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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Establishment of time‐resolved fluoroimmunoassay of IgG4 based on magnetic microspheres

Abstract Background The abnormal increase in serum IgG4 level is an important clinical symptom of IgG4‐related disease (IgG4‐RD), and the detection of serum IgG4 level is a powerful tool for the diagnosis of IgG4‐RD. This study was conducted to establish a simple and rapid immunoassay for the determination of human serum IgG4 levels. Methods Based on the competition method, a novel immunoassay was established for the determination of human serum IgG4 using a combination of time‐resolved fluoroimmunoassay (TRFIA) and magnetic microspheres. IgG4 was coupled with magnetic microspheres and competed with IgG4 in the samples to bind the Eu 3+ ‐labeled anti‐IgG4 antibody. The immunocomplex was separated and washed in a magnetic field, and the fluorescence counts were measured according to the number of dissociated europium ions. Results The analytical sensitivity of IgG4‐TRFIA based on magnetic microspheres was 0.006 g/L, and the detection range was 0.006–20 g/L under optimal conditions. The precision, recovery, and specificity of this immunoassay were demonstrated to be acceptable. The clinical application of IgG4‐TRFIA based on magnetic microspheres was evaluated and compared with that of immunonephelometry. The results showed that the two detection methods had a good correlation, with a correlation coefficient of .9871. Conclusion IgG4‐TRFIA based on magnetic microspheres has the advantages of high sensitivity, wide detection range, and short analysis time and has the potential to become a useful tool for the diagnosis of IgG4‐RD.

Wu, Qingqing↗
DOE OSTI2020

Identifying the mechanisms of cation inhibition of phenol oxidation by acid birnessite

Abstract Many phenolic compounds found as contaminants in natural waters are susceptible to oxidation by manganese oxides. However, there is often variability between oxidation rates reported in pristine matrices and studies using more environmentally relevant conditions. For example, the presence of cations generally results in slower phenolic oxidation rates. However, the underlying mechanism of cation interference is not well understood. In this study, cation co‐solutes inhibit the transformation of four target phenols (bisphenol A, estrone, p ‐cresol, and triclosan) by acid birnessite. Oxidation rates for these compounds by acid birnessite follow the same trend (Na + > K + > Mg 2+ > Ca 2+ ) when cations are present as co‐solutes. We further demonstrate that the same trend applies to these cations when they are absent from solution but pre‐exchanged with the mineral. We analyze valence state, surface area, crystallinity, and zeta potential to characterize changes in oxide structure. The findings of this study show that pre‐exchanged cations have a large effect on birnessite reactivity even in the absence of cation co‐solutes, indicating that the inhibition of phenolic compound oxidation is not due to competition for surface sites, as previously suggested. Instead, the reaction inhibition is attributed to changes in aggregation and the mineral microstructure.

Balgooyen, Sarah↗
DOE OSTI2022

Analysis of the Spontaneous Emission Limited Linewidth of an Integrated III–V/SiN Laser

Abstract This article describes a calculation of the spontaneous emission limited linewidth of a semiconductor laser consisting of hybrid or heterogeneously integrated, silicon and III–V intracavity components. Central to the approach are a) description of the multi‐element laser cavity in terms of composite laser/free‐space eigenmodes, b) use of multimode laser theory to treat mode competition and multiwave mixing, and c) incorporation of quantum‐optical contributions to account for spontaneous emission effects. Application of the model is illustrated for the case of linewidth narrowing in an InAs quantum‐dot laser coupled to a high‐ SiN cavity.

