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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identification of Suitable Vacuum Gas Oils as Plasticizers Using HT-GC × GC-HRMS

Vacuum gas oils (VGOs) have long been heralded as effective plasticizers due to their high boiling-points and lubricating properties. One of these VGOs, HyVac Oil 93050, is a paraffinic plasticizer that is currently utilized in polymer–plasticizer explosive formulations, while this oil effectively increases the elasticity and decreases the sensitivity for current formulations. We seek to identify alternative VGOs with similar density, viscosity, molecular composition, and impurities for future formulations. In this study, 18 VGOs, including HyVac Oil 93050, were initially evaluated for their density and viscosity. The oils were then ranked based on physical characteristics and analyzed for molecular composition using high-temperature comprehensive two-dimensional gas chromatography with high-resolution time-of-flight mass spectrometry (HT-GC × GC-HRMS). Further, the HT-GC × GC-HRMS chromatograms of the top 10 most similar VGOs to HyVac Oil 93050, according to density, were compared utilizing the Pearson correlation coefficient. Three of the oils with densities similar to those of HyVac Oil 93050 had a Pearson correlation within the lot-to-lot variation of HyVac Oil 93050. For the top 3 candidates, Pearson correlation was then utilized as a feature selection technique to discover significant chemical differences. Positive chemical ionization (PCI) and negative chemical ionization (NCI) HT-GC × GC-HRMS chromatograms were also evaluated and aided in the discovery and identification of several key compounds including additives that acted as stabilizers for the VGOs. After this further chemical analysis, two of the original 17 potential VGOs were determined to be physically and chemically similar to HyVac Oil 93050.

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

Force-Free Identification of Minimum-Energy Pathways and Transition States for Stochastic Electronic Structure Theories

Here, the accurate mapping of potential energy surfaces (PESs) is crucial to our understanding of the numerous physical and chemical processes mediated by atomic rearrangements, such as conformational changes and chemical reactions, and the thermodynamic and kinetic feasibility of these processes. Stochastic electronic structure theories, e.g., Quantum Monte Carlo (QMC) methods, enable highly accurate total energy calculations that in principle can be used to construct the PES. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method, previously developed for QMC structural optimization, to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and at saddle points, we show that it is possible to identify MEPs and TSs by using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia (NH 3 ) molecule and the nucleophilic substitution (S N 2) reaction F – + CH 3 F → FCH 3 + F – . We validate our results using Density Functional Theory (DFT)- and Coupled Cluster (CCSD, CCSD(T))-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities, free energy differences, rate constants, and equilibrium constants that incorporates stochastically optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations at significantly reduced computational expense.

Iyer, Gopal R.↗
DOE OSTI2026

Identification of KLHL12 Ligands Using Fragment-Based Methods

Targeted protein degradation can be induced by recruiting a protein of interest to an E3 ligase, resulting in its ubiquitination and subsequent proteasome-mediated degradation. However, only a small number of E3 ligases have been utilized for degradation. Expansion of the repertoire of useful E3 ligases via the identification of ligands to those ligases could broaden the scope and applicability of the degradation paradigm. We have identified KLHL12 as an E3 ligase with higher expression in cancer over normal tissues. We report here the use of NMR-based screening to identify fragments that bind to KLHL12, and X-ray structures of a fragment hit bound to KLHL12. Using this structural information, we optimized the hits, leading to the first reported small molecules that bind to KLHL12 with submicromolar affinity. Derivatives of these compounds may be useful for the construction of PROTACs to selectively degrade protein targets in tumors while sparing normal cells.

Amines↗
DOE OSTI2025

Atom Identification in Bilayer Moiré Materials with Gomb-Net

Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS 2 -WS 2(1–x) Se 2x heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern’s local energetic or electronic modulation. In conclusion, this advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

In Situ Identification of NNH and N 2 H 2 by Using Molecular-Beam Mass Spectrometry in Plasma-Assisted Catalysis for NH 3 Synthesis

