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Although interfacial solvation plays an important role in determining carbon dioxide reduction (CO₂R) kinetics, present understanding of the potential dependent properties of the electrochemical double layer under conditions relevant for CO₂R remains limited. This article summarizes the development and recent applications of plasmon‐enhanced vibrational sum frequency generation (VSFG) spectroscopy to study the effects of cation hydration and interfacial solvation on CO₂R using CO as a vibrational Stark reporter. Results show that electrolyte cations retain their entire solvation shell upon adsorption to inactive sites, while active sites retain only a single water layer between the gold surface and the cation. Measurements also show that the total interfacial electric field can be separated into two contributions: one from the electrochemical double layer (Stern field) and another from the polar solvation environment (Onsager field). Surprisingly, correlating VSFG spectra with reaction kinetics reveals that it is the solvation‐mediated Onsager field that governs the chemical reactivity at the electrode/electrolyte interface. Measuring the interfacial water spectra during electrocatalysis also provides evidence for the proton source during H₂evolution, which competes with CO₂R in aqueous electrolyte. These findings highlight the importance of directly probing cation hydration and interfacial solvation, which mediates reaction kinetics at electrochemical interfaces.

 

Submesoscale currents and internal gravity waves achieve an intense turbulent cascade near the ocean surface [depth of 0–O(100) m], which is thought to give rise to significant energy sources and sinks for mesoscale eddies. Here, we characterize the contributions of nonwave currents (NWCs; including eddies and fronts) and internal gravity waves (IGWs; including near-inertial motions, lee waves, and the internal wave continuum) to near-surface submesoscale turbulence in the Drake Passage. Using a numerical simulation, we combine Lagrangian filtering and a Helmholtz decomposition to identify NWCs and IGWs and to characterize their dynamics (rotational versus divergent). We show that NWCs and IGWs contribute in different proportions to the inverse and forward turbulent kinetic energy cascades, based on their dynamics and spatiotemporal scales. Purely rotational NWCs cause most of the inverse cascade, while coupled rotational–divergent components of NWCs and coupled NWC–IGWs cause the forward cascade. The cascade changes direction at a spatial scale at which motions become increasingly divergent. However, the forward cascade is ultimately limited by the motions’ spatiotemporal scales. The bulk of the forward cascade (80%–95%) is caused by NWCs and IGWs of small spatiotemporal scales (L< 10 km;T< 6 h), which are primarily rotational: submesoscale eddies, fronts, and the internal wave continuum. These motions also cause a significant part of the inverse cascade (30%). Our results highlight the requirement for high spatiotemporal resolutions to diagnose the properties and large-scale impacts of near-surface submesoscale turbulence accurately, with significant implications for ocean energy cycle study strategies.

 

Extreme ultraviolet (XUV) light sources based on high harmonic generation are enabling the development of novel spectroscopic methods to help advance the frontiers of ultrafast science and technology. In this account we discuss the development of XUV-RA spectroscopy at near grazing incident reflection geometry and highlight recent applications of this method to study ultrafast electron dynamics at surfaces. Measuring core-to-valence transitions with broadband, femtosecond pulses of XUV light extends the benefits of x-ray absorption spectroscopy to a laboratory tabletop by providing a chemical fingerprint of materials, including the ability to resolve individual elements with sensitivity to oxidation state, spin state, carrier polarity, and coordination geometry. Combining this chemical state sensitivity with femtosecond time resolution provides new insight into the material properties that govern charge carrier dynamics in complex materials. It is well known that surface dynamics differ significantly from equivalent processes in bulk materials, and that charge separation, trapping, transport, and recombination occurring uniquely at surfaces governs the efficiency of numerous technologically relevant processes spanning photocatalysis, photovoltaics, and information storage and processing. Importantly, XUV-RA spectroscopy at near grazing angle is also surface sensitive with a probe depth of 3 nm, providing a new window into electronic and structural dynamics at surfaces and interfaces. Here we highlight the unique capabilities and recent applications of XUVRA spectroscopy to study photo-induced surface dynamics in metal oxide semiconductors, including photocatalytic oxides (Fe2O3, Co3O4 NiO, and CuFeO2) as well as photoswitchable magnetic oxide (CoFe2O4). We first compare the ultrafast electron self-trapping rates via small polaron formation at the surface and bulk of Fe2O3 where we note that the energetics and kinetics of this process differ significantly at the surface. Additionally, we demonstrate the ability to systematically tune this kinetics by molecular functionalization, thereby, providing a route to control carrier transport at surfaces. We also measure the spectral signatures of charge transfer excitons with site specific localization of both electrons and holes in a series of transition metal oxide semiconductors (Fe2O3, NiO, Co3O4). The presence of valence band holes probed at the oxygen L1-edge confirms a direct relationship between the metal-oxygen bond covalency and water oxidation efficiency. For a mixed metal oxide CuFeO2 in the layered delafossite structure, XUV-RA reveals that the sub-picosecond hole thermalization from O 2p to Cu 3d states of CuFeO2 leads to the spatial separation of electrons and holes, resulting in exceptional photocatalytic performance for H2 evolution and CO2 reduction of this material. Finally, we provide an example to show the ability of XUV-RA to probe spin state specific dynamics in a the photo-switchable ferrimagnet, cobalt ferrite (CoFe2O4). This study provides a detailed understating of ultrafast spin switching in a complex magnetic material with site-specific resolution. In summary, the applications of XUV-RA spectroscopy demonstrated here illustrate the current abilities and future promise of this method to extend molecule-level understanding from well-defined photochemical complexes to complex materials so that charge and spin dynamics at surfaces can be tuned with the precision of molecular photochemistry. 

Nanoparticle catalysts display optimal mass activity due to their high surface to volume ratio and tunable size and structure. However, control of nanoparticle size requires the presence of surface ligands, which significantly influence catalytic performance. In this work, we investigate the effect of dodecanethiol on the activity, selectivity, and stability of Au nanoparticles for electrochemical carbon dioxide reduction (CO 2 R). Results show that dodecanethiol on Au nanoparticles significantly enhances selectivity and stability with minimal loss in activity by acting as a CO 2 -permeable membrane, which blocks the deposition of metal ions that are otherwise responsible for rapid deactivation. Although dodecanethiol occupies 90% or more of the electrochemical active surface area, it has a negligible effect on the partial current density to CO, indicating that it specifically does not block the active sites responsible for CO 2 R. Further, by preventing trace ion deposition, dodecanethiol stabilizes CO production on Au nanoparticles under conditions where CO 2 R selectivity on polycrystalline Au rapidly decays to zero. Comparison with other surface ligands and nanoparticles shows that this effect is specific to both the chemical identity and the surface structure of the dodecanethiol monolayer. To demonstrate the potential of this catalyst, CO 2 R was performed in electrolyte prepared from ambient river water, and dodecanethiol-capped Au nanoparticles produce more than 100 times higher CO yield compared to clean polycrystalline Au at identical potential and similar current. 

Incomplete or inconsistent temporal neuroimaging records of patients over time pose a major challenge to accurately predict clinical scores for diagnosing Alzheimer’s Disease (AD). In this paper, we present an unsupervised method to learn enriched imaging biomarker representations that can simultaneously capture the information conveyed by all the baseline neuroimaging measures and the progressive variations of the available follow-up measurements of every participant. Our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show improved performance in predicting cognitive outcomes thereby demonstrating the effectiveness of our proposed method.