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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Untapped Potential of Fluoride Ions in Maximizing the Electrochemical Stability of Deep Eutectic Solvents

Advancing the batteries is pivotal to propelling our society toward a sustainable, electrified future. The stability of electrolytes forms the backbone of energy storage systems. This is particularly the case for redox flow batteries (RFBs). Their deployability depends on their longevity and dependability. The presence of unstable electrolytes can trigger undesirable reactions, degrade performance, and lead to battery failure. Aqueous electrolytes, with limited electrochemical stability window (ESW), are prone to hydrogen and oxygen evolution. Conversely, non-aqueous electrolytes offer enhanced stability. In this study, we unveil the ESW of a non-aqueous eutectic solvent, comprising choline fluoride and ethylene glycol—a composition that has eluded experimental investigation until now. Our findings show that the stability window, reduction and oxidation potential limits of deep eutectic solvents, are sensitive to variations in the halide component of the ammonium salt. Furthermore, this work not only highlights the benefits of novel deep eutectic solvents but also sets the stage for their strategic use in future battery electrolytes.

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

Transition Metal Dichalcogenide MoS 2 : Oxygen and Fluorine Functionalization for Selective Plasma Processing

Low-temperature plasma processing is a promising technique for tailoring transition metal dichalcogenides (TMDs). For chalcogen substitution processing, a key challenge is to identify the ion energy window that enables selective chalcogen removal while preserving the metal lattice. Using ab initio molecular dynamics (AIMD), we demonstrate that oxygen and fluorine functionalization widen the processing window by significantly lowering the sulfur sputtering energy threshold (E sputt,S ) of MoS 2 from ∼30 to ∼10 eV via formation of sputtering products such as SO 2 and SF n . Additionally, we show that experimentally relevant cryogenic temperatures strongly affect E sputt,S (T). The dependence is confirmed via AIMD and also predicted by a mechanistic parameter-free theory, suggesting that E sputt (T) generalizes to other TMDs, functionalizations, and surface impact conditions. Our results highlight oxygen/fluorine functionalization, ionic impact angle, and material temperature to be key control parameters for selective, damage-controlled chalcogen removal in TMD processing.

Polyachenko, Yury [Princeton Plasma Physics Labora↗
DOE OSTI2021

Extending the Near-Infrared Emission Range of Indium Phosphide Quantum Dots for Multiplexed In Vivo Imaging

This report of the reddest emitting indium phosphide quantum dots (InP QDs) to date demonstrates tunable, near-infrared (NIR) photoluminescence (PL) as well as PL multiplexing in the first optical tissue window while avoiding toxic constituents. This synthesis overcomes the InP “growth bottleneck” and extends the emission peak of InP QDs deeper into the first optical tissue window using an inverted QD heterostructure, specifically ZnSe/InP/ZnS core/shell/shell nanoparticles. The QDs exhibit InP shell thickness-dependent tunable emission with peaks ranging from 515–845 nm. The high absorptivity of InP yields effective photoexcitation of the QDs with UV, visible, and NIR wavelengths. These nanoparticles extend the range of tunable direct-bandgap emission from InP-based nanostructures, effectively overcoming a synthetic barrier that has prevented InPbased QDs from reaching their full potential as NIR imaging agents. Further, multiplexed lymph node imaging in a mouse model demonstrates the potential of the NIR-emitting InP particles for in vivo imaging.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Controllable Phase Transition Properties in VO 2 Films via Metal-Ion Intercalation

