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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Carbon Dioxide Conversion to Nanomaterials: Methods, Applications, and Challenges

Anthropogenic carbon dioxide (CO 2 ) emission is one of the biggest global threats that we are confronted with in the 21st century. A lack of prompt action to mitigate the atmospheric CO 2 level could lead to global catastrophic events. As a result of the significance of the aforementioned threat, multiple nations have agreed to enact carbon capture, storage, utilization, and sequestration (CCUS) technologies at academic and industrial scales with the aim of lowering the atmospheric CO 2 level. Although there are many reviews on CCUS technologies, there is a lack of comprehensive review on CO 2 conversion to more economically valuable products, such as nanomaterials. Carbon dioxide capture and conversion into nanomaterials is an interesting research topic in many ways because it contributes to our fundamental knowledge of material conversion, provides alternative ways to reduce atmospheric pollution, and yields lucrative products, which has not been explored broadly. Aside from the positive outcomes, we have a moral obligation to rectify a global threat caused by anthropogenic emission. Herein, we present current methods of converting CO 2 into organic and inorganic nanomaterials with a focus on operating conditions and challenges as well as the potential of nanomaterials in specifically biomedical applications as opposed to extensively reviewed energy-related uses. Furthermore, our review emphasizes the toxicity issues related to using such nanomaterials in biological settings based on literature and other comparable studies.

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

Utilizing Essential Symmetry Breaking in Auxiliary-Field Quantum Monte Carlo: Application to the Spin Gaps of the C 36 Fullerene and an Iron Porphyrin Model Complex

We present three distinct examples where phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) can be reliably performed with a single-determinant trial wave function with essential symmetry breaking. Essential symmetry breaking was first introduced by Lee and Head-Gordon [ Phys. Chem. Chem. Phys. 2019, 21, 4763-4778, 10.1039/C8CP07613H]. We utilized essential complex and time-reversal symmetry breaking with ph-AFQMC to compute the triplet–singlet energy gap in the TS12 set. We found statistically better performance of ph-AFQMC with complex-restricted orbitals than with spin-unrestricted orbitals. We then showed the utilization of essential spin symmetry breaking when computing the singlet–triplet gap of a known biradicaloid, C 36 . ph-AFQMC with spin-unrestricted Hartree–Fock (ph-AFQMC+UHF) fails catastrophically even with spin-projection and predicts no biradicaloid character. With approximate Brueckner orbitals obtained from regularized orbital-optimized second-order Møller–Plesset perturbation theory (κ-OOMP2), ph-AFQMC quantitatively captures strong biradicaloid character of C 36 . Lastly, we applied ph-AFQMC to the computation of the quintet–triplet gap in a model iron porphyrin complex where brute-force methods with a small active space fail to capture the triplet ground state. We show unambiguously that neither triplet nor quintet is strongly correlated using UHF, κ-OOMP2, and coupled-cluster with singles and doubles (CCSD) performed on UHF and κ-OOMP2 orbitals. There is no essential symmetry breaking in this problem. By virtue of this, we were able to perform UHF+ph-AFQMC reliably with a cc-pVTZ basis set and predicted a triplet ground state for this model geometry. The largest ph-AFQMC in this work correlated 186 electrons in 956 orbitals. Our work highlights the utility, scalability, and accuracy of ph-AFQMC with a single-determinant trial wave function with essential symmetry breaking for systems mainly dominated by dynamical correlation with little static correlation.

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

Revisiting Artifacts of Kohn–Sham Density Functionals for Biosimulation

We revisit the problem of unphysical charge density delocalization/fractionalization induced by the self-interaction error of common approximate Kohn–Sham (KS) density functional theory functionals on simulation of small to medium-sized proteins in a vacuum. Aside from producing unphysical electron densities and total energies, the vanishing of the HOMO–LUMO gap associated with the unphysical charge delocalization leads to an unphysical low-energy spectrum and catastrophic failure of most popular solvers for the KS self-consistent field (SCF) problem. We apply a robust quasi-Newton SCF solver to obtain solutions for some of these difficult cases. The anatomy of the charge delocalization is revealed by the natural deformation orbitals obtained from the density matrix difference between the Hartree–Fock and KS solutions; the charge delocalization not only can occur between charged fragments (such as in zwitterionic polypeptides) but also involves neutral fragments. The vanishing-gap phenomenon and troublesome SCF convergence are both attributed to the unphysical KS Fock operator eigenspectra of molecular fragments (e.g., amino acids or their side chains). Analysis of amino acid pairs suggests that the unphysical charge delocalization can be partially ameliorated by the use of some range-separated hybrid functionals but not by semilocal or standard hybrid functionals. Last, we demonstrate that solutions without the unphysical charge delocalization can be located even for semilocal KS functionals highly prone to such defects, but such solutions have non-Aufbau character and are unstable with respect to mixing of the non-overlapping “frontier” orbitals. Caution should be exercised when unexpectedly small (or vanishing) HOMO–LUMO gaps and atypical SCF convergence patterns (e.g., oscillatory) are observed in KS DFT simulations in any context (bio or otherwise).

