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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ring-Opening Dynamics of the Cyclopropyl Radical and Cation: the Transition State Nature of the Cyclopropyl Cation

Here, we provide compelling experimental and theoretical evidence for the transition state nature of the cyclopropyl cation. Synchrotron photoionization spectroscopy employing coincidence techniques together with a novel simulation based on high-accuracy ab initio calculations reveal that the cation is unstable via its allowed disrotatory ring-opening path. The ring strains of the cation and the radical are similar, but both ring opening paths for the radical are forbidden when the full electronic symmetries are considered. These findings are discussed in light of the early predictions by Longuet-Higgins alongside Woodward and Hoffman; we also propose a simple phase space explanation for the appearance of the cyclopropyl photoionization spectrum. The results of this work allow the refinement of the cyclopropane C–H bond dissociation energy, in addition to the cyclopropyl radical and cation cyclization energies, via the Active Thermochemical Tables approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Indolo[2,3- b ]quinoxaline as a Low Reduction Potential and High Stability Anolyte Scaffold for Nonaqueous Redox Flow Batteries

Redox flow batteries (RFBs) are a promising stationary energy storage technology for leveling power supply from intermittent renewable energy sources with demand. A central objective for the development of practical, scalable RFBs is to identify affordable and high-performance redox-active molecules as storage materials. Herein, we report the design, synthesis, and evaluation of a new organic scaffold, indolo[2,3-b]quinoxaline, for highly stable, low-reduction potential, and high-solubility anolytes for nonaqueous redox flow batteries (NARFBs). The mixture of 2- and 3-(tert-butyl)-6-(2-methoxyethyl)-6H-indolo[2,3-b]quinoxaline exhibits a low reduction potential (-2.01 V vs Fc/Fc + ), high solubility (>2.7 M in acetonitrile), and remarkable stability (99.86% capacity retention over 49.5 h (202 cycles) of H-cell cycling). This anolyte was paired with N-(2-(2-methoxyethoxy)-ethyl)phenothiazine (MEEPT) to achieve a 2.3 V all-organic NARFB exhibiting 95.8% capacity retention over 75.1 h (120 cycles) of cycling.

25 ENERGY STORAGE↗
DOE OSTI2024

Tuning Two-Dimensional Phthalocyanine Dual Site Metal–Organic Framework Catalysts for the Oxygen Reduction Reaction

Metal-organic frameworks (MOFs) offer an interesting opportunity for catalysis, particularly for metal-nitrogen-carbon (M-N-C) motifs by providing an organized porous structural pattern and well-defined active sites for the oxygen reduction reaction (ORR), a key need for hydrogen fuel cells and related sustainable energy technologies. Here, in this work, we leverage electrochemical testing with computational models to study the electronic and structural properties in these systems and their relationship to ORR activity and stability based on dual transitional metal centers. These consists of two M1 metals with amine nodes coordinated to a single M2 metal with a phthalocyanine linker, where M1/M2 = Co, Ni, or Cu. Co-based metal centers, in particular Ni-Co, demonstrate the highest overall activity of all nine tested MOFs. Computationally, we identify the dominance of Co-sites, relative higher importance of the M2 site, and the role of layer M1 interactions on the ORR activity. Selectivity measurements indicate that M1 sites of MOFs, particularly Co, exhibits lowest (< 4%), and Ni demonstrates highest (>46%) two-electron selectivity, in good agreement with computational studies. Direct in-situ stability characterization, measuring dissolved metal ions, and calculations, using an alkaline stability metric, confirm that Co is the most stable metal in the MOF, while Cu exhibits notable instability at the M1. Overall, this study reveals how atomistic coupling of electronic and structural properties affects the ORR performance of dual site MOF catalysts and opens new avenues for tunable design and future development of these systems for practical electrochemical applications.

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

Mg-Ion Conduction in Antiperovskite Solid Electrolytes Revealed by 25 Mg Ultrahigh Field NMR and First-Principles Calculations

