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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 OSTI2023

Confinement of 1D Chain and 2D Layered CuI Modules in K-INA-R Frameworks via Coordination Assembly: Structure Regulation and Semiconductivity Tuning

Herein, we present a new series of CuI-based hybrid materials with tunable structures and semiconducting properties. Furthermore, the CuI inorganic modules can be tailored into a one-dimensional (1D) chain and two-dimensional (2D) layer and confined/stabilized in coordination frameworks of potassium isonicotinic acid (HINA) and its derivatives (HINA-R, R = OH, NO 2 , and COOH). The resulting CuI-based hybrid materials exhibit interesting semiconducting behaviors associated with the dimensionality of the inorganic module; for instance, the structures containing the 2D-CuI module demonstrate significantly enhanced photoconductivity with a maximum increase of five orders of magnitude compared to that of the structures containing the 1D-CuI module. They also represent the first CuI-bearing hybrid chemiresistive gas sensors for NO 2 with boosted sensing performance and sensitivity at multiple orders of magnitude over that of the pristine CuI. Particularly, the sensing ability of CuI-K-INA containing both 1D- and 2D-CuI modules is comparable to those of the best NO 2 chemiresistors reported thus far.

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

Fluoride-Rich, Organic–Inorganic Gradient Interphase Enabled by Sacrificial Solvation Shells for Reversible Zinc Metal Batteries

Zinc metal batteries are strongly hindered by water corrosion, as solvated zinc ions would bring the active water molecules to the electrode/electrolyte interface constantly. Herein, we report a sacrificial solvation shell to repel active water molecules from the electrode/electrolyte interface and assist in forming a fluoride-rich, organic-inorganic gradient solid electrolyte interface (SEI) layer. Additionally, the simultaneous sacrificial process of methanol and Zn(CF 3 SO 3 ) 2 results in the gradient SEI layer with an organic-rich surface (CH 2 OC- and C 5 product) and an inorganic-rich (ZnF 2 ) bottom, which combines the merits of fast ion diffusion and high flexibility. As a result, the methanol additive enables corrosion-free zinc stripping/plating on copper foils for 300 cycles with an average coulombic efficiency of 99.5%, a record high cumulative plating capacity of 10 A h/cm 2 at 40 mA/cm 2 in Zn/Zn symmetrical batteries. More importantly, at an ultralow N/P ratio of 2, the practical VO 2 //20 mu m thick Zn plate full batteries with a high areal capacity of 4.7 mAh/cm 2 stably operate for over 250 cycles, establishing their promising application for grid-scale energy storage devices. Furthermore, directly utilizing the 20 μm thick Zn for the commercial-level areal capacity (4.7 mAh/cm 2 ) full zinc battery in our work would simplify the manufacturing process and boost the development of the commercial zinc battery for stationary storage.

25 ENERGY STORAGE↗
DOE OSTI2024

Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization

High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic activity as the sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multiobjective BO framework for HEAs that simultaneously targets activity, cost-effectiveness, and entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to 10 elements.

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

Triphenylphosphine Oxide-Derived Anolyte for Application in Nonaqueous Redox Flow Battery

Recent advances in redox flow batteries have made them a viable option for grid-scale energy storage, however they exhibit low energy density. One way to boost energy density is by increasing the cell potential using a nonaqueous system. Molecular engineering has proven to be an effective strategy to develop redox-active compounds with extreme potentials but these are usually challenged by resource sustainability of the newly developed redox materials. Here, we investigate the utility of phosphine oxides as anolytes with extremely negative potentials. Specifically, we found that cyclic triphenylphosphine oxide (CPO), has a highly negative potential (–2.4 V vs Fc/Fc + ). Importantly, CPO is synthesized from triphenylphosphine oxide, a common industrial chemical waste with no commercial value. Structural and electrochemical characterization of the reduced radical anion showed that enhanced stability is due to cyclization or extended pi-conjugation. Importantly, mechanistic investigation into the decomposition of CPO under various solvents and electrochemical conditions allowed us to utilize an acetonitrile/DMF binary solvent system to enable a long-lived anolyte which exhibited no fade over 350 cycles. In conclusion, this work led to the development of a waste-derived cyclic phosphine oxide that exhibits excellent cycling stability making it an ideal anolyte toward the development of energy-dense RFBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Energizing Robust Sulfur/Lithium Electrochemistry via Nanoscale-Asymmetric-Size Synergism