In this work, ammonia synthesis at 533 K and atmospheric pressure was investigated in a coaxial dielectric barrier discharge (DBD) plasma reactor without packing and with porous γ-Al 2 O 3 , 5 wt % Ru/γ-Al 2 O 3 , or 5 wt % Co/γ-Al 2 O 3 catalyst particles. Gas-phase species were monitored in situ using an electron impact molecular-beam mass spectrometer (EI-MBMS). Gas-phase species NNH and N 2 H 2 were first identified under common conditions of plasma-assisted ammonia synthesis and were present at levels comparable to that of NH 3 in the plasma discharge. Concentrations of NNH, N 2 H 2 , and NH in a reactor packed with γ-Al 2 O 3 or other particles were lower than those observed in an empty reactor, while the concentration of NH 3 increased. These observations point to the importance of NNH and N 2 H 2 in plasma-assisted surface reactions in ammonia synthesis. Reaction pathways of direct adsorption of gas-phase NNH and N 2 H 2 on solid surfaces and subsequent reactions were proposed. This study demonstrated that in situ identification of gas-phase species via EI-MBMS provides a powerful approach to study the kinetics of plasmaassisted catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Coupling High-Throughput and Targeted Screening for Identification of Nonobvious Metabolic Engineering Targets

Identification of metabolic engineering targets is a fundamental challenge in strain development programs. While high-throughput (HTP) genetic engineering methodologies capable of generating vast diversity are being developed at a rapid rate, a majority of industrially interesting molecules cannot be screened at sufficient throughput to leverage these techniques. We propose a workflow that couples HTP screening of common precursors (e.g., amino acids) that can be screened either directly or by artificial biosensors, with low-throughput targeted validation of the molecule of interest to uncover nonintuitive beneficial metabolic engineering targets and combinations hereof. Using this workflow, we identified several nonobvious novel targets for improving p-coumaric acid (p-CA) and L -DOPA production from two large 4k gRNA libraries each deregulating 1000 metabolic genes in the yeast Saccharomyces cerevisiae. We initially screened yeast cells transformed with gRNA library plasmids for individual regulatory targets improving the production of l-tyrosine-derived betaxanthins, identifying 30 targets that increased intracellular betaxanthin content 3.5–5.7 fold. Hereafter, we screened the targets individually in a high-producing p-CA strain, narrowing down the targets to six that increased the secreted titer by up to 15%. To investigate whether any of the six targets could be additively combined to improve p-CA production further, we created a gRNA multiplexing library and subjected it to our proposed coupled workflow. The combination of regulating PYC1 and NTH2 simultaneously resulted in the highest (threefold) improvement of the betaxanthin content, and an additive trend was also observed in the p-CA strain. Lastly, we tested the initial 30 targets in a L -DOPA producing strain, identifying 10 targets that increased the secreted titer by up to 89%, further validating our screening by proxy workflow. This coupled approach is useful for strain development in the absence of direct HTP screening assays for products of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identification and Catalysis of the Potential-Limiting Step in Lithium-Sulfur Batteries

The Li-S chemistry is thermodynamically promising for high-density energy storage but kinetically challenging. Over the past few years, many catalyst materials have been developed to improve the performance of Li-S batteries and their catalytic role has been increasingly accepted. However, the classic catalytic behavior, i.e., reduction of reaction barrier, has not been clearly observed. Crucial mechanistic questions, including what specific step is limiting the reaction rate, whether/how it can be catalyzed, and how the catalysis is sustained after the catalyst surface is covered by solid products, remain unanswered. Herein, we report the first identification of the potential-limiting step of Li-S batteries operating under lean electrolyte conditions and its catalysis that conforms to classic catalysis principles, where the catalyst lowers the kinetic barrier of the potential-limiting step and accelerates the reaction without affecting the product composition. After carefully examining the electrochemistry under lean electrolyte conditions, we update the pathway of the Li-S battery reaction: S 8 solid is first reduced to Li 2 S 8 and Li 2 S 4 molecular species sequentially; the following reduction of Li 2 S 4 to a Li 2 S 2 -Li 2 S solid with an almost constant ratio of 1:4 is the potential-limiting step; the previously believed Li 2 S 2 -to-Li 2 S solid-solid conversion does not occur; the recharging reaction is relatively fast. We further demonstrate that supported cobalt phthalocyanine molecules can effectively catalyze the potential-limiting step. After Li 2 S 2 /Li 2 S buries the active sites, it can self-catalyze the reaction and continue driving the discharging process.

