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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Theoretical, Solid-State, and Solution Quantification of the Hydrogen Bond-Enhanced Halogen Bond

Proximal noncovalent forces are commonplace in natural systems and understanding the consequences of their juxtaposition is critical. This paper experimentally quantifies for the first time a Hydrogen Bond-Enhanced Halogen Bond (HBeXB) without the complexities of protein structure or preorganization. An HBeXB is a halogen bond that has been strengthened when the halogen donor simultaneously accepts a hydrogen bond. In this work, our theoretical studies suggest that electron-rich halogen bond donors are strengthened most by an adjacent hydrogen bond. Furthermore, stronger hydrogen bond donors enhance the halogen bond the most. X-ray crystal structures of halide complexes (X - =Br - , I - ) reveal that HBeXBs produce shorter halogen bonds than non-hydrogen bond analogues. 19 F NMR titrations with chloride highlight that the HBeXB analogue exhibits stronger binding. Together, these results form the foundation for future studies concerning hydrogen bonds and halogen bonds in close proximity.

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

Propane Activation on Pt Electrodes at Room Temperature: Quantification of Adsorbate Identity and Coverage

Abstract C−H bond activation is the first step in manufacturing chemical products from readily available light alkane feedstock and typically proceeds via carbon‐intensive thermal processes. The ongoing emphasis on decarbonization via electrification motivates low‐temperature electrochemical alternatives that could lead to sustainable chemicals production. Platinum (Pt) electrocatalysts have shown activity towards reacting alkanes; however, little is known about propane electrocatalytic activation and conditions suitable for enabling selective oxidation to valuable products. Herein, we utilize a combination of electrochemical mass spectrometry (ECMS) and density functional theory (DFT) calculations to elucidate the potential dependence of propane activation on Pt electrocatalysts. Results show a strong dependence of adsorption on the applied potential in room‐temperature aqueous acidic electrolyte, with a maximum coverage of propane‐derived adsorbates at 0.30 V vs RHE. Using charge deconvolution and deuterated experiments, the mechanism of adsorption was elucidated, and C 3 H 2 * was determined as the average dehydrogenated propane‐derived adsorbate species. DFT calculations further corroborate these results, showing that the formation of deeply dehydrogenated species is energetically accessible at room temperature. The combined theoretical and experimental findings yield insights for selective activation of paraffinic C−H bonds at room temperature, aqueous conditions—a critical step towards decarbonized chemical manufacturing.

Chemistry↗
DOE OSTI2019

Real‐time quantification and supplementation of bioreactor amino acids to prolong culture time and maintain antibody product quality

Abstract Real‐time monitoring of cell cultures in bioreactors can enable expedited responses necessary to correct potential batch failure perturbations which may normally go undiscovered until the completion of the batch and result in failure. Currently, analytical technologies are dedicated to real‐time monitoring of bioreactor parameters such as pH, dissolved oxygen, and temperature, nutrients such as glucose and glutamine, or metabolites such as lactate. Despite the importance of amino acids as the building blocks of therapeutic protein products, other than glutamine their concentrations are not commonly measured. Here, we present a study into amino acid monitoring, supplementation strategies, and how these techniques may impact the cell growth profiles and product quality. We used preliminary bioreactor runs to establish baselines by determining initial amino acid consumption patterns, the results of which were used to select a pool of amino acids which gets depleted in the bioreactor. These amino acids were combined into blends which were supplemented into bioreactors during a subsequent run, the concentrations of which were monitored using a mass spectrometry based at‐line method we developed to quickly assess amino acid concentrations from crude bioreactor media. We found that these blends could prolong culture life, reversing a viable cell density decrease that was leading to batch death. Additionally, we assessed how these strategies might impact protein product quality, such as the glycan profile. The amino acid consumption data were aligned with the final glycan profiles in principal component analysis to identify which amino acids are most closely associated with glycan outcomes.

