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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Sewage Treatment Plant #1 Area, SWMU 117 Per- and Polyfluoroalkyl Substances (PFAS) Site Assessment Progress Report

This Per- and Polyfluoroalkyl Substances (PFAS) Site Assessment (SA) Progress Report (SAPR) discusses the investigation activities and findings for the Sewage Treatment Plant #1 (STP1) Area located at Kennedy Space Center (KSC), Florida (Figure 1-1). This site has been designated Solid Waste Management Unit (SWMU) 117 under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program, as the sewage treatment plant and associated areas were identified as a potential source of PFAS to the environment. This PFAS SAPR was prepared by Tetra Tech, Inc., for the National Aeronautics and Space Administration (NASA) under Indefinite Delivery Indefinite Quantity Contract 80KSC019D0011-80KSC019F0070. This is the first progress report to document on-going SA activities; supplemental progress reports will be provided as additional data is collected. PFAS SA activities were conducted between April 2020 and March 2022 to collect additional data to supplement the existing datasets to better understand the extent of PFAS impacts to the environment in the STP1 Area. The SA for the STP1 Area covers an approximately 130-acre investigation area with multiple structures and buildings. The focus of the SA is the STP1 Complex and associated structures, including the former Polishing Pond, former Sludge Disposal8 Area, and former Spray Field. The STP1 Complex is located in the KSC Industrial Area, at the southwest corner of 4th Street SE and C Avenue SE. The STP1 Complex is located approximately ¼-mile south and downgradient of the Fire Station #1 site (SWMU 116), which is also currently undergoing a PFAS SA because of potential releases of PFAS-containing aqueous film-forming foam (AFFF). During the SA, a total of seven soil, 131 groundwater direct push technology (DPT), 24 groundwater monitoring well, and 11 surface water samples were collected between April 2020 and March 2022. Monitoring well samples were analyzed for 18 PFAS compounds, with all other samples analyzed for 28 PFAS compounds. The SA sample results were used along with historical results to evaluate the extent of PFAS impacts to the environment in the STP1 Area. Data generated to date and prior results were screened against the United States Environmental Protection Agency (USEPA) May 2022 Tap Water Regional Screening Levels (RSL) for groundwater and residential RSLs for soil (hazard quotient of 0.1). Surface water results were screened against the State of Florida Human Health Surface Water Screening Levels (SWSLs). Results from the SA showed exceedances of the applicable screening criteria for groundwater and surface water. Considering the current and historical dataset, PFOS is the prevalent PFAS compound. Based on these results, additional groundwater DPT and surface water sampling should be considered for PFAS analysis, focused on evaluating surface water bodies in the southeast portion of the Industrial Area, which discharge into the Banana River. Additionally, installation of monitoring wells should be considered to evaluate the interaction between the groundwater and surface water at the site. Collection of additional samples for TOC analysis should also be considered from representative groundwater (saturated soils) and surface water locations to further evaluate potential correlations between PFAS and TOC to provide a more comprehensive dataset to assist in fate and transport analyses.

Sewage Treatment Plant↗
DOE OSTI2023

Environmental exposure to industrial air pollution is associated with decreased male fertility

