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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
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

Investigation of porous polydimethylsiloxane structures with tunable properties induced by the phase separation technique

This article reports the fabrication and characterization of porous polydimethylsiloxane (PDMS) structures developed by the solvent evaporationinduced phase separation technique. Here, ternary systems containing water/ tetrahydrofuran (THF)/PDMS with various concentrations are produced to form a stable solution. The porous PDMS structures are formed by removing the solvent (THF) and nonsolvent (water) phases during the stepping heat treatment procedure. The analytical ternary phase diagram is constructed based on the thermodynamic equilibrium state in the polymer solution to explain the stable/unstable formulations and the possible composition change path. The results show that the isolated pores with the adjustable pore size ranging from 330 to 1900 μm are obtained by tuning the water to the THF ratio. The mechanical properties of the porous PDMS structures are determined by conducting the tensile tests on the prepared dog bone-shaped specimens. A wide range of elastic modulus ranging between 0.49 and 1.05 MPa was achieved without affecting the density of the porous sample by adjusting the solvent and non-solvent content in the solution. It is shown that the flexibility of the porous structures can be improved by reducing the ratio of water to THF and decreasing the PDMS content. The porosity measurements reveal that the PDMS concentration is the major phase controlling the porosity of the structure, while the effect of water/THF is negligible.

42 ENGINEERING↗
DOE OSTI2020

Impacts on product quality attributes of monoclonal antibodies produced in CHO cell bioreactor cultures during intentional mycoplasma contamination events

Abstract A mycoplasma contamination event in a biomanufacturing facility can result in costly cleanups and potential drug shortages. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and penetrate the standard 0.2‐µm filters used in the clarification of harvested cell culture fluid. Previously, we reported a study regarding the ability of Mycoplasma arginini to persist in a single‐use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G 1 (IgG1) antibody. Our previous work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Careful evaluation of certain identified process parameters over time may be used to indicate mycoplasma contamination in CHO cell cultures in a bioreactor before detection from a traditional method. In this report, we studied the changes in the IgG1 product quality produced by CHO cells considered to be induced by the M. arginini contamination events. We observed changes in critical quality attributes correlated with the duration of contamination, including increased acidic charge variants and high mannose species, which were further modeled using principal component analysis to explore the relationships among M. arginini contamination, CHO cell growth and metabolites, and IgG1 product quality attributes. Finally, partial least square models using NIR spectral data were used to establish predictions of high levels (≥10 4 colony‐forming unit [CFU/ml]) of M. arginini contamination, but prediction of levels below 10 4 CFU/ml were not reliable. Contamination of CHO cells with M. arginini resulted in significant reduction of antibody product quality, highlighting the importance of rapid microbiological testing and mycoplasma testing during particularly long upstream bioprocesses to ensure product safety and quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Persistence of Methionine Side Chain Mobility at Low Temperatures in a Nine‐Residue Low Complexity Peptide, as Probed by 2 H Solid‐State NMR

Abstract Methionine side chains are flexible entities which play important roles in defining hydrophobic interfaces. We utilize deuterium static solid‐state NMR to assess rotameric inter‐conversions and other dynamic modes of the methionine in the context of a nine‐residue random‐coil peptide (RC9) with the low‐complexity sequence GGKGMGFGL. The measurements in the temperature range of 313 to 161 K demonstrate that the rotameric interconversions in the hydrated solid powder state persist to temperatures below 200 K. Removal of solvation significantly reduces the rate of the rotameric motions. We employed 2 H NMR line shape analysis, longitudinal and rotation frame relaxation, and chemical exchange saturation transfer methods and found that the combination of multiple techniques creates a significantly more refined model in comparison with a single technique. Further, we compare the most essential features of the dynamics in RC9 to two different methionine‐containing systems, characterized previously. Namely, the M35 of hydrated amyloid‐β 1–40 in the three‐fold symmetric polymorph as well as Fluorenylmethyloxycarbonyl (FMOC)‐methionine amino acid with the bulky hydrophobic group. The comparison suggests that the driving force for the enhanced methionine side chain mobility in RC9 is the thermodynamic factor stemming from distributions of rotameric populations, rather than the increase in the rate constant.

