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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Final Technical Report - Rapid Surface Microanalysis using a Low Temperature Plasma

This project focused on improving our current understanding and scientific knowledge in the area of plasma-surface interactions and plasma assisted material synthesis related to advanced microelectronics and nanotechnology. Current challenges include: controlling the interaction of Low Temperature Plasma (LTP) with a single layer of atoms to manufacture integrated circuits, continued miniaturization of integrated circuits, LTP processing of material surfaces and thin films to enable industrial scale fabrication of advanced microelectronics, synthesis of new materials, nanomaterials, nanotubes, and complex materials, Technology developed in this subtopic is of value to either (i) enable scans of surfaces (~1 sq. cm area) using various microscopies (electron, optical, other) at high resolution (micron or sub-micron resolution) rapidly (hours or days rather than years to complete a high-resolution scan of such a large surface area), or (ii) enable scans of surfaces (~1 sq. cm area) using various microscopies (electron, optical, other) at relatively low resolution rapidly, then apply algorithms to select spots for micron-scale imaging. Sputtering occurs when particles of a solid material are ejected from its surface by energetic particles from a plasma. While the degradation of the solid material and the subsequent deposition of the ejected material onto vulnerable surfaces are the usual subjects of sputtering studies, plasma science has yet to be combined with sputtering to create new diagnostics devices and systems. Small changes in the design of the plasma discharge device make it possible to create broad plasma beams for rapid scanning or small plasma beams to obtain the distribution of ejected elements with micron resolution. In the high-resolution use, the ion flux is extracted from the gas-discharge plasma and focused by a spherical emission surface to micron sizes onto the target specimen, providing very local sputtering and local elemental analysis. We call this “self-focusing”. The radiation from the excited and ionized sputtered atoms is recorded by a spectrometer through a window and fiberglass cable and analyzed with standard software packages used for optical glow discharge spectroscopy. Computer simulations of beam formation were used to verify and optimize the designs to be tested. A prototype was designed, constructed, and used to start experiments of beam formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations

Depressions – inwardly draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous “depressions” also arise in image processing and morphological segmentation, where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. This hierarchy can be used to selectively fill or breach depressions or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on GitHub and Zenodo.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Abstract Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt 3 Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Rare Earth Element-Induced Condensation of the Block V of the Repeats-in-Toxin Domain from CyaA from Bordetella pertussis for Separations

Rare earth elements (REEs) are critical for the development of a range of new technologies. However, the current industrial separation processes of these metals from natural sources, recycled materials, and industrial effluents involve the large consumption of organic solvents, resulting in a sizable environmental footprint. We aim to exploit the high affinity of the block V peptide of the repeats-in-toxin (RTX) domain of the adenylate cyclase protein from Bordetella pertussis for the separation of REEs. This peptide selectively binds with lanthanide (Ln) cations and can undergo Ln-induced phase separation, which can be used in bioseparation processes. Here, we evaluated the self-assembling structures of complexes of the RTX domain peptide folded in the presence of Ln 3+ cations. Size distribution and surface potential measurements of complexes were taken to understand the Ln-induced changes in the complexed peptide. Transmission electron microscopy imaging was used to explore the structures of complexes, while anomalous small-angle X-ray scattering measurements were used to determine the distribution of Ln 3+ ions within the protein-based macrostructures. In the presence of excess Ln 3+ , we observed the formation of coral-like cylindrical structures comprised of Ln 3+ -RTX complexes, with approximately eight trivalent metals per peptide within the nanosized assemblies. These findings provide new insights into the structural organization of assembled RTX domains and their ability to coordinate with REEs, forming nanosized, metal-rich structures that naturally condense, providing a proof-of-concept for protein-based separation processes of these critical materials.

chemical structure↗
DOE OSTI2020

Lagrangian simulations of spray interaction with a surface using a stochastic multi-regime impingement model

Modeling of spray impingement upon the piston or cylinder surface is important for hydrocarbon emission and soot predictions in high-fidelity simulation of internal combustion engines with direct injections. Due to the complexity of the physical process of spray impingement, current computational models heavily rely on the empirical studies which involve large uncertainties. A computational model is presented for spray impingement on solid walls in general computational fluid dynamics (CFD) simulations. The model adopts the Marmanis-Thoroddsen theory for determining the number of secondary droplets and a stochastic method for calculating the velocities of the secondary droplets. The numerical methods and parallel implementation of the impingement model are presented in detail. The model is implemented in Grit, which is a recently developed CPU/GPU performance-portable Lagrangian particle library, for accelerating spray simulations. The model and its implementation are tested for examples of spray impingements on flat and smooth surfaces with different impingement angles.

