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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Estimating cluster masses from SDSS multiband images with transfer learning

ABSTRACT The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We present a transfer-learning approach to estimate cluster masses using the ugriz-band images in the SDSS Data Release 12. The target masses are derived from X-ray or SZ measurements that are only available for a small subset of the clusters. We designed a semisupervised deep learning model consisting of two convolutional neural networks. In the first network, a feature extractor is trained to classify the SDSS photometric bands. The second network takes the previously trained features as inputs to estimate their total masses. The training and testing processes in this work depend purely on real observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232 dex on average and 0.214 dex for the best fold. The performance is comparable to that given by redMaPPer, 0.192 dex. We have further applied a joint integrated gradient and class activation mapping method to interpret such a two-step neural network. The performance of our algorithm is likely to improve as the size of training data set increases. This proof-of-concept experiment demonstrates the potential of deep learning in maximizing the scientific return of the current and future large cluster surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Ultrafast Dynamics of Plasmon-Coupled Excitons in Semiconducting Nanoplatelets

Exciton-plasmon coupling in nanomaterials produces many relevant phenomena for photonics applications including increased light-matter interactions, enhanced radiative rates of quantum emitters, and coherent energy exchange. In the case of exciton coupling to surface plasmon polaritons (SPPs), dispersive interactions controlled by the wavevector of optical excitation create the opportunity for tunable optical emission. Strong temporal impacts on exciton lifetimes can also occur in coupled systems, creating the opportunity for ultrafast control of exciton lifetime via changes in electronic coupling magnitude to a dispersive SPP. The coupling strength can be impacted by the morphology of the nanomaterials. Here, in this study, we utilize colloidal semiconductor nanoplatelets deposited onto thin silver plasmonic films, and compare the results to semiconductor quantum dots deposited on the silver films. We map the dispersion of the coupled systems and measure the ultrafast transient absorption response of the coupled systems. Due to the larger interaction areas of the nanoplatelets that lie flat on the silver films, a greater degree of coupling is found for the nanoplatelets, and much faster temporal responses are found as compared to quantum dots. Fresnel theory calculations that incorporate heavy and light hole features can reproduce the dispersion of the nanoplatelet-silver film, and a simple three-state model is developed to provide insights into the nature of the coupling at different photon energies along the dispersion curve.

colloidal semiconductor nanoplatelets↗
DOE OSTI2025

Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under $0.3σ$) in the $Ω_\mathrm{m}-S_8$ plane. Assuming the $w$CDM model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $Ω_\mathrm{m} = 0.304\pm0.037$-constraints 70% tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

Prat, J. [Nordita; Royal Inst. Tech., Sodertalje; ↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Mapping resonant inelastic x-ray scattering onto electronic structure of iridium compounds

Resonant inelastic X-ray scattering (RIXS) is an indispensable tool that can selectively probe the electronic structure of active sites in energy conversion systems, for example, iridium oxides. However, decoding the RIXS spectra remains challenging due to its inherently complex many-body interactions. Here, we analyze Ir L 3 edge RIXS spectra of Ir, IrCl 3 , and IrO 2 , employing the joint density of states (JDOS) calculated directly from electronic band-structure calculations. The overall energy-loss features in the RIXS spectra are well reproduced by the JDOS, reaffirming the close correspondence between electron–hole excitations and RIXS spectra observed in prior studies. Intriguingly, the RIXS spectra above ∼4 eV in metallic Ir and IrO 2 follow a power-law behavior, 𝐼 RIXS ~Δ −𝑝 , where Δ is the energy loss and p is the associated power-law exponent. This is consistent with edge-singularity behavior commonly found in resonant X-ray scattering from metallic samples. Furthermore, orbital-projected JDOS enables a decomposition of the IrO 2 spectrum into specific dd transitions, providing a clear interpretation of orbital excitations and an efficient strategy for decoding RIXS spectra in iridium-based energy conversion systems.

