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Disentangling Transient Charge Density and Metal–Ligand Covalency in Photoexcited Ferricyanide with Femtosecond Resonant Inelastic Soft X-ray Scattering
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Disentangling the Role of Surface Chemical Interactions on Interfacial Charge Transport at BiVO 4 Photoanodes
Chemical transformations that occur on photoactive materials, such as photoelectrochemical water splitting, are strongly influenced by the surface properties as well as by the surrounding environment. Herein, we elucidate the effects of oxygen and water surface adsorption on band alignment, interfacial charge transfer, and charge-carrier transport by using complementary Kelvin probe measurements and photoconductive atomic force microscopy on bismuth vanadate. By observing variations in surface potential, we show that adsorbed oxygen acts as an electron-trap state at the surface of bismuth vanadate, whereas adsorbed water results in formation of a dipole layer without inducing interfacial charge transfer. The apparent change of trap state density under dry or humid nitrogen, as well as under oxygen-rich atmosphere, proves that surface adsorbates influence charge-carrier transport properties in the material. The finding that oxygen introduces electronically active states on the surface of bismuth vanadate may have important implications for understanding functional characteristics of water splitting photoanodes, devising strategies to passivate interfacial trap states, and elucidating important couplings between energetics and charge transport in reaction environments.
Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy
Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advancement in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts have been aimed at understanding the effect of microstructures on MHP properties, including chemical heterogeneity, strain disorder, phase impurity, etc. It has been found that grain and grain boundary (GB) are tightly related to lots of microscale and nanoscale behavior in MHP thin films. Atomic force microscopy (AFM) is widely used to observe grain and boundary structures in topography and subsequently to study the correlative surface potential and conductivity of these structures. For now, most AFM measurements have been performed in imaging mode to study the static behavior; in contrast, AFM spectroscopy mode allows us to investigate the dynamic behavior of materials, e.g., conductivity under sweeping voltage. However, a major limitation of AFM spectroscopy measurements is that they require manual operation by human operators, and as such only limited data can be obtained, hindering systematic investigations of these microstructures. Here, we designed a workflow combining the conductive AFM measurement with a machine learning (ML) algorithm to systematically investigate grain boundaries in MHPs. The trained ML model can extract GBs locations from the topography image, and the workflow drives the AFM probe to each GB location to perform a current–voltage (IV) curve automatically. Then, we are able to have IV curves at all GB locations, allowing us to systematically understand the property of GBs. Using this method, we discovered that the GB junction points are less conductive, potentially more photoactive, and can play critical roles in MHP stability, while most previous works only focused on the difference between GB and grains.
Disentangling the Physical Processes Responsible for the Kinetic Complexity in Interfacial Electron Transfer of Excited Ru(II) Polypyridyl Dyes on TiO 2
Interfacial electron transfer at titanium dioxide (TiO 2 ) is investigated herein for a series of surface bound ruthenium-polypyridyl dyes whose metal-to-ligand charge-transfer state (MLCT) energetics are tuned through chemical modification. The 12 complexes are of the form Ru II (bpy-A)(L) 2 2+ , where bpy-A is a bipyridine ligand functionalized with phosphonate groups for surface attachment to TiO 2 . Functionalization of ancillary bipyridine ligands (L) enables the potential of the excited state Ru III/ * couple, E + / *, in 0.1 M perchloric acid (HClO 4 (aq)) to be tuned from –0.69 to –1.03 V vs NHE. Each dye is excited by a 200 fs pulse of light in the visible region of the spectrum and probed with a time-delayed supercontiuum pulse (350–800 nm). Decay of the MLCT excited-state absorption at 376 nm is observed without loss of the ground-state bleach, which is a clear signature of electron injection and formation of the oxidized dye. The dye-dependent decays are biphasic with time constants in the 3–30 and 30–500 ps range. The slower injection rate constant for each dye is exponentially distributed relative to E + / *. The correlation between the exponentially diminishing density of TiO 2 sub-band acceptor levels and injection rate is well described using Marcus–Gerischer theory, with the slower decay components being assigned to injection from the thermally equilibrated state and the faster components corresponding to injection from higher energy states within the 3 MLCT manifold. These results and detailed analyses incorporating molecular photophysics and semiconductor density of states measurements indicate that the multiexponential behavior that is often observed in interfacial injection studies is not due to sample heterogeneity. Rather, this work shows that the kinetic heterogeneity results from competition between excited-state relaxation and injection as the photoexcited dye relaxes through the 3 MLCT manifold to the thermally equilibrated state, underscoring the potential for a simple kinetic model to reproduce the complex kinetic behavior often observed at the interface of mesoporous metal oxide materials.
