Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “materials characterization”

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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Decoding diffraction and spectroscopy data with machine learning: A tutorial

This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Robust quantification of the diamond nitrogen-vacancy center charge state via photoluminescence spectroscopy

Nitrogen vacancy (NV) centers in diamond are at the heart of many emerging quantum technologies, all of which require control over the NV charge state. Hence, methods for quantification of the relative photoluminescence intensities of the NV 0 and NV − charge states, i.e., a charge state ratio, are vital. Several approaches to quantify NV charge state ratios have been reported but are either limited to bulk-like NV diamond samples or yield qualitative results. We propose an NV charge state quantification protocol based on the determination of sample- and experimental setup-specific NV 0 and NV − reference spectra. The approach employs blue (400–470 nm) and green (480–570 nm) excitation to infer pure NV 0 and NV − spectra, which are then used to quantify NV charge state ratios in subsequent experiments via least squares fitting. We test our dual excitation protocol (DEP) for a bulk diamond NV sample and 20 and 100 nm nanodiamond particles and compare results with those obtained via other commonly used techniques such as zero-phonon line fitting and non-negative matrix factorization. We find that DEP can be employed across different samples and experimental setups and yields consistent and quantitative results for NV charge state ratios that are in agreement with our understanding of NV photophysics. By providing robust NV charge state quantification across sample types and measurement platforms, DEP will support the development of NV-based quantum technologies.

Color center laser spectroscopy↗
DOE OSTI2025

Mapping causal patterns in crystalline solids

The evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO 3 along the phase transition boundary from the ferroelectric rhombohedral phase to the non-ferroelectric orthorhombic phase is explored using scanning transmission electron microscopy. Localized properties, including polarization, lattice parameter, and chemical composition, are parameterized from atomic-scale imaging, and their causal relationships are reconstructed using a linear non-Gaussian acyclic model. This approach is further extended to explore the spatial variability of the causal coupling using the sliding window transform method, which revealed that new causal relationships emerged at both the expected locations, such as domain walls and interfaces, and at additional regions forming clusters in the vicinity of the walls or spatially distributed features. While the exact physical origins of these relationships are unclear, they likely represent nanophase-separated regions in the morphotropic phase boundaries. Overall, we posit that an in-depth understanding of complex disordered materials away from thermodynamic equilibrium necessitates understanding not only the generative processes that can lead to observed microscopic states but also the causal links between multiple interacting subsystems.

Causal inference↗
DOE OSTI2023

Dominant heterocyclic composition of dissolved organic nitrogen in the ocean: A new paradigm for cycling and persistence

Marine dissolved organic nitrogen (DON) is one of the planet’s largest reservoirs of fixed N, which persists even in the N-limited oligotrophic surface ocean. The vast majority of the ocean’s total DON reservoir is refractory (RDON), primarily composed of low molecular weight (LMW) compounds in the subsurface and deep sea. However, the composition of this major N pool, as well as the reasons for its accumulation and persistence, are not understood. Past characterization of the analytically more tractable, but quantitatively minor, high molecular weight (HMW) DON fraction revealed a functionally simple amide-dominated composition. While extensive work in the past two decades has revealed enormous complexity and structural diversity in LMW dissolved organic carbon, no efforts have specifically targeted LMW nitrogenous molecules. Here, we report the first coupled isotopic and solid-state NMR structural analysis of LMW DON isolated throughout the water column in two ocean basins. Together these results provide a first view into the composition, potential sources, and cycling of this dominant portion of marine DON. Our data indicate that RDON is dominated by 15N-depleted heterocyclic-N structures, entirely distinct from previously characterized HMW material. This fundamentally new view of marine DON composition suggests an important structural control for RDON accumulation and persistence in the ocean. The mechanisms of production, cycling, and removal of these heterocyclic-N-containing compounds now represents a central challenge in our understanding of the ocean’s DON reservoir.

