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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A newly characterized malaria antigen on erythrocyte and merozoite surfaces induces parasite inhibitory antibodies

We previously identified a Plasmodium falciparum (Pf) protein of unknown function encoded by a single-copy gene, PF3D7_1134300, as a target of antibodies in plasma of Tanzanian children in a whole-proteome differential screen. Here we characterize this protein as a blood-stage antigen that localizes to the surface membranes of both parasitized erythrocytes and merozoites, hence its designation as Pf erythrocyte membrane and merozoite antigen 1 (PfEMMA1). Mouse anti-PfEMMA1 antisera and affinity-purified human anti-PfEMMA1 antibodies inhibited growth of P. falciparum strains by up to 68% in growth inhibition assays. Following challenge with uniformly fatal Plasmodium berghei (Pb) ANKA, up to 40% of mice immunized with recombinant PbEMMA1 self-cured, and median survival of lethally infected mice was up to 2.6-fold longer than controls (21 vs. 8 d, P = 0.005). Furthermore, high levels of naturally acquired human anti-PfEMMA1 antibodies were associated with a 46% decrease in parasitemia over 2.5 yr of follow-up of Tanzanian children. Together, these findings suggest that antibodies to PfEMMA1 mediate protection against malaria.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Analytical Tools to Assess Polymer Biodegradation: A Critical Review and Recommendations

Many petroleum-derived plastic materials are highly recalcitrant and persistent in the environment, posing significant threats to human and ecological receptors due to their accumulation in ecosystems. In recent years, research efforts have focused on advancing biological methods for polymer degradation. Enzymatic depolymerization has emerged as particularly relevant for biobased plastic recycling, potentially scalable for industrial use. Biodegradation involves adsorption to the plastic solid surface, followed by an interfacial reaction, resulting in cleavage of bonds of polymer chains exposed on the surface. Here, widely varying substrate-specific kinetics are observed, with the polymer’s properties possessing a significant impact on the rate of this interfacial catalysis. Thus, there is a critical need for sensitive and accurate characterization of the material surface during and after interfacial depolymerization to fully understand the reaction mechanisms. Here, we provide a critical review of a range of techniques used in the analysis of material surfaces to characterize the chemical, topological, and morphological features relevant to the study of enzymatic biocatalysis, including microscopy techniques, spectroscopic techniques (e.g., X-ray diffraction analysis, Fourier transform infrared attenuated total reflectance spectroscopy, and mass spectrometry detection of analytes associated with degradation). Techniques for evaluation of surface energy and topology in their relevancy for sensitive detection of biological surface modifications are also discussed. In addition, this paper provides an overview of the strengths of these techniques and compares their performance in both sensitivity and throughput, including emerging techniques, which can be useful, particularly for the rapid analysis of the surface properties of polymeric materials in high-throughput screening of candidate biocatalysts. This research serves as a starting point in selecting and applying appropriate methodologies that provide direct evidence to the ongoing biotic degradation of polymeric materials.

Colachis, Matthew↗
DOE OSTI2020

Aperiodically ordered nano-graphene on the quasicrystalline substrate

Designing exotic structures in low dimensions is key in today's quest to tailor novel quantum states in materials with unique symmetries. Particularly intriguing materials in this regard are low dimensional aperiodic structures with non-conventional symmetries that are otherwise forbidden in translation symmetric crystals. In our work, we focus on the link between the structural and electronic properties of aperiodically ordered aromatic molecules on a quasicrystalline surface, which has largely been neglected so far. As an exemplary case, we investigate the self-assembly and the interfacial electronic properties of the nano-graphene-like molecule coronene on the bulk truncated icosahedral (i) Al–Pd–Mn quasicrystalline surface using multiple surface sensitive techniques. We find an aperiodically ordered coronene monolayer (ML) film on the i-Al–Pd–Mn surface that is characterized by the same local motifs of the P1 Penrose tiling model as the bare i-Al–Pd–Mn surface. The electronic valence band structure of the coronene/i-Al–Pd–Mn system is characterized by the pseudogap of thebare i-Al–Pd–Mn, which persists the adsorption of coronene confirming the quasiperiodic nature of the interface. In addition, we find a newly formed interface state of partial molecular character that suggests an at least partial chemical interaction between the molecule and the quasicrystalline surface. We propose that this partial chemical molecule–surface interaction is responsible for imprinting the quasicrystalline order of the surface onto the molecular film.

