Single-Entity Electrochemistry of N-Doped Graphene Oxide Nanostructures for Improved Kinetics of Vanadyl Oxidation.

N-doped graphene oxides (GO) are nanomaterials of interest as building blocks for 3D electrode architectures for vanadium redox flow battery applications. N- and O-functionalities have been reported to increase charge transfer rates for vanadium redox couples. However, GO synthesis typically yields heterogeneous nanomaterials, making it challenging to understand whether the electrochemical activity of conventional GO electrodes results from a sub-population of GO entities or sub-domains.

Carbon Thin-Film Electrodes as High-Performing Substrates for Correlative Single Entity Electrochemistry.

Correlative methods to characterize single entities by electrochemistry and microscopy/spectroscopy are increasingly needed to elucidate structure-function relationships of nanomaterials. However, the technical constraints often differ depending on the characterization techniques to be applied in combination. One of the cornerstones of correlative single-entity electrochemistry (SEE) is the substrate, which needs to achieve a high conductivity, low roughness, and electrochemical inertness.

Porous N-Doped Carbon-encapsulated Iron as Novel Catalyst Architecture for the Electrocatalytic Hydrogenation of Benzaldehyde.

Carbon porous materials containing nitrogen functionalities and encapsulated iron-based active sites have been suggested as electrocatalysts for energy conversion, however their applications to the hydrogenation of organic substrates via electrocatalytic hydrogenation (ECH) remain unexplored. Herein, we report on a Fe@C:N material synthesized with an adapted annealing procedure and tested as electrocatalyst for the hydrogenation of benzaldehyde. Using different concentrations of the organic, and electrolysis coupled to gas chromatography experiments, we demonstrate that it is possible to use such architectures for the ECH of unsaturated organics.

Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up.

Background: Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM's predictive ability.

Methods: Patients were identified from a prospectively maintained single-institution database.