A general image misalignment correction method for tomography experiments
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36 MATERIALS SCIENCE↗
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Results for “Analytical method in materials science”
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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Precision Cleaning Verification of Nonvolatile Residues by Using Water, Ultrasonics, and Turbidity Analyses
Chlorofluorocarbons (CFC's) in the atmosphere are believed to present a major environmental problem because they are able to interact with and deplete the ozone layer. NASA has been mandated to replace chlorinated solvents in precision cleaning, cleanliness verification, and degreasing of aerospace fluid systems hardware and ground support equipment. KSC has a CFC phase-out plan which provides for the elimination of over 90 percent of the CFC and halon use by 1995. The Materials Science Laboratory and KSC is evaluating four analytical methods for the determination of nonvolatile residues removal by water: (1) infrared analyses using an attenuated total reflectance; (2) surface tension analyses, (3) total organic content analyses, and (4) turbidity analyses. This research project examined the ultrasonic-turbidity responses for 22 hydrocarbons in an effect to determine: (1) if ultrasonics in heated water (70 C) will clean hydrocarbons (oils, greases, gels, and fluids) from aerospace hardware; (2) if the cleaning process by ultrasonics will simultaneously emulsify the removed hydrocarbons in the water; and (3) if a turbidimeter can be used successfully as an analytical instrument for quantifying the removal of hydrocarbons. Sixteen of the 22 hydrocarbons tested showed that ultrasonics would remove it at least 90 percent of the contaminated hydrocarbon from the hardware in 10 minutes or less giving a good ultrasonic-turbidity response. Six hydrocarbons had a lower percentage removal, a slower removal rate, and a marginal ultrasonic-turbidity response.
Skinner, S. Ballou↗
Data-driven discovery of dynamics from time-resolved coherent scattering
Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.
36 MATERIALS SCIENCE↗
An autonomous laboratory for the accelerated synthesis of novel materials
To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.
36 MATERIALS SCIENCE↗
Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images
The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources. Published by the American Physical Society 2025
Reed, Niko R. (ORCID:0009000305222403)↗
Application of C ( t )-Integral Solutions in Extending ASME BPVC Section XI Division 2 Code Case N-934 for Transient Creep Crack Growth
Creep crack growth is a phenomenon which arises in damaged metallic structures under combined primary and secondary loads in the creep regime. The High Temperature Flaw Evaluation Code Committee of the American Society of Mechanical Engineers Boiler and Pressure Vessel Code (BPVC) is evaluating methods in extending Code Case N-934 to capture transient creep crack growth. Here, this paper provides context and basic examples on the selected approach to transient creep crack growth methods based on the C(t)-integral. The basis for selected C(t)-integral solution as analytical method is established, including an overview of its derivation. Practical assessments of a crack growing under creep conditions in a realistic component are conducted to illustrate the analytical approach. Additional considerations in the application of the analytical methods and limitations are discussed.
36 MATERIALS SCIENCE↗
Decontamination of Genesis Array Materials by UV Ozone Cleaning
Shortly after the NASA Genesis Mission sample return capsule returned to earth on September 8, 2004, the science team discovered that all nine ultra-pure semiconductor materials were contaminated with a thin molecular organic film approximately 0 to 100 angstroms thick. The organic contaminate layer, possibly a silicone, situated on the surface of the materials is speculated to have formed by condensation of organic matter from spacecraft off-gassing at the Lagrange 1 halo orbit during times of solar exposure. While the valuable solar wind atoms are safely secured directly below this organic contamination and/or native oxide layer in approximately the first 1000 angstroms of the ultra-pure material substrate, some analytical techniques that precisely measure solar wind elemental abundances require the removal of this organic contaminate. In 2005, Genesis science team laboratories began to develop various methods for removing the organic thin film without removing the precious material substrate that contained the solar wind atoms. Stephen Sestak and colleagues at Open University first experimented with ultraviolet radiation ozone (UV/O3) cleaning of several non-flight and flown Genesis silicon wafer fragments under a pure flowing oxygen environment. The UV/O3 technique was able to successfully remove organic contamination without etching into the bulk material substrate. At NASA Johnson Space Center Genesis Curation Laboratory, we have installed an UV/O3 cleaning devise in an ambient air environment to further experimentally test the removal of the organic contamination on Genesis wafer materials. Preliminary results from XPS analysis show that the UV/O3 cleaning instrument is a good non-destructive method for removing carbon contamination from flown Genesis array samples. However, spectroscopic ellipsometry results show little change in the thickness of the surface film. All experiments to date have shown UV/O3 cleaning method to be the best non-destructive method for removing organic contamination from the surface of the Genesis materials. The UV/O3 cleaning process can also clean carbon contamination to levels below non-flight standards. This can be seen by comparing sample 60260's carbon 10667 cps with non-flight Si carbon 21675 cps. Therefore, surface carbon contamination should not hinder the analysis of solar wind.
