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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Characterization and microbial mitigation of fugitive methane emissions from oil and gas wells: Example from Indiana, USA

Hydrocarbon gas emissions from active, inactive, and improperly sealed or abandoned oil/gas wells significantly contribute to anthropogenically emitted greenhouse gases, predominantly in the form of methane (CH 4 ). We explored the extent of hydrocarbon gas emissions from 20 active, inactive, plugged and abandoned oil/gas wells in Indiana (USA), where it is estimated that there are more than 80,000 well sites throughout the state. After this initial survey, using a static flux tent, we quantified fugitive CH4 emissions from an active gas well to approximately 2 L h -1 . To evaluate the potential for microbial mitigation of hydrocarbon emissions to the atmosphere, we conducted laboratory microcosm experiments to quantify the CH 4 oxidizing potential of soils collected from sites with varying distances to the leaking gas well. Soils in close proximity to the well (0.5 m) efficiently consumed nearly all (97 %) of the added CH 4 , while only 14 % of added CH 4 was consumed by soils that were more distant from the well (20 m). These results suggest that fugitive CH 4 emissions enrich methanotrophic bacteria in soils immediately adjacent to the well. Consistent with this view, we found that prolonged exposure of soils to elevated concentrations of CH 4 enhanced the methanotrophic activity. Together, these findings prompted us to design a “methanotrophic soil mound” to assess the feasibility of mitigating point sources of CH 4 by harnessing the natural methanotrophic capacity of soil microbial communities. We found that a methanotrophic soil mound from a landfill could sustainably mitigate the CH 4 emission from the artificial source, providing a promising low-cost solution to ameliorate fugitive CH 4 emissions from abandoned oil and gas wells to the atmosphere. Finally, the effectiveness of microbe-based remediation is limited in cold climates and arid environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Temperature controlled transformations of giant unilamellar vesicles of amphiphilic triblock copolymers synthesized via microfluidic mixing

We report on a simple approach for synthesis of temperature-responsive giant unilamellar vesicles (GUVs) from poly(N-vinylcaprolactam) 15 -block-poly(dimethylsiloxane) 65 -block-poly(N-vinylcaprolactam) 15 (PVCL 15 -PDMS65-PVCL 15 ) triblock copolymer and non-temperature responsive small and giant vesicles from novel poly(N-vinylpyrrolidone)-block-poly(dimethylsiloxane)-block-poly(N-vinylpyrrolidone) (PVPON 15 -PDMS 65 -PVPON 15 and PVPON 6- PDMS 30- PVPON 6 ) triblock copolymers using microfluidic mixing at 25 °C. We show that temperature-responsive PVCL 15 -PDMS 65- PVCL 15 GUVs with the average diameter of 1.4 ± 0.2 µm while being stable at room temperature for at least 14 days, transformed irreversibly into small vesicles of 168 ± 40 nm after incubation of their aqueous solution at 42 °C for 24 h. We hypothesized that this transformation is induced by local compressive stresses of the vesicle membrane due to the collapse of PVCL blocks above the copolymer lower critical solution temperature (LCST) leading to the decrease of the vesicle membrane thickness. Consequently, we found that the temperature-induced size transformation of the PVCL-based GUVs at 42°C can be suppressed by substituting PVCL with its hydrophilic homologue PVPON, or by suppressing the PVCL's LCST behavior through hydrogen-bonding with tannic acid molecules. In the former case, novel PVPON n -PDMS m -PVPON n triblock copolymers (n = 15, m = 65 and n = 6, m = 30) assemble into vesicles stable from 25 °C to 55 °C as confirmed by optical and electron microscopy, dynamic light scattering (DLS) and small-angle neutron scattering (SANS). In the latter, hydrogen bonding interactions of PVCL with the polyphenol tannic acid (TA) at room temperature resulted in stable PVCL-based GUVs at 42°C as confirmed by optical, electron, and atomic force microscopies. We also found that physical crosslinking of the PVCL corona through hydrogen bonding with TA in PVCL 15- PDMS 65- PVCL 15 GUVs will delay their low pH-induced degradation at 37°C by 48 h compared to non-modified GUVs. Our findings open opportunities for the development of temperature-regulated stable micro-vehicles that would change their structural characteristics in the physiologically relevant temperature range from 25 to 42°C and can be utilized for cell mimicking studies. The developed GUVs also have potential in theranostic drug delivery as substitutes for polymer microcapsules and lipid microbubbles as well as for stimuli-triggered sensing, protection, and rapid response in an aqueous environment.

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

Manipulating cobalt oxide on N-doped aligned electrospun carbon nanofibers towards instant electrochemical detection of dopamine secreted by living cells

A demand exists for the development of miniaturized biosensors with high sensitivity and a wide dynamic range for rapid detecting dopamine (DA) levels. Here, an electrochemical sensor is created by decorating closely packed cobalt oxide (Co 3 O 4 ) nanograins on nitrogen-doped electrospun carbon nanofibers (NECNFs) via a facile electrodeposition method. The carbon nanofiber matrix provides both a scaffold with ultrahigh surface area for homogeneous dispersion of cobalt oxide nanostructures and facilitates the electrochemical conductivity for the hybrid electrode. The sensing principle is based on the redox reaction between DA and DA quinone that generates electrical signal, which is detectable via differential pulse voltammetry (DPV) and chronoamperometry (CA). Owing to a high electroactive surface area and facilitating electron transfer between electrode surface and the target molecule, the electrical sensing platform exhibits superior sensitivity and selectivity toward DA, with an excellent limit of detection (9 nM) over a wide range of concentrations (0.01 to 100 μM). Moreover, based on its excellent sensing properties and biocompatibility, the sensor has been used for the real-time monitoring exogeneous DA released from pheochromocytoma (PC12) cells. This work demonstrates promise of a novel sensor technology that may render rapid point-of-care testing of DA towards early clinical diagnosis.

