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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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DOE OSTI2021

Mitigating contamination in LSS surveys: a comparison of methods

ABSTRACT Future large-scale structure surveys will measure the locations and shapes of billions of galaxies. The precision of such catalogues will require meticulous treatment of systematic contamination of the observed fields. We compare several existing methods for removing such systematics from galaxy clustering measurements. We show how all the methods, including the popular pseudo-Cℓ Mode Projection and Template Subtraction methods, can be interpreted under a common regression framework and use this to suggest improved estimators. We show how methods designed to mitigate systematics in the power spectrum can be used to produce clean maps, which are necessary for cosmological analyses beyond the power spectrum, and we extend current methods to treat the next-order multiplicative contamination in observed maps and power spectra, which reduced power spectrum errors from $\Delta \chi ^2_{\rm C_\ell }\simeq 10$ to ≃ 1 in simulated analyses. Two new mitigation methods are proposed, which incorporate desirable features of current state-of-the-art methods while being simpler to implement. Investigating the performance of all the methods on a common set of simulated measurements from Year 5 of the Dark Energy Survey, we test their robustness to various analysis cases. Our proposed methods produce improved maps and power spectra when compared to current methods, while requiring almost no user tuning. We end with recommendations for systematics mitigation in future surveys, and note that the methods presented are generally applicable beyond the galaxy distribution to any field with spatial systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Incremental Fermi Large Area Telescope Fourth Source Catalog

We present an incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi Large Area Telescope (LAT) catalog of γ-ray sources. Based on the first 12 years of science data in the energy range from 50 MeV to 1 TeV, it contains 6658 sources. The analysis improves on that used for the 4FGL catalog over eight years of data: more sources are fit with curved spectra, we introduce a more robust spectral parameterization for pulsars, and we extend the spectral points to 1 TeV. The spectral parameters, spectral energy distributions, and associations are updated for all sources. Light curves are rebuilt for all sources with 1 yr intervals (not 2 month intervals). Among the 5064 original 4FGL sources, 16 were deleted, 112 are formally below the detection threshold over 12 yr (but are kept in the list), while 74 are newly associated, 10 have an improved association, and seven associations were withdrawn. Pulsars are split explicitly between young and millisecond pulsars. Pulsars and binaries newly detected in LAT sources, as well as more than 100 newly classified blazars, are reported. We add three extended sources and 1607 new point sources, mostly just above the detection threshold, among which eight are considered identified, and 699 have a plausible counterpart at other wavelengths. We discuss the degree-scale residuals to the global sky model and clusters of soft unassociated point sources close to the Galactic plane, which are possibly related to limitations of the interstellar emission model and missing extended sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Challenge of Disproportionate Importance of Temporal Features in Predicting HPC Power Consumption

In this work, we demonstrate the challenges in predicting HPC cluster power consumption in the face of significant temporal skew in power consumption behavioral patterns. Predicting large power swings that extend several megawatts has significant operational value for HPC centers, however, prediction is challenging due to the relative rarity of such events and also due to the abrupt or disjoint deviation from the average power consumption levels. To study the impact of this challenge, we have trained a recurrent neural network (RNN) as a reasonably sophisticated model to predict power consumption of the one-year worth of node power consumption data from the Summit supercomputer located in the Oak Ridge Leadership Computing Facility. By studying the prediction results, we have found that although simple usage of RNN models can provide good results on average power consumption levels, it would fail at predicting the power swings that have more operational value. With such results, we discuss potential next steps in addressing such issues aiming towards a robust usage of power prediction techniques in HPC operations.

Li, Chengcheng↗
DOE OSTI2025

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

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

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

Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological implications from two decades of spectroscopic surveys at the Apache Point Observatory