Calaway, Michael J.↗
Roadmap on data-centric materials science
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
36 MATERIALS SCIENCE↗
Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification
Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.
36 MATERIALS SCIENCE↗
Time of Flight Secondary Ion Mass Spectrometry for Characterization of Pt-Coated Porous Transport Layers in PEM Water Electrolyzers
Titanium-based porous transport layers (PTLs) and iridium-based catalyst layers (CLs) are two main components of proton exchange membrane water electrolyzers (PEMWEs). PTLs are typically coated with platinum to minimize interfacial losses and to support long-term operation. Optimizing coatings and the PTL-CL interface requires comprehensive characterization. This study establishes time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a valuable technique for PTL characterization, addressing capabilities and limitations related to PTL morphology. A methodology was developed that uses a Cs + sputter beam for dynamic depth profiling, with data collected in both positive-ion (MCs + ) and negative-ion modes to generate depth profiles, 2D ion maps, and 3D ion reconstructions. ToF-SIMS detected relative differences in platinum-layer thickness between samples; these trends were validated by cross-sectional scanning transmission electron microscope (STEM) measurements and flat-titanium substrate controls. Interfacial oxide layers are identified in both ion modes, with enhanced oxide sensitivity in negative mode. The technique’s high sensitivity enables detection of nanometer-scale coatings and trace impurities within the bulk PTL structure. These results provide a methodological framework for analyzing Pt-coated PTLs, with the potential to extend to other components in PEMWEs and other electrolyzer systems.
36 MATERIALS SCIENCE↗
Towards a quantitative understanding of bonding in supersonic single particle impacts: A three-dimensional FIB-SEM exploration
Particle bonding is crucial to coating quality in cold spray, but it has been a challenge to accurately quantify bonding even in single particle impacts. This paper uses FIB-SEM to explicitly map the particle-substrate interface for Cu-on-Cu single microparticle impacts in a full 3D rendering that spans a wide range of impact velocities. This approach permits a detailed quantification of the total bonding area and all of its associated components. In addition to revealing why prior 2D characterization efforts have missed important details about impact bonding, these data quantitatively reveal the evolution of bonding from its onset at the “critical velocity” V cr (where bonding is generally poor, ∼6 %) to its peak at around 1.3‧V cr (where almost 90 % of the particle bonds). Further increase in the velocity to 1.5‧V cr and beyond finds the onset of hydrodynamic penetration and a decrease in bonding. These data then support the development of a simple analytical model based on oxide rarefication and extrusion of bare metal through gaps in the oxide layer as driving the development of bonding. As a result, the model reproduces the experiments and provides guidance on optimization of bonding as a function of material and process parameters.
Analytical methods↗
Basin-Size Mapping: Prediction of Metastable Polymorph Synthesizability Across TaC–TaN Alloys
The sizes of the basins of attraction on the potential energy surface are helpful indicators in determining the experimental synthesizability of metastable phases. In principle, these basins can be controlled with changes in thermodynamic conditions such as composition, pressure, and surface energy. Herein, we use random structure sampling to computationally study how alloying smoothly perturbs basin of attraction sizes. The TaC 1-x N x pseudobinary is an ideal test system given the structural and polymorphic contrast of its parent compounds and their technological relevance as epitaxial substrates for Al 1-x Ga x N. While we find limited thermodynamic stability across all computationally observed phases, random structure sampling shows a significant composition region where the rocksalt basin dominates. As such, we predict the potential for the nonequilibrium synthesis of metastable rocksalt TaC 1-x N x alloys as substrates for Al 1-x Ga x N. At higher nitrogen concentrations, other low-energy metastable polymorphs emerge that continue to retain the hexagonal close packing suitable for III-N growth. Confidence in these trends was established through uncertainty quantification of the basin sizes and energy distributions; such analysis utilized the Beta and Dirichlet distributions. In conclusion, we also find (a) polymorph basin sizes can be rationalized in terms of energetic preferences for different coordination environments; and (b) basin sizes universally shrink with increasing nitrogen content, making the system more prone to amorphous growth.
