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NASA NTRSDate not supplied

Reduction of Spectral Radiance Reflectance During the Annular Solar Eclipse of 21 June 2020 from DSCOVR/EPIC

We have analyzed three EPIC images during the annular solar eclipse on June 21, 2020 when centers of the eclipse were in the Arabian Peninsula (mostly desert), the Himalayas (mostly barren land), and China (mostly cloudy over vegetation) and compared with two images for 2017 American solar eclipse over Casper, WY and Columbia, MO (vegetated surface for both). We found that the global average reductions of spectral reflectance for the three images during 2020 solar eclipse are quite different while the reductions of spectral reflectance for the two images for 2017 solar eclipse are similar. Radiative transfer model simulations suggest that different surface spectral albedo and cloud fraction attribute to the different reduction of spectral reflectance for three images during 2020 eclipse while similar spectral albedo of vegetated surface around Casper and Columbia are the main cause for the similar spectral reflectance reduction for the two images during 2017 solar eclipse.

solar eclipse↗

NASA NTRS2021

Significant Climate Benefits from Near-Term Climate Forcer Mitigation in Spite of Aerosol Reductions

Near-term climate forcers (NTCFs), including aerosols and chemically reactive gases such as tropospheric ozone and methane, offer a potential way to mitigate climate change and improve air quality--so called "win-win" mitigation policies. Prior studies support improved air quality under NTCF mitigation, but with conflicting climate impacts that range from a significant reduction in the rate of global warming to only a modest impact. Here, we use state-of-the-art chemistry-climate model simulations conducted as part of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) to quantify the 21st-century impact of NTCF reductions, using a realistic future emission scenario with a consistent air quality policy. Non-methane NTCF (NMNTCF; aerosols and ozone precursors) mitigation improves air quality, but leads to significant increases in global mean precipitation of 1.3% by mid-century and 1.4% by end-of-the-century, and corresponding surface warming of 0.23 and 0.21 K. NTCF (all-NTCF; including methane) mitigation further improves air quality, with larger reductions of up to 45% for ozone pollution, while offsetting half of the wetting by mid-century (0.7% increase) and all the wetting by end-of-the-century (non-significant 0.1% increase) and leading to surface cooling of -0.15 K by mid-century and -0.50 K by end-of-the-century. This suggests that methane mitigation offsets warming induced from reductions in NMNTCFs, while also leading to net improvements in air quality.

Near-term climate forcers (NTCFs)↗

NASA NTRS2022

Identification and Reduction of Interactional Noise of a Quadcopter in Hover and Forward Flight Conditions

Advanced Air Mobility is a vision for a safe, accessible, and sustainable aviation system to transport people and cargo between places not served by traditional aviation. With this emerging transportation industry, there is motivation to characterize the noise of vehicles to determine their potential impacts on the community. An experimental testing campaign was conducted on a representative model of a small unmanned aircraft system in the NASA Langley Low Speed Aeroacoustic Wind Tunnel as a continuation of a previous testing campaign. The goals of the current test are to identify sources of interactional noise as well as to test custom-designed rotors and noise reduction devices. The tested noise reduction methods involve increasing the vertical distances between the rotors and the vehicle airframe as well as between the forward and aft rotor disk planes. These methods are intended to reduce rotor-airframe interaction noise in hover and fore-aft rotor wake ingestion noise in forward flight. A phased microphone array is also utilized to identify the locations of prominent noise generation for the different vehicle configurations in forward flight. Elevation of the rotors from the vehicle airframe yielded nearly 8 dBA overall noise reduction in forward flight, while yielding up to 4 dB reduction in overall tonal levels for one of the rotors in hover.

