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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 OSTI2024

High-pressure phase transitions in a laser directed energy deposited Fe-33Cu Alloy

Here, additively manufactured Fe-Cu alloys contain both equilibrium face-centered cubic (FCC) and metastable body-centered cubic (BCC) crystal structure Cu precipitates depending on their size. However, the stability of these nanoscale precipitates under extreme conditions such as high pressures has not been reported. This study investigates the phase transformations and microstructural stability of laser directed energy deposition (DED-LB) made Fe67Cu33 alloy (nominal composition in at.%) under high static pressure deformation using in-situ synchrotron X-ray diffraction under pressure, postmortem high-resolution scanning transmission electron microscopy (HR-STEM), and molecular dynamics (MD) simulations. In-situ XRD results reveal a reversible phase transformation from BCC to the hexagonal close-packed (HCP) structure in the Fe grains at an onset pressure of 16.4 GPa, significantly higher than reported in the literature for pure Fe. Although no high-pressure phase transition was observed in the FCC Cu grains through XRD, HR-STEM analysis uncovers a phase transition to the HCP structure in nanoscale metastable BCC Cu precipitates within the BCC Fe matrix. After decompression, the Fe matrix reverted back to the BCC structure with periodic lath martensite, while regions of the nanoscale BCC Cu precipitates retained the metastable HCP structure. MD simulations support the BCC → HCP transition in the embedded nanoscale coherent Cu precipitates, consistent with the classical Burgers mechanism. Thus, by leveraging in-situ XRD observation, postmortem high-resolution S/TEM microscopy, and MD simulations, this study offers profound insights into the distinctive phase transformations induced by high pressure in DED-LB Fe67Cu33 alloy, which is distinguished by its hierarchical microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings

Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfil the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. Furthermore, these examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Urbanization effects on the spatial patterns of spring vegetation phenology depend on the climatic background

Cities have been considered ideal surrogates for evaluating ecological responses to climate warming. Although research has revealed that the urban heat island effect is not the only determinant that drives the rural-urban difference in spring phenology, limited attempts further explore the effect of complex interactions between urbanization and climates on the start of the season (SOS). Here, we employed the percentage of impervious surface area as an indicator of urbanization levels, incorporating the urban heat island (UHI) effect and climates to decipher the multiple effects on the temporal shift in SOS. Our results suggest that urbanization, the UHI effect, and climates jointly drive the phenological timing in cities. Further, the SOS is highly linearly correlated with urbanization levels and UHI effects (p<0.001) in the zones with warm and humid climates, whereas relatively warmer, cooler, or arid climates break down the linearity. Geographically, the SOS is north-south symmetrical for the rural-urban ecosystem at the national scale, which initiates at the mid-latitudes (around 30 °N, 67.58 DOY ± 2.23 days) first and then towards the low (around 20 °N, 105.25 DOY ± 1.31 days) and high-latitudes (45 °N, 123.01 DOY ± 1.45 days). Chilling accumulation and spring temperature jointly contribute to this phenomenon. As the UHI effect is similar to the projected global warming in the future, understanding the phenological responses to urbanization in various climates is insightful for evaluating the impact of future warming on plants' phenological behavior in the global ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Regulating the surface Pt coordination environment in the PtN overlayers on PtCuN hollow nanospheres for efficient oxygen reduction reaction

Engineering the surface Pt coordination environment is a promising strategy for promoting the kinetically sluggish oxygen reduction reaction (ORR) on Pt-based catalysts. Here, in this study, we achieve the compressive strain effect and electronic effect by Cu and N co-doping to synthesize the PtCuN hollow nanospheres with PtN overlayers (PtCuN@PtN HNSs) using a facile solvothermal synthesis. Electrochemical investigations show that the constructed disordered Pt–N coordination structures effectively facilitate the ORR and stabilize Pt atoms in the compressed lattice, whereas an excessive N-doping can lead to the formation of a structurally unstable Pt nitride phase. Theoretical analyses confirm that the oxygen reduction kinetics on the compressed PtN overlayers are regulated by a synergistic effect resulting from N-doping and lattice compression, circumventing the traditional linear scaling relationships (LSR). The optimized PtCuN@PtN HNSs, with the composition of PtCu 0.29 N 1.1 , demonstrate an area-specific activity of 1.98 mA cm –2 and a mass-specific activity of 1.81 A mg Pt –1 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

Extended convex hull-based distributed optimal energy flow of integrated electricity-gas systems

Integrated electricity and gas systems are constructed to facilitate the gas-fired generation, and the distributed operation of these integrated systems have received much attention due to the increased emphasis on data security and privacy between different agencies. This paper proposes an extended convex hull based method to address optimal energy flow problems for the integrated electricity and gas systems in a distributed manner. First, a multi-block electricity-gas system model is constructed by dividing the whole system into N blocks considering both physical and regional differences. This multi-block model is then convexified by replacing the nonconvex gas transmission equation with the extended convex hull-based constraints. The Jacobi-Proximal alternating direction method of multipliers algorithm is adopted to solve the convexified model and minimize its operation cost. Finally, the feasibility of the optimal solution for the convexified model is checked, and a sufficient condition is developed. If the sufficient condition is satisfied, the optimal solution for the original nonconvex problem can be recovered from that for the convexified problem. Simulation results demonstrate that the proposed method is tractable and effective in obtaining feasible optimal solutions for multi-block optimal energy flow problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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↗