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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Buried Interfaces in Organic Photocathodes for H 2 Evolution: Fermi-Level Pinning and Recombination

Herein, we demonstrate how Fermi-level pinning at buried contacts impacts solar fuel generation in all-polymer photocathodes by systematically comparing the effects of work function, hydroxyl coverage, and hydrogen evolution using chemically modified indium tin oxide (ITO) supports. Photovoltages and net photocathode performance are improved when the ITO is passivated using phosphonic acids, independent of work function, suggesting that the passivation reduces Fermi-level pinning at the buried interface arising from blended heterojunction interactions with surface metal hydroxyls. Transient photovoltage decay reveals differences in recombination mechanisms, supported by light intensity-dependent measurements. Briefly, nonpassivated, hydrophilic contacts exhibit trap-assisted recombination, while passivated, hydrophobic contacts follow bimolecular recombination. We then investigate changes in electroactivity of hole-transfer processes as a function of scan rate and repetitive cycling using a diffusion-controlled molecular redox probe, analogous to a hole-only device achieved via the electrolyte. The nonpassivated buried contacts exhibit higher overpotentials for oxidation, indicative of hole injection/extraction barriers. We observe irreversible electron transfer via the hole-transport level of the blended heterojunction and a strong cycle dependence, consistent with changes in the hole trap state density. Passivation results in more reversible redox behaviors, consistent with more Ohmic-like contacts. Collectively, these results provide context toward the realization of durable organic photoelectrodes with optimized photovoltages and net solar-to-hydrogen conversion efficiencies via fundamental understanding of the rates of carrier generation, recombination, and transport in high-dielectric aqueous environments and opportunities to characterize buried interfaces under device-relevant electric fields.

buried interfaces↗
DOE OSTI2021

Injection Parameters That Promote Branching of Hydraulic Cracks

Fluid injection into rock formations can either produce complex branched hydraulic fractures, create simple planar fractures, or be dominated by porous diffusion. Currently, the optimum injection parameters to create branched fractures are unknown. We conducted repeatable hydraulic fracturing experiments using analog-rock samples with controlled heterogeneity to quantify the fluid parameters that promote fracture branching. A large range of injection rates and fluid viscosities were used to investigate their effects on induced fracture patterns. Paired with a simple analytical model, our results identify the threshold at which fracture transitions from an isolated planar crack to branched cracks when closed natural fractures exist. These results demonstrate that this transition can be controlled by injection rate and fluid viscosity. In relation to the field practices, the present model predicts slickwater and lower viscosity fluid injections promote fracture branching, with the Marcellus shale used as an example.

58 GEOSCIENCES↗
DOE OSTI2022

Robust chain aggregation of low-entropy rigid ladder polymers in solution

Conjugated polymers have been widely investigated where ladder-type conjugated polymers receive more attention due to their rigid backbones and extraordinary properties. However, the understanding of how the rigid conformation of ladder polymers translates to material properties is still limited. Here, we systematically investigated the solution aggregation properties of a carbazole-derived conjugated ladder polymer (LP) and its analogous non-ladder control polymer (CP) via light scattering, neutron scattering, and UV-vis absorption spectroscopy characterization techniques, revealing a highly robust, temperature-insensitive aggregation behavior of the LP. The experimental findings were further validated by computational molecular dynamics simulations. Here we found that the peak positions and intensities of the UV spectra of the LP remained constant between 20 °C and 120 °C in chlorobenzene solution. The polymer also showed a stable hydrodynamic radius measured by dynamic light scattering from 20 °C to 70 °C in the chlorobenzene solution. Using small-angle neutron scattering, no Guinier region was reached in the measured q range down to 0.008 Å –1 , even at elevated temperature. In contrast, the non-ladder control polymer CP was fully soluble in the chlorobenzene solvent without the observation of any notable aggregates. The Brownian dynamics simulation showed that during polymer aggregation, the entropy change of the LP was significantly less negative than that of the non-ladder control polymer. These findings revealed the low entropy nature of rigid conjugated ladder polymers and the low entropy penalty for their aggregation, which is promising for highly robust intermolecular interactions at high temperatures. Such a unique thermodynamic feature of rigid ladder polymers can be leveraged in the design and application of next-generation electronic and optoelectronic devices that function under unconventional high temperature conditions.

