arXiv subjects

Alexander Ryzhov

Publications and source records attributed to Alexander Ryzhov.

4 recordsLinked to original sources

arXiv2025

Isotopic Fingerprints of Proton-mediated Dielectric Relaxation in Solid and Liquid Water

We report cross-validated measurements of the isotope effect on dielectric relaxation for four isotopologues of ice and water, including the 1-10^5 Hz region, in which only sporadic and inconsistent measurements were previously available. In ice, the relaxation rates exhibit an activated temperature dependence with an isotope-independent activation energy. Across 248-273 K, the H_2O/D_2O relaxation rate ratio remains constant at 2.0 \pm 0.1. This scaling agrees with Kramers' theory in the high-friction limit if the moving mass is the proton or deuteron, indicating that dielectric relaxation is governed by a classic proton transfer over an energy barrier rather than molecular reorientation.

physics.chem-ph↗

arXiv2024

All-water supercapacitor enabled by 1-nm clay channels

Water confined to channels one nanometer thick exhibits electrochemical behavior distinct from bulk water, including enhanced protonic conductivity and large dielectric anisotropy. Here, we exploit these characteristics to design a scalable electrochemical energy-storage system ("blue capacitor") constructed entirely from naturally abundant materials. By assembling layered clays and conductive graphene, we produce 1-nm-thick channels in which confined water acts as the sole electrolyte. We systematically study different clay types, the electrode composition, and separator thickness using complementary physicochemical and electrochemical techniques. The device operates stably up to 1.6 V, achieves specific capacitances of up to 40 F/g, nearly 100% coulombic efficiency, and stable performance over more than 60,000 charge-discharge cycles. Structural and dynamic analyses validate the device architecture, water purity, and proton transport in the nanopores. These results demonstrate that nanoconfined water can function as an electrolyte in a macroscopic electrochemical device, providing a platform for exploring sustainable aqueous energy-storage systems.

cond-mat.soft↗

arXiv2022

Confinement-controlled Water Engenders High Energy Density Electrochemical-double-layer Capacitance

The renewable energy sector critically needs low-cost and environmentally neutral energy storage solutions throughout the entire device life cycle. However, the limited performance of standard water-based electrochemical systems prevents their use in specific applications. Meanwhile, recent fundamental studies revealed dielectric anomalies of water near solid-liquid interfaces of carbon-based nanomaterials. In contrast to the bulk water properties, these anomalies of water under nano-confinement and in the presence of electric fields have not yet been understood and used. Here, we experimentally study the ability of the interfacial water layer to engender and store charge in electrochemical double-layer capacitance. We demonstrate the prototype of a 'water only' membrane-electrode assembly. The prototype exhibits characteristics with a perspective of competing with existing batteries and supercapacitors without using electrolytes as ionic carriers. The results provide the impetus for developing high-energy-density electrochemical double-layer capacitors and open up other avenues for ecologically-neutral batteries, fuel cells, and nanofluidic devices.

physics.chem-ph↗

arXiv2020

Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach

Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion. To tackle this challenge, an event-triggered -- as opposed to classic time-triggered -- paradigm, is proposed in which learning and control decisions are made when events occur and enough information is collected. Events are characterized by certain design conditions and they occur when the conditions are met, for instance, when a certain state threshold is reached. By systematically adjusting the time of learning and control decisions, the proposed framework can potentially reduce the variance in learning, and consequently, improve the control process. We formulate the micro-climate control problem based on semi-Markov decision processes that allow for variable-time state transitions and decision making. Using extended policy gradient theorems and temporal difference methods in a reinforcement learning set-up, we propose two learning algorithms for event-triggered control of micro-climate in buildings. We show the efficacy of our proposed approach via designing a smart learning thermostat that simultaneously optimizes energy consumption and occupants' comfort in a test building.

eess.SY↗