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

Temperature-Dependent Fracture Resistance of Silicon Nanopillars during Electrochemical Lithiation

During the lithation of silicon anodes, the solid-state diffusion of lithium into Li x Si follows the Arrhenius law, the resulting morphology and fracture behavior are determined by the silicon anode operation temperature. Here, we reveal the temperature dependence of the lithiation mechanics of crystalline silicon nanopillars (SiNPs) via microscopic observations of the anisotropic growth and fracture behavior. In this work, we fabricated 1D SiNP structures with various orientations ($\langle100\rangle, \langle110\rangle$, and $\langle111\rangle$) as working electrodes and operated them at temperatures ranging from –20 to 40 °C. The lithiation of crystalline silicon at low temperatures exhibited preferential volume expansion along $\langle110\rangle$ and decreased fracture resistance. Furthermore, low temperatures caused the catastrophic fracture of amorphous silicon after the second lithiation. Our findings demonstrate the importance of silicon anode temperature control to prevent mechanical fracture during the cycle of lithium-ion batteries in harsh environments (e.g., electric vehicles in winter).

25 ENERGY STORAGE↗
DOE OSTI2024

Factors Affecting the Electron Conductivity in Single Crystal Li 7 La 3 Zr 2 O 12 and Li 7 P 3 S 11

One of the serious challenges in all solid-state Li ion batteries is neutral Li intrusion into the solid-state electrolyte that can ultimately cause catastrophic failure. One possibility for this is due to n-type electron conductivity that induces the reaction Li + + e – → Li 0 at sites where the potential is less than the Li + /Li potential. This paper reports hybrid density functional theory calculations of the electronic conductivity in two prototype single crystalline solid-state electrolytes, cubic Li 7 La 3 Zr 2 O 12 (c-LLZO) and Li 7 P 3 S 11 (LPS). The formation energies of important point defects that can affect electron conductivity are determined, and we find that the mechanism of n-type electron conductivity for both solid-state electrolytes is via “small” electron polaron hopping, where the quotes signify that substantial Li ion rearrangement is associated with the polaron formation and its migration. In both electrolytes, the formation energies for the small polarons at the Fermi energy are too high to generate measurable electron conductivity at room temperature. For c-LLZO, the concentration of electron polarons necessary to ensure charge neutrality from positively charged oxygen vacancies formed in synthesis can be significantly higher. Hence, the electron conductivity could be significant when measured with ion-blocking metal electrodes, and we discuss how the synthesis conditions could affect this magnitude. However, in the solid-state battery, these polarons are replaced by negatively charged Li vacancies so that the electron conductivity should remain minimal. For LPS single crystals, the inherent minimal electron conductivity is independent of synthesis conditions. Here we also show that the cost of forming Li 0 in bulk c-LLZO is enormous due to strain effects so that it could only potentially form at voids, grain boundaries, or around vacancy defects which relax the lattice strain.

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

Self-Sensing Composites via an Embedded 3D-Printed PVDF-MoS 2 Nanosensor for Structural Health Monitoring