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

Enhancing quantum annealing accuracy through replication-based error mitigation *

Abstract Quantum annealers like those manufactured by D-Wave Systems are designed to find high quality solutions to optimization problems that are typically hard for classical computers. They utilize quantum effects like tunneling to evolve toward low-energy states representing solutions to optimization problems. However, their analog nature and limited control functionalities present challenges to correcting or mitigating hardware errors. As quantum computing advances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing (QA). In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is well-suited for currently available noisy intermediate-scale quantum annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being more flexible and compatible with a wider range of hardware connectivity patterns. In comparisons against standard QA without error mitigation on larger problem instances that could not be handled by previous methods, RBM consistently gets better energies and ground state probabilities across parameterized problem sets.

Djidjev, Hristo N. (ORCID:0000000192868824)↗
DOE OSTI2023

Design of stimulus-responsive two-state hinge proteins

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here, in this work, we describe the design of “hinge” proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

X-ray crystallographic and hydrogen deuterium exchange studies confirm alternate kinetic models for homolog insulin monomers

Despite the crucial role of various insulin analogs in achieving satisfactory glycemic control, a comprehensive understanding of their in-solution dynamic mechanisms still holds the potential to further optimize rapid insulin analogs, thus significantly improving the well-being of individuals with Type 1 Diabetes. Here, we employed hydrogen-deuterium exchange mass spectrometry to decipher the molecular dynamics of newly modified and functional insulin analog. A comparative analysis of H/D dynamics demonstrated that the modified insulin exchanges deuterium atoms faster and more extensively than the intact insulin aspart. Additionally, we present new insights derived from our 2.5 Å resolution X-ray crystal structure of modified hexamer insulin analog at ambient temperature. Furthermore, we obtained a distinctive side-chain conformation of the Asn3 residue on the B chain (AsnB3) by operating a comparative analysis with a previously available cryogenic rapid-acting insulin structure (PDB_ID: 4GBN). The experimental conclusions have demonstrated compatibility with modified insulin’s distinct cellular activity, comparably to aspart. Additionally, the hybrid structural approach combined with computational analysis employed in this study provides novel insight into the structural dynamics of newly modified and functional insulin vs insulin aspart monomeric entities. It allows further molecular understanding of intermolecular interrelations driving dissociation kinetics and, therefore, a fast action mechanism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

2025 Intern Poster

The Hot Fuel Examination Facility (HFEF) at the Materials and Fuels Complex (MFC) houses the largest U.S. inert atmosphere hot cell for nuclear material research. Key features include the precision gamma scanning (PGS), Fuel Accident Condition Simulator (FACS), Neutron Radiography Reactor (NRAD), and the focus of this project, the Metallograph Loading Cell (MET Cell). The MET Cell performs tests on spent nuclear fuel, such as microhardness testing, microscopy, and neutron radiography. However, the MET Cell’s existing pressure and lighting control systems are outdated and inefficient, with inadequate documentation for system changes over time. This project aims to design a new automated control system for the MET Cell, ensuring longevity (minimum ten years), ease of troubleshooting/repair, and integration into the building monitoring system. The design process addressed challenges such as space restrictions, varied voltages within enclosures, sourcing new components, and security limitations. Compliance with NFPA 70, UL508A, MFC Physical Security, and INL Engineering standards was essential. The project involves repurposing an existing PLC to manage lighting and pressure control using digital and analog signals, simplifying wiring, and ensuring thorough documentation for future reference.