We present the cosmological implications from final measurements of clustering using galaxies, quasars, and Lyα forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, r d , from eight different samples and six measurements of the growth rate parameter, fσ 8 , from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, ΛCDM model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization, under the same model, the BAO data provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Independent of distance measurements, the SDSS RSD data complement weak lensing measurements from the Dark Energy Survey (DES) in demonstrating a preference for a flat ΛCDM cosmological model when combined with Planck measurements. The combined BAO and RSD measurements indicate σ 8 =0.85±0.03, implying a growth rate that is consistent with predictions from Planck temperature and polarization data and with General Relativity. When combining the results of SDSS BAO and RSD, Planck, Pantheon Type Ia supernovae (SNe Ia), and DES weak lensing and clustering measurements, all multiple-parameter extensions remain consistent with a ΛCDM model. Regardless of cosmological model, the precision on each of the three parameters, Ω Λ , H 0 , and σ 8 , remains at roughly 1%, showing changes of less than 0.6% in the central values between models. In a model that allows for free curvature and a time-evolving equation of state for dark energy, the combined samples produce a constraint Ω K =-0.0022±0.0022. The dark energy constraints lead to w 0 =-0.909±0.081 and w a =$-0.49_{-0.30}^{+0.35}$, corresponding to an equation of state of w p =-1.018±0.032 at a pivot redshift z p =0.29 and a Dark Energy Task Force Figure of Merit of 94. The inverse distance ladder measurement under this model yields H 0 =68.18±0.79 km s -1 Mpc -1 , remaining in tension with several direct determination methods; the BAO data allow Hubble constant estimates that are robust against the assumption of the cosmological model. In addition, the BAO data allow estimates of H 0 that are independent of the CMB data, with similar central values and precision under a ΛCDM model. Our most constraining combination of data gives the upper limit on the sum of neutrino masses at Σm ν <0.115 eV (95% confidence). Finally, we consider the improvements in cosmology constraints over the last decade by comparing our results to a sample representative of the period 2000-2010. We compute the relative gain across the five dimensions spanned by w, Ω k , Σm ν , H 0 , and σ 8 and find that the SDSS BAO and RSD data reduce the total posterior volume by a factor of 40 relative to the previous generation. Finally, adding again the Planck, DES, and Pantheon SN Ia samples leads to an overall contraction in the five-dimensional posterior volume of 3 orders of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Data Mine model for accessible partnerships in data science

Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

Betz, Margaret A.↗
DOE OSTI2021

Cosmological cross-correlations and nearest neighbour distributions

ABSTRACT Cross-correlations between data sets are used in many different contexts in cosmological analyses. Recently, k-nearest neighbour cumulative distribution functions (kNN-CDF) were shown to be sensitive probes of cosmological (auto) clustering. In this paper, we extend the framework of NN measurements to describe joint distributions of, and correlations between, two data sets. We describe the measurement of joint kNN-CDFs, and show that these measurements are sensitive to all possible connected N-point functions that can be defined in terms of the two data sets. We describe how the cross-correlations can be isolated by combining measurements of the joint kNN-CDFs and those measured from individual data sets. We demonstrate the application of these measurements in the context of Gaussian density fields, as well as for fully non-linear cosmological data sets. Using a Fisher analysis, we show that measurements of the halo-matter cross-correlations, as measured through NN measurements are more sensitive to the underlying cosmological parameters, compared to traditional two-point cross-correlation measurements over the same range of scales. Finally, we demonstrate how the NN cross-correlations can robustly detect cross-correlations between sparse samples – the same regime where the two-point cross-correlation measurements are dominated by noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Identification of carbohydrate gene clusters obtained from in vitro fermentations as predictive biomarkers of prebiotic responses

Prebiotic fibers are non-digestible substrates that modulate the gut microbiome by promoting expansion of microbes having the genetic and physiological potential to utilize those molecules. Although several prebiotic substrates have been consistently shown to provide health benefits in human clinical trials, responder and non-responder phenotypes are often reported. These observations had led to interest in identifying, a priori, prebiotic responders and non-responders as a basis for personalized nutrition. In this study, we conducted in vitro fecal enrichments and applied shotgun metagenomics and machine learning tools to identify microbial gene signatures from adult subjects that could be used to predict prebiotic responders and non-responders. Using short chain fatty acids as a targeted response, we identified genetic features, consisting of carbohydrate active enzymes, transcription factors and sugar transporters, from metagenomic sequencing of in vitro fermentations for three prebiotic substrates: xylooligosacharides, fructooligosacharides, and inulin. A machine learning approach was then used to select substrate-specific gene signatures as predictive features. These features were found to be predictive for XOS responders with respect to SCFA production in an in vivo trial. Our results confirm the bifidogenic effect of commonly used prebiotic substrates along with inter-individual microbial responses towards these substrates. We successfully trained classifiers for the prediction of prebiotic responders towards XOS and inulin with robust accuracy (≥ AUC 0.9) and demonstrated its utility in a human feeding trial. Overall, the findings from this study highlight the practical implementation of pre-intervention targeted profiling of individual microbiomes to stratify responders and non-responders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Streamlining heterologous expression of top carbonic anhydrases in Escherichia coli : bioinformatic and experimental approaches