25 ENERGY STORAGE↗
DOE OSTI2020

Using Multiple Signatures to Improve Accuracy of Substorm Identification

Abstract We have developed a new procedure for combining lists of substorm onset times from multiple sources. We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from 1–31 January 2005. We show that this procedure is capable of rejecting false positive identifications and filling data gaps that appear in individual lists. The resulting combined onset lists produce a waiting time distribution that is comparable to previously published results, and superposed epoch analyses of the solar wind driving conditions and magnetospheric response during the resulting onset times are also comparable to previous results. Comparison of the substorm onset list from the MHD model to that obtained from observational data reveals that the MHD model reproduces many of the characteristic features of the observed substorms, in terms of solar wind driving, magnetospheric response, and waiting time distribution. Heidke skill scores show that the MHD model has statistically significant skill in predicting substorm onset times.

Haiducek, John D.↗
DOE OSTI2025

Baseflow Identification via Explainable AI With Kolmogorov‐Arnold Networks

Abstract Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov‐Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We demonstrate KAN's potential on the problem of baseflow identification, a notoriously challenging task plagued by significant uncertainty. KAN‐derived functional dependencies of the baseflow components on the aridity index outperform their original counterparts; they demonstrate that water availability, rather than potential evapotranspiration, drives baseflow by constraining actual evapotranspiration under arid conditions. On a test set, they increase the Nash‐Sutcliffe efficiency (NSE) by 65%, decrease the root mean squared error by 29%, and increase the Kling‐Gupta efficiency by 34%. This superior performance is achieved while reducing the number of fitting parameters from three to two. Next, we use data from 378 catchments across the continental United States to refine the water‐balance equation at the mean‐annual scale. The KAN‐derived equations based on the refined water balance outperform both the current aridity index model, with up to a 105% increase in NSE, and the KAN‐derived equations based on the original water balance. While the performance of our model and tree‐based machine learning methods is similar, KANs offer the advantage of simplicity and transparency and require no specific software or computational tools. This case study focuses on the aridity index formulation, but the approach is flexible and transferable to other hydrological processes. Plain Language Summary Equations used in hydrologic model are often suboptimal, resulting in reduced prediction accuracy and efficiency. We implemented Kolmogorov‐Arnold networks (KAN), a machine learning algorithm for deriving symbolic formulations, to estimate groundwater recharge and showed that it outperforms an existing state‐of‐the‐art semi‐empirical formulation. In hydrology, Nash‐Sutcliffe efficiency (NSE), root mean squared error (RMSE), and Kling‐Gupta efficiency (KGE) are commonly used to evaluate model performance. Higher NSE and KGE values indicate better performance, while lower RMSE values are preferable. Our results show that NSE increased by 71%, RMSE decreased by 32%, and KGE improved by 25%. In addition, KAN identifies an optimal functional form and can be used to derive new analytical formulas using the prior knowledge. The KAN‐inspired equation outperformed the original formulation and reduced the fitting parameters. Furthermore, we refined the water‐balance equation at the mean‐annual scale and showed that, based on the new water‐balance equation, KAN can derive new formulations that are superior to the original aridity index formulations (up to 105% increase in NSE) and KAN‐derived equations based on the original water balance. These findings highlight the significant potential of KAN to advance the scientific understanding of a wide range of hydrologic processes. Key Points Kolmogorov‐Arnold networks (KANs) enhance interpretability of machine‐learned hydrological models KAN‐derived symbolic formulations outperform state‐of‐the‐art semi‐empirical aridity indices KAN‐identified functional form yields an analytical index with fewer fitting parameters and improved performance

baseflow↗
DOE OSTI2022

Computational identification of HCV neutralizing antibodies with a common HCDR3 disulfide bond motif in the antibody repertoires of infected individuals

Despite recent success in hepatitis C virus (HCV) treatment using antivirals, an HCV vaccine is still needed to prevent reinfections in treated patients, to avert the emergence of drug-resistant strains, and to provide protection for people with no access to the antiviral therapeutics. The early production of broadly neutralizing antibodies (bNAbs) associates with HCV clearance. Several potent bNAbs bind a conserved HCV glycoprotein E2 epitope using an unusual heavy chain complementarity determining region 3 (HCDR3) containing an intra-loop disulfide bond. Isolation of additional structurally-homologous bNAbs would facilitate the recognition of key determinants of such bNAbs and guide rational vaccine design. Here we report the identification of new antibodies containing an HCDR3 disulfide bond motif using computational screening with the Rosetta software. Using the newly-discovered and already-known members of this antibody family, we review the required HCDR3 amino acid composition and propose determinants for the bent versus straight HCDR3 loop conformation observed in these antibodies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Identification of the glassy state in nanoparticles by transmission electron microscopy