Vugmeyster, Liliya↗
DOE OSTI2025

Projector‐Based Quantum Embedding Study of Iron Complexes

Projection‐based embedding theory (PBET) is used to calculate and assess the challenging spin‐crossover energies for a selection of small Fe‐containing systems by embedding the metal center into the frozen potential of the ligands. MP2, CCSD, and CCSD(T) are embedded in potentials from the SCAN and r 2 SCAN functionals and compared with the canonical values for the constituent methods and previously reported reference values. Considering the PBET calculations as a correction for the underlying DFT, the embedding calculations are able to provided improvement for most cases. In some cases, the PBET methods are able to compensate for limitations in the wave function methods and produce results similar to more rigorous calculations from the literature. For the systems with spin‐crossover energies near zero, the current methodology fails to provide consistent improvement. In conclusion, the isolated recalculation of the electronic structure around the metal center when embedded into a DFT treatment of the ligand field shows promise as a pragmatic and lower cost treatment compared to the canonical treatment of the whole system of the difficult class of spin‐crossover complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Interface conformal anomalies

We consider two d ≥ 2 conformal field theories (CFTs) glued together along a codimension one conformal interface. The conformal anomaly of such a system contains both bulk and interface contributions. In a curved-space setup, we compute the heat kernel coefficients and interface central charges in free theories. The results are consistent with the known boundary CFT data via the folding trick. In d = 4, two interface invariants generally allowed as anomalies turn out to have vanishing interface charges. These missing invariants are constructed from components with odd parity with respect to flipping the orientation of the defect. We conjecture that all invariants constructed from components with odd parity may have vanishing coefficient for symmetric interfaces, even in the case of interacting interface CFT.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A combination of deep eutectic solvent and ethanol pretreatment for synergistic delignification and enhanced enzymatic hydrolysis for biorefinary process

Here, a novel pretreatment system containing deep eutectic solvents and ethanol (DES-E) for synergistic carbohydrate conversion and delignification was reported in this study. The DES-E pretreatment resulted in an enhanced glucose yield compared to individual DES and ethanol pretreatment for the three tested biomass, including Broussonetia papyrifera, corn stover and pine. To further explore the delignification mechanism, the solubilized lignin and residual lignin from Broussonetia papyrifera was recovered and extracted, then thoroughly characterized. The highest total OH content was found in the DES-E solubilized lignin, which could be used as antioxidant. The presence of ethanol in pretreatment liquor could protect the β-O-4 substructure from breakage and reduce lignin condensation, which favors the subsequent enzymatic hydrolysis. Comparable glucose yield and delignification performance was achieved by recycled DES. DES-E pretreatment offers a promising method for lignin isolation and cellulose digestibility improvement simultaneously.

09 BIOMASS FUELS↗
DOE OSTI2024

Operando carbon corrosion measurements in fuel cells using boron-doped carbon supports

Carbonaceous materials are the most common catalyst supports in proton exchange membrane fuel cell (PEMFCs), yet their corrosion is one of the limiting factors in achieving high durability. Herein, we doped carbon supports with boron (B) to increase the corrosion-resistance of the support. Two types of B-doped carbons were synthesized and studied as platinum support materials. Further, they varied in their morphologies, surface areas, and the types of boron species. The durability of Pt/B-doped carbon catalysts was investigated using the US-DOE catalysts’ supports accelerated stress test (AST) and a mass-spectrometer connected to the fuel cell effluent stream to quantify the mass of corroded carbon support in operando. The addition of boron to the carbon increased the stability of Pt catalysts in long-term usage of PEMFC. After 4000 AST cycles, more than 50% of initial current density was preserved for the boron-containing systems, while less than 30% of it remained with Vulcan carbon (Pt/V). Also, the Pt/B-doped carbon samples demonstrated better electrochemical active surface area (ECSA) stability when compared to Pt/V. Carbon loss measurements showed that B-doped carbons have higher resistance to electrochemical corrosion than unmodified carbon. Specifically, the substitutional boron-doped carbon demonstrated an extremely high stability and low corrosion rate.

25 ENERGY STORAGE↗
DOE OSTI2025

Corrosion resistant hot melt adhesive to bind metals

Hot melt adhesives (HMAs) play an important role in many industries, and their demand is expected to grow. HMAs don't require any solvents, and their application results in the formation of strong bonds with the substrate upon cooling within seconds. These properties differentiate them from liquid glues and make them preferable for practical application. Currently, commercial HMAs are used in bonding lightweight materials such as paper, polymers, and cartons, and have limited usage in areas necessitating the bonding of heavier objects like metals. Here, in this study, we report a design and testing of versatile platform comprising an ion-coordinating polymer and ionic fillers for performance optimization and understanding of structure–property relationships, enabling the rational design of HMAs with improved adhesion to metal surfaces. All-atom Molecular dynamics (MD) simulations and various characterization methods are used to elucidate the adhesion mechanism in model composite system containing polyethylene oxide mixed with chemically diverse salt particles. The maximum adhesion strength is found in composites with Al(OH) 3 and FeCl 3 fillers. Interestingly, the presence of Al(OH) 3 also provides a multifunctional anticorrosion property as measured electrochemically using the Tafel method. The discovered path to formulations with improved adhesion to metal surfaces constitutes an important step toward advancing HMAs for use in the structural and semi-structural metal work domain.