42 - ENGINEERING↗
DOE OSTI2025

Analog Signal Multiplexing System for the Iota Proton Injector

The Fermilab Accelerator Science and Technology (FAST) Facility at FNAL is a dedicated research and development center focused on advancing particle accelerator technologies for future applications worldwide. Currently, a key objective of FAST Operations is to commission the 2.5 MeV IOTA Proton Injector (IPI) and enable proton injection into the Integrable Optics Test Accelerator (IOTA) storage ring. The low and medium-energy sections of the IPI include four frame-style dipole trims and two multi-function correctors with independently controlled coils, requiring readout of 32 analog channels for current and voltage monitoring in total. To reduce cost and optimize rack space within the PLC-based control system, a 32-to-4 analog signal multiplexing system was designed and implemented. This system enables real-time readback of excitation parameters from all magnetic correctors. This paper presents the design, construction, implementation, and performance of the multiplexing system.

MacLean, Daniel R. [Fermilab] (ORCID:0000000210103↗
DOE OSTI2025

Analog Signal Multiplexing System for the IOTA Proton Injector

he Fermilab Accelerator Science and Technology (FAST) Facility at FNAL is a dedicated research and development center focused on advancing particle accelerator technologies for future applications worldwide. Currently, a key objective of FAST Opera-tions is to commission the 2.5 MeV IOTA Proton Injec-tor (IPI) and enable proton injection into the Integrable Optics Test Accelerator (IOTA) storage ring. The low and medium-energy sections of the IPI include four frame-style dipole trims and two multi-function cor-rectors with independently controlled coils, requiring readout of 32 analog channels for current and voltage monitoring in total. To reduce cost and optimize rack space within the PLC-based control system, a 32-to-4 analog signal multiplexing system was designed and implemented. This system enables real-time readback of excitation parameters from all magnetic correctors. This paper presents the design, construction, implementation, and performance of the multiplexing system.

MacLean, Daniel R. [Fermilab] (ORCID:0000000210103↗
DOE OSTI2020

Optimized Quantum Compilation for Near-Term Algorithms with OpenPulse

Quantum computers are traditionally operated by programmers at the granularity of a gate-based instruction set. However, the actual device-level control of a quantum computer is performed via analog pulses. Here, we introduce a compiler that exploits direct control at this microarchitectural level to achieve significant improvements for quantum programs. Unlike quantum optimal control, our approach is bootstrapped from existing gate calibrations and the resulting pulses are simple. Our techniques are applicable to any quantum computer and realizable on current devices. We validate our techniques with millions of experimental shots on IBM quantum computers, controlled via the OpenPulse control interface. For representative benchmarks, our pulse control techniques achieve both 1.6x lower error rates and 2x faster execution time, relative to standard gate-based compilation. These improvements are critical in the near-term era of quantum computing, which is bottlenecked by error rates and qubit lifetimes.

Gokhale, Pranav↗
DOE OSTI2020

Cyber Security Analysis for Nuclear Reactor Control Systems (Final Technical Report)

This project investigated the cyber-security impacts of moving from an all analog, point-to-point, instrumentation and control (I&C) system to a digital I&C system based on Modbus and a shared communication medium. A formalism called a hybrid attack graph was expanded to support the nuclear research reactor system. The hybrid attack graph allows one to check a system for vulnerabilities, in this case cyber-security vulnerabilities, and to document the attack vectors (scenarios) causing those vulnerabilities. In parallel, a simulation of the system was developed to model both the physical reactor parameters and operations, as well as the network interconnects and communications. This simulation platform was modeled on the nuclear research reactor located at Washington State University. The simulation platform provided a sandbox to evaluate and quantify the impact of identified and proposed vulnerabilities in the system and to determine the effectiveness of countermeasures at stopping these attacks. The simulation and hybrid attack graph tools were integrated to provide a streamlined process of generating attack scenarios, playing those scenarios out in the simulation, and then analyzing the results to correlate system state to states in the hybrid attack graph. This process was used to (1) quantify the impact of attack scenarios and (2) to determine if the system moved through the hybrid attack graph as anticipated. The hybrid attack graph tool was extended and customized to produce a tool to automatically identify critical assets (CAs) and critical digital assets (CDAs) as defined by NRC Regulatory Guide 5.71. This tool was verified using the nuclear research reactor at Washington State University. Finally, a series of educational modules covering the findings of the different aspects of this research have been created.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Barriers to adopting artificial intelligence and machine learning technologies in nuclear power

