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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Elucidation of Marcus Relationships for Hydride Transfer Reactions Involving Transition Metal Hydrides

The rate of hydride transfer from three Ir hydride complexes of the type Cp*Ir( R bpy)H + (Cp* = C 5 Me 5 ; R bpy = 4,4′-R-2,2′-bipyridine, R = OMe, H, CO 2 Me) to six N-methylacridinium ( R Acr + ) acceptors with electronically different substituents in the 2- or 2,7-positions were measured. Using the thermodynamic hydricity of the donors and the hydride affinity of the acceptors the thermodynamic driving forces for hydride transfer were determined. Brønsted plots, which correlate kinetic and thermodynamic hydricity, demonstrate distinct linear free energy relationships for each complex, with different Brønsted α values. Thus, at the same driving force hydride transfer from Cp*Ir( OMe bpy)H + is faster than for Cp*Ir(bpy)H + or Cp*Ir( CO2Me bpy)H + . Experimental and computational analyses are consistent with a concerted hydride transfer mechanism for all Ir complexes. As the thermodynamic driving force increases an earlier transition state is observed and all transition states also include π-stacking interactions between the donor and acceptor, which likely contribute to the different α values. The experimental data fits well to the Marcus model, enabling the determination of reorganization energies (λ) that range from 58 to 69 kcal mol -1 . These are lower than λ values for hydride transfer reactions involving organic donors and acceptors. This work provides a rare example of the correlation of kinetic and thermodynamic hydricity using only experimental data and shows that hydride transfer reactions involving metal hydrides can follow Marcus theory. Furthermore, the findings offer insight into controlling metal-catalyzed hydride transfer reactions, which is valuable for designing improved systems for a range of transformations.

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

Revealing Solvent-Assisted Li+ Transport in the Solid Electrolyte Interphase operando

The performance of energy-dense lithium metal batteries is critically influenced by the properties of the solid electrolyte interphase (SEI). Yet, progress in understanding this layer has been limited by the lack of accurate operando characterization because the SEI evolves dynamically during cycling. Here, we apply dynamic electrochemical impedance spectroscopy (dEIS) to resolve the real-time evolution of the SEI on lithium metal in ether-based electrolytes with varying degrees of fluorination. We find that faster stabilization of the compact SEI resistance correlates with improved passivation and higher Coulombic efficiency. Unexpectedly, compact SEI resistance correlates directly with Li+ solvation energy, revealing that weaker Li+ solvation increases not only bulk but also interphase resistance. These findings challenge the conventional view of the SEI as a purely solid-phase conductor and instead support a solvent-assisted Li+ transport mechanism within the compact SEI. This framework emphasizes the need to balance SEI ionic conductivity with the Li+ solvation environment to maximize lithium metal battery performance.

Sacci, Robert [ORNL] (ORCID:0000000200735221)↗
DOE OSTI2026

Distinct Kinetic Signatures of Photodesorption from Metal Nanoparticles

Visible photon fluxes can influence the rate and selectivity of heterogeneously catalyzed reactions on metal nanoparticle surfaces. Models describing the influence of photon fluxes have typically introduced photon flux dependent apparent thermal kinetic parameters (reaction orders, activation energies, binding energies, etc.). This has relied on empirical fitting of reaction rate data, making mechanistic interpretations of how photon fluxes influence elementary step rates challenging and inconsistent with fundamental descriptions of photochemistry on metal surfaces developed from surface science studies. Using the CO adsorption–desorption quasi-equilibrium reaction on Pt/Al 2 O 3 catalysts as a model system, we measured steady state adsorbed CO (CO*) coverages under isothermal and isobaric (1 mbar CO) conditions as a function of temperature (473–573 K) and of 440 nm photon flux ((0.1–5.2) × 10 3 # hv Pt site –1 s –1 ) using in situ IR spectroscopy. Steady state CO* coverage on Pt was photon flux dependent with increasing photon flux causing decreasing coverage, consistent with photons driving CO* desorption rates faster than thermal CO* desorption rates. However, photon flux dependent CO* coverages were essentially temperature independent, inconsistent with models that describe photon effects using perturbations to apparent thermal kinetic parameters. Instead, 120 steady state CO* coverages as a function of temperature and photon flux are quantitatively described by a kinetic model in which the overall desorption rate is a summation of independent thermal and photon induced CO* desorption rates. Site-resolved analysis reveals distinct kinetic parameters for photon driven desorption of CO* from well-coordinated, under-coordinated, and highly under-coordinated Pt sites, with temperature-dependent apparent quantum efficiencies (AQE) consistent with temperature dependence of vibrational quanta distribution of adsorbed CO. The rigorous kinetic rate laws for independent photon and thermal driven pathways allow for predictive modeling of the influence of photon fluxes on the rates of CO* desorption under catalytic conditions. Further, the analysis provides evidence that steady state continuous wave photon fluxes can drive desorption/adsorption reactions on metal surfaces out of thermal equilibrium, reconciling surface science observations of molecular photodesorption with applied catalysis. The work establishes a general kinetic framework to be considered for photon driven processes on metals, and defines catalyst, reaction, and photon flux characteristic design principles for breaking Sabatier limitations.