Sluggish redox kinetics and dendrite growth perplex the fulfillment of efficient electrochemistry in lithium–sulfur (Li–S) batteries. The complicated sulfur phase transformation and sulfur/lithium diversity kinetics necessitate an all-inclusive approach in catalyst design. Herein, a compatible mediator with nanoscale-asymmetric-size configuration by integrating Co single atoms and defective CoTe 2–x (Co SA -CoTe 2–x @NHCF) is elaborately developed for regulating sulfur/lithium electrochemistry synchronously. Substantial electrochemistry and theoretical analyses reveal that CoTe 2–x exhibits higher catalytic activity in long-chain polysulfide transformation and Li 2 S decomposition, while monodispersed Co sites are more effective in boosting sulfur reduction kinetics to regulate Li 2 S deposition. Such cascade catalysis endows Co SA -CoTe 2–x @NHCF with the all-around service of “trapping-conversion-recuperation” for sulfur species during the whole redox reaction. Furthermore, it is demonstrated by in situ transmission electron microscopy that initially formed electronic-conductive Co and ionic-conductive Li 2 Te provide sufficient lithiophilic sites to regulate homogeneous Li plating and stripping with markedly suppressed dendrite growth. Consequently, by coupling the Co SA -CoTe 2–x @NHCF interlayer and Li@Co SA -CoTe 2–x @NHCF anode, the constructed Li–S full batteries deliver superior cycling stability and rate performance, and the flexible pouch cell exhibits stable cycling performance at 0.3 C. In conclusion, the gained insights into the synergistic effect of asymmetric-size structures pave the way for the integrated catalyst design in advanced Li–S systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Remote-Contact Catalysis for Target-Diameter Semiconducting Carbon Nanotube Arrays

Electrostatic catalysis has been an exciting development in chemical synthesis (beyond enzymes catalysis1 ) in recent years, boosting reaction rates and selectively producing certain reaction products2 . Most of the studies to date have been focused on using external electric field (EEF) to rearrange the charge distribution in small molecule reactions such as Diels-Alder addition3 , carbene reaction4 , etc. However, in order for these EEFs to be effective, a field on the order of 1 V/nm (10 MV/cm) is required, and the direction of the EEF has to be aligned with the reaction axis5 . Such a large and oriented EEF will be challenging for large-scale implementation, or materials growth with multiple reaction axis or steps. Here, we demonstrate that the energy band at the tip of an individual single-walled carbon nanotube6 (SWCNT) can be spontaneously shifted in a high-permittivity growth environment, with its other end in contact with a low-work function electrode (e.g., hafnium carbide or titanium carbide7 ). By adjusting the Fermi level at a point where there is a substantial disparity in the density of states (DOS) between semiconducting (s-) and metallic (m-) SWCNTs8 , we achieve effective electrostatic catalysis for s-SWCNT growth assisted by a weak EEF perturbation (200V/cm). This approach enables the production of high-purity (99.92%) s-SWCNT horizontal arrays with narrow diameter distribution (0.95±0.04 nm), targeting the requirement of advanced SWCNT-based electronics for future computing9-11. These findings highlight the potential of electrostatic catalysis in precise materials growth, especially for s-SWCNTs, and pave the way for the development of advanced SWCNT-based electronics12.

Wang, Jiangtao↗
DOE OSTI2025

A Quasi-Ordered Mn-Rich Cathode with Highly Reversible Oxygen Anion Redox Chemistry

Anionic oxygen redox chemistry in Li-rich Mn-based layer oxide cathodes represents a transformative approach for boosting the energy density of next-generation lithium-ion batteries. However, conventional oxygen redox reactions often induce oxygen dimerization at high voltages, leading to irreversible lattice oxygen loss and a rapid voltage fade. Herein, we achieve highly reversible oxygen redox chemistry through a new quasi-ordered structural design that incorporates both intra- and interlayer cation disorder configurations. This unique structure significantly enhances lattice oxygen stability, effectively stabilizes oxidized oxygen, and inhibits the formation of peroxo- or superoxol-like species, thereby enabling anionic redox reactions to proceed reversibly even at deep delithiation states. The quasi-ordered design mitigates irreversible phase transitions and preserves the structural integrity throughout extended cycling. Consequently, the proposed cathode demonstrates exceptional cyclability with negligible capacity and voltage fade, retaining 99% capacity and 98% average voltage after long-term cycling. Finally this work provides fresh insights into addressing issues related to lattice oxygen instabilities and reforming strategies for developing long-life, high-energy-density anionic redox cathode materials for advanced batteries.