Artificial intelligence and machine learning (AI/ML) technologies offer unique opportunities to transform nuclear plant operations and power generation. Benefits will be felt not only within existing analog and digital instrumentation and control, but also within work processes, the integration of people with technology and most importantly, the business case. The application of this new technology can help simplify complex problems and produce more effective decision-making, making nuclear power safer, more efficient, and more economically viable in the current energy market. Nonetheless, there are potential barriers to its adoption that must be overcome. The purpose of this paper is to categorize, review, and discuss barriers to AI/ML adoption within the nuclear power industry, with a focus on existing commercial reactors. Unique considerations for advanced reactors are also offered. Here we provide a comprehensive overview of the historical, technical, and business barriers that the industry faces, as well as stakeholder readiness, and end-user acceptance. We underscore the importance of user experience and offer potential solutions in overcoming each barrier. These include provisions for easier plant data access, a friendly regulatory environment, and investment in user trust and explainable AI.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Improved Beam Loss Accounting with Fast Data Acquisition (DAQ) Chassis

Identifying the source of beam loss events in the CE-BAF accelerator can be a challenging task. However, with our new prototype system, this task becomes more effi-cient. The system, developed in the fall of 2022, utilizes a dispersive beam position monitor (BPM) and the exist-ing switched electrode electronics BPM hardware. Previ-ously a commercial off-the-shelf data acquisition (DAQ) system was employed to capture BPM wire signals at a sample rate of 20 kS/s. The fast shutdown signal triggered the system, which disables the beam at the injector. Analysis of beam position and energy variation before a beam loss event was used to determine if the beam loss event was associated with an energy transient. The proto-type system, implemented using National Instruments hardware and LabVIEW® software, relied on a software trigger. Manual post-processing was required to ascertain whether the fault was due to an un-tripped cavity with a gradient or phase transient. This work focuses on deploying a Fast DAQ Chassis to monitor BPM hardware in real time and during beam loss events. This system was originally developed and in-stalled in CEBAF to monitor the time-domain RF control signals in the legacy analog RF systems. This technology was leveraged to also monitor BPM signals. As the new system employs a hardware trigger, developing tools to automatically identify faults linked to energy transients unrelated to cavity faults will be straightforward. This paper will discuss the project's initial updates, underlin-ing the crucial role of each member of our team in this achievement

Tiskumara, J.↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Precursor Analysis Report: SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003

The SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003 Precursor Analysis Report leverages publicly available information about Davis-Besse’s 2003 cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. On 25 January 2003, the SQL Slammer worm infected more than 90% of vulnerable hosts and crashed the internet in 10 to 15 minutes, making it one of the fastest spreading worms in history. SQL Slammer is a fileless, memory-resident worm that remotely exploits a stack-based buffer overflow vulnerability on local hosts to intensively scan and rapidly self-propagate across the internet. The worm infected approximately 300,000 unpatched hosts running Microsoft Structured Query Language (SQL) Server 2000 or Microsoft Desktop Engine (MSDE) 2000 with SQL Server Resolution Service. The SQL Slammer worm indirectly infected FirstEnergy’s Davis-Besse nuclear power plant by first infecting a consultant’s company network server and then propagating through an external misconfigured connection into Davis-Besse’s site network. The infection caused major network congestion, slow performance, data overloads, and the inability of local hosts to communicate with each other, which eventually caused a loss of availability and a loss of view when the Safety Parameter Display System (SPDS) and Plant Process Computer (PPC) crashed. At the time of the infection, the plant was already offline, the digital monitoring systems had redundant analog backups, and the plant control and safety functions were not affected, so there were no concerns of a safety breach. However, this incident resulted in many lessons learned and spawned important discussions about cybersecurity’s role in nuclear safety and electric power reliability regulation, policy, and guidance. Researchers and analysts identified 10 unique techniques utilized during the attack with a total of 640 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Eight of the identified techniques used during Davis-Besse cyber attack were precursors to the triggering event. Analysis identified 596 observables associated with these precursor techniques, 428 of which were assessed to have an increased likelihood of being perceived in the 331 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Steady Spills, Stronger Signals: Machine Learning for Slow Spill Analysis