Carbonic anhydrase (CA) enzymes facilitate the reversible hydration of CO 2 to bicarbonate ions and protons. Identifying efficient and robust CAs and expressing them in model host cells, such as Escherichia coli, enables more efficient engineering of these enzymes for industrial CO 2 capture. However, expression of CAs in E. coli is challenging due to the possible formation of insoluble protein aggregates, or inclusion bodies. This makes the production of soluble and active CA protein a prerequisite for downstream applications. In this study, we streamlined the process of CA expression by selecting seven top CA candidates and used two bioinformatic tools to predict their solubility for expression in E. coli. The prediction results place these enzymes in two categories: low and high solubility. Our expression of high solubility score CAs (namely CA5-SspCA, CA6-SazCAtrunc, CA7-PabCA and CA8-PhoCA) led to significantly higher protein yields (5 to 75 mg purified protein per liter) in flask cultures, indicating a strong correlation between the solubility prediction score and protein expression yields. Furthermore, phylogenetic tree analysis demonstrated CA class-specific clustering patterns for protein solubility and production yields. Unexpectedly, we also found that the unique N-terminal, 11-amino acid segment found after the signal sequence (not present in its homologs), was essential for CA6-SazCA activity. Overall, this work demonstrated that protein solubility prediction, phylogenetic tree analysis, and experimental validation are potent tools for identifying top CA candidates and then producing soluble, active forms of these enzymes in E. coli. The comprehensive approaches we report here should be extendable to the expression of other heterogeneous proteins in E. coli.

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

Theoretical framework for new magnetic materials for quantum computing and information storage. Final report for the Award No. DE-SC0018910

The focus of this grant was on molecular magnetic materials for information storage and quantum computing. We have been developing robust, first-principle methods for computing relevant electronic and magnetic properties of molecular building blocks (SMMs) of novel magnetic materials and quantum computers. These tools enable theoretical modeling of SMMs’ behavior, facilitating the interpretation of experimental studies and aiding the design of novel magnetic materials. Our strategy is based on the spin-flip (SF) approach, which extends the hierarchy of black-box single-reference methods to strongly correlated systems. Specifically, we developed general scalable algorithms and computer codes for calculating molecular properties, with an emphasis on spin-related properties, such as zero-field splittings, hyperfine couplings, and g-tensors. While our primary focus was on SF wave functions and SF-TDDFT, the underlying theory and computer codes were formulated using reduced density matrices, such that these tools are applicable to a broader class of methods. To extend the scope of applicability of wave-function-based SF methods to larger systems, we developed reduced-scaling approaches for the equation-of-motion coupled-cluster (EOM-CC) methods and continue developing libtensor (our open-source general tensor contraction library for many-body methods). We carried out extensive benchmarks and also carried out several applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The TRANSCEND University Consortium: An Overview - 20106

The UK government is committed to nuclear energy having an important role in delivering a secure, low-carbon and affordable energy future. Projections indicate that between now and 2035, ∼14 GW of new nuclear generating capacity may be built. Successful delivery of this strategy recognises the need for research and development, skills development and international collaboration as key enablers. One key area is the decommissioning and clean-up of all civil nuclear licensed sites and the implementation of geological disposal of highly active wastes, with a central component being a joined-up approach to nuclear R and D across government, industry and academia which benefits the UK economy and ensures the security of supply. Essential to public acceptance of this strategy remains a demonstrable ability to safely manage and dispose of waste, with acceptance directly linked to having scientifically robust routes for its safe clean-up and disposal. There therefore remains an ongoing need for further research in the areas of decommissioning, immobilisation and management of nuclear waste, and there is significant government and stakeholder support for such research. Given the needs of both new nuclear power generation and legacy wastes, there is a requirement to develop innovative approaches to waste management and decommissioning. Following on from earlier, highly successful projects supported by UK Research and Innovation's Energy Programme, the work of the TRANSCEND (Transformative Science and Engineering for Nuclear Decommissioning) consortium is part of the response to these needs, providing support to a strong and internationally leading group of researchers in this key technology area. The consortium is a collaboration between 11 universities, supported by 8 key industry partners, with a total funding of UK Pounds 9.5 M from the UK Engineering and Physical Research Council, the universities and key project partners. The research being carried out is multidisciplinary and covers both fundamental and applied topics, with 40 research projects clustered in four technical themes: integrated waste management; site decommissioning and remediation; spent fuels; and nuclear materials. Key aims of the consortium are to extend and further develop a relevant academic skill base in the UK associated with the needs of nuclear waste management and decommissioning, to train the next generation of UK researchers, equipping them with skills and experiences relevant to nuclear waste management and decommissioning issues, and to carry out cutting-edge research that has the potential to provide new and innovative pathways to better management of both legacy and future nuclear wastes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Validation of the DESI DR2 Ly$α$ forest full-shape analysis

