Catalytic engines can use hydrogen peroxide as a chemical fuel in order to drive motion at the microscale. The chemo-mechanical actuation is a complex mechanism based on the interrelation between catalytic reactions and electro-hydrodynamics phenomena. We studied catalytic micropumps using fluorescence confocal microscopy to image the concentration of protons in the liquid. In addition, we measured the motion of particles with different charges in order to map the spatial distributions of the electric field, the electrostatic potential and the fluid flow. The combination of these two techniques allows us to contrast the gradient of the concentration of protons against the spatial variation in the electric field. We present numerical simulations that reproduce the experimental results. Our work sheds light on the interrelation between the different processes at work in the chemomechanical actuation of catalytic pumps. Our experimental approach could be used to study other electrochemical systems with heterogeneous electrodes.
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http://dx.doi.org/10.1103/PhysRevLett.111.168301 | DOI Listing |
Sci Adv
January 2025
Department of Physics, Princeton University, Princeton, NJ 08544, USA.
Introducing superconductivity in topological materials can lead to innovative electronic phases and device functionalities. Here, we present a unique strategy for quantum engineering of superconducting junctions in moiré materials through direct, on-chip, and fully encapsulated 2D crystal growth. We achieve robust and designable superconductivity in Pd-metalized twisted bilayer molybdenum ditelluride (MoTe) and observe anomalous superconducting effects in high-quality junctions across ~20 moiré cells.
View Article and Find Full Text PDFSoft Matter
January 2025
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria.
In this work, a theoretical approach is developed to investigate the structural properties of ionic microgels induced by a circularly polarized (CP) electric field. Following a similar study on chain formation in the presence of linearly polarized fields [T. Colla , , 2018, , 4321-4337], we propose an effective potential between microgels which incorporates the field-induced interactions a static, time averaged polarizing charge at the particle surface.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Laboratoire PHENIX, Sorbonne Université, CNRS, (Physico-Chimie des Electrolytes et Nanosystèmes Interfaciaux), 4 Place Jussieu, 75005 Paris, France.
In recent years, the theoretical description of electrical noise and fluctuation-induced effects in electrolytes has gained renewed interest, enabled by stochastic field theories like stochastic density functional theory (SDFT). Such models, however, treat solvents implicitly, ignoring their generally polar nature. In the present study, starting from microscopic principles, we derive a fully explicit SDFT theory that applies to ions in a polar solvent.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Stanford University, Department of Mechanical Engineering, Stanford, California 94305, USA.
The extreme electric fields created in high-intensity laser-plasma interactions could generate energetic ions far more compactly than traditional accelerators. Despite this promise, laser-plasma accelerator experiments have been limited to maximum ion energies of ∼100 MeV/nucleon. The central challenge is the low charge-to-mass ratio of ions, which has precluded one of the most successful approaches used for electrons: laser wakefield acceleration.
View Article and Find Full Text PDFMed Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
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