VO 2 has shown great promise for sensors, smart windows, and energy storage devices, because of its drastic semiconductor-to-metal transition (SMT) near 340 K coupled with a structural transition. To push its application toward room-temperature, effective transition temperature (T c ) tuning in VO 2 is desired. Here, in this study, tailorable SMT characteristics in VO 2 films have been achieved by the electrochemical intercalation of foreign ions (e.g., Li ions). By controlling the relative potential with respect to Li/Li + during the intercalation process, T c of VO 2 can be effectively and systematically tuned in the window from 326.7 to 340.8 K. The effective T c tuning could be attributed to the observed strain and lattice distortion and the change of the charge carrier density in VO 2 introduced by the intercalation process. This demonstration opens up a new approach in tuning the VO 2 phase transition toward room-temperature device applications and enables future real-time phase change property tuning.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Hydrogen-Bonding Interactions in Hybrid Aqueous/Nonaqueous Electrolytes Enable Low-Cost and Long-Lifespan Sodium-Ion Storage

Although “water-in-salt” electrolytes have opened a new pathway to expand the electrochemical stability window of aqueous electrolytes, the electrode instability and irreversible proton co-insertion caused by aqueous media still hinder the practical application, even when using exotic fluorinated salts. In this study, an accessible hybrid electrolyte class based on common sodium salts is proposed, and crucially an ethanol-rich media is introduced to achieve highly stable Na-ion electrochemistry. Here, ethanol exerts a strong hydrogen-bonding effect on water, simultaneously expanding the electrochemical stability window of the hybridized electrolyte to 2.5 V, restricting degradation activities, reducing transition metal dissolution from the cathode material, and improving electrolyte–electrode wettability. The binary ethanol–water solvent enables the impressive cycling of sodium-ion batteries based on perchlorate, chloride, and acetate electrolyte salts. Notably, a Na 0.44 MnO 2 electrode exhibits both high capacity (81 mAh g –1 ) and a remarkably long cycle life >1000 cycles at 100 mA g –1 (a capacity decay rate per cycle of 0.024%) in a 1 M sodium acetate system. The Na 0.44 MnO 2 /Zn full cells also show excellent cycling stability and rate capability in a wide temperature range. The gained understanding of the hydrogen-bonding interactions in the hybridized electrolyte can provide new battery chemistry guidelines in designing promising candidates for developing low-cost and long-lifespan batteries based on other (Li + , K + , Zn 2+ , Mg 2+ , and Al 3+ ) systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Theoretical Design of Lithium Chloride Superionic Conductors for All-Solid-State High-Voltage Lithium-Ion Batteries

The development of solid electrolytes (SEs) is a promising pathway to improve the energy density and safety of conventional Li ion batteries. Several lithium chloride SEs, Li 3 MCl 6 (M = Y, Er, In, and Sc), have gained popularity due to their high ionic conductivity, wide electrochemical window, and good chemical stability. This study systematically investigated 17 Li 3 MCl 6 SEs to identify novel and promising lithium chloride SEs. Calculation results revealed that 12 Li 3 MCl 6 (M = Bi, Dy, Er, Ho, In, Lu, Sc, Sm, Tb, TI, Tm, and Y) were stable phase with a wide electrochemical stability window and excellent chemical stability against cathode materials and moisture. Li-ion transport properties were examined using bond valence site energy (BVSE) and ab initio molecular dynamics (AIMD) calculation. Li 3 MCl 6 showed the lower migration energy barrier in monoclinic structures, while orthorhombic and trigonal structures exhibited higher energy barriers due to the sluggish diffusion along the two-dimensional path based on the BVSE model. AIMD results confirmed the slower ion migration along the 2D path, exhibiting lower ionic diffusivity and higher activation energy in orthorhombic and trigonal structures. For the further increase of ionic conductivity in monoclinic structures, Li-ion vacancy was formed by the substitution of M 3+ with Zr 4+ . Zr-substituted phase (Li 2.5 M 0.5 Zr 0.5 Cl 6 , M = In, Sc) exhibited up to a fourfold increase in ionic conductivity. This finding suggested that the optimization of Li vacancy in the Li 3 MCl 6 SEs could lead to superionic Li 3 MCl 6 SEs.