Ge, Wenjun↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2021

Spatio-temporal ablation dynamics and plasma chemistry of aluminum induced by temporally modulated ytterbium fiber laser

Here, in this work, we studied single-pulse ablation dynamics of a temporally modulated continuous wave laser-material interaction with Al using in situ multimodal time-resolved diagnostics that describe in detail the associated physical and chemical processes. Time-resolved scattering, emission imaging, and optical emission spectroscopy unveiled a sequence of events spread out across three distinct phases: (i) early phase ablation process, associated with particle generation and liquid Al column formation (<20 μs), (ii) secondary detonation when sufficient ejected material is accumulated over the surface (20-50 μs), and (iii) molten liquid Al pool oscillation on the surface, followed by large droplet ejection from the liquid pool (100-500 μs). Atomic Al and AlO were observed with optical emission spectroscopy at different ratios during the entire lifetime of the event, verifying the formation of oxidized Al vapor upon its interaction with air. Morphological and compositional characterization confirmed surface oxidation and material re-solidification in the form of protrusions produced during the irradiation process. This work provides insights into the complex physical and chemical mechanisms of single-pulse ablation in the sub-millisecond laser pulse regime, which are critically important for parameter optimization in a variety of laser processing, microfabrication, and deposition applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Computational Analysis of the Effect of Structured Packing Design on Absorption Column Hydrodynamics for Post-Combustion Carbon Capture Applications

Solvent based post-combustion carbon capture technologies have a potential for reducing carbon emissions from fossil-fuel-fired power plants and industrial sources where CO2 emissions are inherently harder to mitigate, such as steel or cement industries. A prominent technology to achieve this is by retrofitting absorption columns to the existing infrastructure. While these systems have been among the less costly alternatives for carbon capture, they still impose a considerable energy penalty to the operation of power plants or industrial facilities. The optimization of CO2 capture rate in solvent-based absorption process is complex as it depends on several factors including CO2 solubility, solvent reaction kinetics and temperature effects on the solubility, reaction rates, surface tension, and thermophysical properties of the solvent and the flue gas. The overall heat and mass transfer also depends on the hydrodynamics, which in turn, is affected by the packing geometry. In the current work, we systematically quantify the effects the design of the structured packing has on the column hydrodynamics, by performing detailed CFD simulations for different geometrical configurations and operating conditions. We then obtain relationships between the key hydrodynamic metrics, such as liquid holdup, interfacial area, wetted area, and pressure drop to the parameters defining the packing geometries and identify new more effective packing designs for the given operating conditions.

Shah, Yash Girish↗
DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Laser-Irradiated Holey Graphene-Supported Single-Atom Catalyst towards Hydrogen Evolution and Oxygen Reduction

Single-atom catalysts (SAC) can boost the intrinsic catalytic activity of hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR). However, the challenge remains due to the complex synthesis process and insufficient stability. A sustainable approach is applied to synthesizing SACs through laser irradiation and gaining mesoporous graphene oxide (MGO). The surface dangling bonds of nitrogen-doped MGO (NMGO) extract metal atoms species from Co or Fe metal foams and convert them to SAC via an appropriate synthesis approach. Notably, the Co-NMGO electrocatalyst requires low potentials of 146 mV to convey a current density of 10 mA cm -2 towards HER. Similarly, the Fe-NMGO electrocatalyst offers an onset of 0.79 V towards ORR in acidic solution. The individual metal atoms are confirmed via aberration-corrected scanning transmission electron microscopy, and X-ray absorption near-edge structure and extended X-ray absorption fine structure. Density functional theory calculations by applying the grand canonical potential kinetics model revealed that Co-NMGO shows the optimum free reaction energy of -0.17 eV at -0.1 V for HER, and Fe-NMGO has less limiting potential than that of Co-NMGO for ORR case. Furthermore, this work opens a new approach towards the synthesis of SAC and its mechanistic understandings.

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

Height-resolved emission spectroscopy and high-speed imaging of the TiAl6V4 vapor plume under laser powder bed fusion conditions

Optical emission spectroscopy is increasingly used as an in situ monitoring technique during laser powder bed fusion (LPBF) because plume emission holds elemental information not found in other in situ sensing techniques. This work explores the shape, stability, and temperature of the emission plume above the melt pool of Ti6Al4 V undergoing laser scans under LPBF-like processing conditions, using high-speed video and height-resolved spectroscopy to study the details of plume dynamics. Optical emission spectroscopy is conducted in the 480 nm to 525 nm region, where Ti emission is strong, with 0.3 mm vertical resolution above the baseplate. The Boltzmann plot method is used to determine temperature at each probed elevation, which indicates that the hottest location of the plume is occasionally elevated 0.3 mm to 0.6 mm above the scanning surface. The results show that the plume shape, stability, temperature, and spectra are highly dependent on the processing conditions. We highlight some of the complexities of optical emission spectroscopy and discuss potential challenges for implementing optical emission spectroscopy across an entire build.