Powers, David N.↗
DOE OSTI2019

Quantification and Statistical Analysis of Errors Related to the Approximate Description of Active Site Models in Metal‐Exchanged Zeolites

Abstract Metal‐exchanged zeolites are complex catalytic systems with great potential for applications in hydrocarbon conversion processes. The numerous combinations of zeotypes and metal‐based Lewis acid motifs create a vast material space that can only be effectively navigated with robust structure‐function relationships. Exhaustive modeling of extraframework metal sites in all crystallographically distinct framework positions is not practical; thus, we quantified the uncertainties arising from the use of simplified active site representations using the C−H bond activation in methane as a probe reaction. Density functional theory calculations and statistical analysis of associated error ensembles suggest that the Lewis acid identity primarily determines the reactivity of the zeolite towards methane activation, with other factors such as zeolite model characteristics deemed secondary. The error ensemble distributions were used to predict the relative probability of creating active C−H bond scission fragments in metal‐exchanged zeolites, and thus can be used to efficiently screen various metal‐exchange candidates in their efficacy towards hydrocarbon conversion.

Thirumalai, Hari↗
DOE OSTI2020

Quantification of Brønsted Acid Sites in Zeolites by Water Desorption Thermogravimetry

The many studies that have been conducted on water adsorption on zeolites were carried out in “adsorption” mode, from adsorption isotherms or single-point measurements, with the samples being first degassed at high temperature, and then exposed to water vapor at variable partial pressure. Here, we report Thermogravimetric Analysis (TGA) studies of HZSM-5 saturated with water at room temperature, thus studying water adsorption in “desorption” mode. In this work, TGA was used to ascertain the amount of water remaining as a function of the pretreatment temperature, and to identify the temperature at which one water molecule per framework Al atom persists. Solid-state 1H NMR spectroscopy, combined with molecular modeling, provided evidence for water molecules hydrogen bonded to Brønsted acid sites and formation of oxonium species for distinct water per framework Al atom. TGA is shown to be a simple and expeditious technique to measure the number of Brønsted acid sites in aluminosilicate zeolites, with advantages over the traditional methods of IR or TPD of dangerous adsorbed ammonia or pyridine. TGA of adsorbed water also provides information on the water clusters formed at the acid sites of HZSM-5, which are proportional to the number of acid sites.

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

Quantification of modeling uncertainty in the Rayleigh damping model

Understanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper proposes a systematic approach to quantify the uncertainty associated with Rayleigh's damping model. Bayesian calibration with embedded model error is employed to treat the coefficients of the Rayleigh model as random variables using modal damping ratios. Through a numerical example, we illustrate how this approach works and how the calibrated model can address modeling uncertainty associated with the Rayleigh damping model.

42 ENGINEERING↗
DOE OSTI2022

Quantification of microcystin production and biodegradation rates in the western basin of Lake Erie

Abstract Cyanobacterial biomass forecasts currently cannot predict the concentrations of microcystin, one of the most ubiquitous cyanotoxins that threaten human and wildlife health globally. Mechanistic insights into how microcystin production and biodegradation by heterotrophic bacteria change spatially and throughout the bloom season can aid in toxin concentration forecasts. We quantified microcystin production and biodegradation during two growth seasons in two western Lake Erie sites with different physicochemical properties commonly plagued by summer Microcystis blooms. Microcystin production rates were greater with elevated nutrients than under ambient conditions and were highest nearshore during the initial phases of the bloom, and production rates were lower in later bloom phases. We examined biodegradation rates of the most common and toxic microcystin by adding extracellular stable isotope‐labeled microcystin‐LR (1 μ g L −1 ), which remained stable in the abiotic treatment (without bacteria) with minimal adsorption onto sediment, but strongly decreased in all unaltered biotic treatments, suggesting biodegradation. Greatest biodegradation rates (highest of −8.76 d −1 , equivalent to the removal of 99.98% in 18 h) were observed during peak bloom conditions, while lower rates were observed with lower cyanobacteria biomass. Cell‐specific nitrogen incorporation from microcystin‐LR by nanoscale imaging mass spectrometry showed that a small percentage of the heterotrophic bacterial community actively degraded microcystin‐LR. Microcystin production and biodegradation rates, combined with the microcystin incorporation by single cells, suggest that microcystin predictive models could be improved by incorporating toxin production and biodegradation rates, which are influenced by cyanobacterial bloom stage (early vs. late bloom), nutrient availability, and bacterial community composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.