Carbon fiber (CF)-reinforced epoxy composites are widely used in vehicle applications, where early damage detection is crucial for reliability and safety. To address this need, we developed a self-sensing epoxy/CF composite by embedding a PVDF-MoS 2 nanosensor via an embedded 3D printing method. By harnessing the intrinsic curing kinetics of epoxy, we tailored its rheological properties to optimize the embedded printing process, enabling precise and reliable support for sensor filaments without compromising the composite’s structural and functional integrity. Through comprehensive rheological and kinetic analysis, we established a quantitative relationship among curing temperature, conversion rate, and resulting yield modulus─defining a narrow processing window essential for successful sensor integration. Specifically, we identified that an epoxy yield modulus range of 180–294 Pa and a conversion rate below 10% are critical to support the PVDF-MoS 2 filament architecture. Here, this embedded 3D printing method produces complex and multimaterial PVDF-MoS 2 sensors within an epoxy matrix with minimal deformation and reduced postprocessing, which is scalable and adaptable for industrial applications. Under cyclic loading, the embedded sensors exhibited stable signals under constant loads and increased voltage signals in response to crack formation (17–35% higher) and catastrophic failure (1 order of magnitude higher), effectively capturing structural changes in real time. This study demonstrates the potential of PVDF-MoS 2 nanocomposite sensor materials for real-time structural health monitoring in epoxy–CF composite systems, enabling early detection of defects and stress anomalies, significantly reducing the risk of unexpected failures, and enhancing structural reliability.

PVDF-MoS2 sensor↗
DOE OSTI2023

Effects of the Hydrophobic Block Length Ratio of Poly(vinylbenzyl N -methylpiperidinium carbonate)- b -polyethylene- b -poly(vinylbenzyl N -methylpiperidinium carbonate) Block Copolymers for Anion Exchange Membrane Electrolysis

Electrolysis of water to produce hydrogen from renewable electricity is an extremely attractive strategy to reduce energy dependence on fossil fuels. Development of membrane and ionomer materials that maintain high performance with long lifetimes is needed. We developed and investigated the performance and durability of a series of polyethylene-based ABA triblock copolymer anion exchange ionomer and membrane (AEI, AEM) materials for anion exchange membrane water electrolysis. Poly(vinylbenzyl N-methylpiperidinium carbonate)-b-polyethylene-b-poly(vinylbenzyl N-methylpiperidinium carbonate) was synthesized with different hydrophobic/hydrophilic block ratios (1.02:1, 2.58:1, and 4.46:1) resulting in a range of ion exchange capacities (1.1–1.8 meq g –1 ) and water swelling (23–154%) characteristics. All AEMs showed full anionic dissociation, as evidenced by linear Arrhenius correlations, and excellent carbonate conductivity of 8–94 mS cm –1 at 50 °C. Hydrophilic phase separation may offer superior chemical durability by only wetting the ion-conducting region of the polymer and avoiding attack at the nonwetted backbone. We evaluated the performance and durability of these AEMs as a function of hydrophobic polyethylene (PE) content. The AEM containing the least amount of PE displayed the highest performance of 1 A cm –2 at 2.3 V but degraded at 40 mV h –1 before catastrophically failing after <3 h at 0.5 A cm –2 . With greater PE-containing AEMs, the degradation rate was reduced by 3 orders of magnitude with only a 0.2 V increase in voltage. Constant current electrolysis for 50 h resulted in a voltage change of 0.3 mV h –1 in a single-cell water electrolyzer and 1 M potassium carbonate. In a 600 h test, the voltage change in the final 200 h was impressively low for an experimental film, 58 μV h –1 . Postmortem analysis indicated that the membrane did not thin and that water becomes more tightly hydrogen-bound in the polymer after electrolysis.

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

Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance

Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitates efficient machine learning surrogates for predicting battery performance. The physics-guided continual learning (PGCL) method proposed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Using a AORFB database with a thousand potential materials generated by a 780 $\text{cm}^2$ interdigitated cell model, PGCL incorporates AORFB physics to optimize the continual learning task formation and training strategies to retain previously learned battery material knowledge. Finally, the trained PGCL demonstrates its capability in assessing emerging ASO materials within the established parameter space when evaluated with the dihydroxyphenazine isomers.

25 ENERGY STORAGE↗
DOE OSTI2026

Degradation Dynamics of Perovskite Solar Cells Under Fixed Reverse-Current Injection

Previous studies of reverse-bias stability in perovskite solar cells have focused primarily on voltage-controlled tests. Here we instead investigate perovskite solar cell degradation under well-defined, constant reverse-current stress. We show that the choice of hole-transport layer dictates the dominant degradation pathway: cells using thick poly(triphenylamine) (PTAA) layers can tolerate high reverse bias but quickly undergo catastrophic breakdown under fixed reverse current near their one-sun maximum power-point. In contrast, cells modified with phosphonic-acid interface layer MeO-2PACz, with poorer indium-doped tin oxide (ITO) coverage compared to PTAA, exhibit gradual and recoverable degradation under certain stress and recovery conditions. For MeO-2PACz devices, degradation increases with both current magnitude and duration. Importantly, when normalized by injected charge (current times duration), lower currents applied over longer times cause more degradation than higher currents over shorter periods. Here, combining electrical measurements with spatially resolved photoluminescence imaging, our observations support an ion- and charge-mediated interfacial electrochemical degradation mode.

