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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structural and biochemical basis for regiospecificity of the flavonoid glycosyltransferase UGT95A1

Glycosylation is a predominant strategy plants use to fine-tune the properties of small molecule metabolites to affect their bioactivity, transport, and storage. It is also important in biotechnology and medicine as many glycosides are utilized in human health. Small molecule glycosylation is largely carried out by family 1 glycosyltransferases. Here, we report a structural and biochemical investigation of UGT95A1, a family 1 GT enzyme from Pilosella officinarum that exhibits a strong, unusual regiospecificity for the 3'-O position of flavonoid acceptor substrate luteolin. We obtained an apo crystal structure to help drive the analyses of a series of binding site mutants, revealing that while most residues are tolerant to mutations, key residues M145 and D464 are important for overall glycosylation activity. Interestingly, E347 is crucial for maintaining the strong preference for 3'-O glycosylation, while R462 can be mutated to increase regioselectivity. The structural determinants of regioselectivity were further confirmed in homologous enzymes. Our study also suggests that the enzyme contains large, highly dynamic, disordered regions. We showed that while most disordered regions of the protein have little to no implication in catalysis, the disordered regions conserved among investigated homologs are important to both the overall efficiency and regiospecificity of the enzyme. This report represents a comprehensive in-depth analysis of a family 1 GT enzyme with a unique substrate regiospecificity and may provide a basis for enzyme functional prediction and engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Regulatory helix plays a key role in genetic ON-OFF switching for the 2’-deoxyguanosine sensing mRNA element

Transcriptional riboswitches, noncoding mRNA elements that operate in cis to regulate gene expression, have a promising potential in medicine, synthetic biology and directed evolution. They bind to cellular metabolites or metal ions with high specificity, leading to conformational rearrangements that facilitate the activation or premature termination of transcription for downstream genes. This elegant mechanism for feedback regulation of metabolic pathways has been identified in prokaryotes and a few in eukaryotes. Our chemical probing of the 2’-deoxyguanosine (2’-dG)-sensing riboswitch demonstrates that the overall conformational state of the full-length riboswitch (dGsw-fl) is unresponsive to the 2’-dG. Although binding proceeds as expected, dGsw-fl exclusively populates an OFF state of transcriptional inhibition. We chemically probed the structure of a known dGsw transcriptional intermediate (dGsw-int) to evaluate the possibility of a cotranscriptional regulatory role. Interestingly, apo dGsw-int adopts an alternative conformation in which a stable anti-terminator helix is formed, leading to an ON state where transcription can proceed. In the presence of 2’-dG, this anti-terminator helix is destabilized to produce a conformation reminiscent of the full-length, OFF-state dGsw. Using a fluorescence quenching assay, we demonstrate that binding 2’-dG to early transcriptional intermediates can inhibit the formation of the anti-terminator helix, locking dGsw in an OFF state. These data suggest that metabolite sensing occurs during a brief window of time between the synthesis of two transcriptional intermediates. Our studies indicate that dGsw does not function as a binary ON−OFF switch, but instead fine-tunes the transcription of downstream genes during RNA synthesis using key intermediates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗
DOE OSTI2021

Electronic impact of Ni2P nanoparticle size on hydrogenation rates

Nickle phosphide, Ni2P, nanoparticles supported on SiO2 show a marked volcano shaped size dependence. Kinetic analysis and physicochemical characterization shows the electron density of Ni to increase with increasing Ni2P size, enhancing in turn the H2 adsorption strength. For particles below 15 nm, H adsorbs weakly causing a low surface coverage of H, gradually increasing with particle size. For large particles, hydrogen binds too strongly causing a high coverage but low catalytic activity. The remarkable size dependence of the electronic properties show that Ni2P particle size can be used to fine-tune its activity for hydrogenation.

