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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
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 OSTI2019

Molecular identification of organic vapors driving atmospheric nanoparticle growth

Abstract Particles formed in the atmosphere via nucleation provide about half the number of atmospheric cloud condensation nuclei, but in many locations, this process is limited by the growth of the newly formed particles. That growth is often via condensation of organic vapors. Identification of these vapors and their sources is thus fundamental for simulating changes to aerosol-cloud interactions, which are one of the most uncertain aspects of anthropogenic climate forcing. Here we present direct molecular-level observations of a distribution of organic vapors in a forested environment that can explain simultaneously observed atmospheric nanoparticle growth from 3 to 50 nm. Furthermore, the volatility distribution of these vapors is sufficient to explain nanoparticle growth without invoking particle-phase processes. The agreement between observed mass growth, and the growth predicted from the observed mass of condensing vapors in a forested environment thus represents an important step forward in the characterization of atmospheric particle growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2019

Consensus on the key characteristics of endocrine-disrupting chemicals as a basis for hazard identification

Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with hormone action, thereby increasing the risk of adverse health outcomes, including cancer, reproductive impairment, cognitive deficits and obesity. A complex literature of mechanistic studies provides evidence on the hazards of EDC exposure, yet there is no widely accepted systematic method to integrate these data to help identify EDC hazards. Inspired by work to improve hazard identification of carcinogens using key characteristics (KCs), we have developed ten KCs of EDCs based on our knowledge of hormone actions and EDC effects. In this Expert Consensus Statement, we describe the logic by which these KCs are identified and the assays that could be used to assess several of these KCs. We reflect on how these ten KCs can be used to identify, organize and utilize mechanistic data when evaluating chemicals as EDCs, and we use diethylstilbestrol, bisphenol A and perchlorate as examples to illustrate this approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Identification of bioactive metabolites using activity metabolomics

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. Furthermore, this concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Identification of a CARM1 Inhibitor with Potent In Vitro and In Vivo Activity in Preclinical Models of Multiple Myeloma

CARM1 is an arginine methyltransferase with diverse histone and non-histone substrates implicated in the regulation of cellular processes including transcriptional co-activation and RNA processing. CARM1 overexpression has been reported in multiple cancer types and has been shown to modulate oncogenic pathways in in vitro studies. Detailed understanding of the mechanism of action of CARM1 in oncogenesis has been limited by a lack of selective tool compounds, particularly for in vivo studies. We describe the identification and characterization of, to our knowledge, the first potent and selective inhibitor of CARM1 that exhibits anti-proliferative effects both in vitro and in vivo and, to our knowledge, the first demonstration of a role for CARM1 in multiple myeloma (MM). EZM2302 (GSK3359088) is an inhibitor of CARM1 enzymatic activity in biochemical assays (IC 50 = 6 nM) with broad selectivity against other histone methyltransferases. Treatment of MM cell lines with EZM2302 leads to inhibition of PABP1 and SMB methylation and cell stasis with IC 50 values in the nanomolar range. Oral dosing of EZM2302 demonstrates dose-dependent in vivo CARM1 inhibition and anti-tumor activity in an MM xenograft model. As a result, EZM2302 is a validated chemical probe suitable for further understanding the biological role CARM1 plays in cancer and other diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Identification of dominant gas transport frequencies during barometric pumping of fractured rock

Abstract We demonstrate that although barometric pressures are complicated signals comprised of numerous frequencies, it is a subset of these frequencies that drive the overwhelming majority of gas transport in fractured rock. Using an inverse numerical analysis, we demonstrate that a single barometric component with seasonally modulated amplitude approximates gas transport due to a measured barometric signal. If past barometric tendencies are expected to continue at a location, the identification of this frequency can facilitate accurate long term predictions of barometrically induced gas transport negating the need to consider stochastic realizations of future barometric variations. Additionally, we perform an analytical analysis that indicates that there is a set of barometric frequencies, consistent with the inverse numerical analysis, with high production efficiency. Based on the corroborating inverse numerical and analytical analyses, we conclude that there is a set of dominant gas transport frequencies in barometric records.