Degradation↗
DOE OSTI2022

Role of the Mineral in the Self-Healing of Cracks in Human Enamel

Human enamel is an incredibly resilient biological material, withstanding repeated daily stresses for decades. The mechanisms behind this resilience remain an open question, with recent studies demonstrating a crack-deflection mechanism contributing to enamel toughness and other studies detailing the roles of the organic matrix and remineralization. Here, we focus on the mineral and hypothesize that self-healing of cracks in enamel nanocrystals may be an additional mechanism acting to prevent catastrophic failure. To test this hypothesis, we used a molecular dynamics (MD) approach to compare the fracture behavior of hydroxyapatite (HAP) and calcite, the main minerals in human enamel and sea urchin teeth, respectively. In this work, we find that cracks heal under pressures typical of mastication by fusion of crystals in HAP but not in calcite, which is consistent with the resilience of HAP enamel that calcite teeth lack. Scanning transmission electron microscopy (STEM) images of structurally intact ("sound") human enamel show dashed-line nanocracks that resemble and therefore might be the cracks healed by fusion of crystals produced in silico . The fast, self-healing mechanism shown here is common in soft materials and ceramics but has not been observed in single crystalline materials at room temperature. The crack self-healing in sound enamel nanocrystals, therefore, is unique in the human body and unique in materials science, with potential applications in designing bioinspired materials.

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

Large Local Currents in a Lithium-Ion Battery during Rest after Fast Charging

Increasing electric vehicle (EV) adoption requires lithium-ion batteries that can be charged quickly and safely. Some EV batteries have caught on fire despite being neither charged nor discharged. While the lithium that plates on graphite during fast charging affects battery safety, so do the internal ionic currents that can occur when the battery is at rest after charging. These currents are difficult to quantify; the external current that can readily be measured is zero. Here we study a graphite electrode at rest after 6C fast charging using operando X-ray microtomography. We quantify spatially resolved current density distributions that originate at plated lithium and end in underlithiated graphite particles. The average current densities decrease from 1.5 to 0.5 mA cm -2 in about 20 min after charging is stopped. Surprisingly, the range of the stripping current density is independent of time, with outliers above 20 mA cm -2 . In conclusion, the persistence of outliers provides a clue as to the origin of catastrophic failure in batteries at rest.

25 ENERGY STORAGE↗
DOE OSTI2025

Locally Confined Polysulfide-Reactive Electrolytes for Shuttle-Free Sodium–Sulfur Batteries

Sodium-sulfur batteries promise high-energy-density and sustainable electrochemical energy storage but suffer from uncontrolled polysulfides dissolution and high sodium reactivity. These challenges fundamentally originate from poor electrolyte-electrode compatibility. Current electrolyte research inadequately addresses the trade-off between minimal polysulfides solvation and stabilizing sodium interfaces. Here, we present a locally-confined polysulfide-reactive electrolyte strategy that mediates the polysulfide dissolution dynamics and sodium stability by leveraging an electrophilic solvating species with a localized high-concentration electrolyte. This design enables shuttle-free cell operation by synergistically restricting the global solvating power of the electrolyte through intermolecular interactions and locally scavenging sparingly dissolved polysulfides via electrolyte electrophilicity. The precisely confined surface reaction facilitates a protective cathode-electrolyte interface, realizing a quasi-solid-state sulfur conversion in our liquid ether-based electrolyte, which crucially avoids crossover-induced catastrophic sodium-metal degradation. The proposed electrolyte demonstrates long-term cycling of high-mass-loading sulfur cathodes (> 3 mg S cm −2 with commercial carbon host and 70 wt% sulfur content), which afford 710 mA h g −1 over 400 cycles in coin cells and steady pouch cell operation over 180 cycles. Furthermore, this work establishes a scalable electrolyte design protocol that regulates the reaction chemistry of highly reactive electrodes, offering a pathway toward sustainable renewable energy storage.