42 ENGINEERING↗
DOE OSTI2020

Design of High Efficient Mid-Wavelength Infrared Polarizer on ORMOCHALC Polymer

While an organically modified chalcogenide (ORMOCHALC) can be used to fabricate a polymeric mid-wavelength infrared (MWIR) polarizer with competitive extinction ratio compared to the commercial wire-grid polarizers, which are composed of fragile inorganic materials, there is still a knowledge gap regarding the systematic design process to obtain high transmission efficiency and extinction ratio. As such, a computational parameter study for design optimization is conducted with the geometric parameters of the bilayer grating ORMOCHALC polarizer. The computational study shows that the Fabry–Pérot cavity is the primary mechanism that determines the transmission behaviors and the extinction ratio. A bilayer grating design, guided by the parameter study, is realized through the thermal nanoimprint and metal deposition processes. The extinction ratios measured with the Fourier-transform infrared are 245, 304, and 351 at the wavelength of 3, 4, and 5 μm, respectively. Compared to the state-of-the-art of the polymeric MWIR linear polarizers, the extinction ratio is improved by 1.4 times, and the transmission efficiency is increased by 2.5 times. Theoretical analysis with the multiple-layer model based on the transfer matrix method predicts a matched transmission behavior with the experiment and a full-wave electromagnetic simulation.

42 ENGINEERING↗
DOE OSTI2025

Solventless Dual‐Cure Liquid Resins Via Circular Use of Phthalic Anhydride for Recyclable Composite Applications

Abstract Fiber‐reinforced composites (FRCs) possess a remarkable strength‐to‐weight ratio, making them ideal light‐weighing alternative materials of metals used in automotive, aerospace, and outdoor equipment applications, but their recycling is challenging. Chemically recyclable thermoset polymers can enable fiber recovery and reuse; however, challenges remain in the separation and purification of depolymerized small molecules for efficient polymer recycling. To this end, a series of liquid resins for chemically recyclable polymer networks is designed based on phthalic anhydride, a widely produced and inexpensive chemical. The straightforward sublimation of phthalic anhydride is leveraged to enable a simple and efficient separation process for polymer recycling. To liquefy phthalic anhydride, five mono‐acryloyl‐phthalates are synthesized to obtain stable liquid resins together with phthalic diglycidyl ester. These liquid resins undergo dual‐cure reactions that comprise photopolymerization of acrylate and, subsequently, heat‐mediated epoxy‐acid polymerization reactions. These liquid resins exhibit moderate viscosities (2600–6400 cP @ 22 °C), fast curing, and robust thermomechanical properties (T g s from 71 to 116 °C). It is demonstrated that hydrolysis of the dual‐cured polymers completes within 2 h at 80 °C, and direct sublimation produces phthalic anhydride with 82% yield. This resin system is expected to provide a cost‐competitive, highly efficient platform for recyclable FRCs.

Polymer Science↗
DOE OSTI2023

Assessing mechanisms for microbial taxa and community dynamics using process models

Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer–resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling

Abstract Using hydrogen (H ) and ammonia (NH ) for renewable energy storage has the potential to enable economical power and heat supply with high renewable penetrations, especially in remote locations which are characterized by high energy costs. In this work we assess the economic competitiveness of renewable combined heat and power (CHP) systems in Mahaka HI, Nantucket MA, and Northwest Arctic Borough (NWAB) AK by optimally designing these systems for scenarios in which power and heat can be purchased over a range of historical energy prices as well as when 100% renewable supply is required. We use a combined optimal design and scheduling model which minimizes annualized net present cost by determining optimal technology selection and size simultaneously with optimal schedules for each period of a system operating horizon aggregated from full year hourly resolution data via a consecutive temporal clustering algorithm. We find that renewable generation meets at least 85% of power demands and 75% of heat demands under the lowest energy prices investigated. Higher conventional energy prices lead to increased renewable penetration which is facilitated by renewable NH as a seasonal energy storage medium, as are 100% renewable CHP systems. NH is used for power generation with heat cogeneration in all three locations, as well as directly for heating in NWAB. On an annual cost basis, NH ‐enabled 100% renewable CHP is only 3% more expensive in Mahaka and NWAB than systems which can purchase energy at the lowest prices, while it is 15% more expensive in Nantucket.

