Search NASA⌕ Search

SEARCH · Search NASA

Results for “NeTS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reduction kinetics of hematite powder using argon/hydrogen plasma with prospects for near net shaping of sustainable iron

Direct reduction of iron ore using hydrogen plasma is being explored as a potential solution to decarbonize the iron and steel sector. The current state-of-the-art demonstrated reduction of hematite pellets via hydrogen plasma using Ar + 10% H 2 but had slow reduction kinetics, requiring 30 minutes of plasma exposure for complete reduction. Here we show that using hematite in a powder form, easily obtainable from beneficiated ore, results in 10× faster kinetics using plasma generated from Ar + 2% H 2 shielding gas compared to the current state-of-the art. Additionally, the increased kinetics using powders and a dilute hydrogen concentration can enable the use of advanced manufacturing techniques like blown powder directed energy deposition using a plasma tungsten arc welding torch to manufacture near net shape components directly from the ore concentrates. This ore to part approach will also reduce the emissions associated with downstream processes like rolling, forging, and machining, thereby further aiding in the sectorial decarbonization efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Net Electronic Charge as an Effective Electronic Descriptor for Oxygen Release and Transport Properties of SrFeO 3 -based Oxygen Sorbents

Perovskite oxides, as oxygen sorbents, exhibit excellent potential in thermochemical redox applications such as chemical looping air separation (CLAS), resulting from their excellent redox properties and high tunability. The structural and compositional flexibility of perovskites, while making these mixed oxides highly versatile, also poses challenges for their design and optimization. Moreover, the elevated operating temperature and dynamic oxygen partial pressure swings during redox processes add additional complications for oxygen sorbent design. From a fundamental standpoint, finding simple yet effective descriptors for the effect of cation substitutions on oxygen sorbents’ redox and oxygen transport properties are of significant importance for rational optimization of perovskite-based oxygen sorbents. In the present study, a series of A/B-site-doped SrFeO 3-δ perovskites are investigated using density functional theory (DFT) + U simulations. From these simulations, an effective electronic structural descriptor, the net electronic charge (Δe) of oxygen anions, is extracted based on its excellent correlations with vacancy formation energy, experimentally derived oxygen capacity, and oxygen vacancy migration barrier. Overall, these findings provide an effective tool to correlate the oxygen sorbents’ redox performances with a simple yet physically meaningful descriptor for rational design and optimization of perovskites in the context of CLAS.

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

Net Present Value Optimization of a Natural Gas Combined Cycle Plant with CO 2 Capture using a Water-Lean Solvent Considering Transient Electricity Price for Multiple Regions

Global CO 2 emissions are increasing at about a 1.5% rate per year. Fossil fuel-based plants are one of the main contributors to this rise. In the power generation industry, fossil fuel plants are dominant, and many plants are under development. In this study, a natural gas combined cycle (NGCC) power plant with postcombustion capture using a leading water-lean solvent is considered. For optimal design and operating schedule, large-scale dynamic optimization is undertaken for net present value (NPV) optimization. The first principle dynamic model of NGCC is developed, including a model of the highly efficient H-class gas turbines. For computational tractability of the dynamic optimization problem, a reduced-order model is developed by using the Hankel singular value decomposition. A waterlean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine, is used for carbon capture. A model of the capture system is developed in Aspen Plus, which is used to develop a reduced-order model by using ALAMO, a machine learning software. In addition, a reduced model of the CO 2 compression system with a dehydration unit is also considered. The integrated system is used for NPV optimization by using the Python-based PYOMO platform. The PCC process is analyzed for three configurations-conventional packed bed, rotating packed bed (RPB), and a combination of RPB and direct contact cooler. The NPV optimization is performed for 14 regional markets by considering year-long clustered and continuous locational marginal price data with a 1 h interval. Optimization results show that the PCC can achieve 90% CO 2 capture with a positive NPV for six regions. Sensitivity studies conducted by using the PCC configurations indicate that the process is economically feasible for 9 regions out of 14 regional electricity markets with NPV values in the range of 33−540 $MM.