Identification of amorphous phases in nanoparticles by atomic-resolution transmission electron microscopy (TEM) requires analyses such as tilt-angle-dependent TEM imaging and single-nanoparticle electron diffraction, rather than relying on a single TEM image. Here, the disordered structures of amorphous nanoparticles offer unique atomic configurations and properties that differ from the properties of their crystalline counterparts. These characteristics have motivated the exploration of such materials for mechanical, sensing and catalytic applications. In addition, the formation of amorphous metal nanoparticles is an important endeavour to understand the process of vitrification and the nature of the glassy state. Atomic-resolution transmission electron microscopy (TEM) is increasingly being used as a tool for characterizing structures of nanoparticles produced by vitrification. In this Comment, we discuss the pitfalls of using TEM for ascertaining whether nanoparticles are amorphous. We also make recommendations of best practices.

Alcorn, Francis M. [Sandia National Laboratories (↗
DOE OSTI2021

Structural modification of fentanyls for their retrospective identification by gas chromatographic analysis using chloroformate chemistry

Abstract The one-step breakdown and derivatization of a panel of nine fentanyls to yield uniquely tagged products that can be detected by Electron Ionization Gas Chromatography-Mass Spectrometry (EI-GC-MS) is presented. The method involves the treatment of the synthetic opioids with 2,2,2-trichloroethoxycarbonyl chloride (TrocCl) at 60 °C for 3 h in dichloromethane and furnishes two products from one fentanyl molecule that can be used to retrospectively identify the original opioid. Parameters that were studied and fully optimized for the method included temperature, solvent, nature of scavenging base and reaction time. One of the two resulting products from the reaction bears the trichloroethoxycarbonyl (Troc) tag attached to the norfentanyl portion of the original opioid and greatly aids in the opioid detection and identification process. The methodology has been applied to the chemical modification of a panel of nine fentanyls and in all cases the molecular ion peak for the Troc-norfentanyl product bearing the distinctive trichloroethyl isotopic signature can be clearly observed. The method’s LLOD was determined to be 10 ng/mL while its LLOQ was found to be 20 ng/mL. This methodology represents the first application of chloroformates in the chemical modification of this class of synthetic opioids that are notoriously inert to common derivatization strategies available for GC–MS analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Identification of the acetaldehyde oxide Criegee intermediate reaction network in the ozone-assisted low-temperature oxidation of trans -2-butene

Uni- and bi-molecular reactions involving Criegee intermediates (CIs) have been the focus of many studies due to the role these molecules play in atmospheric chemistry. The reactivity of CIs is known to strongly depend on their structure. The reaction network of the second simplest CI, acetaldehyde oxide (CH 3 CHOO), is investigated in this work in an atmospheric pressure jet-stirred reactor (JSR) during the ozonolysis of trans-2-butene to explore the kinetic pathways relevant to atmospheric chemistry and low-temperature combustion. The mole fraction profiles of reactants, intermediates, and final products are determined by means of molecular-beam mass spectrometry in conjunction with single-photon ionization employing tunable synchrotron-generated vacuum ultraviolet radiation. A network of CI reactions is identified in the temperature region below 600 K, characterized by CI addition to trans-2-butene, water, formaldehyde, formic acid, and methanol. No sequential additions of the CH 3 CHOO CI are observed, in contrast with the reactivity of the simplest CI (H 2 COO) and the earlier observation of an extensive reaction network with up to four H 2 COO sequential additions (Phys. Chem. Chem. Phys., 2019, 21, 7341–7357). Experimental photoionization efficiency scans recorded at 300 K and 425 K and ab initio threshold energy calculations lead to the identification and quantification of previously elusive intermediates, such as ketohydroperoxide and hydroperoxide species. Specifically, the C 4 H 8 + O 3 adduct is identified as a ketohydroperoxide (KHP, 3-hydroperoxybutan-2-one, CH 3 C([double bond, length as m-dash]O)CH(CH 3 )OOH), while hydroxyacetaldehyde (glycolaldehyde, HCOCH 2 OH) formation is attributed to unimolecular isomerization of the CIs. Other hydroperoxide species such as methyl hydroperoxide (CH 3 OOH), ethyl hydroperoxide (C 2 H 5 OOH), butyl hydroperoxide (OOH), hydroperoxyl acetaldehyde (HOOCH 2 CHO), hydroxyethyl hydroperoxide (CH 3 CH(OH)OOH), but-1-enyl-3-hydroperoxide, and 4-hydroxy-3-methylpentan-2-one (HOCH(CH 3 )CH(CH 3 )C([double bond, length as m-dash]O)CH 3 ) are also identified. Detection of additional oxygenated species such as methanol, ethanol, ketene, and aldehydes suggests multiple active oxidation routes. Finally, these results provide additional evidence that CIs are key intermediates of the ozone-unsaturated hydrocarbon reactions providing critical inputs for improved kinetics models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Enhanced descriptor identification and mechanism understanding for catalytic activity using a data-driven framework: revealing the importance of interactions between elementary steps