36 MATERIALS SCIENCE↗
DOE OSTI2023

In Situ Attenuated Total Reflectance Infrared Spectroelectrochemistry (ATR-IR-SEC) for the Characterization of Molecular Redox Processes on Surface-Proximal Doped Silicon ATR Crystal Working Electrodes

In situ mid-infrared spectroscopy is a powerful technique for understanding the mechanism of CO 2 reduction (CO 2 R) catalysts because it enables the direct detection of catalytic intermediates and products. Moreover, spectroelectrochemistry (SEC), the coupling of spectroscopy with electrochemistry, allows spectroscopic changes to be correlated with applied potentials to reveal potential-dependent intermediates that are often relevant to photoelectrochemical reactions. Hybrid photoelectrodes, comprised of a narrow bandgap semiconductor, like silicon (Si), with a covalently-linked molecular catalyst, are a promising platform for sunlight-driven catalysis, but characterization of the catalytic mechanism(s) is challenging under photoelectrochemical conditions, particularly when the catalyst is present in monolayer or less concentrations. Here, we have developed a new strategy to use multiple-reflection attenuated total reflectance IR spectroscopy (ATR-IR) coupled with electrochemistry to characterize catalysts directly integrated with a semiconductor surface under applied potential. We show that by surface-proximal n-type or p-type doping of the top ~100-200 nm of the crystal surface, Si ATR crystals can be used simultaneously as the internal reflection element and semiconductor working electrode for ATR-IR-SEC measurements. The surface-proximal doping strategy yields a quasi-equipotential surface with excellent infrared transparency that would have been compromised by free carrier absorption if the crystal was uniformly doped. This approach permits the catalytically-active functionalized surface to be directly probed without modification and overcomes signal-to-noise limitations of other strategies that use separately deposited working electrodes on Si ATR crystals. Proof-of-concept ATR-IR-SEC spectra were collected during the reduction and oxidation of monolayers of Re- and Ru-based transition metal carbonyl complexes, respectively, verifying the viability of the technique to probe redox processes associated with CO 2 R catalysts on Si electrode surfaces with high sensitivity.

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

Learning interpretable surface elasticity properties from bulk properties via neural network equation learners

Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.

Equation learning↗
DOE OSTI2020

Enhancing nanostructured nickel-rich lithium-ion battery cathodes via surface stabilization

Layered, nickel-rich lithium transition metal oxides have emerged as leading candidates for lithium-ion battery (LIB) cathode materials. High-performance applications for nickel-rich cathodes, such as electric vehicles and grid-level energy storage, demand electrodes that deliver high power without compromising cell lifetimes or impedance. Nanoparticle-based nickel-rich cathodes seemingly present a solution to this challenge due to shorter lithium-ion diffusion lengths compared to incumbent micrometer-scale active material particles. However, since smaller particle sizes imply that surface effects become increasingly important, particle surface chemistry must be well characterized and controlled to achieve robust electrochemical properties. Moreover, residual surface impurities can disrupt commonly used carbon coating schemes, which result in compromised cell performance. Using x-ray photoelectron spectroscopy, here we present a detailed characterization of the surface chemistry of LiNi0.8Al0.15Co0.05O2 (NCA) nanoparticles, ultimately identifying surface impurities that limit LIB performance. With this chemical insight, annealing procedures are developed that minimize these surface impurities, thus improving electrochemical properties and enabling conformal graphene coatings that reduce cell impedance, maximize electrode packing density, and enhance cell lifetime fourfold. Overall, this work demonstrates that controlling and stabilizing surface chemistry enables the full potential of nanostructured nickel-rich cathodes to be realized in high-performance LIB technology.

Lim, Jin-Myoung↗
DOE OSTI2022

Thermodynamics of electrolyte solutions near charged surfaces: Constant surface charge vs constant surface potential

Electric double layers are ubiquitous in science and engineering and are of current interest, owing to their applications in the stabilization of colloidal suspensions and as supercapacitors. While the structure and properties of electric double layers in electrolyte solutions near a charged surface are well characterized, there are subtleties in calculating thermodynamic properties from the free energy of a system with charged surfaces. These subtleties arise from the difference in the free energy between systems with constant surface charge and constant surface potential. In this work, we present a systematic, pedagogical framework to properly account for the different specifications on charged bodies in electrolyte solutions. Our approach is fully variational—that is, all free energies, boundary conditions, relevant electrostatic equations, and thermodynamic quantities are systematically derived using variational principles of thermodynamics. We illustrate our approach by considering a simple electrolyte solution between two charged surfaces using the Poisson–Boltzmann theory. Our results highlight the importance of using the proper thermodynamic potential and provide a general framework for calculating thermodynamic properties of electrolyte solutions near charged surfaces. Specifically, we present the calculation of the pressure and the surface tension between two charged surfaces for different boundary conditions, including mixed boundary conditions.