25 ENERGY STORAGE↗
DOE OSTI2024

Lamination of >21% Efficient Perovskite Solar Cells with Independent Process Control of Transport Layers and Interfaces

Transport layer and interface optimization is critical for improving the performance and stability of perovskite solar cells (PSCs) but is restricted by the conventional fabrication approach of sequential layer deposition. While the bottom transport layer is processed with minimum constraints, the narrow thermal and chemical stability window of the halide perovskite (HP) layer severely restricts the choice of top transport layer and its processing conditions. To overcome these limitations, we demonstrate lamination of HPs—where two transport layer-perovskite half-stacks are independently processed and diffusion-bonded at the HP-HP interface—as an alternative fabrication strategy that enables self-encapsulated solar cells. Power conversion efficiencies (PCE) of >21% are realized using cells that incorporate a novel transport layer combination along with dual-interface passivation via self-assembled monolayers, both of which are uniquely enabled by the lamination approach. This is the highest reported PCE for any laminated PSC encapsulated between glass substrates. We further show that this approach expands the processing window beyond traditional fabrication processes and is adaptable for different transport layer compositions. The laminated PSCs retained >75% of their initial PCE after 1000 h of 1-sun illumination at 40 °C in air using an all-inorganic transport layer configuration without additional encapsulation. Furthermore, a laminated 1 cm 2 device maintained a V oc of 1.16 V. Finally, the scalable lamination strategy in this study enables the implementation of new transport layers and interfacial engineering approaches for improving performance and stability.

14 SOLAR ENERGY↗
DOE OSTI2020

Assembly of Disordered Cocontinuous Morphologies by Multiblock Copolymers with Random Block Sequence and Length Dispersity

Among the wide variety of nanostructures accessible by self-assembly of block copolymers, cocontinuous morphologies are of great interest for separations, catalysis, sensing, and energy storage due to their ability to integrate distinct properties of the constituent polymers. Furthermore, we investigate the tendency to form disordered cocontinuous phases of multiblock copolymers (MBCs) with random block sequence and length dispersity prepared by step-growth polymerization of pre-made telechelic polymers, specifically using thiol-Michael addition between diacrylate functionalized polystyrene (PS) and poly (D,L - lactide) (PLA) chains and a small molecule dithiol. In the phase diagram so established, disordered cocontinuous nanostructures occupy a wide window (≈ 25 % vol) at a modest segregation strength of χN = 15, close to the critical value for microphase separation. At a higher segregation strength of χN = 30, only a moderately wide (≈ 10 vol %) cocontinuous window is found, with MBCs instead tending to form ordered nanostructures. Although the breadth of the cocontinuous widow is less than that found in prior work on analogous randomly end-linked copolymer networks (RECNs), the MBC architecture offers offsetting advantages in terms of solution and melt processability.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Probing Active-Site Relocation in Cu/SSZ-13 SCR Catalysts during Hydrothermal Aging by in situ EPR Spectroscopy, Kinetic Studies, and DFT Calculations

Cu/SSZ-13 selective catalytic reduction catalysts display activity loss after mild hydrothermal aging without support degradation or loss of isolated Cu ion content. By applying electron paramagnetic resonance spectroscopy to compare hydrated, dehydrated and NH3-saturated catalysts before and after hydrothermal aging, as well as density functional theory calculations, it is found that hydrothermal aging induces Cu relocation, leading to stronger Cu-support interactions. This is particularly evidenced by ZCuOH conversion to Z2Cu, which leads to the generation of a unique structure with two Cu(II) ions positioned in a double 6-membered ring prism. The Cu(II) ions that do not undergo such dramatic changes also appear to stay closer to framework windows as evidenced by the overall increased anisotropy during EPR measurements. In situ EPR measurements under SCR conditions demonstrate that, in comparison to the fresh catalyst, higher percentages of Cu species stay as Cu(II) in the aged catalyst. This suggests that the reduction half-cycle of the SCR redox, i.e., Cu(II) ? Cu(I), becomes slower for the hydrothermally aged catalyst.