We report accurate identification of descriptors for catalytic activities has long been essential to the in-depth understanding of catalysis and recently to set the basis for catalyst screening. However, commonly used methods suffer from low accuracy in predictability. This study reports an enhanced approach to accurately identify the descriptors from a kinetic dataset using a machine learning (ML) surrogate model. CO hydrogenation to methanol over Cu-based catalysts was taken as a case study. Our model captures not only the contribution from individual elementary steps but also the interaction between relevant steps within a reaction network, which was found to be essential for high accuracy. As a result, six effective descriptors are identified, which are accurate enough to ensure the trained gradient boosted regression (GBR) model for good prediction of the methanol turnover frequency (TOF) over metal (M)-doped Cu(111) model surfaces (M = Au, Cu, Pd, Pt, Ni). More importantly, going beyond the purely mathematical ML model, the catalytic role of each identified descriptor can be revealed by using model-agnostic interpretation tools, which enhances the insight into the promoting effect of alloying. The trained GBR model outperforms the conventional derivative-based methods in terms of both the predictability and the mechanism understanding. It opens alternative possibilities toward accurate descriptor-based rational catalyst optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

The eROSITA Final Equatorial-Depth Survey (eFEDS): Identification and characterization of the counterparts to point-like sources

In November 2019, eROSITA on board of the Spektrum-Roentgen-Gamma (SRG) observatory started to map the entire sky in X-rays. After the four-year survey program, it will reach a flux limit that is about 25 times deeper than ROSAT. During the SRG performance verification phase, eROSITA observed a contiguous 140 deg 2 area of the sky down to the final depth of the eROSITA all-sky survey (eROSITA Final Equatorial-Depth Survey; eFEDS), with the goal of obtaining a census of the X-ray emitting populations (stars, compact objects, galaxies, clusters of galaxies, and active galactic nuclei) that will be discovered over the entire sky. This paper presents the identification of the counterparts to the point sources detected in eFEDS in the main and hard samples and their multi-wavelength properties, including redshift. To identify the counterparts, we combined the results from two independent methods (NWAY and ASTROMATCH), trained on the multi-wavelength properties of a sample of 23k XMM-Newton sources detected in the DESI Legacy Imaging Survey DR8. Then spectroscopic redshifts and photometry from ancillary surveys were collated to compute photometric redshifts. Of the eFEDS sources, 24 774 of 27 369 have reliable counterparts (90.5%) in the main sample and 231 of 246 sources (93.9%) have counterparts in the hard sample, including 2514 (3) sources for which a second counterpart is equally likely. By means of reliable spectra, Gaia parallaxes, and/or multi-wavelength properties, we have classified the reliable counterparts in both samples into Galactic (2695) and extragalactic sources (22 079). For about 340 of the extragalactic sources, we cannot rule out the possibility that they are unresolved clusters or belong to clusters. Inspection of the distributions of the X-ray sources in various optical/IR colour-magnitude spaces reveal a rich variety of diverse classes of objects. The photometric redshifts are most reliable within the KiDS/VIKING area, where deep near-infrared data are also available. This paper accompanies the eROSITA early data release of all the observations performed during the performance and verification phase. Together with the catalogues of primary and secondary counterparts to the main and hard samples of the eFEDS survey, this paper releases their multi-wavelength properties and redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