Tross, Michael C.↗
DOE OSTI2023

Extinction Imaging Diagnostics for In Situ Quantification of Soot within Explosively Generated Fireballs

Fireballs produced from the detonation of high explosives often contain particulates primarily composed of various phases of carbon soot. The transport and concentration of these particulates is of interest for model validation and emission characterization. Here, this work proposes ultra-high-speed imaging techniques to observe a fireball's structure and optical depth. An extinction-based diagnostic applied at two wavelengths indicates that extinction scales inversely with wavelength, consistent with particles in the Rayleigh limit and dimensionless extinction coefficients which are independent of wavelength. Within current confidence bounds, the extinction-derived soot mass concentrations agree with expectations based upon literature reported soot yields. Results also identify areas of high uncertainty where additional work is recommended.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Temporal Sampling of Enzymes from Live Cells by Localized Electroporation and Quantification of Activity by SAMDI Mass Spectrometry

Abstract Measuring changes in enzymatic activity over time from small numbers of cells remains a significant technical challenge. In this work, a method for sampling the cytoplasm of cells is introduced to extract enzymes and measure their activity at multiple time points. A microfluidic device, termed the live cell analysis device (LCAD), is designed, where cells are cultured in microwell arrays fabricated on polymer membranes containing nanochannels. Localized electroporation of the cells opens transient pores in the cell membrane at the interface with the nanochannels, enabling extraction of enzymes into nanoliter‐volume chambers. In the extraction chambers, the enzymes modify immobilized substrates, and their activity is quantified by self‐assembled monolayers for matrix‐assisted laser desorption/ionization (SAMDI) mass spectrometry. By employing the LCAD‐SAMDI platform, protein delivery into cells is demonstrated. Next, it is shown that enzymes can be extracted, and their activity measured without a loss in viability. Lastly, cells are sampled at multiple time points to study changes in phosphatase activity in response to oxidation by hydrogen peroxide. With this unique sampling device and label‐free assay format, the LCAD with SAMDI enables a powerful new method for monitoring the dynamics of cellular activity from small populations of cells.

Mukherjee, Prithvijit↗
DOE OSTI2024

Quantification of Swelling in Hematite Pellets Reduced Using Hydrogen–Nitrogen Gas Mixture

Iron ore pellets are reduced in a 50%H 2 –50%N 2 1 atm gas mixture at 750, 800, 850, 900, and 950 °C while simultaneously documenting swelling (change in pellet radius) and weight change. Swelling increases with increasing temperature, with catastrophic swelling (>20% of reduction swelling index) observed at 850, 900, and 950 °C. As the pellet is reduced, the pellet radius increases until 40–50% reduction is achieved, followed by a decrease in diameter beyond 40–50% reduction at 750 and 850 °C. At 950 °C, the pellet radius continues to increase with additional pellet reduction without any subsequent decrease in diameter. Scanning electron microscopy (SEM) analysis shows that the neighboring grains inside the pellet sinter together at 750 and 850 °C, whereas the individual grains sinter internally at 950 °C. SEM analysis and observations suggest that the reduction process at 750 and 850 °C can be approximated as a topochemical reaction process, while the reduction process at 950 °C can no longer be approximated as a topochemical reaction process. In conclusion, an empirical equation for the radius of the pellet is derived with fitting parameters dependent on temperature and the degree of reduction of the pellet undergoing reduction based on the experimental data.