58 GEOSCIENCES↗
DOE OSTI2023

Measurement of Photoneutrons from Depleted Uranium and Comparison Study Using MCNPX-PoliMi

High-energy photon interrogation is a nondestructive technique that is used to detect special nuclear materials and characterize nuclear waste. The development of such systems is complex and requires Monte Carlo simulations to optimize system performance. Monte Carlo simulations rely on various scattering, absorption, and photonuclear cross-section data. While the scattering and absorption cross-section data have been extensively studied and validated with experiments, the results obtained from photonuclear simulations are often found to underpredict measured results, indicating uncertainties in the photonuclear cross sections themselves. Thus, there is a need for new measured results that can be used to quantify underpredictions in simulations using photonuclear cross-section data. In the present work, we interrogated depleted uranium with a 9-MV electron linac and detected photoneutrons with trans-stilbene organic scintillators. The measurement of photoneutrons with organic scintillators is challenging due to the presence of the intense photon flux, which causes issues such as pulse pile-up, detector saturation, and poor signal-to-background ratio. To mitigate these challenges, we used iron and polyethylene shielding of varying thicknesses around the depleted uranium target and a neural network–based digital pulse processing algorithm to recover neutron and photon information from piled-up events. Our goal was to compare the measured photoneutron count rate with the simulated rate obtained using the MCNPX-PoliMi transport code. For a light output window of 0.28 to 2.67 MeVee (1.66- to 6.85-MeV proton recoil energy), we found that the simulated count rate obtained using the ENDF/B-VII photonuclear cross-section library underpredicts the measured rate by 32.8% 3.2%. Additionally, we compared the simulated and measured photoneutron light output distributions. For the least thicknesses of shielding, the simulation was found to underpredict measurements in the 0.70- to 2.67-MeVee light output window. For the greatest thicknesses of shielding, the simulation was found to underpredict the measurement across the entire light output window of 0.28 to 2.67 MeVee.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Low cost switching circuit for van der Pauw resistivity and Hall measurements at various temperatures

The electrical characterization of materials, particularly superconductors, semiconductors, and those undergoing metal-insulator transitions (MITs), relies significantly on resistivity and Hall measurements as a function of temperature. The van der Pauw four-point probe method is commonly used for such measurements, which involves resistance measurements for different circuit configurations connected to sample contacts. However, repeating these measurements at different temperatures is challenging and time consuming. Here, this study introduces a novel approach utilizing a switching circuit controlled by an Arduino device and a LabVIEW program to automate resistance measurements for different electrical configurations. The effectiveness of this setup was demonstrated by testing V 2 O 3 thin film samples deposited on Al 2 O 3 (001) substrates using DC magnetron sputtering. The MIT temperature of the sample was 115 K during heating and 100 K during cooling. The sample exhibited p-type charge carriers with a Hall coefficient of 1.3 ± 0.1 x 10 -4 cm 3 /C and Hall mobility of 1.7 x 10 -1 cm 2 /V∙s, which is consistent with findings from other studies employing commercial equipment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Correlation of critical current density to quasi-biaxial texture and grain boundary cleanliness in fully dense Bi-2212 wires

The distinctive quasi-biaxial texture of Bi 2 Sr 2 CaCu 2 O x (Bi-2212) plays an important role in enabling high critical current density ($J_c$) in Bi-2212 round wires (RWs). Here we studied three over pressure heat treated wires with $J_c$ varying by a factor of ~10, all being fully dense. Using electron backscatter diffraction, we observed the differences in biaxial texture in these three wires. Transmission electron microscopy also revealed differences in grain boundary (GB) cleanliness and connectivity. These analyses showed that high $J_c$ is unambiguously correlated to the best biaxial texture, which is in turn correlated to slow cooling from the liquid melt into solid Bi-2212. However, at 4.2 K, there is a negligible difference in intragrain pinning in the three wires, suggesting that the $J_c$ variation by a factor of ~10 is primarily due to variable filament and intergrain connectivity. In this work, the principal determinants of intergrain connectivity is the quasi-biaxial texture and GB cleanliness. Overall, $J_c$ optimization of the Bi-2212 RW is a complex multi-variable process, but this study shows that maximizing the biaxial texture quality is an important first step in such an optimization process.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cryogenic single-port calibration for superconducting microwave resonator measurements

