Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “IDENTIFICATION”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
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 OSTI2016

Identification of Surface-Exposed Protein Radicals and A Substrate Oxidation Site in A-Class Dye-Decolorizing Peroxidase from Thermomonospora curvata

Dye-decolorizing peroxidases (DyPs) are a family of heme peroxidases in which a catalytic distal aspartate is involved in H 2 O 2 activation to catalyze oxidations under acidic conditions. They have received much attention due to their potential applications in lignin compound degradation and biofuel production from biomass. However, the mode of oxidation in bacterial DyPs remains unknown. We have recently reported that the bacterial TcDyP from Thermomonospora curvata is among the most active DyPs and shows activity toward phenolic lignin model compounds. On the basis of the X-ray crystal structure solved at 1.75 Å, sigmoidal steady-state kinetics with Reactive Blue 19 (RB19), and formation of compound II like product in the absence of reducing substrates observed with stopped-flow spectroscopy and electron paramagnetic resonance (EPR), we hypothesized that the TcDyP catalyzes oxidation of large-size substrates via multiple surface-exposed protein radicals. Among 7 tryptophans and 3 tyrosines in TcDyP consisting of 376 residues for the matured protein, W263, W376, and Y332 were identified as surface-exposed protein radicals. Only the W263 was also characterized as one of the surface-exposed oxidation sites. SDS-PAGE and size-exclusion chromatography demonstrated that W376 represents an off-pathway destination for electron transfer, resulting in the cross-linking of proteins in the absence of substrates. Mutation of W376 improved compound I stability and overall catalytic efficiency toward RB19. While Y332 is highly conserved across all four classes of DyPs, its catalytic function in A-class TcDyP is minimal, possibly due to its extremely small solvent-accessible areas. Identification of surface-exposed protein radicals and substrate oxidation sites is important for understanding the DyP mechanism and modulating its catalytic functions for improved activity on phenolic lignin.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2018

Identification of the Selective Sites for Electrochemical Reduction of CO to C 2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning

Recent experiments have shown that CO reduction on oxide derived Cu nanoparticles (NP) are highly selective toward C 2+ products. However, understanding of the active sites on such NPs is limited, because the NPs have ~200 000 atoms with more than 10 000 surface sites, far too many for direct quantum mechanical calculations and experimental identifications. We show here how to overcome the computational limitation by combining multiple levels of theoretical computations with machine learning. This approach allows us to map the machine learned CO adsorption energies on the surface of the copper nanoparticle to construct the active site visualization (ASV). Furthermore, we identify the structural criteria for optimizing selective reduction by predicting the reaction energies of the potential determining step, $ΔE_{OCCOH}$, for the C 2+ product. Based on this structural criterion, we design a new periodic copper structure for CO reduction with a theoretical faradaic efficiency of 97%.

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 OSTI2013

Moving Away from the Reference Genome: Evaluating a Peptide Sequencing Tagging Approach for Single Amino Acid Polymorphism Identifications in the Genus Populus

The genetic diversity across natural populations of the model organism, Populus, is extensive, containing a single nucleotide polymorphism roughly every 200 base pairs. When deviations from the reference genome occur in coding regions, they can impact protein sequences. Rather than relying on a static reference database to profile protein expression, we employed a peptide sequence tagging (PST) approach capable of decoding the plasticity of the Populus proteome. Using shotgun proteomics data from two genotypes of P. trichocarpa, a tag-based approach enabled the detection of 6,653 unexpected sequence variants. Through manual validation, our study investigated how the most abundant chemical modification (methionine oxidation) could masquerade as a sequence variant (AlaSer) when few site-determining ions existed. In fact, precise localization of an oxidation site for peptides with more than one potential placement was indeterminate for 70% of the MS/MS spectra. We demonstrate that additional fragment ions made available by high energy collisional dissociation enhances the robustness of the peptide sequence tagging approach (81% of oxidation events could be exclusively localized to a methionine). We are confident that augmenting fragmentation processes for a PST approach will further improve the identification of single amino acid polymorphism in Populus and potentially other species as well.