Adhesive and cohesive forces↗
DOE OSTI2022

DFT-FE 1.0: A massively parallel hybrid CPU-GPU density functional theory code using finite-element discretization

In this work, we present DFT-FE 1.0, building on DFT-FE 0.6 [Comput. Phys. Commun. 246, 106853 (2020)], to conduct fast and accurate large-scale density functional theory (DFT) calculations (reaching ~ 100,000 electrons) on both many-core CPU and hybrid CPU-GPU computing architectures. This work involves improvements in the real-space formulation—via an improved treatment of the electrostatic interactions that substantially enhances the computational efficiency—as well high-performance computing aspects, including the GPU acceleration of all the key compute kernels in DFT-FE. We demonstrate the accuracy by comparing the ground-state energies, ionic forces and cell stresses on a wide-range of benchmark systems against those obtained from widely used DFT codes. Further, we demonstrate the numerical efficiency of our implementation, which yields ~ 20× CPU-GPU speed-up by using GPU acceleration on hybrid CPU-GPU nodes. Notably, owing to the parallel-scaling of the GPU implementation, we obtain wall-times of 80–140 seconds for full ground-state calculations, with stringent accuracy, on benchmark systems containing ~ 6, 000 – 15,000 electrons.

pseudopotential↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2024

A coarse-grained model of clay colloidal aggregation and consolidation with explicit representation of the electrical double layer

The aggregation of clay minerals in liquid water exemplifies colloidal self-assembly in nature. These negatively charged aluminosilicate platelets interact through multiple mechanisms with different sensitivities to particle shape, surface charge, aqueous chemistry, and interparticle distance and exhibit complex aggregation structures. Experiments have difficulty resolving the associated colloidal assemblages at the scale of individual particles. Conversely, all-atom molecular dynamics (MD) simulations provide detailed insight on clay colloidal interaction mechanisms, but they are limited to systems containing a few particles. We develop a new coarse-grained (CG) model capable of representing assemblages of hundreds of clay particles with accuracy approaching that of MD simulations, at a fraction of the computational cost. Our CG model is parameterized based on MD simulations of a pair of smectite clay particles in liquid water. A distinctive feature of our model is that it explicitly represents the electrical double layer (EDL), i.e., the cloud of charge-compensating cations that surrounds the clay particles. Our model captures the simultaneous importance of long-range colloidal interactions (i.e., interactions consistent with simplified analytical models, already included in extant clay CG models) and short-range interactions such as ion correlation and surface and ion hydration effects. The resulting simulations correctly predict, at low solid-water ratios, the existence of ordered arrangements of parallel particles separated by water films with a thickness up to ~10 nm and, at high solid-water ratios, the coexistence of crystalline and osmotic swelling states, in agreement with experimental observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Computational tools for the simulation and analysis of spin-polarized EPR spectra

The EPR spectra of paramagnetic species induced by photoexcitation typically exhibit enhanced absorptive and emissive features resulting from sublevel populations that differ from thermal equilibrium. The populations and the resulting spin polarization of the spectra are dictated by the selectivity of the photophysical process generating the observed state. Simulation of the spin-polarized EPR spectra is crucial in the characterization of both the dynamics of formation of the photoexcited state as well as its electronic and structural properties. EasySpin, the simulation toolbox for EPR spectroscopy, now includes extended support for the simulation of the EPR spectra of spin-polarized states of arbitrary spin multiplicity and formed by a variety of different mechanisms, including photoexcited triplet states populated by intersystem crossing, charge recombination or spin polarization transfer, spin-correlated radical pairs created by photoinduced electron transfer, triplet pairs formed by singlet fission and multiplet states arising from photoexcitation in systems containing chromophores and stable radicals. In this paper, we highlight EasySpin’s capabilities for the simulation of spin-polarized EPR spectra on the basis of illustrative examples from the literature in a variety of fields ranging across chemistry, biology, material science and quantum information science.