Superconducting circuit testing and materials loss characterization requires robust and reliable methods for the extraction of internal and coupling quality factors of microwave resonators. Additionally, a common method, imposed by limitations on the device design or experimental configuration, is the single-port reflection geometry, i.e. reflection-mode. However, impedance mismatches in cryogenic systems must be accounted for through calibration of the measurement chain while it is at low temperatures. In this paper, we demonstrate a data-based, single-port calibration using commercial microwave standards and a vector network analyzer with samples at millikelvin temperature in a dilution refrigerator, making this method useful for measurements of quantum phenomena. Finally, we cross reference our data-based, single-port calibration and reflection measurement with over-coupled 2D- and 3D-resonators against well established two-port techniques corroborating the validity of our method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design

With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Automating STEM Aberration Correction via Bayesian Optimization

Multipole aberration correctors have given scanning transmission electron microscopes (STEM) the ability to produce high-quality, atomic-resolution images, enabling STEM to be a key tool in material sciences for characterizing the structure and composition of materials. However, the process of correcting these aberrations typically requires human input and is accomplished using scanned STEM or Ronchigram images at different focii and beam tilts. In this work, we demonstrate an automated on-sample aberration correction system using Bayesian Optimization. We have developed a Python-based server able to communicate with the CEOS DCOR aberration corrector and the Thermo Fischer microscope scripting interface. This server allows us to change aberrations, acquire images and perform basic image analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

π phase shift across stripes in a charge density wave system

Many strongly correlated materials are characterized by deeply intertwined charge and spin order. Besides their high superconducting transition temperatures, one of the central features of these complex patterns in cuprates is a phase shift which occurs across lines of decreased hole density. That is, when doped away from their antiferromagnetic phase, the additional charge is not distributed uniformly, but rather in “stripes.” The sublattices preferentially occupied by up and down spin are reversed across these stripes, a phenomenon referred to as a “π phase shift.” Many of the spin-charge patterns, including the π phase shift, are reproduced by density matrix renormalization group and quantum Monte Carlo calculations of simplified tight binding (repulsive Hubbard) models. In this paper we demonstrate that this sublattice reversal is generic by considering the corresponding phenomenon in the attractive Hubbard Hamiltonian, where a charge density wave phase forms at half filling. Here, we introduce charge stripes via an appropriate local chemical potential; measurements of charge correlation across the resulting lines of lowered density reveal a clear π phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Proximity-induced collective modes in an unconventional superconductor heterostructure

Unconventional superconductors have been long sought for their potential applications in quantum technologies and devices. A key challenge impeding this effort is the difficulty associated with probing and characterizing candidate materials and establishing their order parameter. Here we present a platform that allows one to spectroscopically probe unconventional superconductivity in thin-layer materials via the proximity effect. Here we show that inducing an s-wave gap in a sample with an intrinsic d-wave instability leads to the formation of bound states of quasiparticle pairs, which manifest as a collective mode in the d-wave channel. This finding provides a way to study the underlying pairing interactions vicariously through the collective mode spectrum of the system. Upon further cooling of the system we observe that this mode softens considerably and may even condense, signaling the onset of time-reversal symmetry-breaking superconductivity. Therefore, our proposal also allows for the creation and study of these elusive unconventional states.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Theory of criticality for quantum ferroelectric metals

