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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Materials for Heated Head Automated Thermoplastic Tape Placement

NASA Langley Research Center (LaRC) is currently pursuing multiple paths to develop out of autoclave (OOA) polymeric composite materials and processes. Polymeric composite materials development includes the synthesis of new and/or modified thermosetting and thermoplastic matrix resins designed for specific OOA processes. OOA processes currently under investigation include vacuum bag only (VBO) prepreg/composite fabrication, resin transfer molding (RTM), vacuum assisted resin transfer molding (VARTM) and heated head automated thermoplastic tape placement (HHATP). This paper will discuss the NASA Langley HHATP facility and capabilities and recent work on characterizing thermoplastic tape quality and requirements for quality part production. Samples of three distinct versions of APC-2 (AS4/PEEK) thermoplastic dry tape were obtained from two materials vendors, TENCATE, Inc. and CYTEC Engineered Materials** (standard grade and an experimental batch). Random specimens were taken from each of these samples and subjected to photo-microscopy and surface profilometry. The CYTEC standard grade of APC-2 tape had the most voids and splits and the highest surface roughness and/or waviness. Since the APC-2 tape is composed of a thermoplastic matrix, it offers the flexibility of reprocessing to improve quality, and thereby improve final quality of HHATP laminates. Discussions will also include potential research areas and future work that is required to advance the state of the art in the HHATP process for composite fabrication.

Jensen, Brian J.↗
NASA NTRS1997

Novel software for analysis of root gravitropism: comparative response patterns of Arabidopsis wild-type and axr1 seedlings

In an earlier study (Evans, Ishikawa & Estelle 1994, Planta 194, 215-222) we used a video digitizer system to compare the kinetics of auxin action on root elongation in wild-type seedlings and seedlings of auxin response mutants of Arabidopsis thaliana (L.) Heynh. We have since modified the system software to allow determination of elongation on opposite sides of vertical or gravistimulated roots and to allow continuous measurement of the angle of orientation of sequential subsections of the root during the response. We used this technology to compare the patterns of differential growth that generate curvature in roots of the Columbia ecotype and in the mutants axr1-3, axr1-12 and axr2, which show reduced gravitropic responsiveness and reduced sensitivity to inhibition by auxin. The pattern of differential growth during gravitropism differed in roots of wild-type and axr1 seedlings. In wild-type roots, initial curvature resulted from differential inhibition of elongation in the distal elongation zone (DEZ). This was followed by an acceleration of elongation along the top side of the DEZ. In roots of axr1-3, curvature resulted from differential stimulation of elongation whereas in roots of axr1-12 the response was variable. Roots of axr2 did not exhibit gravitropic curvature. The observation that the pattern of differential growth causing curvature is dramatically altered by a change in sensitivity to auxin is consistent with the classical Cholodny-Went theory of gravitropism which maintains that differential growth patterns induced by gravistimulation are mediated primarily by gravi-induced shifts in auxin distribution. The new technology introduced with this report allows automated determination of stimulus response patterns in the small but experimentally popular roots of Arabidopsis.

Non-NASA Center↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

Building Atomic and Plasmonic Devices via Electron Beams: from Desired Structures to Desired Properties

The evolution of the aberration correction in Scanning Transmission Electron Microscopy (STEM) has enabled multiple advances including high-resolution structural imaging, pm-level position detection of atomic columns, and ~3-5 mV resolution electron energy loss spectroscopy of the chemical and physical functionalities. These developments have further enabled the precise manipulation of the atomic structure of materials, including removal and formation of atomic planes in 3D materials, controlled atomic motion, and even direct atomic assembly of homo- and heteroatomic artificial molecules. Here, we will discuss several recent advances in STEM-EELS automated experiments as applied to quantum and nanoplasmonic systems.

42 ENGINEERING↗
DOE OSTI2024

Recycling of silver from silicon solar cells by laser debonding

A laser-debonding approach to silver electrode recovery from solar cells is presented to address the critical need for efficient and eco-friendly recycling methods. The study explores the use of UV nanosecond and IR continuous-wave lasers to precisely debond silver electrodes from silicon wafers. By optimizing parameters like laser power, scan speed, and the number of passes, intact silver electrical contact lines were recovered from solar cells. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) analysis confirmed the successful separation of silver from silicon wafers. Furthermore, an increase in the number of laser passes led to the production of silver microparticles, as validated by morphology and compositional studies using EDS. The estimated silver is approximately 90 mg for the Si solar cells of 15.4 * 15.4 cm 2 used in the study. To achieve a large area recovery of silver, a MATLAB code incorporating detection algorithms and specific criteria to automate laser scanning by accurately identifying the location of silver electrodes was developed. This presented laser debonding method is not confined to silicon solar cells but can be extended to other solar cell types featuring metal electrodes, offering a versatile solution. By minimizing the use of hazardous chemicals and reducing operational costs, the developed process aligns with the imperative of environmentally responsible photovoltaic waste management.

14 SOLAR ENERGY↗
DOE OSTI2022

Machine learning the metastable phase diagram of covalently bonded carbon

Abstract Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct “metastable” phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n -diamond as a cubic-analog of diaphite-like lonsdaelite phase.