Palys, Matthew J.↗
DOE OSTI2022

A thermodynamic evaluation of metal halides for the recrystallization of Cu(In,Ga)Se 2

CdTe films are deposited at low temperatures and recrystallized to photovoltaic device quality using CdCl 2 treatments, leading to competitive manufacturing costs. Cu(In,Ga)Se 2 (CIGS) typically requires high-temperature, low-rate depositions to produce high-efficiency devices, resulting in higher costs. A similar metal halide treatment of CIGS has been demonstrated by us previously for some metal halide sources. To understand and optimize the process, a thermodynamic evaluation of candidate metal halides for such treatments is presented as a guide for their selection. By comparing bond dissociation energies, mono- and di-halide compounds are proposed to be ideal compounds to act as transport agents. Known recrystallization and temperature reduction benefits by Ag alloying suggests that Ag-halides should be used to aid transport of all species. The high vapor pressure and mobility of Ga compounds still poses a problem for metal halide treatment resulting in Ga etching and removal of intentional Ga gradients. Less severe but similar issues with In compounds may occur. As a result, Cu compounds have low vapor pressures which may limit transport, however, Cu is highly mobile in CIGS and recrystallization still occurs.

14 SOLAR ENERGY↗
DOE OSTI2022

Future-proofing photovoltaics module reliability through a unifying predictive modeling framework

Solar energy, especially photovoltaics (PV), plays a significant role in the global energy transition required for decarbonization. Technological advancements in increasing efficiencies coupled with cost reductions through economies of scale made PV a cost-competitive energy source set to exceed the milestone of 1 TW globally deployed capacity in 2022. However, ongoing downward price pressure coupled with increasing service life expectations creates tremendous challenges for reliability engineers in ensuring the safe and reliable operation of PV modules and systems over the anticipated service life. Today's reliability research efforts aim for module lifetimes of up to 50 years. Still, our current tools fall short in accurately assessing degradation mechanisms and failure modes over such extended periods. We argue that the well-established PV reliability learning cycle needs to be accelerated to keep up with the rapid technological advancements and high expectations that are put on PV and its role in the global energy transition. In this article, we explore the evolution of the PV reliability learning cycle and highlight the significance that predictive modeling capabilities will have on future PV module reliability. We propose creating a unifying modeling framework - which once established will enable the holistic assessment of PV module reliability, accelerate the PV reliability learning cycle, and bring us closer to quantitative service life predictions of PV modules.

14 SOLAR ENERGY↗
DOE OSTI2025

Water‐mediated Decomposition Pathways of ε‐Hexanitrohexaazaisowurtzitane

The dominant initial decomposition mechanism of 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (HNIW) is understood from the literature to be the N─NO 2 bond scission unimolecular mechanism. Here we have found a water-mediated HONO release initial decomposition mechanism energetically competitive with the N─NO 2 bond scission mechanism modeled using ab initio nudged elastic band simulations. The activation energy of the water-mediated HONO mechanism was calculated to be 138.9 kJ/mol with the Perdew, Burke, and Ernzerhof (PBE) functional (177.8 with PBE0), while the double NO 2 release mechanism is 171.9 kJ/mol (203.3 with PBE0). Branching secondary, tertiary, and quaternary decomposition steps were also discovered including oxidation of HONO or H 2 O causing ring-opening that leads to C─N bond breaking and N 2 O or NO release. The reaction rate of HONO oxidation is much faster than HONO release, making the HONO concentration low. The Helmholtz free energy barrier of the double NO 2 release mechanism is lower than the water-mediated HONO release above 438 K (165°C) due to the vibrational contribution to the free energy. This helps explain why the literature reports more NO 2 release at higher temperatures. The present study reveals a physical mechanism for how water can catalyze the decomposition of HNIW at low enough temperatures, and reveals secondary, tertiary, and quaternary reaction mechanisms leading to NO x release. In conclusion, as water is realistically always present, the reported barriers are important for building kinetic models needed to ensure the safety and reliability of HNIW.

