-density performance under low relative humidity, while maintaining comparable performance under humidified conditions. Electrochemical impedance spectroscopy and CO displacement experiments reveal that improvements are linked to reduced ionic resistance and lower sulfonate adsorption on Pt sites, rather than changes in electrochemical surface area. However, excessive IL loading leads to mass transport losses. These results highlight the importance of selective pore filling and efficient IL distribution in achieving kinetic gains without compromising ionic and gas transport.

08 HYDROGEN↗
DOE OSTI2025

Surface-enhanced stimulated Raman spectroscopy with squeezed photonic states

Recent advances in generating light in specific quantum states facilitate new sensing applications and enhance or revitalize established measurement and sensing techniques. Stimulated Raman spectroscopy (SRS), a measurement modality based on Raman scattering, could benefit from tuning the quantum properties of the pump and the Stokes lights or the properties of quantum states of the stimulating and excitation fields. Additionally, this modality could also benefit from field enhancement accompanied by plasmon excitation in the surface regions of metal nanoparticles. We present a theoretical investigation of stimulated Raman scattering involving squeezed states of light. The concept of surface- and quantum-enhanced stimulated Raman scattering is introduced. Furthermore, expressions for the respective SRS transition rates are derived, and their dependence on the quantum states of the optical field is discussed, with particular emphasis on the squeezing parameters characterizing these states. For cases involving surface enhancements, we also employ classical computational electrodynamics to guide our exploration of atomically large systems that support plasmon excitation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

DESI DR2 Reference Mocks: Clustering results from UCHUU ELGs and QSOs

High-redshift galaxy clustering provides a powerful probe of the growth of structure, testing models of dark matter, dark energy, and galaxy formation during the epoch when the Universe was rapidly evolving. Emission line galaxies (ELGs) and quasars (QSOs) are used as tracers of dark matter by the Dark Energy Spectroscopic Instrument (DESI) to probe this redshift regime. We present results from ELG and QSO mock catalogs created from the Uchuu N-body simulation and tuned to DESI Data Release 2 (DR2) clustering. Employing a modified subhalo abundance matching (SHAM) technique, we populate Uchuu halos and subhalos with QSOs between 0.8 < z < 2.1. For ELGs, we modify this method to select satellite galaxies with low velocities relative to their associated central halos, and populate a separate set of Uchuu halos and subhalos with ELGs between 0.8 < z < 1.6. In this paper, we reproduce the redshift evolution of number density and clustering statistics across the fitted range of scales. We also measure the large-scale clustering bias of both the data and mock samples. These results improve simulated lightcone construction from cosmological models and enhance our understanding of the galaxy-halo connection.

Vaisakh, R. [Southern Methodist U.] (ORCID:0009000↗
DOE OSTI2020

Bandgap tuning of two-dimensional materials by sphere diameter engineering

Developing a precise and reproducible bandgap tuning method that enables tailored design of materials is of crucial importance for optoelectronic devices. Towards this end, we report a sphere diameter engineering (SDE) technique to manipulate the bandgap of two-dimensional (2D) materials. A one-to-one correspondence with an ideal linear working curve is established between the bandgap of MoS 2 and the sphere diameter in a continuous range as large as 360 meV. Fully uniform bandgap tuning of all the as-grown MoS 2 crystals is realized due to the isotropic characteristic of the sphere. More intriguingly, both a decrease and an increase of the bandgap can be achieved by constructing a positive or negative curvature. By fusing individual spheres in the melted state, post-synthesis bandgap adjustment of the supported 2D materials can be realized. In conclusion, this SDE technique, showing good precision, uniformity and reproducibility with high efficiency, may further accelerate the potential applications of 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tuning Electrogenerated Chemiluminescence Intensity Enhancement Using Hexagonal Lattice Arrays of Gold Nanodisks

Electrogenerated chemiluminescence (ECL) microscopy shows promise as a technique for mapping chemical reactions on single nanoparticles. The technique’s spatial resolution is limited by the quantum yield of the emission and the diffusive nature of the ECL process. To improve signal intensity, ECL dyes have been coupled with plasmonic nanoparticles, which act as nanoantennas. Here, we characterize the optical properties of hexagonal arrays of gold nanodisks and how they impact the enhancement of ECL from the coreaction of tris(2,2'-bipyridyl)dichlororuthenium(II) hexahydrate and tripropylamine. Here, we find that varying the lattice spacing results in a 23-fold enhancement of ECL intensity because of increased dye-array near-field coupling as modeled using finite element method simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