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

Multi-phase characterization of pitch-carbon coated nano-silicon anodes for lithium-ion batteries

Silicon (Si) is a leading next-generation Li-ion battery anode candidate that meets rigorous performance demands for portable power including enhanced power and energy density with robust cycling performance. However, a series of complex and interrelated reactions lead to reduced calendar life in Si-containing systems and therefore challenge practical adoption. In the present work, we probe the mechanisms underlying observed performance improvements by adding a pitch-carbon coating onto nano-Si material. We pair solid-phase (X-ray photoemission spectroscopy, Fourier-transform infrared), semi-volatile phase (solid-phase microextraction-gas chromatography-mass spectrometry), and gas-phase (gas chromatography-flame-ionization detector) characterization signals to comprehensively evaluate the impact of pitch-carbon coating on the evolution of the Si solid-electrolyte interphase (SEI) and the associated impacts on electrode/electrolyte reactivity. The pitch-carbon is found to serve as a physicochemical barrier, reducing the electro-active surface area for Si/electrolyte reactivity and preventing Si oxidation. Further, the pitch-carbon coating promotes the evolution of a more-favorable SEI by subsuming substantial functionality typically associated with the fluoroethylene carbonate (FEC) electrolyte additive - such as alkoxide scavenging and suppression of transesterification pathways - and by shifting the competitive electrolyte degradation pathways' favorability. The multi-phase characterization approach enables holistic end-products evaluation from complex (electro)chemical interfacial reactions, which informs a robust interpretation of the carbon coating's role in electrochemical performance improvements. The present mechanistic evaluation aids the rational design for improved nano-Si materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Sub-critical assembly die-away analysis with organic scintillators

Determining the reactivity of systems containing fissile material is essential for verifying safety, and possession and enrichment levels. The goal of this experiment was to characterize a subcritical nuclear fuel assembly irradiated by a pulsed neutron generator through differential die-away analysis. Similar characterizations are commonly performed with thermal neutron detectors, but these detectors are bulky and cumbersome, the materials are not widely available, and timing can be clouded by the required thermalization time. In this work, an organic scintillator capable of detecting both neutrons and photons was used to measure the die-away profiles of both particles. These time profiles were then used to estimate the reactivity of the assembly. Here, the calculated reactivity estimates compare favorably to simulated estimates, and therefore provide a useful tool for fissile material characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Investigating the role of strangeness in baryon–antibaryon annihilation at the LHC

Annihilation dynamics plays a fundamental role in the baryon–antibaryon interaction ($B–\bar{B}$) at low-energy and its strength and range are crucial in the assessment of possible baryonic bound states. Experimental data on annihilation cross sections are available for the $p–\bar{p}$ system but not in the low relative momentum region. Data regarding the $B–\bar{B}$ interaction with strange degrees of freedom are extremely scarce, hence the modeling of the annihilation contributions is mainly based on nucleon–antinucleon ($N–\bar{N}$) results, when available. In this letter we present a measurement of the $p–\bar{p}$, $p–\bar{Λ}$⊕$p–{Λ}$ and $Λ–\bar{Λ}$ interaction using correlation functions in the relative momentum space in high-multiplicity triggered pp collisions at $\sqrt{s}$=13 TeV recorded by ALICE at the LHC. In the $p–\bar{p}$ system the couplings to the mesonic channels in different partial waves are extracted by adopting a coupled-channel approach with recent χEFT potentials. The inclusion of these inelastic channels provides good agreement with the data, showing a significant presence of the annihilation term down to zero momentum. Predictions obtained using the Lednický–Lyuboshits formula and scattering parameters obtained from heavy-ion collisions, hence mainly sensitive to elastic processes, are compared with the experimental $p–\bar{Λ}$⊕$p–{Λ}$ and $Λ–\bar{Λ}$ correlations. The model describes the $Λ–\bar{Λ}$ data and underestimates the $p–\bar{Λ}$⊕$p–{Λ}$ data in the region of momenta below 200 MeV/c. The observed deviation indicates a different contribution of annihilation channels to the two systems containing strange hadrons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Multi-fidelity kinetic theory-based approach for the prediction of particle attrition: Application to jet cup attrition system

The timescale difference between the “fast” flow dynamics of fluidized bed reactors and the relatively “slow” rate of particle degradation makes the direct computational prediction of attrition challenging. An approach to this challenge is a multi-fidelity strategy where a high fidelity model for the flow dynamics is coupled with a lower fidelity model for the long-time resolution of the bulk attrition of the reactor inventory. We implement this approach using high-fidelity kinetic theory simulations to calculate the flow dynamics which are post-processed to calculate the frequency and intensity of the particle-particle and particle-wall collisions (e.g. collision energy spectra). This is combined with the particle breakage properties to construct the coefficients for a low-fidelity model [e.g. Monazam et al., 2018, Powder Technology 340, p. 528-536]. Simulations are performed of a jet cup attrition system containing Canadian hematite (Monazam et al. 2018). Furthermore, these are first analyzed using the collision energy spectra. Quantitative predictions of the mass loss are made using a low-fidelity model derived from the collision-spectra and a calibrated material breakage coefficient. The results are found to compare favorably with the experimental measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