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

Synthesis of aviation biofuel precursors from biomass-derived ketones: Substrate adsorption configuration-catalytic activity

The production of aviation biofuel precursors from biomass-derived ketones by heterogeneous catalysis has been hindered by the low catalytic activity. Herein, a series of Cu-doped metal oxide catalysts were prepared for the conversion of biomass-derived ketones to aviation biofuel precursors. Solvent-free cyclopentanone conversion via aldol condensation reached 91.1 % over Cu/Al 2 O 3 with 100 % selectivity toward dimer and trimer oxygenated species, all of which are aviation biofuel precursors. This catalyst primarily contains Cu 2 O and Cu nanoparticles which are uniformly dispersed across the Al 2 O 3 surface. From in situ DRIFTS and DFT results, the incorporation of Cu species onto Al 2 O 3 not only increased the diversity of Lewis acidic sites, but also changed the adsorption of C = O groups, which lead to the increased aldol condensation activity of Cu/Al 2 O 3 . In conclusion, this study provides insight on the design of heterogeneous catalysts suitable for the solvent-free synthesis of aviation biofuel precursors from biomass-derived ketones.

Adsorption configuration↗
DOE OSTI2023

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m 2 ), 10.0 W/m 2 , 11.1 W/m 2 , 7.1 W/m 2 , and 4.7 W/m 2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m 2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. Here, the comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Paw-Net: Stacking ensemble deep learning for segmenting scanning electron microscopy images of fine-grained shale samples

Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to differentiate the boundaries for different mineral objects to facilitate subsequent analyses, such as porosity calculation. Recently, various machine learning methods, especially convolutional neural networks (CNNs), have been explored to segment SEM images of fine-grained shale samples. However, we found that general CNNs do not yield optimal performance due to insufficient training data and imbalanced objects in SEM images. This work has revised the U-Net architecture, a popular approach for biomedical image analyses, by incorporating a loss function that addresses the imbalance issue. Furthermore, we used the ensemble learning method to train multiple models and combined the results to improve the overall performance of segmentation. We prepared 2162 sub-images from raw SEM images in our experiments and divided them into training, validation, and testing datasets. The overall results show that our method improves the average Intersection over Union (IOU) of mineral objects from 0.49 to 0.58, compared to the original U-Net model. Our method can clearly distinguish each object from others with boundaries, even in highly imbalanced images. Training our models takes less than three minutes using a single GPU, while manual labeling can take up to three hours for each image. Furthermore, the method helps geoscientists gain insights quickly and effectively by building neural network models from a small dataset of SEM images.

58 GEOSCIENCES↗
DOE OSTI2023

Model-parallel Fourier neural operators as learned surrogates for large-scale parametric PDEs

Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches. Once trained, FNOs can achieve speed-ups of multiple orders of magnitude over conventional numerical PDE solvers. However, due to the high dimensionality of their input data and network weights, FNOs have so far only been applied to two-dimensional or small three-dimensional problems. To remove this limited problem-size barrier, we propose a model-parallel version of FNOs based on domain-decomposition of both the input data and network weights. Here, we demonstrate that our model-parallel FNO is able to predict time-varying PDE solutions of over 2.6 billion variables on Perlmutter using up to 512 A100 GPUs and show an example of training a distributed FNO on the Azure cloud for simulating multiphase CO 2 dynamics in the Earth’s subsurface.

58 GEOSCIENCES↗
DOE OSTI2022

Spatial resolution of different discretizations over long-time for the Dirac equation with small potentials

In this report we compare the long-time error bounds and spatial resolution of finite difference methods with different spatial discretizations for the Dirac equation with small electromagnetic potentials characterized by $\varepsilon \in (0, 1]$ a dimensionless parameter. We begin with the simple and widely used finite difference time domain (FDTD) methods, and establish rigorous error bounds of them, which are valid up to the time at $O(1/\varepsilon)$. In the error estimates, we pay particular attention to how the errors depend explicitly on the mesh size $h$ and time step $\tau$ as well as the small parameter $\varepsilon$. Based on the results, in order to obtain "correct" numerical solutions up to the time at $O(1/\varepsilon)$, the $\varepsilon$-scalability (or meshing strategy requirement) of the FDTD methods should be taken as $h = O(\varepsilon^{1/2})$ and $\tau = O(\varepsilon^{1/2})$. To improve the spatial resolution capacity, we apply the Fourier spectral method to discretize the Dirac equation in space. Error bounds of the resulting finite difference Fourier pseudospectral (FDFP) methods show that they exhibit uniform spatial errors in the long-time regime, which are optimal in space as suggested by the Shannon's sampling theorem. Extensive numerical results are reported to confirm the error bounds and demonstrate that they are sharp.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Integral boundary conditions in phase field models

Modeling the chemical, electric and thermal transport as well as phase transitions and the accompanying mesoscale microstructure evolution within a material in an electronic device setting involves the solution of partial differential equations often with integral boundary conditions. Employing the familiar Poisson equation describing the electric potential evolution in a material exhibiting insulator to metal transitions, we exploit a special property of such an integral boundary condition, and we properly formulate the variational problem and establish its well-posedness. Next, we compare our method with the commonly-used Lagrange multiplier method that can also handle such boundary conditions. Numerical experiments demonstrate that our new method achieves optimal convergence rate in contrast to the conventional Lagrange multiplier method. Furthermore, the linear system derived from our method is symmetric positive definite, and can be efficiently solved by Conjugate Gradient method with algebraic multigrid preconditioning.

97 MATHEMATICS AND COMPUTING↗