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Impact of - and -Direction External Side Chains in Y-Series Acceptors on the Molecular Packing and Charge Carrier Dynamics of Organic Photovoltaics.
ACS Appl Mater Interfaces
January 2025
Department of Hydrogen and Renewable Energy, Kyungpook National University, Daegu 41566, Republic of Korea.
The side-chain directions in nonfullerene acceptors (NFAs) strongly influence the intermolecular interactions in NFAs; however, the influence of these side chains on the morphologies and charge carrier dynamics of Y6-based acceptors remains underexplored. In this study, we synthesize four distinct Y6-based acceptors, i.e.
View Article and Find Full Text PDFExtreme Diffusion Measures Statistical Fluctuations of the Environment.
Phys Rev Lett
December 2024
University of Oregon, Department of Physics and Materials Science Institute, Eugene, Oregon 97403, USA.
We consider many-particle diffusion in one spatial dimension modeled as "random walks in a random environment." A shared short-range space-time random environment determines the jump distributions that drive the motion of the particles. We determine universal power laws for the environment's contribution to the variance of the extreme first passage time and extreme location.
View Article and Find Full Text PDFGranular Aluminum Parametric Amplifier for Low-Noise Measurements in Tesla Fields.
Phys Rev Lett
December 2024
Karlsruhe Institute of Technology, IQMT, 76131 Karlsruhe, Germany.
Josephson junction parametric amplifiers have become essential tools for microwave quantum circuit readout with minimal added noise. Even after improving at an impressive rate in the past decade, they remain vulnerable to magnetic fields, which limits their use in many applications such as spin qubits, Andreev and molecular magnet devices, dark matter searches, etc. Kinetic inductance materials, such as granular aluminum (grAl), offer an alternative source of nonlinearity with innate magnetic field resilience.
View Article and Find Full Text PDFTensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States.
Phys Rev Lett
December 2024
California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, USA.
We introduce a change of perspective on tensor network states that is defined by the computational graph of the contraction of an amplitude. The resulting class of states, which we refer to as tensor network functions, inherit the conceptual advantages of tensor network states while removing computational restrictions arising from the need to converge approximate contractions. We use tensor network functions to compute strict variational estimates of the energy on loopy graphs, analyze their expressive power for ground states, show that we can capture aspects of volume law time evolution, and provide a mapping of general feed-forward neural nets onto efficient tensor network functions.
View Article and Find Full Text PDFEvidence of Dirac Quantum Spin Liquid in YbZn_{2}GaO_{5}.
Phys Rev Lett
December 2024
Duke University, Department of Physics, Durham, North Carolina 27708, USA.
The emergence of a quantum spin liquid (QSL), a state of matter that can result when electron spins are highly correlated but do not become ordered, has been the subject of a considerable body of research in condensed matter physics [1,2]. Spin liquid states have been proposed as hosts for high-temperature superconductivity [3] and can host topological properties with potential applications in quantum information science [4]. The excitations of most quantum spin liquids are not conventional spin waves but rather quasiparticles known as spinons, whose existence is well established experimentally only in one-dimensional systems; the unambiguous experimental realization of QSL behavior in higher dimensions remains challenging.
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