Magnesium-ion batteries hold the potential to outperform the energy density of lithium-ion batteries, given the divalent charge carried by each Mg 2+ cation, but remain in an early stage of development. Here, in this study, 25 Mg solid-state nuclear magnetic resonance (ssNMR) is used to gain insight into the local structure and Mg-ion dynamics of candidate Mg-ion solid electrolytes, the antiperovskites Mg 3 SbN and Mg 3 AsN. Using the highest available magnetic field (35.2 T) for high-resolution solid-state NMR, the largest 25 Mg quadrupole coupling constants (C Q ) yet measured of up to 22 MHz are reported and corroborated by first-principles calculations. Predicted C Q values are shown to correlate with the antiperovskite’s tolerance factor; thus, 25 Mg NMR linewidths can report on lattice distortions and phase stability of these antiperovskites. Variable-temperature 25 Mg NMR spectra demonstrate changes at elevated temperatures, ascribed to Mg-ion motional effects. 25 Mg T 1 relaxometry measurements at ultrahigh field reveal a lower activation energy for the more distorted Mg 3 AsN phase, matching computational predictions of a lower energy barrier for Mg 2+ ion migration and suggesting that additional scrutiny of antiperovskites as Mg-ion conductors is warranted. Given the inherent challenges of 25 Mg NMR, this work demonstrates the benefits of combining ultrahigh field NMR spectroscopy, advanced pulse sequences, modern signal processing, and first-principles calculations to facilitate NMR of quadrupolar nuclei as a tool to probe the local structure and ion dynamics in beyond-Li battery materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.

global warming↗
DOE OSTI2026

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences↗
DOE OSTI2024

All-optical image denoising using a diffractive visual processor

Abstract Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30–40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Carbon footprint of global natural gas supplies to China

As natural gas demand surges in China, driven by the coal-to-gas switching policy, widespread attention is focused on its impacts on global gas supply-demand rebalance and greenhouse gas (GHG) emissions. Here, for the first time, we estimate well-to-city-gate GHG emissions of gas supplies for China, based on analyses of field-specific characteristics of 104 fields in 15 countries. Results show GHG intensities of supplies from 104 fields vary from 6.2 to 43.3 g CO 2 eq MJ -1 . Due to the increase of GHG-intensive gas supplies from Russia, Central Asia, and domestic shale gas fields, the supply-energy-weighted average GHG intensity is projected to increase from 21.7 in 2016 to 23.3 CO 2 eq MJ -1 in 2030, and total well-to-city-gate emissions of gas supplies are estimated to grow by ~3 times. While securing gas supply is a top priority for the Chinese government, decreasing GHG intensity should be considered in meeting its commitment to emission reductions.

03 NATURAL GAS↗
DOE OSTI2020

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Distance makes a difference in crystalline photoluminescence

Crystallization-induced photoluminescence weakening was recently revealed in ultrasmall metal nanoparticles. However, the fundamentals of the phenomenon are not understood yet. By obtaining conformational isomer crystals of gold nanoclusters, we investigate crystallization-induced photoluminescence weakening and reveal that the shortening of interparticle distance decreases photoluminescence, which is further supported by high-pressure photoluminescence experiments. To interpret this, we propose a distance-dependent non-radiative transfer model of excitation electrons and support it with additional theoretical and experimental results. This model can also explain both aggregation-induced quenching and aggregation-induced emission phenomena. This work improves our understanding of aggregated-state photoluminescence, contributes to the concept of conformational isomerism in nanoclusters, and demonstrates the utility of high pressure studies in nanochemistry.

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

Enhanced piezoelectricity from highly polarizable oriented amorphous fractions in biaxially oriented poly(vinylidene fluoride) with pure β crystals

Piezoelectric polymers hold great potential for various electromechanical applications, but only show low performance, with |d 33 |< 30 pC/N. We prepare a highly piezoelectric polymer (d 33 = -62 pC/N) based on a biaxially oriented poly(vinylidene fluoride) (BOPVDF, crystallinity = 0.52). After unidirectional poling, macroscopically aligned samples with pure β crystals are achieved, which show a high spontaneous polarization (P s ) of 140 mC/m 2 . Given the theoretical limit of P s,β = 188 mC/m 2 for the neat β crystal, the high P s cannot be explained by the crystalline-amorphous two-phase model (i.e., P s,β = 270 mC/m 2 ). Instead, we deduce that a significant amount (at least 0.25) of an oriented amorphous fraction (OAF) must be present between these two phases. Experimental data suggest that the mobile OAF resulted in the negative and high d 33 for the poled BOPVDF. The plausibility of this conclusion is supported by molecular dynamics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Role of an ancient light-harvesting protein of PSI in light absorption and photoprotection

Diverse algae of the red lineage possess chlorophyll a-binding proteins termed LHCR, comprising the PSI light-harvesting system, which represent an ancient antenna form that evolved in red algae and was acquired through secondary endosymbiosis. However, the function and regulation of LHCR complexes remain obscure. Here we describe isolation of a Nannochloropsis oceanica LHCR mutant, named hlr1, which exhibits a greater tolerance to high-light (HL) stress compared to the wild type. We show that increased tolerance to HL of the mutant can be attributed to alterations in PSI, making it less prone to ROS production, thereby limiting oxidative damage and favoring growth in HL. HLR1 deficiency attenuates PSI light-harvesting capacity and growth of the mutant under light-limiting conditions. We conclude that HLR1, a member of a conserved and broadly distributed clade of LHCR proteins, plays a pivotal role in a dynamic balancing act between photoprotection and efficient light harvesting for photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Double sulfur vacancies by lithium tuning enhance CO2 electroreduction to n-propanol