Nikolas S Zawodny↗

NASA NTRS2022

NASA Satellite Measurements Show Global-scale Reductions in Free Tropospheric Ozone in 2020 and Again in 2021 During COVID-19

NASA satellite measurements show that ozone reductions throughout the Northern Hemisphere (NH) free troposphere reported for spring-summer 2020 during the Corona VIrus Disease 2019 (COVID-19) pandemic have occurred again in spring-summer 2021. The satellite measurements show that tropospheric column ozone (TCO) (mostly representative of the free troposphere) for 20oN-60oN during spring-summer for both 2020 and 2021 averaged ~3 Dobson Units (DU) (or ~7-8%) below normal. These ozone reductions in 2020 and 2021 were the lowest in the 2005- 2021 record. We also include satellite measurements of tropospheric NO2that exhibit reductions of ~10-20% in the NH in early spring-to-summer 2020 and 2021, suggesting that reduced pollution was the main cause for the low anomalies in NH TCO in 2020 and 2021. Reductions of TCO ~2DU (7 %) are also measured in the Southern Hemisphere in austral summer but are 26not associated with reduced NO2.

Tropospheric Ozone↗

NASA NTRSDate not supplied

Decoupling the Effects of Anthropogenic Emission Reductions from the Meteorology and Natural Emissions in TROPOMI NO 2 Retrievals During the 2020 COVID-19 Lockdowns

Satellite measurements during the COVID-19 lockdowns that began in 2020 revealed unprecedented reductions in NO 2 tropospheric vertical column densities (VCD). These reductions have largely been attributed to reduced anthropogenic emissions associated with abrupt decreases in road traffic and other power consuming business activities. Although decreased emissions tended to be the main contributor to the observed NO 2 VCD reduction, meteorological variability also played a role. Whereas the observed VCD changes were predominantly negative in places where public health policies were strictly enforced, meteorology had both positive and negative effects over short time intervals. Here, we present results from a global study of the NO 2 reductions aimed at disentangling the meteorological and natural emission variability from the anthropogenic emissions over the world’s most populated megacities. For this study, NASA’s TROPOMI NO 2 algorithm was used in conjunction with the Global Modeling Initiative (GMI) chemical transport model to separate the contributions due emissions and meteorology. A priori NO 2 profiles were generated from two GMI simulations performed for 2020 at a resolution of 0.25° longitude x 0.25° latitude. The first simulation used updated, COVID-impacted NOX emissions based on Forster et al. (2020), while the second simulation used the 2019 emissions with the 2020 meteorology, referred to here as 2020BAU. The 2020BAU data set allowed for the decoupling of the emission component from the meteorology. When compared to the same period in 2019, NO 2 column amounts during the lockdowns were reduced in 35 out of 36 cities. While reduced emissions contributed most to the observed total change in NO 2 during the lockdowns, the effects of meteorology were significant, ranging between 40% (Chennai) and 15% (Beijing). In China, an increase in NO 2 levels due to meteorology were observed in five out of the seven cities considered in the study. Use of different a-priori NO 2 profiles from the two simulations in our TROPOMI retrievals allowed for a determination of retrieval errors that ranged from -1.7% to -11.0%. We used the quality assurance flag > 0.75 to select the highest quality scenes in the study period. Using the GMI, we estimated the sampling biases to be in the range -11% to 10% of the total change for the cities in our study.

Brad Fisher↗

NASA NTRSDate not supplied

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1%in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.

Machine Learning↗

NASA NTRSDate not supplied

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Machine Learning↗

NASA NTRSDate not supplied

Uncertainty Reduction With Multi-Model Monte Carlo for Crystal Plasticity Simulations of Additively Manufactured Metals

In this work, multi-model Monte Carlo estimators are developed to reduce uncertainty in quantities of interest (QoIs) extracted from crystal plasticity simulations of additively manufactured (AM) metals. A significant concern in AM parts is uncertainty in mechanical properties caused in part by complex microstructures that arise from the AM process. Quantifying uncertainty in microstructure-sensitive behavior using experiments alone is costly, especially when mechanical allowables must be established. Quantitative relationships among microstructure, micromechanical metrics like slip accumulation, crack initiation, and failure are also difficult to capture with limited experiments. Crystal plasticity material models instead enable computational prediction of micromechanical stress and strain fields given a discretized microstructure. However, high-fidelity finely discretized crystal plasticity simulations are computationally expensive, while lower-fidelity models are less accurate and generally biased, making uncertainty quantification and reduction computationally difficult as well. Multi-model Monte Carlo methods leverage correlations between high- and low-fidelity models to produce unbiased estimators for QoIs with reduced uncertainty relative to standard Monte Carlo. Crystal plasticity QoIs considered in this work include yield strength and the mean and extreme values of micromechanical fields that are relevant to crack initiation. Multi-model Monte Carlo estimators are developed for each individual QoI and several groups of QoIs. The results of this work establish relationships among model correlations, sample allocation, and uncertainty reduction for different combinations of QoIs and demonstrate a trend of less uncertainty reduction as QoIs become more sensitive to local microstructure. Limitations from using pilot samples to estimate model covariances and train low-fidelity models are also addressed. The uncertainty reduction achieved by multi-model Monte Carlo is an important step toward using computational mechanics models to predict microstructure-sensitive crack initiation and failure in AM parts.