We present the validation of the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) Lyman-$α$ (Ly$α$) forest full-shape analysis. This analysis combines three-dimensional Ly$α$ forest auto-correlations and cross-correlations with quasars to extract information from both the baryon acoustic oscillation (BAO) feature and the broadband clustering signal, with primary emphasis on the Alcock-Paczynski (AP) measurement. Compared to the DESI DR1 analysis, the DR2 validation uses substantially larger and more realistic mock datasets, including CoLoRe 2LPT and AbacusSummit Ly$α$ forest simulations. The modeling framework is also improved through analytic marginalization over small scales ($<10$$h^{-1}$Mpc) and the impact of ultraviolet background fluctuations. The validation program was completed prior to unblinding and defines quantitative requirements for the cosmological parameters of interest, which are evaluated using hundreds of mock realizations. We further test the analysis through independent fits to the auto- and cross-correlations, multiple catalog splits, and a broad suite of analysis and modeling variations applied to both mocks and blinded observational data. We find that the BAO and AP parameters satisfy all validation requirements and remain stable across all tests. In contrast, mock studies reveal a significant bias in the inferred growth-rate parameter $fσ_8$, leading us to exclude this measurement from the final analysis. The consistency across mocks, data splits, and robustness tests demonstrates that the DR2 Ly$α$ full-shape analysis provides a reliable and substantially improved broadband AP measurement over previous Ly$α$ forest studies.

Herbold, M. [Chicago U., KICP; Ohio State U.] (ORC↗
DOE OSTI2025

Toward a Balanced Description of Ground and Excited States with Transcorrelated F12 Methods

By correlating only the 1-particle states occupied in the reference determinant, the conventional design for the single-reference R12/F12 explicitly correlated methods biases them toward the ground-state description, thereby making the treatment of response properties of the ground state, and energies and other properties of excited states less robust. While the use of multireference methods and/or extensions of the standard SP-projected geminals can achieve a more balanced description of ground and excited states, here we show that the same goals can be achieved by extending the action of F12 correlators to the occupied and valence unoccupied 1-particle states only. This design choice reflects the strong dependence of the optimal correlation length scale of the F12 ansatz on the orbital energies/structure, and helps to avoid the unphysical raising of the ground-state energy if the F12 geminals are used to correlate pairs of all 1-particle states. The improved F12 geminal design is incorporated into the unitary transcorrelation framework to produce a unitary 2-body Hamiltonian that incorporates the short-range dynamical correlation physics for ground and low-energy excited states in a balanced manner. This explicitly correlated effective Hamiltonian reduces the basis set requirement on the correlation-consistent basis cardinal number by 1 or more over the uncorrelated counterpart for the description of ground-state coupled-cluster singles and doubles (CCSD) energies, vertical excitation energies, and harmonic vibrational frequencies of equation-of-motion CCSD low-energy excited states.

Hamiltonians↗
DOE OSTI2025

Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine Learning and Simulations

We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using the arepo code. One suite consists of uniform-box simulations covering a ${(25\,{h}^{-1}\,{\rm{Mpc}})}^{3}$ volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales.

cosmological parameters↗
DOE OSTI2020

Planck 2018 results - VIII. Gravitational lensing

We present measurements of the cosmic microwave background (CMB) lensing potential using the final Planck 2018 temperature and polarization data. Using polarization maps filtered to account for the noise anisotropy, we increase the significance of the detection of lensing in the polarization maps from 5σ to 9σ. Combined with temperature, lensing is detected at 40σ. We present an extensive set of tests of the robustness of the lensing-potential power spectrum, and construct a minimum-variance estimator likelihood over lensing multipoles 8 ≤ L ≤ 400 (extending the range to lower L compared to 2015), which we use to constrain cosmological parameters. We find good consistency between lensing constraints and the results from the Planck CMB power spectra within the ΛCDM model. Combined with baryon density and other weak priors, the lensing analysis alone constrains σ 8 Ωm 0.25 = 0.589 ± 0.020 (1σ errors). Also combining with baryon acoustic oscillation data, we find tight individual parameter constraints, σ8 = 0.811 ± 0.019, H0 = 67.9-1.3+1.2 km s -1 Mpc -1 , and Ω m = 0.303 -0.018 +0.016 . Combining with Planck CMB power spectrum data, we measure σ 8 to better than 1% precision, finding σ 8 = 0.811 ± 0.006. CMB lensing reconstruction data are complementary to galaxy lensing data at lower redshift, having a different degeneracy direction in σ 8 - Ω m space; we find consistency with the lensing results from the Dark Energy Survey, and give combined lensing-only parameter constraints that are tighter than joint results using galaxy clustering. Using the Planck cosmic infrared background (CIB) maps as an additional tracer of high-redshift matter, we make a combined Planck-only estimate of the lensing potential over 60% of the sky with considerably more small-scale signal. We additionally demonstrate delensing of the Planck power spectra using the joint and individual lensing potential estimates, detecting a maximum removal of 40% of the lensing-induced power in all spectra. The improvement in the sharpening of the acoustic peaks by including both CIB and the quadratic lensing reconstruction is detected at high significance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Unveiling the chemical fingerprint of phosphorus-rich stars: I. In the infrared region of APOGEE-2