25 ENERGY STORAGE↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2023

Purification of Lithium-Ion Battery Black Mass through Tailored Alkaline Corrosion

Obtaining high-purity material outputs is crucial to the viability of novel process aimed at direct recycling of lithium-ion batteries. Metallic impurities in recycled cathodes have been shown to inhibit performance, thereby threatening mainstream acceptance of recycled battery products. Thus, shredded black mass (BM) must be purified to remove metallic contaminants, and specifically Al and Cu originating from the electrode current collectors. We herein explore a process to ionize solid copper and aluminum to ionic form based on tailored alkaline chemistry, without incurring damage to the target cathode material (Li(NixMnyCo1-x-y)O2; NMC). Al and Cu corrosion may be enhanced through the addition of chloride salt, elevated temperatures, and the use of ultrasonication - all of which disrupt the formation of passivating films on the metallic surface, and thereby increase corrosion rate. We demonstrate optimized parameters for Al and Cu corrosion both from a kinetic and overall process cost perspective. Further, we analyze the impact of these conditions on the structural (XRD, SEM), chemical (EDS, ICP), and electrochemical (impedance, cycling, dQ/dV) properties of NMC, and suggest that the present purification method does not significantly disrupt NMC performance. Finally, we present preliminary results from a promising bench-scale demonstration of this purification process applied to a simulated black mass.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2020

Amorphization and Plasticity of Olivine During Low-Temperature Micropillar Deformation Experiments

Experimentally quantifying the viscoplastic rheology of olivine at the high stresses and low temperatures of the shallow lithosphere is challenging due to olivine's propensity to deform by brittle mechanisms at these conditions. In this study, we use microscale uniaxial compression tests to investigate the rheology of an olivine single crystal at room pressure and temperature. Pillars with nominal diameters of 1.25 μm were prepared using a focused ion beam milling technique and were subjected to sustained axial stresses of several gigapascal. The majority of the pillars failed after dwell times ranging from several seconds to a few hours. However, several pillars exhibited clear evidence of plastic deformation without failure after 4–8 hr under load. The corresponding creep strain rates are estimated to be on the order of 10-6 to 10 -7 s -1 . The uniaxial stresses required to achieve this deformation (4.1–4.4 GPa) are in excellent agreement with complementary data obtained using nanoindentation techniques. Scanning transmission electron microscopy observations indicate that deformation occurred along amorphous shear bands within the deformed pillars. Electron energy loss spectroscopy measurements revealed that the bands are enriched in Fe and depleted in Mg. We propose that inhomogeneities in the cation distribution in olivine concentrate stress and promote the amorphization of the Fe-rich regions. Finally, the time dependence of catastrophic failure events suggests that the amorphous bands must grow to some critical length scale to generate an unstable defect, such as a shear crack.

58 GEOSCIENCES↗
DOE OSTI2022

Frictional Behavior Downdip Along the Subduction Megathrust: Insights From Laboratory Experiments on Exhumed Samples at In Situ Conditions

Subduction megathrusts exhibit a spectrum of slip modes, including catastrophic earthquakes. Although the mechanical and frictional properties of materials sampled from subduction zones have been studied extensively, few datasets have been collected for compositions and at pressure and temperature conditions representative of those in situ. The Nankai subduction zone in southwest Japan is a well-studied margin, and abundant data provide an opportunity to advance our understanding of fault and earthquake physics. Here, we use samples exhumed in the Shimanto and Sanbagawa Belts on Shikoku Island of southwest Japan that represent analogs for materials along the present-day megathrust at depths of ~5–>25 km, and we shear these at their peak in situ pressure-temperature (P-T) conditions. Effective normal stresses range from 28 to 192 MPa, and temperatures from 105°C to 470°C. We used pore fluid pressures of 45–240 MPa, corresponding to fluid overpressure ratios λ of 0.65 and 0.90. Slip velocities of 0.1–100 μm/s were used, in order to focus on the nucleation of instability and earthquakes. We found predominantly velocity-strengthening (inherently stable) behavior under all conditions for λ = 0.65. For λ = 0.90, velocity-weakening behavior was observed at 350°C, with velocity-strengthening behavior at lower and higher temperatures. The rate/state frictional stability parameter (a-b) increases with slip velocity at temperatures up to ~200°C and remains constant or decreases with slip velocity at higher temperatures. Altogether, our results demonstrate the potentially important roles of both temperature and slip velocity in controlling the distribution of stress and frictional rheology along subduction thrusts.

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↗
DOE OSTI2023

Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

Abstract Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.

54 ENVIRONMENTAL SCIENCES↗