Pt Particles on a Dynamic TiO 2 Support in Near-Ambient Conditions−Disentangling Size, Pressure, and Support Effects
Platinum particles on reducible oxides are known to form complex and highly dynamic catalyst systems at elevated pressures and temperatures, often adopting active structures that differ from those found at room temperature and under ultrahigh vacuum (UHV). Here, we study the oxidation and structural evolution of subnanometer Pt clusters and nanoparticles supported on rutile TiO 2 (110) across an oxygen pressure range from UHV to 0.1 mbar, using near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS), scanning tunneling microscopy (STM) under UHV and NAP conditions, and low-energy ion scattering (LEIS). Our results reveal distinct differences in oxidation behavior and thermal stability between Pt nanoparticles and clusters, which are further modulated by the support stoichiometry and oxygen pressure. Small Pt clusters become oxidized even at room temperature but are susceptible to accelerated sintering in 0.1 mbar O 2 at elevated temperatures. In contrast, well-crystallized Pt nanoparticles on near-stoichiometric TiO 2 show weaker oxidation. On a reduced, defective TiO 2 support, Pt instead quickly becomes deeply buried by new titania layers, which are formed during support reoxidation. This process appears to result primarily from interactions of the support with the gas phase, unlike the classical, selflimited encapsulation that is induced by the strong metal−support interaction (SMSI). Finally, we address the full complexity of real catalysts in a direct side-by-side comparison of the single-crystalline model system with a Pt-loaded TiO 2 powder catalyst (P25). We conclude that the stoichiometry of the model supports must be carefully chosen and controlled to accurately reproduce the expected state of powder supports during redox reactions.
Disentangling the Microphysical Effects of Fire Particles on Convective Clouds Through A Case Study
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Disentangling the Effects of Temperature, Moisture, and Substrate Availability on Soil CO 2 Efflux
Soil respiration ($R_s$), the largest carbon emission flux in ecosystems, is usually modeled as an empirically parameterized function of temperature, and sometimes water availability. The likely contribution by other factors, such as carbohydrate substrate supply from photosynthesis, has been recognized, but modeling capacity to use this information is limited. Wavelet transformations of the residuals of a seasonal Q 10 temperature response model indicated structure at different temporal scales. In this work, we hypothesize that this indicates the lack of explicit representation of relevant processes in the models. Using cross-spectral analysis, we found that time series of photosynthetically active radiation explained most of the diurnal variation, temperature, explained variability at multiple time scales (diurnal-synoptic), whereas volumetric soil water content correlated with variability in Rs at scales 15-30 days. The results suggest that the time domains of influence for different driving variables of $R_s$ are discrete, and largely nonoverlapping, and represent functional relationships between soil biological activity and its constraints. Analysis of phase angles showed that $R_s$ lagged photosynthetically active radiation by 1.5-3.0 hr. As this time lag was the same in both young and mature trees, with more than fivefold difference in transport distances, we hypothesize that this finding adds to the body of literature that support the pressure-concentration-wave model of carbohydrate availability in plants.
Disentangling the Coupled Atmosphere‐Ocean‐Ice Interactions Driving Arctic Sea Ice Response to CO 2 Increases
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Disentangling the Impact of the COVID‐19 Lockdowns on Urban NO 2 From Natural Variability
Abstract TROPOMI satellite data show substantial drops in nitrogen dioxide (NO 2 ) during COVID‐19 physical distancing. To attribute NO 2 changes to NO x emissions changes over short timescales, one must account for meteorology. We find that meteorological patterns were especially favorable for low NO 2 in much of the United States in spring 2020, complicating comparisons with spring 2019. Meteorological variations between years can cause column NO 2 differences of ~15% over monthly timescales. After accounting for solar angle and meteorological considerations, we calculate that NO 2 drops ranged between 9.2% and 43.4% among 20 cities in North America, with a median of 21.6%. Of the studied cities, largest NO 2 drops (>30%) were in San Jose, Los Angeles, and Toronto, and smallest drops (<12%) were in Miami, Minneapolis, and Dallas. These normalized NO 2 changes can be used to highlight locations with greater activity changes and better understand the sources contributing to adverse air quality in each city.