cabon capture↗
DOE OSTI2025

Atom Identification in Bilayer Moiré Materials with Gomb-Net

Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS 2 -WS 2(1–x) Se 2x heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern’s local energetic or electronic modulation. In conclusion, this advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Entanglement of Square Nets in Covalent Organic Frameworks

Herein, two entangled 2D square covalent organic frameworks (COFs) have been synthesized from 4,4',4'',4'''-(9,9'-spirobi[fluorene]-2,2',7,7'-tetrayl)-tetrabenzaldehhyde (SFTB) and p -phenylenediamine (PPA) and benzidine (BZD) to form COF-38, [(SFTB)(PPA) 2 ] imine , and its isoreticular form COF-39, [(SFTB)(BZD) 2 ] imine . We also report the single-crystal electron diffraction structure of COF-39 and find that it is composed of mutually entangled 2D square nets ( sql ). These COFs represent the first examples of entangled 2D COF structures, which, as we illustrate, were made possible by our strategy of using the distorted tetrahedral SFTB building unit. SFTB overcomes the propensity of 2D COFs to stack through π-π stacking and allows entanglements to form. This work significantly adds to the design principles of COFs.

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

Uncertainty Quantification of Global Net Methane Emissions From Terrestrial Ecosystems Using a Mechanistically Based Biogeochemistry Model

Quantification of methane (CH4) emissions from wetlands and its sinks from uplands is still fraught with large uncertainties. Here, a methane biogeochemistry model was revised, parameterized, and verified for various wetland ecosystems across the globe. The model was then extrapolated to the global scale to quantify the uncertainty induced from four different types of uncertainty sources including parameterization, wetland type distribution, wetland area distribution, and meteorological input. We found that global wetland emissions are 212 ± 62 and 212 ± 32 Tg CH4 year -1 (1Tg = 10 12 g) due to uncertain parameters and wetland type distribution, respectively, during 2000–2012. Using two wetland distribution data sets and three sets of climate data, the model simulations indicated that the global wetland emissions range from 186 to 212 CH 4 year -1 for the same period. The parameters were the most significant uncertainty source. After combining the global methane consumption in the range of -34 to -46 Tg CH 4 year -1 , we estimated that the global net land methane emissions are 149–176 Tg CH 4 year -1 due to uncertain wetland distribution and meteorological input. Spatially, the northeast United States and Amazon were two hotspots of methane emission, while consumption hotspots were in the Eastern United States and eastern China. During 1950–2016, both wetland emissions and upland consumption increased during El Niño events and decreased during La Niña events. This study highlights the need for more in situ methane flux data, more accurate wetland type, and area distribution information to better constrain the model uncertainty.

58 GEOSCIENCES↗
DOE OSTI2020

Soil Biogenic Volatile Organic Compound Flux in a Mixed Hardwood Forest: Net Uptake at Warmer Temperatures and the Importance of Mycorrhizal Associations

Abstract Biogenic volatile organic compounds (bVOCs) play important roles in ecological interactions and Earth system processes, yet the biological and physical processes that drive soil bVOC exchanges remain poorly understood. In temperate forests, nearly all tree species associate with arbuscular mycorrhizal (AM) or ectomycorrhizal (ECM) fungi. Given well‐established differences in soil biogeochemistry between AM‐dominated and ECM‐dominated stands, we hypothesized that bVOC exchanges with the atmosphere would differ between soils from the two stand types. We measured bVOC fluxes at the soil‐atmosphere interface in plots dominated by AM‐ and ECM‐associated trees in a deciduous forest in south‐central Indiana, USA during the early and late vegetative growing season. Soils in both AM‐ and ECM‐dominated plots were a net bVOC sink following leaf‐out and were a greater bVOC sink or smaller source at warmer soil temperatures ( T s ). The flux of different bVOCs from ECM plots was often related to soil water content in addition to T s . Methanol dominated total bVOC fluxes, and ECM soils demonstrated greater uptake relative to AM‐dominated plots, on the order of 170 nmol m −2 hr −1 during the early growing season. Our results demonstrate the importance of soil dynamics characterized by mycorrhizal associations to bVOC dynamics in forested ecosystems and emphasize the need to study bidirectional soil bVOC uptake and emission processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Partitioning Net Ecosystem Exchange (NEE) of CO 2 Using Solar-Induced Chlorophyll Fluorescence (SIF)