5d↗
DOE OSTI2025

Effect of Stoichiometry on the Structure and Polarization of BaTiO 3

Barium titanate (BaTiO 3 ) is a material of interest for photonic device applications due to its strong optical non-linearity. However, BaTiO 3 -based devices have not found widespread adoption, in part due to the challenges associated with synthesizing high quality thin-films. Here, high-resolution scanning transmission electron microscope (STEM) imaging is used to investigate the atomic structure of both on- and off-stoichiometric BaTiO 3 synthesized by molecular beam epitaxy (MBE). Here, this investigation reveals an asymmetry in the way the BaTiO 3 atomic lattice accommodates off-stoichiometry growth and unveils features beyond what is expected from diffraction or surface characterization techniques. Excess titanium incorporates into the BaTiO 3 lattice to form pervasive defects despite titanium-rich films having a low surface roughness and high-quality appearance in diffraction. Excess barium forms a rough, water-soluble surface layer but does not significantly impact the quality of the BaTiO 3 lattice. STEM is used to map titanium atom displacement in real-space. The average displacement distance is 30–60 pm in the strained thin-films, higher than the <20 pm displacement in bulk BaTiO 3 . Additionally, the titanium atom displacement direction deviates from the c-axis of the unit cell, which may have implications for the material's electro-optic tensor and thus for electro-optic device design.

Cavanagh, Ashley E. [Harvard Univ., Cambridge, MA ↗
DOE OSTI2021

Generating rotating spacetime in Ricci-based gravity: naked singularity as a black hole mimicker

Motivated by the lack of rotating solutions sourced by matter in General Relativity as well as in modified gravity theories, in this work we extend a recently discovered exact rotating solution of the minimal Einstein-scalar theory to its counterpart in Eddington-inspired Born-Infeld gravity coupled to a Born-Infeld scalar field. This is accomplished with the implementation of a well-developed mapping between solutions of Ricci-Based Palatini theories of gravity and General Relativity. The new solution is parametrized by the scalar charge and the Born-Infeld coupling constant apart from the mass and spin of the compact object. Compared to the spacetime prior to the mapping, we find that the high-energy modifications at the Born-Infeld scale are able to suppress but not remove the curvature divergence of the original naked null singularity. Depending on the sign of the Born-Infeld coupling constant, these modifications may even give rise to an additional timelike singularity exterior to the null one. In spite of that, both of the naked singularities before and after the mapping are capable of casting shadows, and as a consequence of the mapping relation, their shadows turn out to be identical as seen by a distant observer on the equatorial plane. Even though the scalar field induces a peculiar oblateness to the appearance of the shadow with its left and right endpoints held fixed, the closedness condition for the shadow contour sets a small upper bound on the absolute value of the scalar charge, which leads to observational features of the shadow closely resembling those of a Kerr black hole.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Shocks in the stacked Sunyaev-Zel’dovich profiles of clusters II: Measurements from SPT-SZ + Planck Compton- y map

ABSTRACT We search for the signature of cosmological shocks in stacked gas pressure profiles of galaxy clusters using data from the South Pole Telescope (SPT). Specifically, we stack the latest Compton-y maps from the 2500 deg2 SPT-SZ survey on the locations of clusters identified in that same data set. The sample contains 516 clusters with mean mass $\langle M_{\rm 200m}\rangle = 10^{14.9} \, {\rm M}_\odot$ and redshift 〈z〉 = 0.55. We analyse in parallel a set of zoom-in hydrodynamical simulations from the three hundred project. The SPT-SZ data show two features: (i) a pressure deficit at R/R200m = 1.08 ± 0.09, measured at 3.1σ significance and not observed in the simulations, and; (ii) a sharp decrease in pressure at R/R200m = 4.58 ± 1.24 at 2.0σ significance. The pressure deficit is qualitatively consistent with a shock-induced thermal non-equilibrium between electrons and ions, and the second feature is consistent with accretion shocks seen in previous studies. We split the cluster sample by redshift and mass, and find both features exist in all cases. There are also no significant differences in features along and across the cluster major axis, whose orientation roughly points towards filamentary structure. As a consistency test, we also analyse clusters from the Planck and Atacama Cosmology Telescope Polarimeter surveys and find quantitatively similar features in the pressure profiles. Finally, we compare the accretion shock radius ($R_{\rm sh,\, acc}$) with existing measurements of the splashback radius (Rsp) for SPT-SZ and constrain the lower limit of the ratio, $R_{\rm sh,\, acc}/R_{\rm sp}\gt 2.16 \pm 0.59$.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes

By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.

Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗
DOE OSTI2021

Reactive binder and aggregate interfacial zones in the mortar of Tomb of Caecilia Metella concrete, 1C BCE, Rome

Integrated spectroscopic analyses and synchrotron X-ray microdiffraction investigations provide insights into the long-term reactivity of volcanic aggregate components and calcium-aluminum-silicate-hydrate (C-A-S-H) binder in mortar samples from the robust concrete of the sepulchral corridor of the Tomb of Caecilia Metella, 1st C BCE, Rome. The results of innovative micrometer-scale analytical maps indicate that Pozzolane Rosse tephra components–scoria groundmass, clinopyroxene, and leucite crystals–contributed to pozzolanic production of C-A-S-H binder and then remained reactive long after hydrated lime (Ca(OH) 2 ) was fully consumed. The C-A-S-H binding phase is reorganized into wispy halos and tendril-like strands, some with nanocrystalline preferred orientation or, alternatively, split into elongate features with short silicate chain lengths. These microstructures apparently record chemical and structural destabilization of C-A-S-H during excessive incorporation of Al 3+ and K + released through leucite dissolution. Resistance to failure may result from the intermittent toughening of interfacial zones of scoriae and clinopyroxene crystals with post-pozzolanic strätlingite and Al-tobermorite mineral cements and from long-term remodeling of the pozzolanic C-A-S-H binding phase. Roman builders’ selection of a leucite-rich facies of Pozzolane Rosse tephra as aggregate and construction of the tomb in an environment with high surface and ground water exposure apparently increased beneficial hydrologic activity and reactivity in the concrete.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Carbon Storage Open Database

The Carbon Storage Open Database is a collection of spatial data obtained from publicly available sources published by several NATCARB Partnerships and other organizations. The carbon storage open database was collected from open-source data on ArcREST servers and websites in 2018, 2019, 2021, and 2022. The original database was published on the former GeoCube, which is now EDX Spatial, in July 2020, and has since been updated with additional data resources from the Energy Data eXchange (EDX) and external public data resources. The shapefile geodatabase is available in total, and has also been split up into multiple databases based on the maps produced for EDX spatial. These are topical map categories that describe the type of data, and sometimes the region for which the data relates. The data is separated in case there is only a specific area or data type that is of interest for download. In addition to the geodatabases, this submission contains: 1. A ReadMe file describing the processing steps completed to collect and curate the data. 2. A data catalog of all feature layers within the database. Additional published resources are available that describe the work done to produce the geodatabase: Morkner, P., Bauer, J., Creason, C., Sabbatino, M., Wingo, P., Greenburg, R., Walker, S., Yeates, D., Rose, K. 2022. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104945 Morkner, P., Bauer, J., Shay, J., Sabbatino, M., and Rose, K. An Updated Carbon Storage Open Database - Geospatial Data Aggregation to Support Scaling -Up Carbon Capture and Storage. United States: N. p., 2022. Web. https://www.osti.gov/biblio/1890730 Morkner, P., Rose, K., Bauer, J., Rowan, C., Barkhurst, A., Baker, D.V., Sabbatino, M., Bean, A., Creason, C.G., Wingo, P., and Greenburg, R. Tools for Data Collection, Curation, and Discovery to Support Carbon Sequestration Insights. United States: N. p., 2020. Web. https://www.osti.gov/biblio/1777195 Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

carbon storage↗
DOE OSTI2022

Hybrid Power Plants: Status of Operating and Proposed Plants, 2022 Edition [Slides]