Ni2P size effect, Electron density, H2 adsorption ↗
DOE OSTI2021

Analysis and prediction of reaction kinetics using the degree of rate control

“Degree of rate control” (DRC) analysis provides a quantitative approach for analysing the kinetics of multi-step reaction mechanisms that has been widely applied to both heterogeneous and homogeneous catalysis research, as well as electrocatalysis. The DRC of any given transition state or intermediate is defined as a partial derivative such that it approximately equals the fractional increase in net rate to the product of interest per differential decrease in its standard-state free energy for that species (÷RT), holding constant the standard-state free energies of all other transition states and intermediates. Even very complex mechanisms usually have only a few species with non-zero DRCs and are thus the species whose interactions with the catalyst most strongly affect the net rate. These key DRC values thus offer a simple and intuitive route to optimize catalyst materials, especially with the assistance of computational methods like density functional theory (DFT). These high-DRC species are also the species whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. In simple cases with a single “rate-determining step”, the DRC for its transition state (TS) is + 1. Catalyst-bound intermediates, on the other hand, often have negative DRCs equal to a small integer times their fractional occupancy of catalyst sites. The apparent activation energy equals a weighted average of the standard-state enthalpies (relative to reactants) of all the species (intermediates, transition states and products) in the reaction mechanism, each weighted by its DRC (+RT). It has been shown that the apparent transfer coefficient in electrocatalysis, an inverted form of the Tafel slope, is a weighted average of the number of electrons transferred to generate each intermediate or product species in the mechanism, weighted again by the DRC. Quantitative analysis of kinetic isotope effects (KIEs, or the ratio of net rates for different reactant isotopes) in complex mechanisms has shown that the logarithm of the KIE equals the weighted average over all species in the mechanism of the difference between the two isotopes in their standard-state free energies (÷RT), again weighted by the DRC. The reaction orders with respect to fluid-phase concentrations of reactants, products and intermediates have also been proven to be directly related to DRCs. Thus, there are numerous experimental observables which equate to short linear combinations of DRCs, so that combinations of experimental measurements might provide access to DRC values. Since its invention for transition states in 1994 and its generalization to include intermediates in 2009, the DRC has thus far mainly been calculated for microkinetic models based either entirely on DFT or on DFT with the key energies (i.e., those for high-DRC species) being fine-tuned to match experiments. The relationships summarized in this work provide new opportunities for using experiments earlier in the development and optimization of microkinetic models that require input from computational catalysis.

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

Bimetallic NiCu catalysts supported on a Metal-Organic framework for Non-oxidative ethanol dehydrogenation

Non-oxidative ethanol dehydrogenation is a promising route to produce acetaldehyde and hydrogen from sustainable feedstock. Bimetallic NiCu catalysts have shown high efficiency and selectivity for this chemical transformation. In this study, we leverage the high porosity and uniform catalyst deposition sites on a Zr-based metal–organic framework (MOF) catalyst support, NU-1000, to understand how the changes in Cu:Ni ratio affects the reactivity for non-oxidative ethanol dehydrogenation. We found that increasing the Ni 2+ concentration significantly reduces the activation energy of the reaction due to the role of Ni 2+ in suppressing the onset of Cu reduction. Furthermore, this study illustrates how MOFs can be used as catalyst supports to fine-tune the catalyst compositions and understand their effect on the overall catalytic performances.

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

Phase diagrams and microstructures of aqueous short alkyl chain polyethylene glycol ether carboxylate and carboxylic acid triblock surfactant solutions

The surfactant C 8 Eo 8 CH 2 COOH (Akypo LF2) and its salts have a small hydrophobic and a significantly longer hydrophilic part. As a consequence, there must be a significant steric constraint, once these surfactant molecules form micelles. In addition, the partially charged headgroups should bring some additional fine-tuning via electrostatic interactions to this "essentially non-ionic" surfactant. Experiments: Phase diagrams of binary mixtures of water and C 8 Eo 8 CH 2 COOH are established over large concentration and temperature ranges, also at different pHs and in the presence of sodium and calcium ions. Surface tensions and osmotic pressures are measured to understand the systems. To evaluate the microstructures, also Dynamic Light Scattering and Small-Angle X-ray Scattering are performed.