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

Nature of Terminating Hydroxyl Groups and Intercalating Water in Ti 3 C 2 T x MXenes: A Study by 1 H Solid-State NMR and DFT Calculations

Since the discovery of two-dimensional transition metal carbides, referred to as MXenes, research efforts targeted their applications in energy storage, as lithium-ion batteries and supercapacitors. This interest is attributable to MXenes’ large volumetric capacitance, high rate handling capability and stable cycling performance, which largely rely on surface chemistry provided by the terminating groups, such as -OH, -O, and -F. However, the atomic-scale characterization of these surface terminations challenges the diffraction methods. Solid-state (SS)NMR spectroscopy, especially 1 H SSNMR, is a promising approach for scrutinizing the surface terminations and the intercalated water in an atomistic-scale, yet only a few SSNMR studies in MXenes have been reported to date offering conflicting results and limited understanding of -OH terminations. Here, we used 1 H SSNMR experiments in concert with the DFT calculations of NMR parameters to identify multiple types of -OH groups, residing on the external and internal surfaces, in a commonly studied MXene, Ti 3 C 2 T x . The study also identifies bulk-like water trapped between the MXene flakes and interfacial water stranded on the surface. Lastly, two-dimensional 1 H -1 H correlation spectra elucidated the water-surface interactions and the mechanism of de-intercalation of water upon annealing.

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

Correlating Surface Processing of Nb Superconducting RF Cavities with the Evolution of Surface Electronic States

Superconducting-radio frequency (RF) cavities provide an efficient way to accelerate particle beams with extremely high acceleration gradients while generating very small power dissipation. The few nanometers of the surface play a critical role in defining the RF performance of superconducting Nb based cavities. Over the past two decades, several pioneering surface treatment and processing methods have emerged, enabling remarkable improvements in superconducting cavity performance by simultaneously achieving high quality factors with increasing maximum acceleration gradients. These processing approaches include chemical polishing, distinct multi-step thermal treatments under ultra-high vacuum (UHV) conditions over low to high temperature regimes, as well as high-temperature treatments under controlled nitrogen atmospheres. Beyond their macroscopic impact on RF performance, these methods produce distinct surface oxide configurations characterized by different valence states, oxide thicknesses, chemical uniformity, and oxygen concentration profiles extending into the near-surface bulk of niobium. In this work, we are trying to understand how the surface-processing methods and the resulting oxide/oxygen profiles affect the electronic structure of surface and the mechanism of superconductivity. Using a combination of X-ray photoemission and X-ray absorption spectroscopies, we investigate how the valence-band structure and the electronic density of states (DoS) near the Fermi level evolve under different surface treatments. By employing tunable photon energies across multiple elemental absorption edges, we use resonant photoemission to disentangle and identify the elemental contributions to specific valence-band features. Our observations show that different surface-processing methods lead to distinct temperature evolutions of the DoS and valence-band states near the Fermi level. Our results suggest variations in different Nb-O orbital hybridizations in distinct processes and point towards the possibility of different underlying mechanisms of superconductivity governed by surface chemistry and oxide configuration. We also correlate these distinct superconducting mechanisms with RF cavity performance, specifically focusing on measured surface resistance, the nature of the Q-slope, and quench fields observed in superconducting RF measurements. These results will enable us to identify the potential limiting factors and relevant controllable parameters that can be further optimized to improve the performance of superconducting RF cavities.