36 MATERIALS SCIENCE↗
DOE OSTI2026

4D-STEM Coupled with Unsupervised Machine Learning to Reveal at Large-Scale the Microstructural Evolution in Li- and Mn-Rich Cathodes

Li- and Mn-rich (LMR) layered oxides are known to exhibit a thin surface reconstruction layer, which grows during electrochemical cycling in a manner that depends on exposed crystallographic facets, cycling conditions, and electrolyte chemistry. Direct characterization of this layer has traditionally relied on high-resolution electron microscopy, which is inherently limited to small fields of view. Here, we employ four-dimensional scanning transmission electron microscopy (4D-STEM) combined with unsupervised machine-learning clustering to quantitatively map phase distributions over large areas and track their evolution in LMR cathodes during electrochemical aging. Our results show that the surface reconstruction layer consists predominantly of a rocksalt phase, whose thickness varies across different facets following activation cycling and becomes substantially thicker and more uniform during calendar aging. In contrast, a spinel-like phase is observed within the particle bulk. Large-area phase mapping and correlative high-resolution imaging reveal that this spinel-like phase preferentially nucleates at bulk crystallographic defects, including boundaries between 60°-rotated layered domains and associated mixed-phase regions, rather than exclusively at the particle surface. Our findings establish a mechanistic distinction between surface-driven rocksalt formation and bulk-defect-mediated spinel nucleation while demonstrating the unique capability of 4D-STEM to provide statistically robust, mesoscale insight into complex phase-evolution processes in LMR cathodes.

4D-STEM↗
DOE OSTI2020

Understanding Microstructure Variability in Vapor-Deposited Energetic Materials by Using Phase-Field Methods

Critical components, such as detonators, in Sandia's stockpile contain heterogeneous materials whose performance and reliability depend on accurate, predictive models of coupled, complex phenomena to predict their synthesis, processing, and operation. Ongoing research in energetic materials has shown that microstructural properties, such as density, pore-size, morphology, and specific surface area are correlated to their initiation threshold and detonation behavior. However, experiments to study these specific characteristics of energetic materials are challenging and time consuming. Therefore, in this work, we turn to mesoscale modeling methods that may be capable of reproducing some observed phenomena to refine and predict outcomes beforehand. Even so, we have no physics-based modeling capability to predict how the microstructure of an energetic material will evolve over near- and long-term time scales. Thus, the goal of this work is to (i) identify any knowledge gaps in how the underlying microstructure forms and evolves during the synthesis process, and (ii) develop and test a mesoscale phase-field model for vapor deposition to capture critical mechanisms of microstructure formation, evolution, and variability in vapor-deposited energetic materials, such as processing conditions, material properties, and substrate interactions. Based on this work, the phase-field method is shown to be a valuable tool for developing the necessary models containing coupled, complex phenomena to investigate and understand the synthesis and processing of energetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mechanistic understanding of speciated oxide growth in high entropy alloys

Abstract Complex multi-element alloys are gaining prominence for structural applications, supplementing steels, and superalloys. Understanding the impact of each element on alloy surfaces due to oxidation is vital in maintaining material integrity. This study investigates oxidation mechanisms in these alloys using a model five-element equiatomic CoCrFeNiMn alloy, in a controlled oxygen environment. The oxidation-induced surface changes correlate with each element’s interactive tendencies with the environment, guided by thermodynamics. Initial oxidation stages follow atomic size and redox potential, with the latter becoming dominant over time, causing composition inversion. The study employs in-situ atom probe tomography, transmission electron microscopy, and X-ray absorption near-edge structure techniques to elucidate the oxidation process and surface oxide structure evolution. Our findings deconvolute the mechanism for compositional and structural changes in the oxide film and will pave the way for a predictive design of complex alloys with improved resistance to oxidation under extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantification of complex protective surface oxide layer formed during plasma jet exposure of multicomponent ultra-high temperature carbides

Multicomponent ultra-high temperature ceramics (MC-UHTCs) has garnered attention due to their improved thermo-mechanical and oxidation properties. MC-UHTC was progressively formed by adding a UHTC component at each step, producing binary, ternary and quaternary UHTC systems, processed by spark plasma sintering (SPS). Here, we investigate the role of multi-elements and thermochemical stability of the surface oxide layer formed during plasma arc jet exposure (T > 2500 °C) of MC-UHTCs using core-level X-ray photoelectron spectroscopy (XPS) correlated with the structural investigation. The results indicate that adding a UHTC component improves oxidation resistance due to the assemblage of refractory phases forming complex mixed oxides via preferential oxidation. XPS spectra of Ta4f, Nb3d, Ti1p, and Hf4f showed that the nature of the chemical bonds switched from metal carbides to metal oxides (metal oxy-carbides) in the binary system with retained metal carbides in ternary and quaternary UHTCs. The absence of a metal carbide peak in the high-resolution C1s scan of the binary system also supports this finding. The principal complex mixed oxide phases emanate in the exiguous oxidation of MC-UHTCs are quantified using Rietveld analysis. Overall, this study suggests that MC-UHTC is a promising material for possible application in thermal protection systems (TPS).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Complex Dependence of Calcite Crack Kinetics on Salinity: The Role of DLVO and Hydration Forces