High energy collisional dissociation↗
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 OSTI2012

Identification and characterization of a bacterial hydrosulphide ion channel

The hydrosulphide ion (HS{sup -}) and its undissociated form, hydrogen sulphide (H{sub 2}S), which are believed to have been critical to the origin of life on Earth, remain important in physiology and cellular signalling. As a major metabolite in anaerobic bacterial growth, hydrogen sulphide is a product of both assimilatory and dissimilatory sulphate reduction. These pathways can reduce various oxidized sulphur compounds including sulphate, sulphite and thiosulphate. The dissimilatory sulphate reduction pathway uses this molecule as the terminal electron acceptor for anaerobic respiration, in which process it produces excess amounts of H{sub 2}S. The reduction of sulphite is a key intermediate step in all sulphate reduction pathways. In Clostridium and Salmonella, an inducible sulphite reductase is directly linked to the regeneration of NAD{sup +}, which has been suggested to have a role in energy production and growth, as well as in the detoxification of sulphite. Above a certain concentration threshold, both H{sub 2}S and HS{sup -} inhibit cell growth by binding the metal centres of enzymes and cytochrome oxidase, necessitating a release mechanism for the export of this toxic metabolite from the cell. Here we report the identification of a hydrosulphide ion channel in the pathogen Clostridium difficile through a combination of genetic, biochemical and functional approaches. The HS{sup -} channel is a member of the formate/nitrite transport family, in which about 50 hydrosulphide ion channels form a third subfamily alongside those for formate (FocA) and for nitrite (NirC). The hydrosulphide ion channel is permeable to formate and nitrite as well as to HS{sup -} ions. Such polyspecificity can be explained by the conserved ion selectivity filter observed in the channel's crystal structure. The channel has a low open probability and is tightly regulated, to avoid decoupling of the membrane proton gradient.

08 HYDROGEN↗
DOE OSTI2015

Infrared identification of the Criegee intermediates syn - and anti -CH 3 CHOO, and their distinct conformation-dependent reactivity

The Criegee intermediates are carbonyl oxides that play critical roles in ozonolysis of alkenes in the atmosphere. So far, the mid-infrared spectrum of only the simplest Criegee intermediate CH 2 OO has been reported. Methyl substitution of CH 2 OO produces two conformers of CH 3 CHOO and consequently complicates the infrared spectrum. Here we report the transient infrared spectrum of syn - and anti -CH 3 CHOO, produced from CH 3 CHI + O 2 in a flow reactor, using a step-scan Fourier-transform spectrometer. Guided and supported by high-level full-dimensional quantum calculations, rotational contours of the four observed bands are simulated successfully and provide definitive identification of both conformers. Furthermore, anti -CH 3 CHOO shows a reactivity greater than syn -CH 3 CHOO towards NO/NO 2 ; at the later period of reaction, the spectrum can be simulated with only syn -CH 3 CHOO. Without NO/NO 2 , anti -CH 3 CHOO also decays much faster than syn -CH 3 CHOO. The direct infrared detection of syn - and anti -CH 3 CHOO should prove useful for field measurements and laboratory investigations of the Criegee mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Identification of phases, symmetries and defects through local crystallography

Here we report that advances in electron and probe microscopies allow 10 pm or higher precision in measurements of atomic positions. This level of fidelity is sufficient to correlate the length (and hence energy) of bonds, as well as bond angles to functional properties of materials. Traditionally, this relied on mapping locally measured parameters to macroscopic variables, for example, average unit cell. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. Here we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighbourhoods. Clustering and multivariate algorithms such as principal component analysis explore the connectivity of lattice and bond structure, as well as identify minute structural distortions, thus allowing for chemical description and identification of phases. This analysis lays the framework for building image genomes and structure–property libraries, based on conjoining structural and spectral realms through local atomic behaviour.

36 MATERIALS SCIENCE↗
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↗