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

Intrinsic scintillation performance & europium concentration effects in RbSr 2 I 5 and RbSr 2 Br 5 scintillators

Scintillators play crucial roles in homeland security applications like gamma ray spectroscopy and high energy X-ray radiography. For promising new scintillators, fine-tuning the luminescent dopant concentration is one avenue to further improve their performance and tailor their properties. In this work, the effects of europium dopant concentrations on the crystal growth, luminescence and scintillation properties of RbSr 2 Br 5 and RbSr 2 I 5 crystals was investigated. Nine transparent 7 mm diameter single crystals were grown via the Vertical Bridgman method. Here, the optical band gap of RbSr 2 Br 5 was 5.9 eV and that of RbSr 2 I 5 was 4.7 eV. High scintillation performance was achieved with a relatively low europium concentration of 1 mol%. For both RbSr 2 Br 5 :Eu and RbSr 2 I 5 :Eu crystals, light yield of 60–90,000 ph/MeV, energy resolution 2.8–4.0 % at 662 keV, and X-ray afterglow 0.79–1.5 % at 2 ms were obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Magnetic properties of Fe-substituted NiBr 2 single crystals

NiBr 2 is a multiferroic material with an incommensurate magnetic ground state that has easy-plane anisotropy. This manuscript reports how the magnetic properties evolve with Fe substitution in Ni 1-x Fe x Br 2 single crystals for 0 0.17. A particular emphasis is placed on the = 0.055 crystal, for which magnetic phase diagrams were developed on the basis of anisotropic magnetization measurements. The observed phase evolution reveals a phase transition in the x = 0.055 crystal that is not present in NiBr 2 . The data also demonstrate that the easy-plane anisotropy of NiBr is altered by Fe substitution, with a finite easy-axis anisotropy developing at even the low concentration of x = 0.055. Overall, these results demonstrate that Fe substitution enables a fine-tuning of the magnetism in NiBr 2 , especially the magnetic anisotropy, and may therefore promote complex magnetic phases.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.

battery aging reconstruction↗
DOE OSTI2023

Synthesis, structure and canted antiferromagnetism of layered cobalt hydroxide sorbate

Layered hybrid organic–inorganic compounds of the transition metal hydroxides, in which part of the hydroxide ions are exchanged for organic ligands that act as spacers of the 2-dimensional inorganic layers, elicit new or enhanced properties. The interlayer ligands enable fine-tuning of the physicochemical properties of the metal hydroxide layers, including their magnetism. Specifically, within the Co(OH) 2 family of transition ion hydroxides, this approach has been leveraged to stabilize antiferromagnetic and ferrimagnetic ground states for pillared and layered complexes, respectively. Here, in this work, we further investigate the effects of changing dimensionality, local Co coordination, and interlayer spacing on the magnetic ground state of brucite-like β-Co(OH) layered complexes. We report the hydrothermal synthesis of a new crystalline material, Co(OH)(sorb), where the inorganic layers are spaced by monotopic, doubly unsaturated sorbate ligands (sorb = [C 6 H 7 O 2 ] – ). The material was structurally characterized using powder X-ray and neutron diffraction measurements and the crystal structure was solved ab initio from powder diffraction data and refined using the Rietveld method. The magnetic susceptibility and magnetization measurements reveal canted antiferromagnetic ordering with T N = 41.7 K. This report augments our understanding of tuning magnetism through dimensionality in layered complexes and represents a step forward in the design of new 2D layered hybrid compounds.

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

Impact of electrode porosity architecture on electrochemical performances of 1 mm-thick LiFePO4 binder-free Li-ion electrodes fabricated by Spark Plasma Sintering

Thick electrodes with high active material loadings have been intensively studied over the last couple of decades in pursuit of achieving high energy density systems. To optimize and enhance the electrochemical performance of such electrodes, one has to control the pore morphology by, for example, varying the pore size and shape, and the level of porosity. In the present work, the fabrication of thick binder-free LiFePO4 (LFP) electrodes with two different pore sizes (12 and 20 mu m) and porosities (21 vol% and 44 vol%) using Spark Plasma Sintering (SPS) and templating approach is reported. The well-controlled porous architecture inside the thick electrodes is realized by fine-tuning experimental parameters. The impact of porosity architecture on electrochemical performance is quantified and correlated with the 3D tortuosity values determined from both micro-computed tomography and electrochemical impedance-based experimental methods. Based on the micro-computed tomography data analysis, estimated tortuosity values along X, Y, and Z axes reveal an anisotropic effect perpendicularly to the SPS compression axis (Z-direction). This is particularly profoundly observed in the samples with larger pores (20 mu m). The correlation between morphological properties and the rate capability performance is established indicating that the capacity loss happens mainly due to the Li-ion diffusion limitations.

