SEARCH · Search NASA

Results for “Assist”

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.

SourceResearch connectionFrom yearThrough year

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

At least 595 records · Page 33

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Revealing Solvent-Assisted Li+ Transport in the Solid Electrolyte Interphase operando

The performance of energy-dense lithium metal batteries is critically influenced by the properties of the solid electrolyte interphase (SEI). Yet, progress in understanding this layer has been limited by the lack of accurate operando characterization because the SEI evolves dynamically during cycling. Here, we apply dynamic electrochemical impedance spectroscopy (dEIS) to resolve the real-time evolution of the SEI on lithium metal in ether-based electrolytes with varying degrees of fluorination. We find that faster stabilization of the compact SEI resistance correlates with improved passivation and higher Coulombic efficiency. Unexpectedly, compact SEI resistance correlates directly with Li+ solvation energy, revealing that weaker Li+ solvation increases not only bulk but also interphase resistance. These findings challenge the conventional view of the SEI as a purely solid-phase conductor and instead support a solvent-assisted Li+ transport mechanism within the compact SEI. This framework emphasizes the need to balance SEI ionic conductivity with the Li+ solvation environment to maximize lithium metal battery performance.

Sacci, Robert [ORNL] (ORCID:0000000200735221)↗

DOE OSTI2020

Matrix-Assisted Ionization of Molecular Uranium Species

Matrix-assisted ionization (MAI) demonstrates high sensitivity for a variety of organic compounds; however, few studies have reported the application of MAI for the detection and characterization of inorganic analytes. Trace-level uranium analysis is important in the realms of nuclear forensics, nuclear safeguards, and environmental monitoring. Traditional mass spectrometry methods employed in these fields require combinations of extensive laboratory chemistry sample preparation and destructive ionization methods. There has been recent interest in exploring ambient mass spectrometry methods that enable timely sample analysis and higher sensitivity than what is attainable by field-portable radiation detectors. Rapid characterization of uranium at nanogram levels is demonstrated in this study using MAI techniques. Mass spectra were collected on an atmospheric pressure mass spectrometer for solutions of uranyl nitrate, uranyl chloride, uranyl acetate, and uranyl oxalate utilizing 3-nibrobenzonitrile as the ionization matrix. The uranyl complexes investigated were detectable, and the chemical speciation was preserved. Sample analysis was accomplished in a matter of seconds, and limits of detection of 5 ng of uranyl nitrate, 10 ng of uranyl oxalate, 100 ng of uranyl chloride, and 200 ng of uranyl acetate were achieved. The observed gas-phase speciation was similar to negative-ion electrospray ionization of uranyl compounds with notable differences. Six matrix-derived ions were detected in all negative-ion mass spectra, and some of these ions formed adducts with the uranyl analyte. Subsequent analysis of the matrix suggests that these molecules are not matrix contaminants and are instead created during the ionization process.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

Abstract A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Ligand assisted growth of perovskite single crystals with low defect density

A low defect density in metal halide perovskite single crystals is critical to achieve high performance optoelectronic devices. Here we show the reduction of defect density in perovskite single crystals grown by a ligand-assisted solution process with 3-(decyldimethylammonio)-propane-sulfonate inner salt (DPSI) as an additive. DPSI ligands anchoring with lead ions on perovskite crystal surfaces not only suppress nucleation in solution, but also regulate the addition of proper ions to the growing surface, which greatly enhances the crystal quality. The grown CH 3 NH 3 PbI 3 crystals show better crystallinity and a 23-fold smaller trap density of 7 × 10 10 cm -3 than the optimized control crystals. The enhanced material properties result in significantly suppressed ion migration and superior X-ray detection sensitivity of CH 3 NH 3 PbI 3 detectors of (2.6 ± 0.4) × 10 6 µC Gy -1 air cm -2 for 60 kVp X-ray and the lowest detectable dose rate reaches (5.0 ± 0.7) nGy s -1 , which enables reduced radiation dose to patients in medical X-ray diagnostics.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Probing subwavelength in-plane anisotropy with antenna-assisted infrared nano-spectroscopy