uncertainty quantification↗

DOE OSTI2024

Synergizing Fe 2 O 3 Nanoparticles on Single Atom Fe‐N‐C for Nitrate Reduction to Ammonia at Industrial Current Densities

The electrochemical reduction of nitrates (NO 3 − ) enables a pathway for the carbon neutral synthesis of ammonia (NH 3 ), via the nitrate reduction reaction (NO 3 RR), which has been demonstrated at high selectivity. However, to make NH 3 synthesis cost-competitive with current technologies, high NH 3 partial current densities (j NH3 ) must be achieved to reduce the levelized cost of NH 3 . Here, the high NO 3 RR activity of Fe-based materials is leveraged to synthesize a novel active particle-active support system with Fe 2 O 3 nanoparticles supported on atomically dispersed Fe–N–C. The optimized 3×Fe 2 O 3 /Fe–N–C catalyst demonstrates an ultrahigh NO 3 RR activity, reaching a maximum j NH3 of 1.95 A cm −2 at a Faradaic efficiency (FE) for NH 3 of 100% and an NH 3 yield rate over 9 mmol hr −1 cm −2 . Operando XANES and post-mortem XPS reveal the importance of a pre-reduction activation step, reducing the surface Fe 2 O 3 (Fe 3+ ) to highly active Fe 0 sites, which are maintained during electrolysis. Durability studies demonstrate the robustness of both the Fe 2 O 3 particles and Fe–N x sites at highly cathodic potentials, maintaining a current of −1.3 A cm −2 over 24 hours. This work exhibits an effective and durable active particle-active support system enhancing the performance of the NO 3 RR, enabling industrially relevant current densities and near 100% selectivity.

active support↗

DOE OSTI2021

Photochemical CO 2 Reduction Using Rhenium(I) Tricarbonyl Complexes with Bipyridyl-Type Ligands with and without Second Coordination Sphere Effects

In this study, the effect of phenyl, phenol, aniline, amino, and CF 3 substituents of the 2,2'-bipyridine ligand in fac-ReCl(L)(CO) 3 (L=2,2'-bipyridine derivative) catalysts on photochemical CO 2 reduction in dimethylacetamide is examined, in order to understand the structure-function relationships and to compare the catalytic activities with the previously published electrochemical results. All complexes including ReCl(bpy)(CO) 3 have similar excited-state lifetimes and emission spectra, but complex 1 with the Ph-NH 2 moiety exhibits a significantly larger molar absorption coefficient for its metal-to-ligand charge transfer (MLCT) band. All complexes we tested produce CO with only a negligible amount of H 2 and formate in self-sensitized systems in the presence of triethanolamine (TEOA) and in some cases, BIH (1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]-imidazole). The presence of the Ph-NH 2 moiety (complex 1) has a beneficial effect on both electrochemical and photochemical activity, allowing a turnover number (TON) of 32 and 120 for photochemical CO production (without and with BIH, respectively). In the case of the Ph-OH group in the second coordination sphere (complexes 4 and 6), these complexes are active for photochemical CO 2 reduction, despite the formation of a stable 6-coordinate Re-OPh intermediate via reductive deprotonation as previously observed under electrochemical conditions. Overall, BIH accelerates the rate of formation of the one-electron reduced species (OERS) of the Re catalysts and allows higher turnover frequency (TOF) and TON for CO formation. The X-ray structures of complexes 1 and 4 were determined to have distorted octahedral Re centres, and show π-π stacking interactions with neighboring molecules as well as intramolecular hydrogen bonds to the internal chloride ligands. The unusually high absorptivity of the MLCT absorption of complex 1 has been explained by TD-DFT calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Selective reduction of CO to acetaldehyde with CuAg electrocatalysts