Objective: To understand how chronic exposure to industrial air pollution is associated with male fertility through semen parameters. Design: Retrospective cohort study. Subjects: Men in the Subfertility, Health and Assisted Reproduction cohort who underwent a semen analysis 2005-2017 with ≥1 measured semen parameter (N=21,563). Intervention(s): Residential histories for each man were constructed using locations from administrative records linked through the Utah Population Database. Industrial facilities with air emissions of nine endocrine disrupting compound chemical classes were identified from the Environmental Protection Agency Risk-Screening Environmental Indicators microdata. Chemical levels were linked with residential histories for the 5 years prior to each semen analysis. Main Outcome Measures: Semen analyses were classified as azoospermic or oligozoospermic (< 15 M/mL) using World Health Organization cutoffs for concentration. Bulk semen parameters such as concentration, total count, ejaculate volume, total motility, total motile count, and total progressive motile count were also measured. Multivariable regression models with robust standard errors were used to associate exposure quartiles for each of the nine chemical classes with each semen parameter, adjusting for age, race, and ethnicity, as well as neighborhood socioeconomic disadvantage. Results: After adjustment for demographic covariates, several chemical classes were associated with azoospermia and decreased total motility and volume. For exposure in the 4th relative to 1st quartile, significant associations were observed for acrylonitrile (β total motility = -0.87 pp), aromatic hydrocarbons (odds ratio [OR]azoospermia = 1.53; β volume = -0.14 mL), dioxins (OR azoospermia = 1.31; β volume = -0.09 mL; β total motility = -2.65 pp), heavy metals (β total motility = -2.78pp), organic solvents (OR azoospermia = 1.75; β volume = -0.10 mL), organochlorines (OR azoospermia = 2.09; β volume = -0.12 mL), phthalates (OR azoospermia = 1.44; β volume = -0.09 mL; β total motility = -1.21 pp), and silver particles (OR azoospermia = 1.64; β volume = -0.11 mL). All semen parameters significantly decreased with increasing socioeconomic disadvantage. Men who lived in the most disadvantaged areas had concentration, volume, and total motility of 6.70 M/mL, 0.13 mL, and 1.79 pp lower, respectively. Count, motile count, and total progressive motile count all decreased by 30–34 M.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

A covalent inhibitor targeting the papain-like protease from SARS-CoV-2 inhibits viral replication

Covalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-2 have great potential as antivirals, but their non-specific reactivity with thiols has limited their development. In this report, we performed an 8000 molecule electrophile screen against PLpro and identified an α-chloro amide fragment, termed compound 1, which inhibited SARS-CoV-2 replication in cells, and also had low non-specific reactivity with thiols. Compound 1 covalently reacts with the active site cysteine of PLpro, and had an IC50 of 18 μM for PLpro inhibition. Compound 1 also had low non-specific reactivity with thiols and reacted with glutathione 1–2 orders of magnitude slower than other commonly used electrophilic warheads. Finally, compound 1 had low toxicity in cells and mice and has a molecular weight of only 247 daltons and consequently has great potential for further optimization. Collectively, these results demonstrate that compound 1 is a promising lead fragment for future PLpro drug discovery campaigns.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Effect of Controlled Artificial Disorder on the Magnetic Properties of EuFe 2 (As 1– x P x ) 2 Ferromagnetic Superconductor

Static (DC) and dynamic (AC, at 14 MHz and 8 GHz) magnetic susceptibilities of single crystals of a ferromagnetic superconductor, EuFe 2 ( As 1 – x P x ) 2 ( x = 0.23), were measured in pristine state and after different doses of 2.5 MeV electron or 3.5 MeV proton irradiation. The superconducting transition temperature, T c ( H ) , shows an extraordinarily large decrease. It starts at T c ( H = 0 ) ≈ 24 K in the pristine sample for both AC and DC measurements, but moves to almost half of that value after moderate irradiation dose. Remarkably, after the irradiation not only T c moves significantly below the FM transition, its values differ drastically for measurements at different frequencies, ≈16 K in AC measurements and ≈12 K in a DC regime. We attribute such a large difference in T c to the appearance of the spontaneous internal magnetic field below the FM transition, so that the superconductivity develops directly into the mixed spontaneous vortex-antivortex state where the onset of diamagnetism is known to be frequency-dependent. We also examined the response to the applied DC magnetic fields and studied the annealing of irradiated samples, which almost completely restores the superconducting transition. Overall, our results suggest that in EuFe 2 ( As 1 – x P x ) 2 superconductivity is affected by local-moment ferromagnetism mostly via the spontaneous internal magnetic fields induced by the FM subsystem. Another mechanism is revealed upon irradiation where magnetic defects created in ordered Eu 2 + lattice act as efficient pairbreakers leading to a significant T c reduction upon irradiation compared to other 122 compounds. On the other hand, the exchange interactions seem to be weakly screened by the superconducting phase leading to a modest increase of T m (less than 1 K) after the irradiation drives T c to below T m . Our results suggest that FM and SC phases coexist microscopically in the same volume.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Searching for ternary magnetic Zr–Fe–B compounds through deep machine learning