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

Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Furthermore, our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamic Nanopeptide Assemblies for Trans-Tympanic Drug Delivery

Aim: Otitis media is a common otolaryngologic diagnosis worldwide. Invasive methods to curtail and treat frequent occurrences are undesirable, thus necessitating the identification and production of a non-invasive approach to treating the disease. Due to tympanic membrane thickness, ototopical drug delivery is challenging. In this preliminary study, formulations integrating nanopeptides and thermoresponsive polymeric hydrogels are utilized to improve the efficiency of trans-tympanic membrane drug delivery. Methods: Peptides were synthesized using standard Fmoc (fluorenylmethoxycarbonyl protecting group) based solid state peptide synthesis on an automated peptide synthesizer. Ciprofloxacin release was simulated using multiwell microplates with porous inserts. Rate of Ciprofloxacin release was measured over a 48-hour period using a 200 uL solution of peptide fibers and Ciprofloxacin at 1 wt% each, and the labeled peptide at 0.1 wt% in PBS at pH of 7.4. The cytotoxicity of the PA (peptide amphiphile, specifically c16-AHL 3 K 3 -CO 2 H) micelle and fiber with and without ciprofloxacin was investigated by examining epidermal keratinocyte viability in the presence of the material at various concentrations. Laser scanning confocal microscopy was performed with excitation of the calcein dye at 485 nm and the PA-TAMRA (rhodamine labeled peptide) at 515 nm. Results: We have demonstrated the potential viability of a self-assembled peptide amphiphile hydrogel capable of transitioning from a network of 1D nanoscale fibers to 0D micelles. This dissociative mechanism of action yields a peptide that is an effective cell penetrating peptide (CPP) while temporally controlling the release of the antibiotic ciprofloxacin. Conclusion: This work highlights the potential utility of the dynamic process of an engineered peptide hydrogel capable of dissociating into CPPs capable of facilitating drug delivery across the tympanic membrane.

cell culture↗
DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effects of weave path parameters on the geometry of wire arc additive manufactured features

Here, the present work investigates the effect of process parameters on the geometry of wire arc additive manufactured parts. The geometric accuracy of features produced with a weaving strategy is compared to what can be accomplished with a typical overlapping bead strategy. In this work, single-layer and multi-layer geometries were deposited under varying process and path parameters. The wavelength, amplitude, and torch speed of the weaving path were varied, while the power, wire feed speed, and contact tip to work distance remained constant. The geometric deposition efficiencies of several samples produced with a weave strategy are directly compared to samples generated with two parallel overlapping beads with torch speeds defined to match the deposition rate of the weave samples. Feature geometries were characterized using optical microscopy and laser scanning data. The results indicate that implementing a weave path strategy can improve the geometric accuracy of wire arc additive features, thus increasing the effective volumetric deposition rate of the process. It is shown that the most consistent improvements resulted from the combination of low wavelength and high amplitude, which correlate to wider and taller printed layers.

GMAW↗
NASA NTRS1998

Thermomechanical Fatigue Durability of T650-35/PMR-15 Sheet Molding Compound

Although polyimide based composites have been used for many years in a wide variety of elevated temperature applications, very little work has been done to examine the durability and damage behavior under more prototypical thermomechanical fatigue (TMF) loadings. Synergistic effects resulting from simultaneous temperature and load cycling can potentially lead to enhanced, if not unique, damage modes and contribute to a number of nonlinear deformation responses. The goal of this research was to examine the effects of a TMF loading spectrum, representative of a gas turbine engine compressor application, on a polyimide sheet molding compound (SMC). High performance SMCs present alternatives to prepreg forms with great potential for low cost component production through less labor intensive, more easily automated manufacturing. To examine the issues involved with TMF, a detailed experimental investigation was conducted to characterize the durability of a T650-35/PMR-15 SMC subjected to TMF mission cycle loadings. Fatigue damage progression was tracked through macroscopic deformation and elastic stiffness. Additional properties, such as the glass transition temperature (T(sub g) and dynamic mechanical properties were examined. The fiber distribution orientation was also characterized through a detailed quantitative image analysis. Damage tolerance was quantified on the basis of residual static tensile properties after a prescribed number of TMF missions. Detailed microstructural examinations were conducted using optical and scanning electron microscopy to characterize the local damage. The imposed baseline TMF missions had only a modest impact on inducing fatigue damage with no statistically significant degradation occurring in the measured macroscopic properties. Microstructural damage was, however, observed subsequent to 100 h of TMF cycling which consisted primarily of fiber debonding and transverse cracking local to predominantly transverse fiber bundles. The TMF loadings did introduce creep related effects (strain accumulation) which led to rupture in some of the more aggressive stress scenarios examined. In some cases this creep behavior occurred at temperatures in excess of 150 C below commonly cited values for T(sub g). Thermomechanical exploratory creep tests revealed that the SMC was subject to time dependent deformation at stress/temperature thresholds of 150 MPa/230 C and 170 MPa/180 C.