Falling battery prices and the growth of variable renewable generation are driving a surge of interest in “hybrid” power plants that combine, for example, wind or solar generating capacity with co-located batteries. While most of the current interest involves pairing photovoltaic (PV) plants with batteries, other types of hybrid or co-located plants with wide-ranging configurations have been part of the U.S. electricity mix for decades. This annually updated briefing tracks and maps existing hybrid or co-located plants across the United States while also synthesizing data mined from power purchase agreements (PPAs) and generation interconnection queues to shed light on near- and long-term development pipelines. The scope includes co-located hybrid plants that pair two or more generators and/or that pair generation with storage at a single point of interconnection, and full hybrids that feature co-location and co-control. The focus is on plants with one megawatt (MW) or more of capacity; smaller (often behind-the-meter) projects are also increasingly common, but are not included in this data synthesis. Key findings from the latest briefing include: -At the end of 2021, there were nearly 300 hybrid plants (>1 MW) operating across the United States, totaling nearly 36 gigawatts (GW) of generating capacity and 3.2 GW/8.1 GWh of energy storage. PV+storage plants are by far the most common, dominating in terms of plant number (140), storage capacity (2.2 GW/7.0 GWh), storage:generator ratio (53%), and storage duration (3.2 hours). But there are nearly twenty other hybrid plant configurations as well, including several different fossil hybrid categories (each dominated by the fossil component) as well as wind+storage, wind+PV, wind+PV+storage, geothermal+PV, and others. -Last year was a breakout year for PV+storage hybrids in particular: 67 of the 74 hybrids added in 2021 were PV+storage. By the end of 2021, there were more GW of battery capacity installed in PV+storage hybrids (2.2 GW) than as standalone storage plants (1.8 GW). The difference is even starker in energy terms, with PV+storage plants hosting twice as much battery capacity as standalone storage plants (7 GWh vs. 3.5 GWh, respectively). Much of the battery capacity added in hybrid form in 2021 was a battery retrofit to a pre-existing PV plant. -Data on plants under development from the interconnection queues of all seven ISOs/RTOs plus 35 individual utilities suggest that these hybridization trends are likely to continue. At the close of 2021, there were more than 670 GW of solar plants in the nation’s queues; 285 GW (~42%) of this capacity was proposed as a hybrid, most typically pairing PV with battery storage (PV+storage represented nearly 90% of all hybrid capacity in the queues). For wind, 247 GW of capacity sat in the queues, with 19 GW (~8%) proposed as a hybrid, again most-often pairing wind with storage (wind+storage represented ~4% of all hybrid capacity in the queues). Meanwhile, nearly half of all storage in the queues is estimated to be part of a hybrid plant. While many of these proposed plants will not ultimately reach commercial operations, the depth of interest in hybrid plants—especially PV+storage—is notable.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Laser-heated diamond anvil cell synthesis and recovery of metastable MnSb 2 and YbZn 2 for post-synthesis transport studies

The creation and exploration of new materials under extreme pressure–temperature conditions has become increasingly reliant on laser-heated diamond anvil cell (LHDAC) techniques, which provide direct access to previously unexplored regions of multinary phase diagrams. Whereas numerous high-pressure phases have been identified in situ, systematic recovery and post-synthesis physical property characterization of these materials remain significant challenges. In this work, we describe the setup and implementation of an LHDAC-based synthesis and recovery workflow and demonstrate its application to metastable MnSb 2 and YbZn 2 phases. Synchrotron x-ray diffraction and spatial mapping confirm dominant formation of the targeted phases, whereas laboratory-based refinement quantifies phase fractions despite intrinsic microstrain and minor secondary phases. High-pressure transport measurements on recovered samples reveal pressure-tunable electronic instabilities in both systems. In MnSb 2 , pressure suppresses two high-temperature magnetic ordering anomalies, observed in transport, by ∼5 GPa and, for higher pressures, induces a new low-temperature feature that increases with further pressure increase. In hexagonal high-pressure YbZn 2 , an electronic reconstruction emerges at ∼11 GPa, characterized by semiconducting-like behavior from ∼30 to 300 K and a broad low-temperature coherence crossover near 30 K. Our results establish LHDAC synthesis not only as a structural discovery tool but also as an experimental platform for investigating correlated quantum states stabilized far from equilibrium thermodynamic conditions.

Huyan, S. [Iowa State University, Ames, IA (United↗
DOE OSTI2022

Multi-frame, ultrafast, x-ray microscope for imaging shockwave dynamics

Inertial confinement fusion (ICF) holds increasing promise as a potential source of abundant, clean energy, but has been impeded by defects such as micro-voids in the ablator layer of the fuel capsules. It is critical to understand how these micro-voids interact with the laser-driven shock waves that compress the fuel pellet. At the Matter in Extreme Conditions (MEC) instrument at the Linac Coherent Light Source (LCLS), we utilized an x-ray pulse train with ns separation, an x-ray microscope, and an ultrafast x-ray imaging (UXI) detector to image shock wave interactions with micro-voids. To minimize the high- and low-frequency variations of the captured images, we incorporated principal component analysis (PCA) and image alignment for flat-field correction. After applying these techniques we generated phase and attenuation maps from a 2D hydrodynamic radiation code (xRAGE), which were used to simulate XPCI images that we qualitatively compare with experimental images, providing a one-to-one comparison for benchmarking material performance. Moreover, we implement a transport-of-intensity (TIE) based method to obtain the average projected mass density (areal density) of our experimental images, yielding insight into how defect-bearing ablator materials alter microstructural feature evolution, material compression, and shock wave propagation on ICF-relevant time scales.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Microstructure‐Informed Analysis Framework for Lattice Structures: Guiding Topology‐Material Synergy in Titanium Alloys