42 ENGINEERING↗
DOE OSTI2021

SMART Perovskite Growth: Enabling a Larger Range of Process Conditions

Cost-effective, high-throughput industrial applications of metal-halide perovskites require a highly tolerant method (i.e., wide process window) that produces high-quality materials with a short annealing time. Here, we introduce a Seed Modulation for ARTificially controlled nucleation (SMART) process that enables rapid fabrication of high-quality perovskite films under a wide set of initial input parameters. Additionally, we characterized the thin-film evolution from the perspective of crystallinity, surface potential, diffusivity, surface carrier dynamics, and interfacial recombination. We find that surface and subsurface defects primarily determine the performance of materials and devices. By modulating the seeds for perovskite nucleation, we were able to improve the overall crystallization. We achieved a >20% power conversion efficiency using only a 5 min annealing step, and we found that the annealing window is widened such that differing initial conditions achieve similar quality. Furthermore, we demonstrated reproducibility and performance improvement in larger-area perovskite cells by incorporating the SMART process.

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

Short-Wave Infrared Upconverting Nanoparticles

Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.

Qi, Xiao↗
DOE OSTI2021

ESI-MS Identification of the Cationic Phosphine-Ligated Gold Clusters Au1-Au22: Insight into the Gold-Ligand Ratio and Abundance of Larger Clusters

Triphenylphosphine (PPh3)-ligated gold clusters offer promising potential applications due to their relative ease of synthesis and usefulness in forming advanced cluster architectures. While previous studies reported cationic PPh3-ligated gold clusters with core sizes of Au1 - Au4, Au6 - Au11, and Au¬13 - Au14, there has not been definitive identification by mass spectrometry of larger clusters in the Au12 - Au25¬ range. Herein, we survey a polydisperse solution of cationic PPh3-ligated gold clusters using high mass-resolution (M/?M = 60,000) electrospray ionization mass spectrometry (ESI-MS). To improve the sensitivity and mass resolution of larger clusters for unambiguous identification, we increased the number of scan averages and reduced the range of mass collection windows to 200 m/z, thereby mitigating potential mass and ion abundance bias resulting from smaller “building block” gold clusters and other solution components present in higher abundance. In addition to the previously reported clusters, we identified several new species including Au5(PPh3)5+, Au12(PPh3)9HCl2+, Au15(PPh3)9Cl2+, Au16(PPh3)10Cl22+, Au17(PPh3)113+, Au18(PPh3)102+, Au19(PPh3)10Cl2+, Au20(PPh3)12H33+, Au21(PPh3)10Cl2+, and Au22(PPh3)10Cl22+, indicating that a full range of clusters between Au1 - Au22 may be observed in a single polydisperse solution. Considering all of the observed clusters, our findings provide evidence that the “magic number” icosahedral Au13 may be the transition point in cluster growth between smaller clusters, exhibiting a 1:1 gold-to-ligand ratio, and larger clusters, wherein subsequent gold atoms are added to the core without an equal number of accompanying ligands. Our method demonstrates that reducing the range of m/z collection windows and increasing the number of scan averages can improve instrument sensitivity for cationic gold clusters and enable a more complete survey of polydisperse solutions, thereby providing new insights to guide and validate the results of other characterization methods and theoretical calculations. This work was supported by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences. MH acknowledges support from the DOE Science Undergraduate Laboratory Internship (SULI) program. HH acknowledges support from the DOE Office of Workforce Development for Teachers and Scientist (WDTS) under the Visiting Faculty Program (VFP). This work was performed using EMSL, a national scientific user facility sponsored by the DOE's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is a multiprogram national laboratory operated for DOE by Battelle.

Hewitt, Michael↗
DOE OSTI2022

Predicting Future Laboratory Fault Friction Through Deep Learning Transformer Models

Machine learning models using seismic emissions as input can predict instantaneous fault characteristics such as displacement and friction in laboratory experiments, and slow slip in Earth. Here, we address whether the seismic/acoustic emission (AE) from laboratory experiments contains information about future frictional behavior. The approach uses a convolutional encoder-decoder containing a transformer model in the latent space, similar to models used for natural language processing. We test the model limits using progressively larger AE input time windows and progressively larger output friction time windows. The results demonstrate that very near-term friction predictions are indeed contained in the AE signal, and predictions are progressively worse farther into the future. The future predictions by the model of impending failure in the near-term are remarkably robust. This first effort predicting future fault frictional behavior with machine learning will aid in guiding efforts for applications in Earth.