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

Understanding the Performance Gap between Polycrystalline and Single-Crystal Nickel-Rich Layered Oxide Cathodes

Singe-crystal (SC) nickel-rich layered oxide cathodes, composed of boundary-free particles with high tap density, offer significant advantages in volumetric energy density and mechanical strength compared with polycrystalline (PC) cathode materials. However, as the nickel content increases (≥80%), SC Ni-rich cathodes often suffer from faster performance degradation than PC cathodes of the same composition, and the underlying causes of this discrepancy remain poorly understood. Herein, we reveal the distinct Ni redox behaviors that govern the electrochemical performance of SC and PC Ni-rich cathodes using multiscale and operando characterization techniques. Our results indicate that the increasingly heterogeneous Ni oxidation process in SC cathodes leads to the additional irreversible oxygen redox activity that deteriorates both the mechanical and chemical structures. In contrast, PC cathodes, despite with more pronounced surface reconstruction, exhibit greater chemomechanical stability due to homogeneous redox reactions during charging. Consequently, we find that bulk degradation, more than surface reactions, ultimately leads to fast capacity decay of SC Ni-rich cathodes during cycling. In conclusion, this work offers a comprehensive view on the impact of Ni redox evolutions on the chemomechanical stability in Ni-rich layered oxide cathodes, providing new insights into the longstanding performance gap between SC and PC cathodes, and guiding the rational design of Ni-rich cathode architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Surface-Functionalized, Two-Dimensional Polymer Electrochromic Layers as Ultrafast, Multi-State Infrared Optical Gates

Electrochromic devices have widespread application potential, but the currently available switching speeds limit broad real-world implementation of this technology. Here, we report surface-engineered two-dimensional polymers with ionophilic pores that offer unprecedented switching speeds in solid-state, two-terminal, electrochromic devices. In particular, we demonstrate that a crystalline donor–acceptor 2DP functionalized with ethylene glycol oligomers exhibits multistate infrared absorption that is 4× faster (t c = 320 ms) with 3× coloration efficiency (491 cm 2 C –1 ) compared to an alkyl functionalized 2DP constructed from the same chromophores. The functionalized nanoporous surfaces enable rapid switching in these materials under either oxidative or reductive conditions, allowing us to access a range of robust, stable optical responses in a single electrochromic layer. These attributes led us to leverage surface-functionalized 2DPs as multistate infrared logic gates. Collectively, this work demonstrates that surface engineering of nanoporous crystalline lattices is a promising approach to co-optimize the electronic and ionic conductivities required to achieve rapidly switchable electrochromic layers. Beyond speed and efficiency, the demonstration of multistate infrared characteristics shows that electrochromic frameworks are useful in integrated optoelectronic circuits. This positions surface-engineered 2DPs as improved electrochromic coatings and a new material platform for photonic information processing and adaptive devices.

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

A Mesopore-Confined and Graphene Oxide-Localized Ruthenium Catalyst Increases Rates of Mid-Chain Polyolefin Hydrogenolysis