Accurate partitioning of net ecosystem exchange (NEE) of CO 2 to gross primary production (GPP) and ecosystem respiration (R eco ) is crucial for understanding carbon cycle dynamics under changing climate. However, it remains as a long-standing problem in global ecology due to lack of independent constraining information for the two offsetting component fluxes. solar-induced chlorophyll fluorescence (SIF), a mechanistic proxy for photosynthesis, holds great promise to improve NEE partitioning by constraining GPP. We developed a parsimonious SIF-based approach for NEE partitioning and examined its performance using synthetic simulations and field measurements. This approach outperforms conventional approaches in reproducing simulated GPP and R eco , especially under high vapor pressure deficit. For field measurements, it results in lower daytime GPP and R eco than conventional approaches. Furthermore, this study made the first attempt to demonstrate SIF's potential for improving NEE partitioning accuracy and sets the stage for future efforts to examine its robustness and scalability under real-world environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO 2 Flux and its Inter-Annual Variability

Drylands occupy ~40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO 2 fluxes. Here, for this work, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions

Abstract Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve.

Aghazadeh, Amirali (ORCID:0000000302230873)↗
DOE OSTI2023

Weyl nodal ring states and Landau quantization with very large magnetoresistance in square-net magnet EuGa 4

Magnetic topological semimetals allow for an effective control of the topological electronic states by tuning the spin configuration. Among them, Weyl nodal line semimetals are thought to have the greatest tunability, yet they are the least studied experimentally due to the scarcity of material candidates. Here, using a combination of angle-resolved photoemission spectroscopy and quantum oscillation measurements, together with density functional theory calculations, we identify the square-net compound EuGa 4 as a magnetic Weyl nodal ring semimetal, in which the line nodes form closed rings near the Fermi level. The Weyl nodal ring states show distinct Landau quantization with clear spin splitting upon application of a magnetic field. At 2 K in a field of 14 T, the transverse magnetoresistance of EuGa 4 exceeds 200,000%, which is more than two orders of magnitude larger than that of other known magnetic topological semimetals. Our theoretical model suggests that the non-saturating magnetoresistance up to 40 T arises as a consequence of the nodal ring state.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tree foliage as a net accumulator of highly toxic methylmercury

Tree canopies are known to elevate atmospheric inputs of both mercury (Hg) and methylmercury (MeHg). While foliar uptake of gaseous Hg is well documented, little is known regarding the temporal dynamics and origins of MeHg in tree foliage, which represents typically less than 1% of total Hg in foliage. In this work, we examined the foliar total Hg and MeHg content by following the growth of five individual trees of American Beech (Fagus grandifolia) for one growing season (April–November, 2017) in North Carolina, USA. We show that similar to other studies foliar Hg content increased almost linearly over time, with daily accumulation rates ranging from 0.123 to 0.161 ng/g/day. However, not all trees showed linear increases of foliar MeHg content along the growing season; we found that 2 out of 5 trees showed elevated foliar MeHg content at the initial phase of the growing season but their MeHg content declined through early summer. However, foliar MeHg content among all 5 trees showed eventual increases through the end of the growing season, proving that foliage is a net accumulator of MeHg while foliar gain of biomass did not “dilute” MeHg content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Emergence of heavy-fermion behavior and distorted square nets in partially vacancy-ordered Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5)