Tripathi, Malvika [Fermilab] (ORCID:00000001989251↗
DOE OSTI2022

MULTI-TECHNIQUE CHARACTERIZATION OF SUPERCONDUCTING MATERIALS FOR PARTICLE ACCELERATOR APPLICATIONS

We investigated the performance limitations of superconducting radio-frequency (SRF) cavities and materials using multiple experimental techniques. In particular, this study focuses on understanding the surface properties of nitrogen-doped Nb cavities and super?conducting thin films with higher Tc such as Nb3Sn. The main goal of this work is to use different techniques to better understand each aspect of the complex loss mechanism in superconductors to further improve the already highly efficient SRF cavities. Nitrogen doping applied to a Nb SRF cavity significantly improves the quality factor Q0 compared to a conventional Nb cavity, at an expense of reduced maximum accelerating gradient. The early quench mechanism was analyzed by using temperature maps before and during the quenching. The temperature maps revealed insignificant heating before the quench, and we concluded that nitrogen doping reduces critical magnetic fields in local regions, leading to premature quenching. To understand the origin of the increasing Q0 with the rf field, the density of states (DOS) of cold spots from nitrogen-doped and standard cavities were measured and analyzed using scanning tunneling microscopy. The results suggested that nitrogen doping reduces the spatial inhomogeneity of superconducting properties and shrinks the metallic suboxide layers, which tunes the DOS in such a way as to produce the field-induced reduction in the surface resistance. To characterize SRF thin films, an experimental setup for measuring a coplanar waveguide (CPW) resonator was developed and tested. A surface impedance measurement of the Nb film showed good agreement with the BCS calculation. The preliminary results from measurements of Nb3Sn and NbTiN films are also presented here. The nonlinear Meissner effect was investigated in Nb3Sn film CPW resonators by mea?suring the resonant frequency as a function of a parallel magnetic field. Contrary to a conventional quadratic dependence of the penetration depth ?(B) on the applied magnetic field B, as expected in s-wave superconductors, nearly a linear increase of ?(B) with B was observed. It was concluded that this behavior of ?(B) is due to weakly linked grain bound?aries on the polycrystalline Nb3Sn films, which can mimic the NLME expected in a clean d-wave superconductor.

Makita, Junki↗
DOE OSTI2024

Enhanced charge carrier extraction and transport with interface modification for efficient tin-based perovskite solar cells

Interface modification improves charge carrier extraction in tin-based perovskite solar cells. Tin-based perovskites have become the most promising non-lead perovskites due to their ideal band gap and low toxicity. Although the open circuit voltage of tin-based perovskite solar cells (TPSCs) continues to approach the theoretical value, the short-circuit current is still far from the theoretical value. Here, we describe an interface modification method by regulating the property of hole transport layer, PEDOT:PSS, which improves the surface molecular morphology and the energy level alignment of PEDOT:PSS/perovskite interface. Advanced GIWAXS and IR s-SNOM characterization are conducted to achieve multi-dimensional characterization of nanoscale surface morphology and chemical distribution of PEDOT:PSS. With the multi-attribute optimization, charge carrier extraction and non-radiative recombination are also improved. The resultant TPSCs exhibit a higher power conversion efficiency of 13.32% in compared with the control device of 10.50%, accompanied with an increase in the short-circuit current from 18.10 to 20.50 mA cm −2 and FF from 68.23% to 76.43%. This work demonstrates a reliable strategy for improving charge carrier extraction and device performance for lead-free TPSCs.

Zhao, Zhenzhu↗
DOE OSTI2026

Correlating surface adsorbate configuration and electrochemical performance of IrO 2 during seawater-relevant electrolysis

Seawater electrolysis alleviates freshwater demand to produce clean hydrogen while eliminating the need for water purification steps. The anodic process, seawater oxidation, typically requires high overpotentials and yields low selectivity to oxygen via the oxygen evolution reaction (OER), primarily due to the competing chlorine evolution reaction (CER) and hypochlorite evolution reaction (HCER) in pH-neutral conditions. Here, combining in situ surface-enhanced Raman characterization, grand canonical density functional theory-based calculations, and kinetic Monte Carlo simulations, we report the evolution of surface adsorbate configurations driven by applied potential and pH during seawater-relevant OER over IrO 2 , a highly OER-active and chloride-corrosion-resistant catalyst. As a result, the chemical properties of active sites, and thereby the kinetics of OER and CER/HCER, are effectively tuned. However, it is revealed that there is no optimal combination of potential and pH to achieve both high activity and high selectivity for seawater-relevant OER. To address this limitation, we establish a correlation between activity/selectivity and surface adsorbate configurations, enabling the optimization of highly active and OER-selective IrO 2 -based catalysts in seawater-relevant oxidation by modulating the local adsorbate environment of active sites.

08 HYDROGEN↗
DOE OSTI2021

Fractional chiral hinge insulator

We propose and study a wave function describing an interacting three-dimensional fractional chiral hinge insulator (FCHI) constructed by Gutzwiller projection of two noninteracting second-order topological insulators with chiral hinge modes at half filling. We use large-scale variational Monte Carlo computations to characterize the model states via the entanglement entropy and charge-spin fluctuations. We show that the FCHI possesses fractional chiral hinge modes characterized by a central charge c = 1 and Luttinger parameter K = 1/2, like the edge modes of a Laughlin 1/2 state. The bulk and surface topology is characterized by the topological entanglement entropy (TEE) correction to the area law. While our computations indicate a vanishing bulk TEE, we show that the gapped surfaces host an unconventional two-dimensional topological phase. In a clear departure from the physics of a Laughlin 1/2 state, we find a TEE per surface compatible with (ln √2 )/2, half that of a Laughlin 1/2 state. This value cannot be obtained from topological quantum field theory for purely two-dimensional systems. For the sake of completeness, we also investigate the topological degeneracy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Surface Oxidation Behavior of FeNi-based Metal Amorphous Nanocomposite (MANC) Alloys for High-Speed Motor Applications