Abstract Subcritical crack growth (SCG) plays an important role in many geological processes such as delayed earth rupture and rock weathering. The complex dependency of SCG on the in‐crack fluid chemistry, however, is still poorly understood. In this study, we utilize the newly developed surface force‐based fracture theory (SFFT) to elucidate the relative contributions of surface forces and solute transport to the crack growth kinetics of calcite in NaCl solutions. Expanding on Barenblatt's cohesive crack model, SFFT introduces an effective stress intensity at the crack tip that encompasses all the relevant intermolecular forces across the crack in addition to the external far‐field stresses. The nonlinear system of equations portraying the crack opening profile, the solute distribution in a propagating crack, and the crack growth velocity are numerically solved via an implicit scheme. After carefully calibrating the model for calcite‐water systems, the SFFT is used to predict the SCG response of calcite at different NaCl concentrations, based on various hypotheses. These predictions are then compared to existing SCG data from the literature. We demonstrate that the experimentally observed variation of SCG rate with NaCl concentration cannot be explained solely by DLVO forces (electrostatic and Van der Waals interactions). This can be remediated by introducing an exponentially decaying hydration force with a nonlinear, nonmonotonic dependence on NaCl concentration. Furthermore, we demonstrate that accounting for both diffusive and advective transport of ions is important in explaining the absence of a stage‐II SCG response for calcite in electrolyte solutions. Plain Language Summary Subcritical crack growth (SCG) refers to the slow propagation of cracks in materials under a stress below the threshold for catastrophic failure. SCG is a key process in many geological events, for example, delayed earth ruptures and rock weathering. New initiatives such as underground CO 2 and H 2 storage in carbonate reservoirs further call for better understanding of SCG in carbonate minerals subjected to varying fluid chemistry. This study examines the SCG of calcite, a key mineral found in carbonate rocks, intergranular cement in sandstones, and filling material in mineral veins and faults, determining their deformation and strength. A mathematical model is developed to describe how the crack opens and propagates, how solutes (like salts) distribute within the crack, and how the crack surfaces interact with each other. We used the model to predict calcite SCG in water at different salt concentrations and compared it with experimental data. Our results revealed that the hydration force is the dominating factor in determining the complex, non‐linear dependency of SCG on salinity. We also found that both the movement of ions by diffusion and by bulk water flow are crucial for explaining the SCG rates, especially when the cracks grow quickly. Key Points Surface Force‐Based Fracture Theory predicts the complex subcritical crack growth patterns of calcite crystals immersed in NaCl solutions Results highlight the dominant role of hydration forces in altering the fracture behavior of calcite compared to VdW and electric double‐layer forces Advective solute transport explains the absence of stages‐II and ‐III subcritical crack growth responses in solid‐liquid systems

DLVO↗
DOE OSTI2023

The autophagy receptor NBR1 directs the clearance of photodamaged chloroplasts

The ubiquitin-binding NBR1 autophagy receptor plays a prominent role in recognizing ubiquitylated protein aggregates for vacuolar degradation by macroautophagy. Here, we show that upon exposing Arabidopsis plants to intense light, NBR1 associates with photodamaged chloroplasts independently of ATG7, a core component of the canonical autophagy machinery. NBR1 coats both the surface and interior of chloroplasts, which is then followed by direct engulfment of the organelles into the central vacuole via a microautophagy-type process. The relocalization of NBR1 into chloroplasts does not require the chloroplast translocon complexes embedded in the envelope but is instead greatly enhanced by removing the self-oligomerization mPB1 domain of NBR1. The delivery of NBR1-decorated chloroplasts into vacuoles depends on the ubiquitin-binding UBA2 domain of NBR1 but is independent of the ubiquitin E3 ligases SP1 and PUB4, known to direct the ubiquitylation of chloroplast surface proteins. Compared to wild-type plants, nbr1 mutants have altered levels of a subset of chloroplast proteins and display abnormal chloroplast density and sizes upon high light exposure. We postulate that, as photodamaged chloroplasts lose envelope integrity, cytosolic ligases reach the chloroplast interior to ubiquitylate thylakoid and stroma proteins which are then recognized by NBR1 for autophagic clearance. This study uncovers a new function of NBR1 in the degradation of damaged chloroplasts by microautophagy.

59 BASIC BIOLOGICAL SCIENCES↗