Disorders in intermetallic systems belonging to the CeNiSi 2 -family are frequently overlooked. Even compounds presumed to be stoichiometric, such as YFeGe 2 , can be misidentified. Here, in this study, we report a series of Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5) compounds and show, using high-resolution synchrotron X-ray diffraction, that they feature asymmetrical structural distortions in the Fe and Ge sites that lead to a superstructure with partially ordered Fe vacancies and distorted Ge square-net in the triclinic crystal system, space group P[1 with combining macron] with a = 11.4441(3) Å, b = 32.7356(7) Å, c = 11.4456(3) Å, α = 79.6330(10)°, β = 88.3300(10)°, and γ = 79.6350 (10)°. The unit cell is 16 times the conventional orthorhombic cell with the space group Cmcm. We identified the lower and upper limits for Fe in Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5). Our physical property measurements yielded a Sommerfeld coefficient γ = 39.8 mJ mole −1 K −2 , a Kadowaki–Woods ratio of 1.2 × 10 −5 μΩ cm mole 2 K 2 mJ −2 , and a Wilson ratio of 1.83, suggesting heavy fermion behavior in the absence of f electrons, a rather rare case. Furthermore, we observed strong spin frustration and noted findings indicating possible superconductivity associated with the Fe content.

Zhao, Hengdi [Argonne National Laboratory (ANL), A↗
DOE OSTI2024

Empirical correlations between the function of entropy ( Z S ) and net artificial viscous work in a shock physics hydrocode

Entropy is a state variable that may be obtained from any thermodynamically complete equation of state (EOS). However, hydrocode calculations that output the entropy often contain numerical errors; this is not because of the EOS, but rather the solution techniques that are used in hydrocodes (especially Eulerian) such as convection, remapping, and artificial viscosity. Here, in this work, empirical correlations are investigated to reduce the errors in entropy without altering the solution techniques for the conservation of mass, momentum, and energy. Specifically, these correlations are developed for the function of entropy Z S , and they depend upon the net artificial viscous work, as determined via Sandia National Laboratories’ shock physics hydrocode CTH. These results are a continuation of a prior effort to implement the entropy-based CREST reactive burn model in CTH, and they are presented here to stimulate further interest from the shock physics community. Future work is planned to study higher-dimensional shock waves, shock wave interactions, and possible ties between the empirical correlations and a physical law.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels

Biofuel and bioenergy systems are integral to most climate stabilization scenarios for displacement of transport sector fossil fuel use and for producing negative emissions via carbon capture and storage (CCS). However, the net greenhouse gas mitigation benefit of such pathways is controversial due to concerns around ecosystem carbon losses from land use change and foregone sequestration benefits from alternative land uses. Here, we couple bottom-up ecosystem simulation with models of cellulosic biofuel production and CCS in order to track ecosystem and supply chain carbon flows for current and future biofuel systems, with comparison to competing land-based biological mitigation schemes. Analyzing three contrasting US case study sites, we show that on land transitioning out of crops or pasture, switchgrass cultivation for cellulosic ethanol production has per-hectare mitigation potential comparable to reforestation and severalfold greater than grassland restoration. In contrast, harvesting and converting existing secondary forest at those sites incurs large initial carbon debt requiring long payback periods. We also highlight how plausible future improvements in energy crop yields and biorefining technology together with CCS would achieve mitigation potential 4 and 15 times greater than forest and grassland restoration, respectively. Finally, we show that recent estimates of induced land use change are small relative to the opportunities for improving system performance that we quantify here. While climate and other ecosystem service benefits cannot be taken for granted from cellulosic biofuel deployment, our scenarios illustrate how conventional and carbon-negative biofuel systems could make a near-term, robust, and distinctive contribution to the climate challenge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km 2 ), we found that 24.1, 24.2, and 14.5 Tg C y −1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y −1 ) and other disturbances (50.3 Tg C y −1 ) were partially counterbalanced by forest growth (44.1 Tg C y −1 ). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y −1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

54 ENVIRONMENTAL SCIENCES↗