New interest in high performance soft magnetic materials (SMMs) have been fueled by the need to lower losses at higher operating frequencies while maintaining high flux density and tunable permeability in electrical motors, transformer, and generator applications. Conventional SMMs like electrical steels and Fe-based metal amorphous nanocomposite (MANC) alloys are dominated by eddy current losses at high frequencies. Recent breakthrough in high-performance FeNi MANC have shown promise in reducing eddy current losses as compared to electrical steels. Their intrinsic adherent native surface oxide layer provides sufficient electrical insulation to reduce interlaminate eddy current losses. However, notwithstanding advances in MANCs, there exists a gap in literature on investigations of the surface oxide layer responsible for significant reduction of interlaminate eddy current losses in magnetic cores. This work presents a detailed characterization of the surface oxide, oxidation behavior, and relationship between oxide thickness and resistivity of a new FeNi MANC alloy (Fe70Ni30)80Nb4B14Si2.

Egbu, James↗
DOE OSTI2023

Multifunctional Effect of Fe Substitution in Na Layered Cathode Materials for Enhanced Storage Stability

In this study, developing stable cathode materials that are resistant to storage degradation is essential for practical development and industrial processing of Na-ion batteries, as many sodium layered oxide materials are susceptible to hygroscopicity and instability when exposed to ambient air. Among the various layered compounds, Fe-substituted O 3 -type Na(Ni 1/2 Mn 1/2 ) 1-x Fe x O 2 materials have emerged as a promising option for high-performance and low-cost cathodes. While previous reports have noted the decent air-storage stability of these materials, the role and origin of Fe substitution in improving storage stability remain unclear. In this study, we investigate the air resistant effect of Fe substitution in O 3 -Na(Ni 1/2 Mn 1/2 ) 1-x Fe x O 2 cathode materials by performing systematic surface and structural characterizations. We find that the improved storage stability can be attributed to the multifunctional effect of Fe substitution, which forms a surface protective layer containing an Fe-incorporated spinel phase and decreases the thermodynamical driving force for bulk chemical sodium extraction. With these mechanisms, the Fe-containing cathodes can suppress the cascades of cathode degradation processes and better retain the electrochemical performance after air storage.

25 ENERGY STORAGE↗
DOE OSTI2022

Fabrication and Characterization of Iron-Based Catalysts for the Dehydrogenation of Fossil Fuels

For a prosperous and sustainable future, hydrogen is an encouraging solution due to its simple transition for industrial decarbonization and synergy for economic development. Paradoxically, current hydrogen production pathways release substantial amount of greenhouse gases into the atmosphere contributing to climate change. To keep up with increasing demand, hydrogen could be produced through microwave-assisted thermocatalytic dehydrogenation of fossil fuels without emitting carbon dioxide. This requires specified catalysts to meet the requirements of hydrogen yield and selectivity. The objective of the present research is to fabricate, characterize, and compare iron-based alumina (FeAlxOy) catalysts produced via solution combustion synthesis and iron-based catalysts on silicon carbide support (Fe/SiC) produced via incipient wetness impregnation. Heat mode, fuel type, and oxidizer mole ratio were varied for FeAlxOy catalysts, and metal loading was varied for Fe/SiC catalysts. Each resultant product was characterized by X-ray diffraction analysis, scanning electron microscopy, laser diffraction particle size analysis, and Brunauer-Emmett-Teller (BET) surface area analysis. Characterization suggests that fabrication of iron-based alumina nanocomposites through solution combustion synthesis in a muffle furnace using citric acid as the fuel yields the most promising catalyst.

Reyes, Victoria Isabel↗
DOE OSTI2025

Error field measurements and correction on MUSE permanent magnet stellarator

The magnetic field topology and error fields are measured in the MUSE permanent magnet stellarator with an electron beam and fluorescent rod. The effective magnetization of the magnets is determined by measuring rational flux surface locations. Electron magnetic drift effects must be considered self-consistently due to the small magnetic shear on MUSE. Error fields are characterized by flux surface shape deviation and magnetic island chain width and phase. Error fields are then corrected by adjusting the permanent magnet (PM) holders as rigid bodies. Resonant error fields are on the order of or smaller than 3 x 10 -6 of the toroidal field after correction and surface shape matches well with the design. Resonant components of the quasi-symmetry deviation are estimated, using a novel magnetic drift measurement, to be in the 10 −4 range. The PM approach is validated as a low-cost and precise way to build optimized stellarators.

electron beam mapping↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