The origin of phosphorus, one of the essential elements for life on Earth, is currently unknown. Prevalent models of Galactic chemical evolution (GCE) systematically underestimate the amount of P compared to observations, especially at low metallicities. The recently discovered P-rich ([P/Fe] ≳ 1.2 dex) and metal-poor ([Fe/H] ≃ –1.0 dex) giants further challenge the GCE models, calling current theories on stellar nucleosynthesis into question. Since the observed low-mass giants are not expected to produce their high P contents themselves, our primary goal is to find clues on their progenitor or polluter. By increasing the number of known P-rich stars, we aim to narrow down a statistically reliable chemical abundance pattern that defines these peculiar stars. In this way, we place more robust constraints on the nucleosynthetic mechanism that causes the unusually high P abundances. In the long term, identifying the progenitor of the P-rich stars may contribute to the search for the source of P in our Galaxy. We performed a detailed chemical abundance analysis based on the high-resolution near-infrared (H band) spectra from the latest data release (DR17) of the APOGEE-2 survey. Employing the BACCHUS code, we measured the abundances of 13 elements in the inspected sample, which is mainly composed of a recent collection of Si-enhanced giants. We also analyzed the orbital motions and compared the abundance results to possible nucleosynthetic formation scenarios, and also to detailed GCE models. These models were produced with the OMEGA+ chemical evolution code, using four different massive star yield sets to investigate different scenarios for massive star evolution. We enlarged the sample of confirmed P-rich stars from 16 to a group of 78 giants, which represents the largest sample of P-rich stars to date. The sample includes the first detection of a P-rich star in a Galactic globular cluster. Significant enhancements in O, Al, Si, and Ce, as well as systematic correlations among the studied elements, unveil the unique chemical fingerprint of the P-rich stars. In contrast, the high [Mg/Fe] and [(C+N)/Fe] found in some of the P-rich stars with respect to P-normal stars is not confirmed over the full sample because of the current uncertainties. Strikingly, the strong overabundance in the α-element Si is accompanied by normal Ca and S abundances. This is at odds with current stellar nucleosynthesis models of massive stars. Our analysis of the orbital motion showed that the P-rich stars do not belong to a locally specific population in the Galaxy. In addition, we confirm that the majority of the sample stars are not part of binary systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Role of Metal Selection in the Radiation Stability of Isostructural M-UiO-66 Metal–Organic Frameworks

Robust and versatile metal–organic frameworks (MOFs) have emerged as sophisticated scaffolds to meet the critical needs of the nuclear community, but their performance depends on their underexplored structural integrities in high- radiation fields. The contributions of selected metal nodes in the radiation stability of MOFs within the isostructural M-UiO-66 series (where M = Zr, Ce, Hf, Th, and Pu; Zr-UiO-66 experiments were executed in a previous work) have been determined. Ce-, Hf-, and Th-UiO-66 MOF samples were irradiated via gamma and He-ion methodologies to obtain doses up to 3 MGy and 85 MGy, respectively, the latter strikingly higher than that obtained in most other studies. Appreciable self-irradiation constituted the total absorbed doses, up to 31 MGy of the gamma-irradiated Pu-UiO-66 samples. Structural degradation was ascertained by powder X-ray diffraction, X-ray total scattering, vibrational spectroscopy, and, where possible, N 2 physisorption isotherms. Diffuse reflectance infrared Fourier transform spectroscopy provided atomic-level mechanistic insights to reveal that the node-linker connection was most susceptible to radiation damage. Density functional theory calculations were performed on cluster models to evaluate the binding energy of the linkers to each metal node. Here, while the isostructures disclosed the same breakdown signatures, distinct radiation sensitivity as a function of metal selection was evident and followed the trend Hf-UiO-66 ~ Zr-UiO-66 > Th-UiO-66 > Pu-UiO-66 > Ce-UiO-66. We anticipate that these endeavors will contribute to the rational design of radiation-resistant materials for targeted applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