We use deep machine learning (ML) combined with first-principles calculations to search for energetically favorable ternary magnetic zirconium–iron borides. We show that an iterative ML approach enables efficient screening of vast structural libraries, effectively selecting promising candidates for subsequent first-principles investigations. Twenty-two new Fe-rich ternary compounds with formation energies within 60 meV atom −1 above the known ternary convex hull and with magnetic polarization larger than 0.6 T are identified, among which ten structures exhibit significant uniaxial anisotropy with magnetocrystalline anisotropy constant K 1 ⩾ 0.8 MJ m −3 , including a Zr 2 Fe 14 B phase. Such an ML-guided approach dramatically accelerates the discovery of rare-earth-free permanent magnetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy

We employ a machine learning (ML)-guided framework to explore rare earth free magnetic materials, specifically focusing on Fe–Co–C ternary compounds for potential use in permanent magnets. Utilizing a specifically trained crystal graph convolutional neural network model, we efficiently screen a vast space of nearly a million substitutional structures to select 620 promising structures for further investigation by first-principles calculation. We predict five low-energy metastable Fe–Co–C compounds with formation energy less than 150 meV/atom above the convex hull. These compounds exhibit high magnetization (Js > 1.0 T) and significant magnetic anisotropy (K1 > 1.0 MJ/m3), making them promising candidates for permanent magnet applications. The phonon calculations indicate these compounds are dynamically stable. Our ML-guided framework demonstrates the utility of rapidly identifying novel materials with tailored magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Phenothiazines Rapidly Induce Laccase Expression and Lignin-Degrading Properties in the White-Rot Fungus Phlebia radiata

Phlebia radiata is a widespread white-rot basidiomycete fungus with significance in diverse biotechnological applications due to its ability to degrade aromatic compounds, xenobiotics, and lignin using an assortment of oxidative enzymes including laccase. In this work, a chemical screen with 480 conditions was conducted to identify chemical inducers of laccase expression in P. radiata. Among the chemicals tested, phenothiazines were observed to induce laccase activity in P. radiata, with promethazine being the strongest laccase inducer of the phenothiazine-derived compounds examined. Secretomes produced by promethazine-treated P. radiata exhibited increased laccase protein abundance, increased enzymatic activity, and an enhanced ability to degrade phenolic model lignin compounds. Transcriptomics analyses revealed that promethazine rapidly induced the expression of genes encoding lignin-degrading enzymes, including laccase and various oxidoreductases, showing that the increased laccase activity was due to increased laccase gene expression. Finally, the generality of promethazine as an inducer of laccases in fungi was demonstrated by showing that promethazine treatment also increased laccase activity in other relevant fungal species with known lignin conversion capabilities including Trametes versicolor and Pleurotus ostreatus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Revisiting Five Years of CASMI Contests with EPA Identification Tools

Software applications for high resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) continue to enhance chemical identification capabilities. Given the variety of available applications, determining the most fit-for-purpose tools and workflows can be difficult. The Critical Assessment of Small Molecule Identification (CASMI) contests were initiated in 2012 to provide a means to evaluate compound identification tools on a standardized set of blinded tandem mass spectrometry (MS/MS) data. Five CASMI contests have resulted in recommendations, publications, and invaluable datasets for practitioners of HRMS-based screening studies. The US Environmental Protection Agency’s (EPA) CompTox Chemicals Dashboard is now recognized as a valuable resource for compound identification in NTA studies. However, this application was too new and immature in functionality to participate in the five previous CASMI contests. In this work, we performed compound identification on all five CASMI contest datasets using Dashboard tools and data in order to critically evaluate Dashboard performance relative to that of other applications. CASMI data was accessed via the CASMI webpage and processed for use in our spectral matching and identification workflow. Relative to applications used by former contest participants, our tools, data, and workflow performed well, placing more challenge compounds in the top five of ranked candidates than did the winners of three contest years and tying in a fourth. In addition, we conducted an in-depth review of the CASMI structure sets and made these reviewed sets available via the Dashboard. Our results suggest that Dashboard data and tools would enhance chemical identification capabilities for practitioners of HRMS-based NTA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High‐Throughput Screening Assisted Discovery of a Stable Layered Anti‐Ferromagnetic Semiconductor: CdFeP 2 Se 6