Castelli, Michael G.↗
DOE OSTI2021

Ultrafast Electron Microscopy for Spatial-Temporal Mapping of Charge Carriers

This LDRD supported efforts to significantly advance the scanning ultrafast electron microscope (SUEM) for spatial-temporal mapping of charge carrier dynamics in semiconductor materials and microelectronic devices. Sandia's SUEM capability in Livermore, CA, was built and demonstrated with previous LDRD funding; however, the stability and usability of the tool limited the throughput for analyzing samples. A new laser alignment strategy improved the stability of the SUEM, and the design and characterization of a new micro-channel plate (MCP)- based detector improved the signal-to-noise of the SUEM signal detection. These enhancements to the SUEM system improved throughput by over two orders of magnitude (before, a single time series of SUEM measurements would take several days to several weeks to acquire; now, the same measurements can be completed in~90 minutes in an automated fashion). The SUEM system can now be routinely used as an analytical instrument and will be a central part of several multi-year projects starting in FY22.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Measuring 3D Profilometry of SAVY-4000 Nuclear Material Storage Containers: Novacam TubeInspect Capabilities Report

The SAVY-4000 container series is a general-purpose interim storage container for nuclear materials, developed and maintained by Los Alamos National Laboratory (LANL). It is the first vented, general-use nuclear material container to be demonstrated as meeting the requirements outlined in DOE M 441.1-1, the Nuclear Material Packaging Manual. Due to the challenging radiation, thermal, and corrosive storage conditions that the SAVY containers must endure, continuous surveillance techniques are employed to ensure the containers meet all safety standards and specifications. These inspections are typically performed by human operators, who check for issues such as corrosion, O-ring deterioration, corrosion, filter integrity, and potential manufacturing defects. However, human inspections alone are not sufficient, and automated inspection technologies, such as the ATIS system, as well as other automated systems are also utilized. The MicroCam TubeInspect, developed by Novacam Technologies Inc., is designed to address the challenges of understanding how manufacturing variations in the SAVY-4000 container series may affect performance. It is a 3D profilometry measurement system that enables detailed analysis of surface features, including defects, surface roughness, and manufacturing variations. This advanced tool significantly enhances rapid surveillance techniques for both pristine and used containers. In this study, container properties such as surface roughness, thickness, and geometric attributes like circularity are measured for SAVY-4000 containers. Artificially corroded or dented containers are examined to demonstrate the MicroCam's ability to quantify defects. A sensitivity analysis is also conducted, comparing the MicroCam results to those obtained using more precise instruments such as confocal microscopy. This comparison aims to provide valuable insights into container quality, durability, and potential improvements in manufacturing processes.

42 ENGINEERING↗
DOE OSTI2024

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries

The rapid growth of automated and autonomous instrumentation brings forth opportunities for the co-orchestration of multimodal tools that are equipped with multiple sequential detection methods or several characterization techniques to explore identical samples. This is exemplified by combinatorial libraries that can be explored in multiple locations via multiple tools simultaneously or downstream characterization in automated synthesis systems. In co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, an orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Herein, we propose and implement a co-orchestration approach for conducting measurements with complex observables, such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure and integration into an iterative workflow via multi-task Gaussian Processes (GPs). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GPs. We illustrate this method for different modes of piezoresponse force microscopy and micro-Raman spectroscopy on a combinatorial Sm-BiFeO3 library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of the measured signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Intense Pulse Light Annealing of Perovskite Photovoltaics Using Gradient Flashes

Perovskite solar cells (PSCs) have been fabricated through high-speed and low-cost depositions but often require long annealing. Intense pulse light (IPL) can anneal thin films in seconds after deposition by inducing very high temperatures lasting for milliseconds, and multiple flashes can be used to tune the temperature profile. In this study, a gradient flash annealing (GFA) approach is introduced and compared to uniform flash annealing (UFA) by investigating the crystallinity, morphology, and phase evolution of the CH 3 NH 3 PbI 3 perovskite films and their impact on PSC performance. Unlike UFA, low-intensity pulsed irradiation during the pre-annealing stage of GFA played a significant role on enhancing the PSC performance by forming pure-phase CH 3 NH 3 PbI 3 perovskite thin films with superior morphology and high crystallinity. To understand the kinetics, a transient thermal simulation using ANSYS was developed and confirmed with an experimental setup. The results combined with microscopy and spectrophotometry were used to visualize how duration, delay time, photon flux, and flash count parameters participated in crystallization, phase, and morphology evolution. The IPL annealing induced rapid surface temperature increase, reaching as high as 800 °C, while produced well-developed and bound perovskite grains without any surface defects in an uncontrolled ambient environment with high humidity (>60%). The study resulted in PSCs with maximum efficiency and fill factor of 9.27 and 69.92% for the UFA and 11.75 and 68% when annealed through the GFA approach, respectively. Lastly, this work utilized IPL as the sole thermal source for post-deposition annealing to rapidly fabricate efficient PSCs in only 10 s, which opens the pathway for high-speed, low-cost, and large-scale automated fabrication of perovskite photovoltaics and semiconductors.

14 SOLAR ENERGY↗
DOE OSTI2024

High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

Characterization and Analytical Technique↗