Electrochemical CO 2 reduction can produce valuable products with high energy densities but the process is plagued by poor selectivities and low yields. Propanol represents a challenging product to obtain due to the complicated C 3 forming mechanism that requires both stabilization of *C 2 intermediates and subsequent C 1 –C 2 coupling. Herein, density function theory calculations revealed that double sulfur vacancies formed on hexagonal copper sulfide can feature as efficient electrocatalytic centers for stabilizing both CO* and OCCO* dimer, and further CO–OCCO coupling to form C 3 species, which cannot be realized on CuS with single or no sulfur vacancies. The double sulfur vacancies were then experimentally synthesized by an electrochemical lithium tuning strategy, during which the density of sulfur vacancies was well-tuned by the charge/discharge cycle number. The double sulfur vacancy-rich CuS catalyst exhibited a Faradaic efficiency toward n-propanol of 15.4 ± 1% at -1.05 V versus reversible hydrogen electrode in H-cells, and a high partial current density of 9.9 mA cm -2 at -0.85 V in flow-cells, comparable to the best reported electrochemical CO 2 reduction toward n-propanol. Our work suggests an attractive approach to create anion vacancy pairs as catalytic centers for multi-carbon-products.

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

Universal scaling laws of keyhole stability and porosity in 3D printing of metals

Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. We leverage ultrahigh-speed synchrotron X-ray imaging and high-fidelity multiphysics modeling to identify simple yet universal scaling laws for keyhole stability and porosity in metal 3D printing. The laws apply broadly and remain accurate for different materials, processing conditions, and printing machines. We define a dimensionless number, the Keyhole number, to predict aspect ratio of a keyhole and the morphological transition from stable at low Keyhole number to chaotic at high Keyhole number. Furthermore, we discover inherent correlation between keyhole stability and porosity formation in metal 3D printing. By reducing the dimensions of the formulation of these challenging problems, the compact scaling laws will aid process optimization and defect elimination during metal 3D printing, and potentially lead to a quantitative predictive framework.

42 ENGINEERING↗
DOE OSTI2021

Electron donation of non-oxide supports boosts O 2 activation on nano-platinum catalysts

Activation of O 2 is a critical step in heterogeneous catalytic oxidation. Here, the concept of increased electron donors induced by nitrogen vacancy is adopted to propose an efficient strategy to develop highly active and stable catalysts for molecular O 2 activation. Carbon nitride with nitrogen vacancies is prepared to serve as a support as well as electron sink to construct a synergistic catalyst with Pt nanoparticles. Extensive characterizations combined with the first-principles calculations reveal that nitrogen vacancies with excess electrons could effectively stabilize metallic Pt nanoparticles by strong p-d coupling. The Pt atoms and the dangling carbon atoms surround the vacancy can synergistically donate electrons to the antibonding orbital of the adsorbed O 2 . This synergistic catalyst shows great enhancement of catalytic performance and durability in toluene oxidation. The introduction of electron-rich non-oxide substrate is an innovative strategy to develop active Pt-based oxidation catalysts, which could be conceivably extended to a variety of metal-based catalysts for catalytic oxidation.

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

Source sector and fuel contributions to ambient PM 2.5 and attributable mortality across multiple spatial scales

Ambient fine particulate matter (PM 2.5 ) is the world’s leading environmental health risk factor. Reducing the PM 2.5 disease burden requires specific strategies that target dominant sources across multiple spatial scales. We provide a contemporary and comprehensive evaluation of sector- and fuel-specific contributions to this disease burden across 21 regions, 204 countries, and 200 sub-national areas by integrating 24 global atmospheric chemistry-transport model sensitivity simulations, high-resolution satellite-derived PM 2.5 exposure estimates, and disease-specific concentration response relationships. Globally, 1.05 (95% Confidence Interval: 0.74–1.36) million deaths were avoidable in 2017 by eliminating fossil-fuel combustion (27.3% of the total PM 2.5 burden), with coal contributing to over half. Other dominant global sources included residential (0.74 [0.52–0.95] million deaths; 19.2%), industrial (0.45 [0.32–0.58] million deaths; 11.7%), and energy (0.39 [0.28–0.51] million deaths; 10.2%) sectors. Our results show that regions with large anthropogenic contributions generally had the highest attributable deaths, suggesting substantial health benefits from replacing traditional energy sources.

54 ENVIRONMENTAL SCIENCES↗