Cathode material↗
DOE OSTI2023

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Systematic mapping of the conformational landscape and dynamism of soluble fibrinogen

Background: Fibrinogen is a soluble, multisubunit, and multidomain dimeric protein, which, upon its proteolytic cleavage by thrombin, is converted to insoluble fibrin, initiating polymerization that substantially contributes to clot growth. Fibrinogen contains numerous, transiently accessible “cryptic” epitopes for hemostatic and immunologic proteins, suggesting that fibrinogen exhibits conformational flexibility, which may play functional roles in its temporal and spatial interactions. Hitherto, there have been limited integrative approaches characterizing the solution structure and internal flexibility of fibrinogen. Methods: Here, utilizing a multipronged, biophysical approach involving 2 solution-based techniques, temperature-dependent hydrogen-deuterium exchange mass spectrometry and small angle X-ray scattering, corroborated by negative stain electron microscopy, we present a holistic, conformationally dynamic model of human fibrinogen in solution. Results: Our data reveal 4 major and distinct conformations of fibrinogen accommodated by a high degree of internal protein flexibility along its central scaffold. We propose that the fibrinogen structure in the solution consists of a complex, conformational landscape with multiple local minima. This is further supported by the location of numerous point mutations that are linked to dysfibrinogenemia and posttranslational modifications, residing near the identified fibrinogen flexions. Conclusion: Further, this work provides a molecular basis for the structural “dynamism” of fibrinogen that is expected to influence the broad swath of its functionally diverse macromolecular interactions and fine-tune the structural and mechanical properties of blood clots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A safe reinforcement learning algorithm for supervisory control of power plants

Traditional control theory-based methods require tailored engineering for each system and constant fine-tuning. In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly. Model-free Reinforcement learning (RL) has emerged as a promising solution for control tasks due to its ability to learn from trial-and-error interactions with the environment. It eliminates the need for explicitly modeling the environment’s dynamics, which is potentially inaccurate. However, the direct imposition of state constraints in power plant control raises challenges for standard RL methods. To address this, we propose a chance-constrained RL algorithm based on Proximal Policy Optimization for supervisory control. Our method employs Lagrangian relaxation to convert the constrained optimization problem into an unconstrained objective, where trainable Lagrange multipliers enforce the state constraints. In conclusion, our approach achieves the smallest distance of violation and violation rate in a load-follow maneuver for an advanced Nuclear Power Plant design.

constrained optimization↗
DOE OSTI2025

Evolution from topological Dirac metal to flat-band-induced antiferromagnet in layered K x Ni 4 S 2 (0 ≤ x ≤ 1)

Condensed matter systems with coexisting Dirac cones and flat bands and a switchable control between them within a single system are desirable but remarkably uncommon. Here, we report a layered quantum material system, K x Ni 4 S 2 (0 <= x <= 1), that simultaneously hosts both characteristics without involving typical Kagome/honeycomb lattices. Enabled by a topochemical K-deintercalation process, the Fermi surface can be fine-tuned continuously over a wide range of energies. Consequently, a non-magnetic Dirac-metal state with a topological nontrivial Z 2 index of 1;(000), supported by first-principles calculations and high mobility up to 1,471 cm 2 V -1 s -1 , is observed on the K-rich x = 1 side, whereas a flat-band-induced antiferromagnetic state with T N up to 10.1 K emerges as the K-content approaches 0. The K x Ni 4 S 2 system offers a versatile platform for exploring emerging phenomena and underscores aviable pathway for in situ control of quantum materials dominated by Dirac cones, flat bands, and their interplay.

dirac metal↗