Recent advances in 2D magnetism have heightened interest in layered magnetic materials due to their potential for spintronics. In particular, layered semiconducting antiferromagnets exhibit intriguing low-dimensional semiconducting behavior with both charge and spin as carrier controls. However, synthesis of these compounds is challenging and remains rare. Here, first-principles based high-throughput search is conducted to screen potentially stable mixed metal phosphorous trichalcogenides (MM ' P 2 X 6 , where M and M ' are transition metals and X is a chalcogenide) that have a wide range of tunable bandgaps and interesting magnetic properties. Among the potential candidates, a stable semiconducting layered magnetic material, CdFeP 2 Se 6 , that exhibits a short-range antiferromagnetic order at T N = 21 K with an indirect bandgap of 2.23 eV is successfully synthesized . This work suggests that high-throughput screening assisted synthesis can be an effective method for layered magnetic materials discovery.

2D materials↗
DOE OSTI2021

Small molecule microarray identifies inhibitors of tyrosyl-DNA phosphodiesterase 1 that simultaneously access the catalytic pocket and two substrate binding sites

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a member of the phospholipase D family of enzymes, which catalyzes the removal of both 3'- and 5'-DNA phosphodiester adducts. Importantly, it is capable of reducing the anticancer effects of type I topoisomerase (TOP1) inhibitors by repairing the stalled covalent complexes of TOP1 with DNA. It achieves this by promoting the hydrolysis of the phosphodiester bond between the Y723 residue of human TOP1 and the 3'-phosphate of its DNA substrate. Blocking TDP1 function is an attractive means of enhancing the efficacy of TOP1 inhibitors and overcoming drug resistance. Previously, we reported the use of an X-ray crystallographic screen of more than 600 fragments to identify small molecule variations on phthalic acid and hydroxyquinoline motifs that bind within the TDP1 catalytic pocket. Yet, the majority of these compounds showed limited (millimolar) TDP1 inhibitory potencies. We now report examining a 21 000-member library of drug-like Small Molecules in Microarray (SMM) format for their ability to bind Alexa Fluor 647 (AF647)-labeled TDP1. The screen identified structurally similar N,2-diphenylimidazo[1,2-a]pyrazin-3-amines as TDP1 binders and catalytic inhibitors. We then explored the core heterocycle skeleton using one-pot Groebke–Blackburn–Bienayme multicomponent reactions and arrived at analogs having higher inhibitory potencies. Solving TDP1 co-crystal structures of a subset of compounds showed their binding at the TDP1 catalytic site, while mimicking substrate interactions. Although our original fragment screen differed significantly from the current microarray protocol, both methods identified ligand–protein interactions containing highly similar elements. Importantly inhibitors identified through the SMM approach show competitive inhibition against TDP1 and access the catalytic phosphate-binding pocket, while simultaneously providing extensions into both the substrate DNA and peptide-binding channels. As such, they represent a platform for further elaboration of trivalent ligands, that could serve as a new genre of potent TDP1 inhibitors.

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

Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an ‘in-house’ rare-earth database with more than 600 + compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu 2 type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X-ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce-Fe-Cu based compound was also analyzed to get an in-depth understanding of the electronic origin of phase stability. The interpretable analytical models in combination with density-functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structural constraint integration in a generative model for the discovery of quantum materials

Billions of organic molecules have been computationally generated, yet functional inorganic materials remain scarce due to limited data and structural complexity. Here, in this work, we introduce Structural Constraint Integration in a GENerative model (SCIGEN), a framework that enforces geometric constraints, such as honeycomb and kagome lattices, within diffusion-based generative models to discover stable quantum materials candidates. SCIGEN enables conditional sampling from the original distribution, preserving output validity while guiding structural motifs. This approach generates ten million inorganic compounds with Archimedean and Lieb lattices, over 10% of which pass multistage stability screening. High-throughput density functional theory calculations on 26,000 candidates shows over 95% convergence and 53% structural stability. A graph neural network classifier detects magnetic ordering in 41% of relaxed structures. Furthermore, we synthesize and characterize two predicted materials, TiPd 0.22 Bi 0.88 and Ti 0.5 Pd 1.5 Sb, which display paramagnetic and diamagnetic behaviour, respectively. Our results indicate that SCIGEN provides a scalable path for generating quantum materials guided by lattice geometry.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hidden Spin-Valley Locking Stabilizes Nanosecond Spin Polarization in 2D Perovskites