08 HYDROGEN↗
DOE OSTI2021

Quantification of energy and cost reduction from decreasing dissolved oxygen levels in full‐scale water resource recovery facilities

Abstract Aeration systems often lack the efficiency to maintain a desired residual dissolved oxygen (DO) concentration in the tank in part because little consideration is given to the dynamic daily and seasonal loading conditions. Although advanced aeration controllers exist, the majority of plants have DO set points typically based on common practice and literature values rather than site‐specific conditions, which can result in DO set points higher than those necessary to meet treatment objectives. DO set point reduction strategies have primarily been proposed through either static or dynamic simulations. In this study, the substantial improvements associated with DO set point reduction are demonstrated at full scale. A yearlong characterization of full‐scale aeration dynamics captured the effect of diurnal and seasonal fluctuations on oxygen transfer and energy demand and so facilitated the estimation of the potential savings of DO reduction strategies. Full‐scale validation provided direct evidence of DO reduction strategies inducing an overall enhancement of oxygen transfer efficiency along the different bioreactors, while confirming that energy savings as high as 20% were feasible. This study quantifies the influence of oxygen transfer efficiency on operating choices and site‐specific conditions (control strategy, loading conditions, and influent flow variability). Practitioner points We quantified the energy reduction and cost savings associated with a DO reduction in an aeration tank. For each 0.2 mg/L of DO decreased, the average power demand reduction per unit water treated exceeded 17%. Field measurements of dynamic alpha values eliminate the uncertainty in estimating aeration energy and cost savings from DO variations.

Pasini, Federico↗
DOE OSTI2019

Second-order statistical bootstrap for the uncertainty quantification of time-temperature-superposition analysis

Time-temperature superposition (TTS), which for decades has been a powerful method for long-term prediction from accelerated aging data, involves rigid-shifting isotherms in logarithmic time to produce a single master prediction curve. For simple thermo-rheological properties that accurately follow the TTS principle, the shifts can be easily determined, even manually by the eye. However, for many properties of interest, where the principle is obeyed only approximately, or the data is noisy, it is imperative to develop objective shifting techniques along with reliable uncertainty bounds. This work analyzes in detail the method of arclength-minimization as an unsupervised algorithm to determining optimum shifts and demonstrates that the method is nearly unbiased for all practical datasets with a variety of noise distributions. Moreover, if averaged over with-replacement (bootstrap) resamples, the predicted shifts follow a normal distribution, a fact that can be used to construct confidence interval for the master curve through a second-order bootstrap procedure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Bayesian Approach for In-Situ Stress Prediction and Uncertainty Quantification for Subsurface Engineering

Many subsurface engineering applications require accurate knowledge of the in-situ state of stress for their safe design and operation. Existing methods to meet this need primarily include field measurements for estimating one or more of the principal stresses from a borehole, or optimization methods for constructing a 3D geomechanical model in terms of geophysical measurements. These methods, however, often contain considerable uncertainty in estimating the state of stress. Here, in this paper, we build on a Bayesian approach to quantify uncertainty in stress estimations for subsurface engineering applications. This approach can provide an estimate of the 3D distribution of stress throughout the volume of interest and provide an estimate of the uncertainty arising from the stress measurement, the rheology parameters, and a paucity of measurements. The value of this approach is demonstrated using stress measurements from the In Salah carbon storage site, which was one of the world’s first industrial carbon capture and storage projects. This demonstration shows the application of this Bayesian approach for estimating the initial state of stress for In Salah and quantifying the uncertainty in the estimated stress. Also, an assessment of a maximum injection pressure to prevent geomechanical risks from CO 2 injection pressures is provided in terms of the probability distribution of the minimum principal stress quantified by the approach. With the In Salah case study, this paper demonstrates that using the Bayesian approach can provide additional insights for site explorations and/or project operations to make informed-site decisions for subsurface engineering applications.

58 GEOSCIENCES↗