Steele, Brad A. [Lawrence Livermore National Labor↗
DOE OSTI2024

Screening a knowledge‐based library of low molecular weight compounds against the proline biosynthetic enzyme 1‐pyrroline‐5‐carboxylate 1 ( PYCR1)

Abstract Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 1 (PYCR1) is the last enzyme of proline biosynthesis and catalyzes the NAD(P)H‐dependent reduction of Δ 1 ‐pyrroline‐5‐carboxylate toL‐proline. High PYCR1 gene expression is observed in many cancers and linked to poor patient outcomes and tumor aggressiveness. The knockdown of thePYCR1gene or the inhibition of PYCR1 enzyme has been shown to inhibit tumorigenesis in cancer cells and animal models of cancer, motivating inhibitor discovery. We screened a library of 71 low molecular weight compounds (average MW of 131 Da) against PYCR1 using an enzyme activity assay. Hit compounds were validated with X‐ray crystallography and kinetic assays to determine affinity parameters. The library was counter‐screened against human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3 and proline dehydrogenase (PRODH) to assess specificity/promiscuity. Twelve PYCR1 and one PRODH inhibitor crystal structures were determined. Three compounds inhibit PYCR1 with competitive inhibition parameter of 100 μM or lower. Among these, (S)‐tetrahydro‐2H‐pyran‐2‐carboxylic acid (70 μM) has higher affinity than the current best tool compoundN‐formyl‐l‐proline, is 30 times more specific for PYCR1 over human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3, and negligibly inhibits PRODH. Structure‐affinity relationships suggest that hydrogen bonding of the heteroatom of this compound is important for binding to PYCR1. The structures of PYCR1 and PRODH complexed with 1‐hydroxyethane‐1‐sulfonate demonstrate that the sulfonate group is a suitable replacement for the carboxylate anchor. This result suggests that the exploration of carboxylic acid isosteres may be a promising strategy for discovering new classes of PYCR1 and PRODH inhibitors. The structure of PYCR1 complexed withl‐pipecolate and NADH supports the hypothesis that PYCR1 has an alternative function in lysine metabolism.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Ligand‐induced CaMKIIα hub Trp403 flip, hub domain stacking, and modulation of kinase activity

Abstract γ‐Hydroxybutyric acid (GHB) analogs are small molecules that bind competitively to a specific cavity in the oligomeric CaMKIIα hub domain. Binding affects conformation and stability of the hub domain, which may explain the neuroprotective action of some of these compounds. Here, we describe molecular details of interaction of the larger‐type GHB analog 2‐(6‐(4‐chlorophenyl)imidazo[1,2‐b]pyridazine‐2‐yl)acetic acid (PIPA). Like smaller‐type analogs, PIPA binding to the CaMKIIα hub domain promoted thermal stability. PIPA additionally modulated CaMKIIα activity under sub‐maximal CaM concentrations and ultimately led to reduced substrate phosphorylation. A high‐resolution X‐ray crystal structure of a stabilized CaMKIIα (6x mutant) hub construct revealed details of the binding mode of PIPA, which involved outward placement of tryptophan 403 (Trp403), a central residue in a flexible loop close to the upper hub cavity. Small‐angle X‐ray scattering (SAXS) solution structures and mass photometry of the CaMKIIα wild‐type hub domain in the presence of PIPA revealed a high degree of ordered self‐association (stacks of CaMKIIα hub domains). This stacking neither occurred with the smaller compound 3‐hydroxycyclopent‐1‐enecarboxylic acid (HOCPCA), nor when Trp403 was replaced with leucine (W403L). Additionally, CaMKIIα W403L hub was stabilized to a larger extent by PIPA compared to CaMKIIα hub wild type, indicating that loop flexibility is important for holoenzyme stability. Thus, we propose that ligand‐induced outward placement of Trp403 by PIPA, which promotes an unforeseen mechanism of hub domain stacking, may be involved in the observed reduction in CaMKIIα kinase activity. Altogether, this sheds new light on allosteric regulation of CaMKIIα activity via the hub domain.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Modeling SARS-CoV-2 proteins in the CASP-commons experiment

Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Protein model quality assessment using rotation–equivariant transformations on point clouds

Machine learning research concerning protein structure has seen a surge in popularity over the last years with promising advances for basic science and drug discovery. Working with macromolecular structure in a machine learning context requires an adequate numerical representation, and researchers have extensively studied representations such as graphs, discretized 3D grids, and distance maps. As part of CASP14, we explored a new and conceptually simple representation in a blind experiment: atoms as points in 3D, each with associated features. These features—initially just the basic element type of each atom—are updated through a series of neural network layers featuring rotation-equivariant convolutions. Starting from all atoms, we further aggregate information at the level of alpha carbons before making a prediction at the level of the entire protein structure. We find that this approach yields competitive results in protein model quality assessment despite its simplicity and despite the fact that it incorporates minimal prior information and is trained on relatively little data. As a result, its performance and generality are particularly noteworthy in an era where highly complex, customized machine learning methods such as AlphaFold 2 have come to dominate protein structure prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

RNA target highlights in CASP15 : Evaluation of predicted models by structure providers

Abstract The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA‐only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x‐ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA–protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Modeling Protein–Protein and Protein–Ligand Interactions by the ClusPro Team in CASP16

ABSTRACT In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein–protein and protein–ligand complex structures. For protein–protein docking, we combined physics‐based sampling—using ClusPro FFT docking and molecular dynamics—with AlphaFold (AF)‐based sampling, followed by AF‐based refinement. Our method produced numerous high‐accuracy complex models, including cases where AF alone failed, underscoring the critical role of physics‐based sampling alongside deep learning‐based refinement. For protein–ligand docking, we integrated the ClusPro LigTBM template‐based approach with a machine learning‐based confidence model for rescoring. The method preserves conserved interaction fragments derived from homologous complexes, followed by local resampling using physics‐based sampling and a diffusion model. Our template‐based strategy achieved a mean lDDT‐PLI of 0.69 across 233 targets, which was highly competitive. These results demonstrate that combining physics‐based modeling with AI‐driven refinement can significantly enhance the accuracy of both protein–protein and protein–ligand structure predictions.

Ashizawa, Ryota [Department of Applied Mathematics↗
DOE OSTI2026

A multi‐variable framework for selecting WRF physics configurations at convection‐permitting scales: An Amazon wet‐season case study

Tropical convection over rainforests modulates atmospheric circulation and the energy and hydrological cycles across multiple scales. However, the scarcity of observations still limits our understanding of these processes. Although numerical models are utilized to investigate atmospheric physical processes, their performance depends on the choice of parameterizations and grid resolution. Here, in this work, we introduce and apply a multi‐variable, multi‐physics framework to evaluate and rank the Advanced Research Weather Research and Forecasting (WRF–ARW) configurations at 1‐km resolution over the central Amazon during the wet season. A 48‐member ensemble combines three land‐surface models (LSMs), four planetary boundary layer (PBL), and four microphysics (MP) schemes. Model performance for seven convection‐related near‐surface and boundary‐layer variables is assessed using Taylor diagrams and the Taylor skill score (TSS), analysis of variance (ANOVA)‐based sensitivity metrics, and non‐parametric rank tests. We then construct a combined, weighted TSS to identify configurations that are comparatively robust across variables. Results showed that most configurations reproduce near‐surface temperature, sensible and latent heat fluxes, and boundary‐layer height reasonably well, whereas humidity and rainfall remain challenging. LSM choice has the strongest impact on the surface fluxes and a secondary influence on near‐surface temperature and humidity, PBL schemes dominate boundary‐layer height, and MP schemes exert the largest control on rainfall. No single configuration is optimal for all variables, but the combination of Noah (LSM), Yonsei University (PBL), and Morrison (MP) emerges as the most robust configuration for this case, with the WRF single‐moment six‐class scheme (WSM6) providing a competitive, computationally cheaper MP alternative. The framework is general and can be applied to other regions, seasons, and convective regimes.

Amazon rainforest↗