Electrochemical CO reduction can serve as a sequential step in the transformation of CO 2 into multicarbon fuels and chemicals. In this study, we provide insights on how to steer selectivity for CO reduction almost exclusively toward a single multicarbon oxygenate by carefully controlling the catalyst composition and its surrounding reaction conditions. Under alkaline reaction conditions, we demonstrate that planar CuAg electrodes can reduce CO to acetaldehyde with over 50% Faradaic efficiency and over 90% selectivity on a carbon basis at a modest electrode potential of −0.536 V vs. the reversible hydrogen electrode. The Faradaic efficiency to acetaldehyde was further enhanced to 70% by increasing the roughness factor of the CuAg electrode. Density functional theory calculations indicate that Ag ad-atoms on Cu weaken the binding energy of the reduced acetaldehyde intermediate and inhibit its further reduction to ethanol, demonstrating that the improved selectivity to acetaldehyde is due to the electronic effect from Ag incorporation. These findings will aid in the design of catalysts that are able to guide complex reaction networks and achieve high selectivity for the desired product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Power System Network Reduction for Power Hardware-in-the-Loop Simulation

This paper proposes single-port equivalent and two-port equivalent network reduction methods to respectively reduce single-port and two-port areas in a large power network. Parameters of the reduced systems are rigorously derived, which guarantees that the electrical quantities at the port(s) remain unchanged over the reduction, including voltage magnitude and phase and active and reactive power injections into the area to be reduced. The proposed techniques are applied to reduce a practical Maui grid, where the total numbers of buses, lines and transformers are respectively reduced from 212, 106 and 108 to 45, 30 and 13. Dynamic behaviors between the full model and the reduced model are compared in detail to illustrate the efficacy and accuracy of the proposed network reduction.

model reduction↗

DOE OSTI2026

Surface water nitrogen and sediment potential nitrate reduction rates, nutrient stocks, and stable isotopes from nine wetlands at the Tanglewood Biological Station, Alabama

This dataset supports a broader study investigating wetland hydrologic and biogeochemical responses to inundation disturbances. Bimonthly surface water and sediment sampling events were conducted at nine wetland sites situated within the Tanglewood Biological Station in Alabama from April 2023 to February 2024. The contents included in the data package include surface water nitrogen (nitrogen oxides and ammonium) and sediment potential nitrate reduction rates (measured as potential denitrification and dissimilatory nitrate reduction to ammonium processing), nutrient stocks (total carbon, total nitrogen, and organic matter), and stable isotopes (carbon and nitrogen). Water level data related to each wetland location can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/2530253 (Kirker et al., 2024) and related water geochemistry data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3001967 (Forbes et al., 2025). In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata and international generic sample numbers (IGSNs); (4) readme; (5) the field protocol; and (6) a subfolder with sample data. The sample data subfolder contains (1) sediment potential denitrification rate, (2) sediment potential dissimilatory nitrate reduction to ammonium (DNRA) rate, (3) sediment total carbon and nitrogen content, (4) sediment stable isotopes (delta nitrogen-15 and delta carbon-13), (5) sediment percent organic matter, (6) surface water nitrous oxides, (7) surface water ammonium, and (8) methods codes. All files are .csv or .pdf.