Room-temperature spin control in semiconductors is fundamental to spin-optoelectronics. Although inversion symmetry breaking offers one path for spin control in semiconductors, strong spin dephasing at elevated temperatures remains a persistent limitation. Hidden spin polarization without global symmetry breaking provides another promising material design strategy, but robust spin stabilization from this effect has yet to be experimentally realized. Here we show spin stabilization at room temperature in two-dimensional hybrid organic-inorganic perovskites through hidden spin-valley locking. We use functional non-primary ammonium cations to induce symmetry-breaking distortions in the metal-halide layers, producing giant local spin splitting while preserving global inversion symmetry. Time-resolved circular dichroism measurements reveal optically generated spin-polarized carriers that persist for 686 ps in (AzOH)2PbI4 and 4.7 ns in the lead-free analogue (AzOH)2SnI4 at room temperature, without an external magnetic field. First-principles calculations suggest that these long lifetimes arise from hidden spin valleys, enhanced dielectric screening and reduced spin-orbit-induced scattering in the Sn-based compound. Our design paradigm unlocks inversion-symmetric semiconductors with ultralong spin lifetimes, broadening the materials options for light-driven spin control.

14 SOLAR ENERGY↗
DOE OSTI2025

Energy filtering–induced ultrahigh thermoelectric power factors in Ni 3 Ge

Traditional thermoelectric materials rely on low thermal conductivity to enhance their efficiency but suffer from inherently limited power factors. Innovative pathways to optimize electronic transport are thus crucial. Here, we achieve ultrahigh power factors in Ni 3 Ge-based systems through an unconventional thermoelectric materials design principle. When overlapping flat and dispersive bands are engineered to the Fermi level, charge carriers can undergo intense interband scattering, yielding an energy filtering effect similar to what has long been predicted in certain nanostructured materials. Via a multistep DFT-based screening method developed here, we find a family of L1 2 -ordered binary compounds with ultrahigh power factors up to 11 mW m −1 K −2 near room temperature, which are driven by an intrinsic phonon-mediated energy filtering mechanism. Our comprehensive experimental and theoretical study of these intriguing materials paves the way for understanding and designing high-performance scattering-tuned metallic thermoelectrics.

Science & Technology - Other Topics↗
DOE OSTI2022

Cryo-GCMS Outgassing Screening: Thermal Filler Materials

A study was conducted to investigate the outgassing characteristics of four thermal filler materials. The purpose of this screening was to identify any outgassing products that might be considered reactive, specifically compounds that could result in corrosion in the systems where these materials are used. A range of compounds was observed in the sample headspaces, though most do not stand out as being known reactive species of concern. However, several halogenated compounds and sulfurous compounds- classes compounds known to facilitate corrosion reactions under certain conditions- were observed in low concentrations. The TFLEX 760 exhibited the highest total outgassing, while the GR130 had the lowest. Therm-a-Gap75 and the Si thermal grease exhibited very similar outgassing profiles. It is difficult to predict the extent to which any given compound observed in an analysis of this type might pose a risk in an actual system; factors such as temperature, system geometry, concentration, and gas conductance all play a role in the kinetics governing chemical reactions. It is recommended that the results of these analyses are shared with pertinent materials SMEs familiar with the system(s) in question to evaluate potential risks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methods for treating diseases related to the wnt pathway

The present invention relates to methods for treating a disease, in which the disease arises from dysregulation of the Wnt signaling pathway. In some instances, the disease can be treated by administering a Wnt pathway inhibitory compound. In other instances, the method optionally includes conducting a genome-wide screening to determine one or more genes resulting in a reduced disease state and then identifying the gene(s) as being involved in the Wnt signaling pathway.

59 BASIC BIOLOGICAL SCIENCES↗