Disentangling the Effects of Vapor Pressure Deficit and Soil Water Availability on Canopy Conductance in a Seasonal Tropical Forest During the 2015 El Niño Drought
In land models used in Earth system models, vegetation water stress is heuristically related to soil water matric potential, and root water uptake in soil layers is simply proportional to root density to meet the transpiration demand. The efficiency of roots utilizing deeper soil water under water stress cannot be well represented, but it is of critical importance for transpiration regulation and carbon flux. To improve modeling of tropical forest response to drought, a plant hydrodynamic scheme (HYDRO), adapted from a trait-based model in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), has been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to represent the hydraulic dynamics and root water uptake strategy. The adapted model conforms to the big-leaf photosynthesis model in ELM. Vegetation water stress in the model is a function of prognostic leaf water potential and is used to regulate stomatal conductance. Point simulations were performed at two tropical forest sites in the Amazon and Panama. Model sensitivity to the numerical solver, plant hydraulic traits, soil properties, and atmospheric forcing has been evaluated. Our results showed that, even though water stress factors calculated at our study sites using HYDRO are comparable to that simulated by the heuristic water stress model, soil moisture prediction is improved with soil water redistributions through roots. Simulated results using HYDRO is not as sensitive to plant hydraulic traits measured for different tree species as to the soil properties and atmospheric forcing. Simplified numerical solver may cause inaccuracy as to where roots uptake water. This study suggests that plant hydrodynamic representation is critical for improved simulations of soil moisture under water stresses.
Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability
Snow plays a critical role in carbon cycling, vegetation dynamics, and permafrost hydrology at high latitudes by influencing surface energy exchange. Predicting snow distribution patterns is essential for understanding the evolution of Arctic ecosystems, yet scaling process-level knowledge to landscape predictions remains challenging. Here, we analyze snow depth (2019 and 2022), terrain elevation, and vegetation height from a watershed on the Seward Peninsula, Alaska, to examine how topography and shrubs shape snow redistribution across spatial scales. We find that snow depth is strongly coupled to terrain at scales below ∼60 m but becomes increasingly decoupled at larger scales. The topographic model of snow depth variation, which transforms terrain data to align with these scale-dependent snow patterns, is well correlated with local snow depth variations (linear fit R 2 > 0.5 for 85% of 100-m patches). A machine learning reconstruction of shrub canopy snow trapping reveals a simple exponential relationship between canopy structure and snow accumulation ( R 2 = 0.59), highlighting the combined influence of topography and vegetation on snow distribution. Together, these empirical relationships capture much of the observed snow variability in the watershed ( R 2 = 0.49, root mean square error (RMSE) = 30 cm), though systematic limitations persist in areas of strong scour and at coarser scales where wind-terrain interactions are more complex. These findings provide a framework for more efficient snow depth prediction and offer insights to improve snow-vegetation feedback representation in Earth System Models.
Disentangling Cooper-pair formation above the transition temperature from the pseudogap state in the cuprates
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Disentangling conical intersection and coherent molecular dynamics in methyl bromide with attosecond transient absorption spectroscopy
Attosecond probing of core-level electronic transitions provides a sensitive tool for studying valence molecular dynamics with atomic, state, and charge specificity. In this report, we employ attosecond transient absorption spectroscopy to follow the valence dynamics of strong-field initiated processes in methyl bromide. By probing the 3d core-to-valence transition, we resolve the strong field excitation and ensuing fragmentation of the neutral σ* excited states of methyl bromide. The results provide a clear signature of the non-adiabatic passage of the excited state wavepacket through a conical intersection. We additionally observe competing, strong field initiated processes arising in both the ground state and ionized molecule corresponding to vibrational and spin-orbit motion, respectively. The demonstrated ability to resolve simultaneous dynamics with few-femtosecond resolution presents a clear path forward in the implementation of attosecond XUV spectroscopy as a general tool for probing competing and complex molecular phenomena with unmatched temporal resolution.