chemical structure↗
DOE OSTI2025

Magnetic Blocking in Fluoflavine Radical-Bridged Dilanthanide Complexes

Magnetic exchange coupling is difficult to foster in polynuclear lanthanide (Ln) complexes and poorly understood. While coupling Ln ions through closed-shell ligands is inherently weak due to the contracted 4f orbitals, placing open-shell ligands instead has proven to promote orders of magnitude stronger coupling, giving rise to single-molecule magnets (SMMs) innate to real magnetic memory effect in the case of the anisotropic Ln ions. Notably, the impact of radical bridges with differing oxidation states on magnetic blocking remains unexplored due to lack of Ln SMMs with radicals in two distinct charge states. Herein, the first dilanthanide complexes (Ln = Gd, Dy) containing fluoflavine (flv) bridges, [(Cp* 2 Ln) 2 (μ-flv z )]X, (where X = [Al(OC{CF 3 } 3 ) 4 ] − (z = 1−•), 1-Ln; X = 0 (z = 2−), 2-Ln; X =[K(crypt-222)] + (z = 3−•), 3-Ln) are reported. 1-Ln and 3-Ln, comprising the flv 1−• and flv 3−• radical bridges, were investigated via single-crystal X-ray diffraction (SCXRD), ultraviolet−visible (UV−vis) spectroscopy, Superconducting Quantum Interference Device (SQUID) magnetometry, high-field electron paramagnetic resonance (HF-EPR) spectroscopy and broken-symmetry density functional theory (BS-DFT) calculations. 1-Dy and 3-Dy constitute the first SMMs innate to radicals in two differing oxidation states. 1-Dy exhibits a spin-reversal barrier U eff of 28.36 cm −1 and open magnetic hysteresis loops below 3 K. By contrast, 3-Dy displays a much higher U eff of 143(2) cm −1 and open hysteresis loops until 9.5 K, representing a record for dilanthanide SMMs containing an organic radical bridge. The boost in SMM properties in 3-Dy is attributed to spin-phonon coupling and improved frontier orbital structure. This study paves the way for advanced design strategies of polynuclear Ln SMMs.

Benner, Florian [Michigan State Univ., East Lansin↗
DOE OSTI2026

Plasmon-Assisted Electrochemical Epoxidation using Water as an Oxidant

Olefin epoxidation, an important industrial reaction, often uses hazardous oxidants, causing challenges in waste disposal. Here, we demonstrate the use of water as an oxidant by a plasmon-assisted electrochemical strategy. The electrocatalyst is comprised of a hybrid of a water oxidation catalyst, manganese oxide, and plasmonic gold nanoparticles. Visible-light irradiation of the electrocatalyst enhanced the epoxidation of 4-styrenesulfonate 5-fold as compared to dark conditions at the same temperature. From electrochemical analyses conducted under plasmon excitation conditions, complemented by real-time time-dependent density functional tight binding simulations, it is found that the plasmonic boost of the electrochemical styrene epoxidation is due to energetic holes generated by the excitation of localized surface plasmon resonances of gold nanoparticles. These photogenerated holes activate adsorbed water for oxidation and enhance the binding of 4-styrenesulfonate at interfacial sites. Here, this work demonstrates a proof of concept and establishes the mechanistic basis for plasmon-assisted activation of water as an O atom source for electrochemical epoxidations.

Gold↗
DOE OSTI2026

Photocatalytic Ammonia Synthesis using Fe-Based MOFs: The Role of Ligand Functionalization