In this work we discuss a microstructure‐informed analysis framework for lattice structures that maps the material's microstructural response to guide topology selection and mechanical performance optimization. By coupling geometrical topology with intrinsic material behavior, we demonstrate how anisotropic microstructural response can inform the design of optimized lattice structures. To illustrate this concept, we focus on two distinct classes of titanium alloys: Ti5553 (Ti‐5Al‐5Mo‐5V‐3Cr wt%), which exhibits a predominantly ‐phase microstructure, and Ti64 (Ti‐6Al‐4V wt%), which features a dual‐phase structure. These alloys exhibit markedly different mechanical responses under multiaxial loading in “fully dense” solid form. The strut‐level stress analysis of these alloys reveals how specific microstructural characteristics can guide the selection of appropriate lattice topologies. Two representative lattice configurations, one stretching‐dominated and one bending‐dominated, are evaluated under identical loading conditions to explore how microstructure‐driven design can lead to topology choices that are better suited to accommodate shear or other critical local stress states, thereby enhancing mechanical performance. A strut‐level mechanics‐based analysis is performed to evaluate shear stress distribution and highlight the role of topology‐microstructure synergy and compatibility in determining overall lattice behavior. The findings emphasize the importance of designing structures that are both load‐aware and microstructure‐responsive, enabling more effective material utilization in advanced engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Liquid Interfacial Electron Microscopy Identifies Nanogalvanic Corrosion in Pearlitic Steel

The nanoscale mechanisms of localized corrosion in low carbon steels have remained elusive due to the complexity of studying the degradative material behavior at nanoscale solid-liquid interfaces. We identified various steps in the nanogalvanic corrosion processes using in-situ liquid-cell scanning transmission electron microscopy (STEM) using a microfluidic holder by Hummingbird Scientific. Initial work, performed at low magnification, identified the initiation point on a 1018 low-carbon steel surface. This initiation point was determined to be a triple junction of two ferrite grains bridging a cementite grain in contact with a baseline electrolyte of 6 uM CO2 dissolved in a buffered (2.78 uM Na2SO4) aqueous solution, pH 6.1. The pre-etched low-carbon steel surface was prepared using focused ion beam lift-out procedures to extract a cross-section of the low-carbon steel surface, which then was thinned to about 150 nm and transferred to a SiN membrane microfluidic window. The transfer was made using a lift-out needle to attach the low-carbon steel lamella to the corner of the SiN window, and then Pt/C deposition held the lamella in contact with the window while it was released from the lift out needle. To identify the triple point on the low carbon steel lamella, prior to attachment on the SiN window, the sample was characterized for compositional variations with energy dispersive x-ray spectroscopy mapping, grain orientation and phase mapping with precession electron diffraction, and thickness mapping with energy filtered transmission electron microscopy. This pre-characterization prior to the in-situ experiment provided a map of the multiphase and multigrain structure, where the in-situ liquid cell imaging provided a clear understanding of the initiation point on the sample. These data were cross-correlated to paint a holistic picture of the triple junction site, enabling low electron-fluence in-situ snapshot imaging to avoid dominating the native corrosion reactions with effects from the incident electron beam. This initial result identified that localized, nanogalvanic corrosion at the phase interface was the dominant corrosion process in the low-carbon steel, so we next targeted the observation of an array of these nanogalvanic features phase boundaries in a pearlite grain. Near-surface ferrite/cementite phase interfaces that typify pearlitic low-carbon steel were extracted, pre-characterized, and imaged for the in-situ corrosion processes. The sample was a cross-section from a pearlite grain, with alternating ferrite and cementite grains that extended microns down from the pre-etched low-carbon steel pipe surface. After contact with a buffered aqueous solution, the phase boundaries between the ferrite and cementite began to dissolve, with observable material loss and thickness changes in the dark-field and bright-field STEM images. Within minutes, the corrosion front proceeded deeper into the material, claiming a thin layer of ferrite around all exposed phase boundaries before progressing laterally into the ferrite matrix, converting the ferrite to corrosion product normal to each buried cementite grain. Formation of the corrosion product causes a volumetric expansion, creating a lateral wedging force that mechanically ejects the cementite grains from their grooves and leaves behind percolation channels into the steel substructure. Rapid and deleterious, this nanogalvanic corrosion pathway represents an important target for understanding and preventing run-away degradation in this common building material. Observation of this corrosion mechanism was enabled by the combination of pre-characterization using standard structural, grain, and compositional analysis in the TEM, which provides maps for understanding the reaction propagation captured in low-dose, in-situ, liquid-cell STEM.

corrosion↗