Disentangling top-down drivers of mortality underlying diel population dynamics of Prochlorococcus in the North Pacific Subtropical Gyre
Photosynthesis fuels primary production at the base of marine food webs. Yet, in many surface ocean ecosystems, diel-driven primary production is tightly coupled to daily loss. This tight coupling raises the question: which top-down drivers predominate in maintaining persistently stable picocyanobacterial populations over longer time scales? Motivated by high-frequency surface water measurements taken in the North Pacific Subtropical Gyre (NPSG), we developed multitrophic models to investigate bottom-up and top-down mechanisms underlying the balanced control of Prochlorococcus populations. Here, we find that incorporating photosynthetic growth with viral- and predator-induced mortality is sufficient to recapitulate daily oscillations of Prochlorococcus abundances with baseline community abundances. In doing so, we infer that grazers in this environment function as the predominant top-down factor despite high standing viral particle densities. The model-data fits also reveal the ecological relevance of light-dependent viral traits and non-canonical factors to cellular loss. Finally, we leverage sensitivity analyses to demonstrate how variation in life history traits across distinct oceanic contexts, including variation in viral adsorption and grazer clearance rates, can transform the quantitative and even qualitative importance of top-down controls in shaping Prochlorococcus population dynamics.
Disentangling plant- and environment-mediated drivers of active rhizosphere bacterial community dynamics during short-term drought
Abstract Mitigating the effects of climate stress on crops is important for global food security. The microbiome associated with plant roots, the rhizobiome, can harbor beneficial microbes that alleviate stress, but the factors influencing their recruitment are unclear. We conducted a greenhouse experiment using field soil with a legacy of growing switchgrass and common bean to investigate the impact of short-term drought severity on the recruitment of active bacterial rhizobiome members. We applied 16S rRNA and 16S rRNA gene sequencing for both crops and metabolite profiling for switchgrass. We included planted and unplanted conditions to distinguish environment- versus plant-mediated rhizobiome drivers. Differences in community structure were observed between crops and between drought and watered and planted and unplanted treatments within crops. Despite crop-specific communities, drought rhizobiome dynamics were similar across the two crops. The presence of a plant more strongly explained the rhizobiome variation in bean (17%) than in switchgrass (3%), with a small effect of plant mediation during drought observed only for the bean rhizobiome. The switchgrass rhizobiome was stable despite changes in rhizosphere metabolite profiles between planted and unplanted treatments. We conclude that rhizobiome responses to short-term drought are crop-specific, with possible decoupling of plant exudation from rhizobiome responses.
Disentangling climate and policy uncertainties for the Colorado River post-2026 operations
Abstract Lakes Mead and Powell in the Colorado River Basin underpin water and hydropower supply for the western United States. While the policies currently regulating the basin will expire by 2026, planning remains challenging due to intertwined climate variability and policy uncertainties. Based on streamflow projections from 10 dynamically downscaled CMIP6 global climate models and unique methods that add and remove internal variability, we evaluate future conditions at Powell and Mead under existing and alternative policies. Due to projected streamflow declines, under existing policy, both reservoirs will face substantial risks (>80% likelihood) of reaching dead pool before 2060. Adopting recently proposed alternative policies reduces but doesn’t eliminate such risks. All policies also exhibit tipping points where reservoir levels can change rapidly with a slight change in streamflow. A sustainable policy may require larger reductions to further reduce the reservoirs’ dead pool risks and provide better buffers from sudden changes.
Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns
Abstract A fast, robust pipeline for strain mapping of crystalline materials is important for many technological applications. Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions, but this technique is limited when the electron beam undergoes multiple scattering. Deep-learning methods have the potential to invert these complex signals, but require a large number of training examples. We implement a Fourier space, complex-valued deep-neural network, FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures, orientations, thicknesses, and microscope parameters, which are augmented with experimental artifacts. We evaluated FCU-Net against simulated and experimental datasets, where it substantially outperforms conventional analysis methods. Our code, models, and training library are open-source and may be adapted to different diffraction measurement problems.