Photocatalytic ammonia (NH 3 ) synthesis offers a carbon-neutral alternative to the Haber−Bosch process, which generates 42 million metric tons of CO 2 equivalent emissions annually. However, solar-to-ammonia conversion with contemporary photocatalysts remains far from practical requirements, and understanding the limiting factors in systems with well-defined active sites is crucial. Here, we show how the μ 3 -oxo-centered trinuclear Fe cluster in MIL-101(Fe) functions as the catalytic motif for N 2 -to-NH 3 conversion through combined experimental and computational investigations. Comparative studies with a molecular analogue demonstrate that the cluster is stabilized within the MOF framework, sustaining redox cycling and maintaining high catalytic activity. We systematically functionalized the dicarboxylate ligands of MIL-101(Fe) with −NH 2 , −Br, −NO 2 , −F, and −CF 3 to probe how ligand chemistry modulates Fe electron density, N 2 adsorption capacity, and proton availability, correlating these properties with catalytic performance using spectroscopic and surface characterization techniques alongside timeresolved infrared to assess excited-state lifetimes. F-functionalization optimally balances N 2 activation, proton availability at Fe active sites, and excited-state lifetimes, boosting NH 3 production by ∼ 60% relative to unmodified MIL-101(Fe). This study of ligandfunctionalized MIL-101(Fe) MOFs uncovers the underlying structure-activity relationships and advances design principles for solardriven NH 3 synthesis.

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

Molecular-Level Control over Ionic Conduction and Ionic Current Direction by Designing Macrocycle-Based Ionomers

Poor ionic conductivity of the catalyst-binding, submicrometer- thick ionomer layers in energy conversion and storage devices is a huge challenge. However, ionomers are rarely designed keeping in mind the specific issues associated with nanoconfinement. Here, we designed nature-inspired ionomers (calix-2) having hollow, macrocyclic, calix[4]arene-based repeat units with precise, sub-nanometer diameter. In ≤100 nm-thick films, the in-plane proton conductivity of calix-2 was up to 8 times higher than the current benchmark ionomer Nafion at 85% relative humidity (RH), while it was 1-2 orders of magnitude higher than Nafion at 20-25% RH. Confocal laser scanning microscopy and other synthetic techniques allowed us to demonstrate the role of macrocyclic cavities in boosting the proton conductivity. The systematic self-assembly of calix-2 chains into ellipsoids in thin films was evidenced from atomic force microscopy and grazing incidence small-angle X-ray scattering measurements. Moreover, the likelihood of alignment and stacking of macrocyclic units, the presence of one-dimensional water wires across this macrocycle stacks, and thus the formation of long-range proton conduction pathways were suggested by atomistic simulations. We not only did see an unprecedented improvement in thin-film proton conductivity but also saw an improvement in proton conductivity of bulk membranes when calix-2 was added to the Nafion matrices. Nafion-calix-2 composite membranes also took advantage of the asymmetric charge distribution across calix[4]arene repeat units collectively and exhibited voltage-gating behavior. The inclusion of molecular macrocyclic cavities into the ionomer chemical structure can thus emerge as a promising design concept for highly efficient ion-conducting and ion-permselective materials for sustainable energy applications.

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

Design and Evaluation of Pyridinyl Sulfonyl Piperazine LpxH Inhibitors with Potent Antibiotic Activity Against Enterobacterales

Enterobacterales, a large order of Gram-negative bacteria, including Escherichia coli and Klebsiella pneumoniae, are major causes of urinary tract and gastrointestinal infections, pneumonia, and other diseases in healthcare settings and communities. ESBL-producing Enterobacterales and carbapenem-resistant Enterobacterales can break down commonly used antibiotics, with some strains being resistant to all available antibiotics. This public health threat necessitates the development of novel antibiotics, ideally targeting new pathways in these bacteria. Gram-negative bacteria possess an outer membrane enriched with lipid A, a saccharolipid that serves as the membrane anchor of lipopolysaccharides and the active component of the bacterial endotoxin, causing septic shock. The biosynthesis of lipid A is crucial for the viability of Gram-negative bacteria, and as an essential enzyme in this process, LpxH has emerged as a promising target for developing novel antibiotics against multidrugresistant Gram-negative pathogens. Here, we report the development of pyridinyl sulfonyl piperazine LpxH inhibitors. Among them, ortho-substituted pyridinyl compounds significantly boost LpxH inhibition and antibiotic activity over the original phenyl series. Structural and QM/MM analyses reveal that these improved activities are primarily due to the enhanced interaction between F141 of the LpxH insertion lid and the pyridinyl group. Incorporation of the N-methyl-N-phenyl-methanesulfonamide moiety into the pyridinyl sulfonyl piperazine backbone results in JHLPH- 106 and JH-LPH-107, both of which exhibit potent antibiotic activity against wild-type Enterobacterales such as K. pneumoniae and E. coli. JH-LPH-107 exhibits a low rate of spontaneous resistance and a high safety window in vitro, rendering it an excellent lead for further clinical development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone

The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK). It was previously shown that the presence of sterane molecular biomarkers in this sample can be detected via ToF-SIMS (Pasterski, M. J. et al., Astrobiology 2023, 23, 936). We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithms─k-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)─to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. In terms of predictive accuracy and balanced accuracy, KNN was the best performing model and RPART the worst. The feature importance, or the specific features of the ToF-SIM spectra used by the models to make classifications, cannot be determined for KNN, preventing posthoc model interpretation. Nevertheless, the feature importance extracted from the other models was useful for interpreting spectra. In conclusion, we determined that some of the organic ions used to classify biomarker containing spectra may be fragment ions derived from kerogen which is abundant in this mudstone sample.

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

Wildfire‐Smoke Aerosols Lead to Increased Light Use Efficiency Among Agricultural and Restored Wetland Land Uses in California's Central Valley

Abstract There are few observational studies measuring the ecosystem‐scale productivity effects of changes in incident diffuse photosynthetically active radiation (PAR diffuse ), especially related to wildfire smoke. Climate change‐induced increases to the duration and intensity of fire conditions have made smoke a common occurrence across western North America, with largely unquantified ecosystem feedbacks. Under equivalent amounts of radiation, increased atmospheric particulate matter could lead to a boost in productivity as scattering redistributes photons throughout multilayer canopies. In this work, we leverage a meso‐network of eddy covariance measurement sites across a unique array of managed and restored C 3 and C 4 canopy types to understand how recent wildfire smoke affected ecosystem productivity during the summer of 2018, an especially smoky year in the agriculturally productive Central Valley. We find that diffuse PAR diffuse increased by more than a third compared to the previous growing season, while total PAR was only slightly diminished. These conditions caused nearly a doubling of light use efficiency over the range of diffuse fraction observed, with the highest sensitivity to diffuse fraction exhibited by corn and alfalfa crops. We utilized an empirical model to assess the trade‐off between enhanced diffuse fraction and reduced total PAR. Under mean radiation conditions, daily integrated gross ecosystem productivity increased by 1.2–4.2% compared to the previous growing season. Finally, we explore the potential negative effect of heightened ozone, a copollutant often associated with wildfire. In addition to the effects of wildfire smoke, the results of this natural experiment can help validate future predictions of aerosol‐productivity feedbacks.

Hemes, Kyle S.↗
DOE OSTI2021

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗
DOE OSTI2020

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120–600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Learning the Low Frequency Earthquake Activity on the Central San Andreas Fault

Abstract Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow‐slip beneath the seismogenic zone within the more ductile portion of the crust. Monitoring efforts over 15 years detected >1 million LFEs. We train a gradient boosted tree model using statistical features describing the seismic waveforms to estimate the hourly LFE event count. The burst‐like LFE behavior is reproduced, while lower amplitudes are predicted during the most active periods. The hourly event counts are up to 18% greater than the catalog. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational‐intensive template‐matching catalog. Similar waveform statistical features are found between detecting LFEs and tremors, which provides additional evidence tremors are composed of LFEs. The approach extracts information contained in continuous seismic waveforms that might benefit detecting precursory signals.

58 GEOSCIENCES↗
DOE OSTI2022

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Abstract The recent wildfires in the western United States during 2018 and 2020 caused record‐breaking fire damage and casualties. Despite remarkable advances in fire modeling and weather forecasting, it remains challenging to anticipate catastrophic wildfire events and associated damage. One key missing component is a fire weather prediction system with sufficiently long lead time capable of providing useful regional details. Here, we develop a hybrid prediction model of wildfire danger called CFS with super resolution (CFS‐SR) as a proof of concept to fill that void. The CFS‐SR model is constructed by integrating the Climate Forecast System version 2 with a deep learning (DL) technique from Single Image Super Resolution, a method widely used in enhancing image resolution. We show that for the 2018–2019 fire season, the CFS‐SR model significantly improves accuracy in forecasting fire weather at lead times of up to 7 days with an enhanced spatial resolution up to 4 km. This level of high resolution provides county‐level fire weather forecast, making it more practical for allocating resources to mitigate wildfire danger. Our study demonstrates that a proper combination of ensemble climate predictions with DL techniques can boost predictability at finer spatial scales, increasing the utility of fire weather forecasts for practical applications.

54 ENVIRONMENTAL SCIENCES↗