A catalyst architecture with mesoporous silica coating Ru nanoparticles on reduced graphene oxide (mSiO 2 /Ru/rGO) localizes the 2 nm Ru solely at the closed bottoms of 2.9 nm diameter mesopores. mSiO 2 /Ru/rGO catalyzes the rapid, selective hydrogenolysis of polyolefins at wax formation rates (ν wax ) up to 1700 g wax ·g Ru –1 ·h –1 and a turnover frequency (TOF) for C–C bond cleavage of 130 ± 8 min –1 . The ν wax and TOF for mSiO 2 /Ru/rGO are 23× and 16× those of nonporous Ru/rGO, respectively, indicating that faster chain cleavage is also more selective inside mesopores. The methane yield from mSiO 2 /Ru/rGO is ca. 30% of the value obtained from Ru/rGO. Methane decreases, and wax selectivity improves with narrow-pore (2.3 nm) mSiO 2 /Ru/rGO. The reaction rate decreases at lower and higher H 2 pressures from its maximum at 37 bar. Log(TOF)-log(P H2 ) plots reveal rates ∝ [P H2 ] 2.8 or [P H2 ] −2.0 in the lower or higher pressure ranges, respectively. Corresponding plots for Ru/C give rates ∝ [P H2 ] 1 or [P H2 ] −2.4 . Ru is hydrocarbon-covered at low pressure; thus, the higher H 2 order for mSiO 2 /Ru/rGO indicates that mesopores increase the density of cleavable unsaturated moieties on the Ru surface. The lower H 2 order on Ru/C implies a lower density of cleavable groups because saturated segments occupy a larger fraction of the Ru surface. Higher surface occupancy by the hydrocarbon reactant correlates with more methane formation. Therefore, pore confinement, narrower pore diameter in mSiO 2 /Ru/rGO, or higher H 2 pressure leads to lower methane yields. Ru nanoparticles in mSiO 2 /Ru/rGO maintain equivalent activity and selectivity over five recycling tests.

Catalysts↗
DOE OSTI2026

Metal-Dependent Photodissociation of Hydrazone Photoswitches from Rare-Earth Complexes

Rare-earth element separation processes often rely on a small decrease in ionic radii along the series of elements. Separation processes based on the distinct optical properties of REs remain less explored, although photochemical methods may offer a viable alternative. Accurate selection of the synthetic precursors of a photoswitchable acylhydrazonic ligand led to a system that could quantitatively isomerize (E−to−Z) upon irradiation with commercial LED lights. Coordination of the photoswitch with RE III nitrates (RE = La−Lu except Pm and Y) resulted in the retention of the photoswitching properties observed in solution. The lower binding affinity of the generated Z−isomer with RE III ions yielded the dissociation of the complexes upon irradiation (photodissociation) with the release of RE−nitrates in solution. The rate of the reaction was found to be dependent on the optical properties of the RE III ions, with nonemissive complexes (no 4f excited states) dissociating faster than emissive ones (having accessible 4f excited states). A thorough solid-state characterization of the complexes was performed by using crystallographic and photochemical methods. Ultimately, the accessibility of the 4f excited states of the metals following light irradiation and excitation of the ligand led to a decrease in the rate of the reaction due to quenching of the ligand excited state. These results demonstrate that the direct modulation of the metal coordination environment, combined with the metal-dependent reaction rate, could provide a strategy for the development of RE−separation processes based on differences in their optical properties.

Ions↗
DOE OSTI2026

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li + , by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH – dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li + -coupled electron transfer along the PAN backbone, causing ∼10 4 times faster sequential ring-formation of the remaining nitriles. PAN’s extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

Chahal-Crockett, Rajni [Oak Ridge National Laborat↗
DOE OSTI2026

Selective Reduction of Carbon Dioxide in Water Using [M(bpy2+)(CO)3(I)]2+ (M = Mn, Re) Electrocatalysts with Pendent Cations

Manganese(I) carbonyl complexes are promising electrocatalysts for CO2 reduction, yet their application in homogeneous aqueous media remains limited by poor solubility and selectivity. Here, we report water-soluble Mn(I) and Re(I) complexes fac-[M(bpy2+)(CO)3X]2+ (X = I or Cl), featuring bipyridine ligands functionalized with -Ph-CH2-(NMe3)+ cationic ammonium groups that integrate water solubility with secondary-sphere stabilization. In bicarbonate buffer at pH 6.8, the Mn catalyst is completely selective for CO production at a low overpotential (η = 0.3 V), operating by a protonation-first mechanism with observed rates of ~10 s−1. Pulse radiolysis reveals that the one-electron reduced Mn species undergoes dimerization in the absence of CO2 but uniquely reacts competitively with CO2 through an initial pre-equilibrium followed by fast formation of a dinuclear CO2-bridged species (ΔGo = −12.4 kcal mol−1). At a higher 0.6 V over-potential, a faster reduction-first pathway (~100 s−1) is available upon reduction of the metallo-carboxylic acid intermediate, Mn-CO2H2+; however, this regime is functionally limited by the formation of a resistive, noncatalytic film on the electrode surface. Comparison to the analo-gous water-soluble Re catalyst (kobs = 440 s−1, η = 0.6 V) highlights the distinct mechanistic ad-vantages of earth-abundant Mn in low-potential catalysis. These results demonstrate how cati-onic second-sphere design enables selective, homogeneous CO2 reduction in water while reveal-ing competing radical and electrode-mediated processes that govern catalytic performance.