36 MATERIALS SCIENCE↗
Machine Learning based Correlation of the Mechanical Properties of Sub-sized and Standard-sized Specimens
Mechanical testing with sub-sized specimens is essential in the nuclear industry, offering the ability to conduct tests in confined spaces with lower irradiation and expediting material qualification. However, smaller specimens exhibit different material behavior across scales, a phenomenon known as the "specimen size effect". In this study, we compiled over 1,000 tensile testing records, covering 54 parameters such as material type, composition, manufacturing details, irradiation conditions, specimen dimensions, and tensile properties through a comprehensive literature review. We focus on correlating sub-sized and standard specimens’ tensile mechanical properties on SS316 alloy, which has the most extensive dataset available. We explore ML-based models and uncertainty quantification for tensile properties, analyze key factors influencing these properties, and compare the effectiveness of ML models with existing analytical methods in addressing the specimen size effect.
tensile properties↗
Microgravity Environment Characterization Program
The Microgravity Science and Applications Division (MSAD), a division within NASA's Office of Life and Microgravity Science and Applications, sponsors a broad range of space-based research in biotechnology, combustion science, fluid physics, fundamental physics, and materials science. To better understand and exploit the orbital environment, MSAD has developed methods and hardware to characterize accelerations on microgravity experiment carriers. MSAD supports research to verify analytically derived acceleration requirements for experiments and provides vibration isolation for sensitive experiments. The Microgravity Measurement and Analysis Project (MMAP), supported by MSAD, incorporates four projects: the Space Acceleration Measurement System (SAMS), the Orbital Acceleration Research Experiment (OARE), the SAMS for International Space Station (SAMS-II), and the Principal Investigator Microgravity Services (PIMS). SAMS was developed to record microgravity accelerations and the OARE was developed to record very low-frequency microgravity accelerations on-board the NASA Orbiters. The SAMS is also used for cooperative investigations on the Russian Mir space station. The SAMS-II is being developed for the same function on-board the International Space Station (ISS). PIMS utilizes microgravity acceleration data to develop a description of each microgravity mission's acceleration environment and to support microgravity investigators in interpreting possible effects of the acceleration environment on their experiments. These elements of the MSAD program will be used to define acceleration requirements for future Orbiter and ISS payloads. This paper describes the MMAP and summarizes the products and services available to principal investigators and other users. This paper also presents some microgravity acceleration characterization results from the last six years of Orbiter microgravity missions.
DeLombard, Richard↗
Lifetime Energy Savings Via Advanced Manufacturing of Low Density Steels for Transportation Applications
The purpose of this “Low Density Steels for Transportation Applications” project was to develop an alloy composition and processing parameters that would result in a material suitable for use in automotive structural components at a reduced density over the current advanced high strength steel (AHSS) materials used. The project work successfully developed a robust alloy capable of exceeding project mechanical property targets at each stage of development, with an 8% density reduction over benchmark AHSS materials (7.8 g/cm3). The developed alloy has the potential to offer significant vehicle lightweighting and improved fuel economy, without sacrificing the increased passenger safety of more traditional AHSS. Through the three tasks of the project, (1) Alloy design and small-scale laboratory evaluation, (2) Laboratory development of hot rolled material and (3) Laboratory development of a cold rolled material, the laboratory work utilized advanced characterization and analytical methods on novel alloy compositions subjected to both conventional and non-conventional processing operations.
36 MATERIALS SCIENCE↗
An Edge Alignment-Based Orientation Selection Method for Neutron Tomography
Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.
Yang, Diyu↗
Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions
Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.
36 MATERIALS SCIENCE↗
Dimensional Evolution Guides Property Control in the A n Cu 4– n TiS 4 Semiconductor Series
Through progressive reduction of the three-dimensional (3D) covalent network of Cu 4 TiS 4 , we isolate seven new members of the A n Cu 4–n TiS 4 family (A = alkali metal; n = 0–4), spanning 3D, 2D, 1D, and 0D structural fragments. The dimensional reduction is rational, as it preserves the edge-sharing connectivity between [CuS 4 ] 7– and [TiS 4 ] 4– tetrahedra across the series. This structural evolution is driven by the stepwise substitution of Cu with alkali metals, guiding the formation of fragments with reduced dimensionality. The effects of “n” and “A” on the crystal structures, stabilities, electronic structures, and optoelectronic properties are profound, demonstrating that the manipulation of alkali metal size and A n Cu 4–n TiS 4 stoichiometry enables predictable variations in structure and properties. For example, the n = 0 and n = 4 end members of the A n Cu 4–n TiS 4 family set the range of achievable band gaps with 2.00 eV for Cu 4 TiS 4 , 2.60 eV for Na 4 TiS 4 , and intermediate values for the n = 1–3 members. Notably, CsCu 3 TiS 4 exhibits exceptional air stability and congruent melting, with density functional theory (DFT) calculating moderate hole and electron effective masses in specific crystallographic directions (mh = 1.24m 0 , me = 0.87m 0 ). Additionally, A 3 CuTiS 4 (A = Na, K, Rb) displays direct band gap behavior and long photoluminescence lifetimes of 2.3–8.6 μs, and K 3 CuTiS 4 has a PLQY of 5.19%. These findings underscore the potential of the A n Cu 4–n TiS 4 family for applications in optoelectronics and demonstrate widely applicable design concepts that unveil rational stoichiometries within a given composition space to generate a series of crystal structures related through an evolving covalent dimensionality that corresponds to a predictable electronic structure and property progression.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