Particle accelerator experiments rely on stable, consistent proton beams to maximize scientific discovery. This presentation introduces beam spills, duty factor, and beam stability using a meteor shower analogy before exploring how feedback control and machine learning, including recurrent neural networks (RNNs), can analyze spill data, identify patterns, and predict beam behavior. Together, these approaches support beam optimization and improve our understanding of accelerator performance for experiments such as Mu2e.

Prescott, Matthew J. [Fermilab]↗
DOE OSTI2023

Carbonic Anhydrase Enhanced UV-Crosslinked PEG-DA/PEO Extruded Hydrogel Flexible Filaments and Durable Grids for CO 2 Capture

In this study, poly (ethylene glycol) diacrylate/poly (ethylene oxide) (PEG-DA/PEO) interpenetrating polymer network hydrogels (IPNH) were extruded into 1D filaments and 2D grids. The suitability of this system for enzyme immobilization and CO 2 capture application was validated. IPNH chemical composition was verified spectroscopically using FTIR. The extruded filament had an average tensile strength of 6.5 MPa and elongation at break of 80%. IPNH filament can be twisted and bent and therefore is suitable for further processing using conventional textile fabrication methods. Initial activity recovery of the entrapped carbonic anhydrase (CA) calculated from esterase activity, showed a decrease with an increase in enzyme dose, while activity retention of high enzyme dose samples was over 87% after 150 days of repeated washing and testing. IPNH 2D grids that were assembled into spiral roll structured packings exhibited increased CO 2 capture efficiency with increasing enzyme dose. Long-term CO 2 capture performance of the CA immobilized IPNH structured packing was tested in a continuous solvent recirculation experiment for 1032 h, where 52% of the initial CO 2 capture performance and 34% of the enzyme contribution were retained. These results demonstrate the feasibility of using rapid UV-crosslinking to form enzyme-immobilized hydrogels by a geometrically-controllable extrusion process that uses analogous linear polymers for both viscosity enhancement and chain entanglement purposes, and achieves high activity retention and performance stability of the immobilized CA. Potential uses for this system extend to 3D printing inks and enzyme immobilization matrices for such diverse applications as biocatalytic reactors and biosensor fabrication.

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

The SAGA Survey. VI. The Size–Mass Relation for Low-mass Galaxies Across Environments

We investigate how Milky Way (MW)–like environments influence the sizes and structural properties of low mass galaxies by comparing satellites of MW analogs from the Satellites Around Galactic Analogs (SAGA) Survey with two control samples: an environmentally agnostic population from the SAGA background sample and isolated galaxies from the Sloan Digital Sky Survey NASA-Sloan Atlas. All sizes and structural parameters are measured uniformly using pysersic to ensure consistency across samples. We find the half-light sizes of SAGA satellites are systematically larger than those of isolated galaxies, with the magnitude of the offset ranging from 0.05 to 0.12 dex (10%–24%) depending on the comparison sample and completeness cuts. This corresponds to physical size differences between 85 and 200 pc at log 10 (M$_{{\star}}$/M ⊙ ) = 7.5 and 220–960 pc at log 10 (M$_{{\star}}$/M ⊙ ) = 10. This offset persists among star-forming galaxies, suggesting that environment can influence the structure of low-mass galaxies even before it impacts quenching. The intrinsic scatter in the size–mass relation is lower for SAGA satellites than isolated galaxies, and the Sérsic index distributions of satellites and isolated galaxies are similar. In comparison to star-forming satellites, quenched SAGA satellites have a slightly shallower size–mass relation and rounder morphologies at low mass, suggesting that quenching is accompanied by structural transformation and that the processes responsible differ between low- and high-mass satellites. Our results show that environmental processes can imprint measurable structural differences on satellites in MW-mass halos.

Asali, Yasmeen [Yale Univ., New Haven, CT (United ↗