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

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2025

Unsteady Land-Sea Breeze Circulations in the Presence of a Synoptic Pressure Forcing

Unsteady land-sea breezes (LSBs) that result from time-varying surface temperature contrasts Δθ(t) are explored in the presence of a constant synoptic pressure forcing, M g , oriented from sea to land (α = 0°) or land to sea (α = 180°). Large eddy simulations reveal the development of four distinctive regimes, depending on the joint interaction between M g , α, and Δθ(t) in modulating the fine-scale dynamics. Time lags, computed as the shifts that maximize correlation coefficients of the velocity between the unsteady and the corresponding steady scenarios at Δθ = Δθ max , are found to be significant and to extend 2 hr longer for α = 0° compared to α = 180°. These diurnal dynamics result in nonequilibrium conditions that are significantly affected by the flow history, and that behave differently over the two patches for the different α’s. Turbulence is found to be out of equilibrium with the mean flow, and the mean itself is found to be out of equilibrium with the thermal forcing. The sea surface heat flux is consistently more sensitive than its land counterpart to the time-varying external forcing Δθ(t), and more so for synoptic forcing from land to sea (α = 180°). Hence, although the land reaches equilibrium faster, the sea patch is found to exert a stronger control on the turbulence-mean flow equilibrium response. Finally, the vertical velocity profile at the shore and shore-normal velocity transects at the first grid level are shown to encode the multiscale regimes of the LSBs evolution and can thus be used to identify these regimes using k-means clustering.

58 GEOSCIENCES↗
DOE OSTI2024

Phenology of Photosynthesis in Winter‐Dormant Temperate and Boreal Forests: Long‐Term Observations From Flux Towers and Quantitative Evaluation of Phenology Models

Abstract We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (Start GPP and End GPP ) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO 2 to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d −1 ) than ENF (1.10% d −1 ), and their active season length (End GPP –Start GPP ) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in Start GPP , End GPP , or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier Start GPP (1.3–2.5 days °C −1 ) or later End GPP (1.5–1.8 days °C −1 , depending on forest type and month). Finally, we tested whether existing phenological models could predict Start GPP and End GPP . For ENF forests, air temperature‐ and daylength‐based models provided best predictions for Start GPP , while a chilling‐degree‐day model was best for End GPP . The root mean square errors (RMSE) between predicted and observed Start GPP and End GPP were 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days).

Environmental Sciences & Ecology↗
DOE OSTI2024

Simulating Global Terrestrial Carbon and Nitrogen Biogeochemical Cycles With Implicit and Explicit Representations of Soil Microbial Activity

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux

Abstract In this work, we address the computational challenge of large‐scale physics‐based simulation models for the ring current. Reduced computational cost allows for significantly faster than real‐time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring current, valuable for space weather monitoring and mitigation efforts. Additionally, it can also be used for a comprehensive investigation of the system. Thus, we aim to create an emulator for the Ring current‐Atmosphere interactions Model with Self‐Consistent magnetic field (RAM‐SCB) particle flux that not only improves efficiency but also facilitates forecasting with reliable estimates of prediction uncertainties. The probabilistic emulator is built upon the methodology developed by Licata and Mehta (2023), https://doi.org/10.1029/2022sw003345 . A novel discrete sampling is used to identify 30 simulation periods over 20 years of solar and geomagnetic activity. Focusing on a subset of particle flux, we use Principal Component Analysis for dimensionality reduction and Long Short‐Term Memory (LSTM) neural networks to perform dynamic modeling. Hyperparameter space was explored extensively resulting in about 5% median symmetric accuracy across all data sets for one‐step dynamic prediction. Using a hierarchical ensemble of LSTMs, we have developed a reduced‐order probabilistic emulator (ROPE) tailored for time‐series forecasting of particle flux in the ring current. This ROPE offers accurate predictions of omnidirectional flux at a single energy with no pitch angle information, providing robust predictions on the test set with an error score below 11% and calibration scores under 8% with bias under 2% providing a significant speed up as compared to the full RAM‐SCB run.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗
DOE OSTI2025