Infrared nano-spectroscopy based on scattering-type scanning near-field optical microscopy (s-SNOM) is commonly employed to probe the vibrational fingerprints of materials at the nanometer length scale. However, due to the elongated and axisymmetric tip shank, s-SNOM is less sensitive to the in-plane sample anisotropy in general. In this article, we report an easy-to-implement method to probe the in-plane dielectric responses of materials with the assistance of a metallic disk micro-antenna. As a proof-of-concept demonstration, we investigate here the in-plane phonon responses of two prototypical samples, i.e. in (100) sapphire and x-cut lithium niobate (LiNbO 3 ). In particular, the sapphire in-plane vibrations between 350 cm–1 to 800 cm–1 that correspond to LO phonon modes along the crystal b- and c-axis are determined with a spatial resolution of < λ/10, without needing any fitting parameters. In LiNbO 3 , we identify the in-plane orientation of its optical axis via the phonon modes, demonstrating that our method can be applied without prior knowledge of the crystal orientation. Furthermore, our method can be elegantly adapted to retrieve the in-plane anisotropic response of a broad range of materials, i.e. subwavelength microcrystals, van-der-Waals materials, or topological insulators.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Mechanism of Rad26-assisted rescue of stalled RNA polymerase II in transcription-coupled repair

Transcription-coupled repair is essential for the removal of DNA lesions from the transcribed genome. The pathway is initiated by CSB protein binding to stalled RNA polymerase II. Mutations impairing CSB function cause severe genetic disease. Yet, the ATP-dependent mechanism by which CSB powers RNA polymerase to bypass certain lesions while triggering excision of others is incompletely understood. Here we build structural models of RNA polymerase II bound to the yeast CSB ortholog Rad26 in nucleotide-free and bound states. This enables simulations and graph-theoretical analyses to define partitioning of this complex into dynamic communities and delineate how its structural elements function together to remodel DNA. We identify an allosteric pathway coupling motions of the Rad26 ATPase modules to changes in RNA polymerase and DNA to unveil a structural mechanism for CSB-assisted progression past less bulky lesions. Our models allow functional interpretation of the effects of Cockayne syndrome disease mutations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Machine learning assisted quantum super-resolution microscopy

One of the main characteristics of optical imaging systems is spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super-resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of $\sqrt{n}$ improvement in the spatial resolution by measuring the n -th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Plasma-assisted manipulation of vanadia nanoclusters for efficient selective catalytic reduction of NO x

Supported nanoclusters (SNCs) with distinct geometric and electronic structures have garnered significant attention in the field of heterogeneous catalysis. However, their directed synthesis remains a challenge due to limited efficient approaches. This study presents a plasma-assisted treatment strategy to achieve supported metal oxide nanoclusters from a rapid transformation of monomeric dispersed metal oxides. As a case study, oligomeric vanadia-dominated surface sites were derived from the classic supported V 2 O 5 -WO 3 /TiO 2 (VWT) catalyst and showed nearly an order of magnitude increase in turnover frequency (TOF) value via an H 2 -plasma treatment for selective catalytic reduction of NO with NH 3 . Such oligomeric surface VO x sites were not only successfully observed and firstly distinguished from WO x and TiO 2 by advanced electron microscopy, but also facilitated the generation of surface amide and nitrates intermediates that enable barrier-less steps in the SCR reaction as observed by modulation excitation spectroscopy technologies and predicted DFT calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Universal salt-assisted assembly of MXene from suspension on polymer substrates

Abstract Two-dimensional carbides and nitrides, known as MXenes, are promising for water-processable coatings due to their excellent electrical, thermal, and optical properties. However, depositing hydrophilic MXene nanosheets onto inert or hydrophobic polymer surfaces requires plasma treatment or chemical modification. This study demonstrates a universal salt-assisted assembly method that produces ultra-thin, uniform MXene coatings with exceptional mechanical stability and washability on various polymers, including high-performance polymers for extreme temperatures. The salt in the Ti 3 C 2 T x colloidal suspension reduces surface charges, enabling electrostatically hydrophobized MXene deposition on polymers. A library of salts was used to optimize assembly kinetics and coating morphology. A 170 nm MXene coating can reduce radiation temperature by ~200 °C on a 300 °C PEEK substrate, while the coating on Kevlar fabric provides comfort in extreme conditions, including outer space and polar regions.