Ammonium↗

DOE OSTI2021

Network Reduction for Power Hardware-in-the-Loop (PHIL) Simulation

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2020

Mesoporous PdAg Nanospheres for Stable Electrochemical CO 2 Reduction to Formate

Palladium is a promising material for electrochemical CO 2 reduction to formate with high Faradaic efficiency near the equilibrium potential. It unfortunately suffers from problematic operation stability due to CO poisoning on surface. In this work, it is demonstrated that alloying is an effective strategy to alleviate this problem. Mesoporous PdAg nanospheres with uniform size and composition are prepared from the co-reduction of palladium and silver precursors in aqueous solution using dioctadecyldimethylammonium chloride as the structure-directing agent. The best candidate can initiate CO 2 reduction at zero overpotential and achieve high formate selectivity close to 100% and great stability even at <-0.2 V versus reversible hydrogen electrode. The high selectivity and stability are believed to result from the electronic coupling between Pd and Ag, which lowers the d-band center of Pd and thereby significantly enhances its CO tolerance, as evidenced by both electrochemical analysis and theoretical simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Porosity as a Design Element for Developing Catalytic Molecular Materials for Electrochemical and Photochemical Carbon Dioxide Reduction

The catalytic reduction of carbon dioxide (CO 2 ) using sustainable energy inputs is a promising strategy for upcycling of atmospheric carbon into value-added chemical products. This goal has inspired the development of catalysts for selective and efficient CO 2 conversion using electrochemical and photochemical methods. Among the diverse array of catalyst systems designed for this purpose, two- and three-dimensional platforms that feature porosity offer the potential to combine carbon capture and conversion. Included are covalent organic frameworks (COFs), metal-organic frameworks (MOFs), porous molecular cages, and other hybrid molecular materials developed to increase active site exposure, stability, and water compatibility while maintaining precise molecular tunability. This mini-review showcases catalysts for the CO 2 reduction reaction (CO 2 RR) that incorporate well-defined molecular elements integrated into porous materials structures. Selected examples provide insights into how different approaches to this overall design strategy can augment their electrocatalytic and/or photocatalytic CO 2 reduction activity. This article is protected by copyright. All rights reserved.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Selection of solvents for integrated CO 2 absorption and electrochemical reduction systems

Abstract Solvent‐based electrochemical CO 2 reduction (CO 2 R) enables the production of chemicals or fuels using CO 2 from a preceding absorption process. Employing previously tested CO 2 capture solvents does not ensure their suitability for either CO 2 R or integrated CO 2 absorption‐reduction. We propose solvent selection criteria that include the CO 2 solubility, kinetic constant, ionic conductivity, concentration of the bicarbonate, carbamate, and solvent cation in the CO 2 ‐loaded solution, and sustainability indicators. They are implemented for solvent selection (a) from novel, aqueous mixtures of N ‐methylcyclohexylamine (MCA) with piperazine (PZ), 2‐amino‐2‐methyl‐1‐propanol (AMP), potassium hydroxide (KOH), and potassium chloride (KCl) and (b) from aqueous monoethanolamine (MEA), AMP, KOH, MCA, and PZ solutions. Versions of a modified Kent‐Eisenberg model for strong bases, carbamate, and non‐carbamate‐forming amine solutions are developed and parameterized through experimental equilibrium measurements. CO 2 R experimental results are presented for solutions of KOH and MCA + KOH, as these indicate desired trade‐offs for CO 2 absorption and reduction.

Amines↗

DOE OSTI2025

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by the Circle Economy Foundation states that the circularity score for the global economy is declining. The US Environmental Protection Agency (EPA) tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste was destined for landfills. EPA estimates that US industry is responsible for 2.7 Gt of solid nonhazardous waste annually in the US mostly linear economy model. The circular economy framework aims to decouple economic value generation from the extraction of virgin materials from nature. The linear model of material extraction and disposal at the end of life is highly unsustainable. Manufacturing companies are adopting ambitious waste reduction targets to achieve sustainability. Through the Better Plants program, US DOE has established the Waste Reduction Network, which offers technical assistance to partners to achieve their ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is unstandardized metrics for quantifying waste performance that may not be well suited to demonstrate progress. This paper studies traditional methods used to measure waste performance and highlights advantages, disadvantages, and limitations of each method. It also examines the suitability of applying the methods in different manufacturing circumstances. Finally, the paper presents a case study of a large manufacturer that faced inconsistencies in its tracked measurement metric. A solution was proposed and implemented to alter the methodology to enable more accurate waste performance tracking against a baseline.

circular economy↗