58 GEOSCIENCES↗
DOE OSTI2024

Comment on “Five Decades of Observed Daily Precipitation Reveal Longer and More Variable Drought Events Across Much of the Western United States”

Abstract Changes in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293 ) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293 ) preprocess their annual (or seasonal) averages to compute 5‐year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Exploring the Relative Importance of the MJO and ENSO to North Pacific Subseasonal Predictability

Abstract Here we explore the relative contribution of the Madden‐Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO) to midlatitude subseasonal predictive skill of upper atmospheric circulation over the North Pacific, using an inherently interpretable neural network applied to pre‐industrial control runs of the Community Earth System Model version 2. We find that this interpretable network generally favors the state of ENSO, rather than the MJO, to make correct predictions on a range of subseasonal lead times and predictand averaging windows. Moreover, the predictability of positive circulation anomalies over the North Pacific is comparatively lower than that of their negative counterparts, especially evident when the ENSO state is important. However, when ENSO is in a neutral state, our findings indicate that the MJO provides some predictive information, particularly for positive anomalies. We identify three distinct evolutions of these MJO states, offering fresh insights into opportune forecasting windows for MJO teleconnections.

58 GEOSCIENCES↗
DOE OSTI2024

Empowering Machine Learning Forecasting of Labquake Using Event‐Based Features and Clustering Characteristics

Abstract Following recent advances of machine learning (ML), we present a novel approach to extract spatiotemporal seismo‐mechanical features from Acoustic Emission (AE) catalogs to empower ML‐based forecasting. The AE data were recorded during laboratory stick‐slip experiments on granite samples cut by rough faults. Based on the features computed for a past time window, a random forest (RF) classifier is used to forecast the occurrence of a large magnitude event ( M AE > 3.5) in the next time window. Event‐based features allow us to associate informative time‐space characteristics to each feature and nearest‐neighbor clustering analysis enables us to separate background and clustered seismicity and train individual models. The results show that the separation of AEs enhances the forecasting accuracy from 73.2% for the entire catalog up to 82.1% and 89.0% if background and clustered events are used separately. The presented new approach may be upscaled for applications to forecast tectonic earthquakes.

Karimpouli, Sadegh↗
DOE OSTI2025

Nickase fidelity drives EvolvR-mediated diversification in mammalian cells

Abstract In vivo genetic diversifiers have previously enabled efficient searches of genetic variant fitness landscapes for continuous directed evolution. However, existing genomic diversification modalities for mammalian genomic loci exclusively rely on deaminases to generate transition mutations within target loci, forfeiting access to most missense mutations. Here, we engineer CRISPR-guided error-prone DNA polymerases (EvolvR) to diversify all four nucleotides within genomic loci in mammalian cells. We demonstrate that EvolvR generates both transition and transversion mutations throughout a mutation window of at least 40 bp and implement EvolvR to evolve previously unreported drug-resistantMAP2K1variants via substitutions not achievable with deaminases. Moreover, we discover that the nickase’s mismatch tolerance limits EvolvR’s mutation window and substitution biases in a gRNA-specific fashion. To compensate for gRNA-to-gRNA variability in mutagenesis, we maximize the number of gRNA target sequences by incorporating a PAM-flexible nickase into EvolvR. Finally, we find a strong correlation between predicted free energy changes underlying R-loop formation and EvolvR’s performance using a given gRNA. The EvolvR system diversifies all four nucleotides to enable the evolution of mammalian cells, while nuclease and gRNA-specific properties underlying nickase fidelity can be engineered to further enhance EvolvR’s mutation rates.

Science & Technology - Other Topics↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