Science & Technology - Other Topics↗

DOE OSTI2021

Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

Abstract The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine learning assisted derivation of minimal low-energy models for metallic magnets

Abstract We consider the problem of extracting a low-energy spin Hamiltonian from a triangular Kondo Lattice Model (KLM). The non-analytic dependence of the effective spin-spin interactions on the Kondo exchange excludes the use of perturbation theory beyond the second order. We then introduce a Machine Learning (ML) assisted protocol to extract effective two- and four-spin interactions. The resulting spin model reproduces the phase diagram of the original KLM as a function of magnetic field and single-ion anisotropy and reveals the effective four-spin interactions that stabilize the field-induced skyrmion crystal phase. Moreover, this model enables the computation of static and dynamical properties with a much lower numerical cost relative to the original KLM. A comparison of the dynamical spin structure factor in the fully polarized phase computed with both models reveals a good agreement for the magnon dispersion even though this information was not included in the training data set.

Sharma, Vikram (ORCID:0000000189105519)↗

DOE OSTI2025

Quantum annealing-assisted lattice optimization

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Nuclear spin assisted magnetic field angle sensing

Abstract Quantum sensing exploits the strong sensitivity of quantum systems to measure small external signals. The nitrogen-vacancy (NV) center in diamond is one of the most promising platforms for real-world quantum sensing applications, predominantly used as a magnetometer. However, its magnetic field sensitivity vanishes when a bias magnetic field acts perpendicular to the NV axis. Here, we introduce a different sensing strategy assisted by the nitrogen nuclear spin that uses the entanglement between the electron and nuclear spins to restore the magnetic field sensitivity. This, in turn, allows us to detect small changes in the magnetic field angle relative to the NV axis. Furthermore, based on the same underlying principle, we show that the NV coupling strength to magnetic noise, and hence its coherence time, exhibits a strong asymmetric angle dependence. This allows us to uncover the directional properties of the local magnetic environment and to realize maximal decoupling from anisotropic noise.

Physics↗

DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Interfacial reaction during friction stir assisted scribe welding of immiscible Fe and Mg alloy system

We report on interfacial characteristics and chemistry of bonded Mg-Fe interfaces welded using friction stir assisted scribe technique (FaST). Two pairs of dissimilar joints: (AZ31-DP590) and (Pure Mg-DP590) were studied to shed light on joining mechanisms responsible for bonding of “immiscible” pairs of Mg and Fe. We present first direct experimental evidence of presence of oxide layer, Al segregation by atom probe tomography and nano steel grains close to interface by transmission electron microscopy study.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Ion-beam assisted sputtering of titanium nitride thin films

Abstract Titanium nitride is a material of interest for many superconducting devices such as nanowire microwave resonators and photon detectors. Thus, controlling the growth of TiN thin films with desirable properties is of high importance. This work aims to explore effects in ion beam-assisted sputtering (IBAS), were an observed increase in nominal critical temperature and upper critical fields are in tandem with previous work on Niobium nitride (NbN). We grow thin films of titanium nitride by both, the conventional method of DC reactive magnetron sputtering and the IBAS method, to compare their superconducting critical temperatures $$T_{c}$$ T c as functions of thickness, sheet resistance, and nitrogen flow rate. We perform electrical and structural characterizations by electric transport and x-ray diffraction measurements. Compared to the conventional method of reactive sputtering, the IBAS technique has demonstrated a 10% increase in nominal critical temperature without noticeable variation in the lattice structure. Additionally, we explore the behavior of superconducting $$T_c$$ T c in ultra-thin films. Trends in films grown at high nitrogen concentrations follow predictions of mean-field theory in disordered films and show suppression of superconducting $$T_c$$ T c due to geometric effects, while nitride films grown at low nitrogen concentrations strongly deviate from the theoretical models.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A laser-assisted chlorination process for reversible writing of doping patterns in graphene

Chemical doping can be used to control the charge-carrier polarity and concentration in two-dimensional van der Waals materials. However, conventional methods based on substitutional doping or surface functionalization result in the degradation of electrical mobility due to structural disorder, and the maximum doping density is set by the solubility limit of dopants. Here we show that a reversible laser-assisted chlorination process can be used to create high doping concentrations (above 3 × 10 13 cm -2 ) in graphene monolayers with minimal drops in mobility. The approach uses two lasers—with distinct photon energies and geometric configurations—that are designed for chlorination and subsequent chlorine removal, allowing highly doped patterns to be written and erased without damaging the graphene. To illustrate the capabilities of our approach, we use it to create rewritable photoactive junctions for graphene-based photodetectors.

42 ENGINEERING↗