Glacial‐Interglacial and Millennial‐Scale Changes in Nitrous Oxide Emissions Pathways and Source Regions

During the transition from the Last Glacial Maximum (LGM) to the Holocene, the atmospheric N 2 O mole fraction increased by 80 nmol mol −1 . Using ice core measurements of N 2 O isotopomer ratios, we show that this increase was driven by increases in both nitrification and denitrification, with the relative partitioning between both production pathways depending on the assumed isotopic end-member source signatures. Similarly, we also attribute a 35 nmol mol −1 N 2 O mole fraction increase during the Heinrich Stadial 4/Dansgaard Oeschger 8 (HS4/DO8) millennial-scale event to increases in both N 2 O production pathways. In contrast, the 25 nmol mol −1 N 2 O mole fraction decrease during the Younger Dryas was driven almost exclusively by a decrease in nitrification. The deglacial and HS4/DO8 increases in N 2 O production occurred in both marine and terrestrial environments, with the terrestrial source responding faster to warming by about two centuries. Constraints on changes in nitrification and denitrification emissions are robust and consistent with previous studies showing the sensitivity of N 2 O emissions to abrupt Northern Hemisphere warming. This study demonstrates for the first time the importance of both denitrification and nitrification pathways in driving source changes. Absolute emissions are more uncertain due to uncertainty about source isotopomer signatures. For instance, the contribution of denitrification to emissions at the LGM shifts from (65 ± 10) % to (91 ± 6) % when factoring in isotope enrichment due to partial reduction of N 2 O to N 2 during denitrification. Reducing uncertainty in source signatures will increase the power of ice core N 2 O isotope records in deducing environmental change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Rapid Acceleration Bursts in the Van Allen Radiation Belt

Abstract The fast Van Allen radiation belt electron dynamics during geomagnetic storms have not yet been fully explained, in part due to limitations of standard satellite missions in both orbit and the number of spacecraft. Here we overcome these limitations using measurements from the Global Positioning System (GPS) constellation during an acceleration event on 26 August 2018. We show that the acceleration of relativistic electrons occurs in two distinct bursts, each dominated by a different acceleration mechanism. The first burst enhances the radiation belt electrons by four orders of magnitude in 2 hr and is consistent with ULF‐wave radial diffusion. The second burst is likely caused by the local acceleration and delivers an order‐of‐magnitude increase in 20 min. This work demonstrates how distributed, operational measurements can be used to resolve phenomena not observable with previous capabilities, and that rapid energization of the radiation belt can occur much faster than previously reported.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Air‐Ice‐Ocean Coupling During a Strong Mid‐Winter Cyclone: Observing Coupled Dynamic Interactions Across Scales

Abstract Arctic cyclones are key drivers of sea ice and ocean variability. During the 2019–2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, joint observations of the coupled air‐ice‐ocean system were collected at multiple spatial scales. Here, we present observations of a strong mid‐winter cyclone that impacted the MOSAiC site as it drifted in the central Arctic pack ice. The sea ice dynamical response showed spatial structure at the scale of the evolving and translating cyclonic wind field. Internal ice stress and ocean stress play significant roles, resulting in timing offsets between the atmospheric forcing and the ice response and post‐cyclone inertial ringing in the ice and ocean. Ice motion in response to the wind field then forces the upper ocean currents through frictional drag. The strongest impacts to the sea ice and ocean from the passing cyclone occur as a result of the surface impacts of a strong atmospheric low‐level jet (LLJ) behind the trailing cold front and changing wind directions between the warm‐sector LLJ and post cold‐frontal LLJ. Impacts of the cyclone are prolonged through the coupled ice‐ocean inertial response. Local impacts of the approximately 120 km wide LLJ occur over a 12 hr period or less and at scales of a kilometer to a few tens of kilometers, meaning that these impacts occur at combined smaller spatial scales and faster time scales than most satellite observations and coupled Earth system models can resolve.

Meteorology & Atmospheric Sciences↗