58 GEOSCIENCES↗
DOE OSTI2019

Identification of an AgS2 Complex on Ag(110)

Adsorbed sulfur has been investigated on the Ag(110) surface at two different coverages, 0.02 and 0.25 monolayers. At the lower coverage, only sulfur adatoms are present. At the higher coverage, there are additional bright features which we identify as linear, independent AgS 2 complexes. Here, this identification is based upon density functional theory (DFT) and its comparison with experimental observations including bias dependence and separation between complexes. DFT also predicts the absence of AgS 2 complexes at low coverage, and the development of AgS 2 complexes around a coverage of 0.25 monolayers of sulfur, as is experimentally observed. To our knowledge, this is the first example of an isolated linear sulfur-metal-sulfur complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2018

Identification of the active complex for CO oxidation over single-atom Ir-on-MgAl2O4 catalysts

Supported single atoms provide the best utilization of noble metals and provide an opportunity to design new heterogeneous catalysts. However, identification of the active single atoms structure is required for understanding the reaction mechanism and guiding catalyst design. We used in-situ infrared spectroscopy, in-operando x-ray adsorption spectroscopy and quantum chemical calculations to show that Ir(CO)/MgAl2O4 is the active single atom complex for low temperature CO oxidation and Ir(CO)(O) is the most stable intermediate. In contrast to poisoning of nanoparticles by CO, the formation of Ir(CO) on single atoms results in a different reaction mechanism and high activity for low temperature CO oxidation. This is due to the ability of single atoms to coordinate with multiple ligands, where CO, in Ir(CO)(O), acts as a facilitator by lowering the reaction barrier between gas phase CO and adsorbed O (Eley-Rideal mechanism).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Unambiguous identification and discovery of bacterial siderophores by direct injection 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry

Under iron-limiting conditions, bacteria produce low molecular mass Fe(III) binding molecules known as siderophores to sequester the Fe(III), along with other elements, increasing their bioavailibility. Siderophores are thought to influence iron cycling and biogeochemistry in both marine and terrestrial ecosystems and hence the need for rapid, confident characterization of these compounds has increased. In this study, the type of siderophores produced by two marine bacterial species, Synechococcus sp. PCC 7002 and Vibrio cyclitrophicus 1F53, were characterized using a newly developed 21T Fourier Transform Ion Cyclotron Resonance Mass Spectrometer (FTICR MS) with direct injection electrospray ionization. This technique allowed for the rapid detection of synechobactins from Synechococcus sp. PCC 7002 as well as amphibactins from Vibrio cyclitrophicus 1F53 based on high mass accuracy and resolution allowing for observation of specific Fe isotopic peaks and fine isotopic structure enables highly confident identification of these sideropohores. When combined with molecular network analysis two new amphibactins were discovered and verified by tandem MS. These results show that high-field FTICR MS is a powerful technique that will greatly improve the ability to rapidly identify and discover metal binding species in the environment.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

Identification of the Criegee intermediate reaction network in ethylene ozonolysis: Impact on energy conversion strategies and atmospheric chemistry

The reaction network of the simplest Criegee Intermediate (CI) CH2OO has been studied experimentally during the ozonolysis of ethylene. The results provide valuable information about plasma- and ozone-assisted combustion processes and atmospheric aerosol formation. A network of CI reactions was identified, which can be described best by the sequential addition of CI with ethylene, water, formic acid, and other molecules containing hydroxy, aldehyde, and hydroperoxy functional groups. Species resulting from as many as four sequential CI addition reactions were observed, and these species are highly oxygenated oligomers that are known components of secondary organic aerosols in the atmosphere. Insights into these reaction pathways were obtained from a near-atmsopheric pressure jet-stirred reactor coupled to a high-resolution molecular-beam mass spectrometer. The mass spectrometer employs single-photon ionization with synchrotron-generated, tunable vacuum-ultraviolet radiation to minimize fragmentation via near-threshold ionization and to observe mass-selected photoionization efficiency (PIE) curves. Species identification is supported by comparisons of the mass-selected, experimentally observed photo-ionization thresholds with theoretical calculations for the ionization energies. A variety of multi-functional peroxide species are identified, including hydroxymethyl hydroperoxide (HOCH2OOH), hydroperoxymethyl formate (HOOCH2OCHO), methoxymethyl hydroperoxide (CH3OCH2OOH), ethoxymethyl hydroperoxide (C2H5OCH2OOH), 2-hydroxyethyl hydroperoxide (HOC2H4OOH), dihydroperoxy methane (HOOCH2OOH), and 1-hydroperoxypropan-2-one [CH3C(=O)CH2OOH]. A semi-quantitative analysis of the signal intensities as function of successive CI additions and temperature provides mechanistic insights and valuable information for future modeling work of the associated energy conversion processes and atmospheric chemistry. Furthermore this work provides further evidence that the CI is a key intermediate in the formation of oligomeric species via the formation of hydroperoxides.

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↗