A variety of compounds, for example, doped paraelectrics and polar metals, exhibit both ferroelectricity and correlated electronic phenomena such as low-density superconductivity and anomalous transport. Characterizing such properties is tied to understanding the quantum dynamics of inversion symmetry breaking in the presence of itinerant electrons. Here, we present a comprehensive analysis of the properties of a metal near a quantum critical transition to a ferroelectric state, in both two and three dimensions. In this study, starting from a minimal model of electrons coupled to a transverse polar phonon via a Rashba-type spin-orbit interaction, we compute the dynamical response of both electrons and phonons. We find that the system can evince both Fermi and non-Fermi liquid phases, as well as enhanced pairing in both singlet and triplet channels. Furthermore, we systematically compute corrections to one-loop theory and find a tendency to quantum order-by-disorder, leading to a phase diagram that can include second-order, first-order, and finite-momentum phase transitions. Finally, we show that the entire phase diagram can be controlled via application of external strain, either compressive or volume-preserving. Our results provide a map of the dynamical and thermodynamical phase space of quantum ferroelectic metals, which can serve in characterizing existing materials and in seeking applications for quantum technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Phase and contrast moiré signatures in two-dimensional cone beam interferometry

Neutron interferometry has played a distinctive role in fundamental science and characterization of materials. Moiré neutron interferometers are candidate next-generation instruments: they offer microscopy-like magnification of the signal, enabling direct camera recording of interference patterns across the full neutron wavelength spectrum. Here we demonstrate the extension of phase-grating moiré interferometry to two-dimensional geometries. Our fork-dislocation phase gratings reveal phase singularities in the moiré pattern, and we explore orthogonal moiré patterns with two-dimensional phase gratings. Our measurements of phase topologies and gravitationally induced phase shifts are in good agreement with theory. These techniques can be implemented in existing neutron instruments to advance interferometric analyses of emerging materials and precision measurements of fundamental constants. Published by the American Physical Society 2024

Sarenac, D. (ORCID:0000000185753367)↗
DOE OSTI2024

Open-source electrochemical cell for in situ X-ray absorption spectroscopy in transmission and fluorescence modes

X-ray spectroscopy is a valuable technique for the study of many materials systems. Characterizing reactions in situ and operando can reveal complex reaction kinetics, which is crucial to understanding active site composition and reaction mechanisms. In this project, the design, fabrication and testing of an open-source and easy-to-fabricate electrochemical cell for in situ electrochemistry compatible with X-ray absorption spectroscopy in both transmission and fluorescence modes are accomplished via windows with large opening angles on both the upstream and downstream sides of the cell. Using a hobbyist computer numerical control machine and free 3D CAD software, anyone can make a reliable electrochemical cell using this design. Onion-like carbon nanoparticles, with a 1:3 iron-to-cobalt ratio, were drop-coated onto carbon paper for testing in situ X-ray absorption spectroscopy. Cyclic voltammetry of the carbon paper showed the expected behavior, with no increased ohmic drop, even in sandwiched cells. Chronoamperometry was used to apply 0.4 V versus reversible hydrogen electrode, with and without 15 min of oxygen purging to ensure that the electrochemical cell does not provide any artefacts due to gas purging. The XANES and EXAFS spectra showed no differences with and without oxygen, as expected at 0.4 V, without any artefacts due to gas purging. The development of this open-source electrochemical cell design allows for improved collection of in situ X-ray absorption spectroscopy data and enables researchers to perform both transmission and fluorescence simultaneously. It additionally addresses key practical considerations including gas purging, reduced ionic resistance and leak prevention.

08 HYDROGEN↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
DOE OSTI2020

Understanding PV Polymer Backsheet Degradation through X-Ray Scattering

Understanding how photovoltaic (PV) module backsheet polymers age and degrade in response to environmental stresses is important for designing polymers that can maintain their structural integrity after decades of outdoor use. X-ray scattering is a powerful technique for exploring backsheet polymer structure at the angstrom- (wide-angle, WAXS) and nanometer- (small-angle, SAXS) length-scales. We present the use of SAXS and WAXS to study pristine and aged polymer backsheets. The structural insight from these techniques can be used to compare the degradation induced by accelerated testing with the degradation seen in field-aged PV materials.

14 SOLAR ENERGY↗