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Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials
Understanding the thermal transport properties of CALF-20, a recent addition to the metal-organic framework family, is crucial for its effective utilization in greenhouse gas capture. Here, we report the thermal transport study of CALF-20 using artificial neural network-based machine learning potentials. We use the Green-Kubo approach based on equilibrium molecular dynamics, with a heat-flux renormalization technique, to determine the thermal conductivity (κ) of CALF-20. We predict that the anisotropic thermal transport in CALF-20, with κ below 1 Wm−1K−1 at 300 K, is ideal for thermoelectric applications. Our analysis reveals a weak temperature dependence (κ ~ 1/T0.56) and near invariance with pressure in κ value of CALF-20, which stands out from the typical trend observed in crystalline materials. The outcome of the study, leveraging advanced computational techniques for predictive modeling, offers valuable insights into more suitable applications of CALF-20 with tailored thermal properties.
Controlling Coulomb correlations and fine structure of quasi-one-dimensional excitons by magnetic order
Many surprising properties of quantum materials result from Coulomb correlations defining electronic quasiparticles and their interaction chains. In van der Waals layered crystals, enhanced correlations have been tailored in reduced dimensions, enabling excitons with giant binding energies and emergent phases including ferroelectric, ferromagnetic and multiferroic orders. Yet, correlation design has primarily relied on structural engineering. Here we present quantitative experiment–theory proof that excitonic correlations can be switched through magnetic order. By probing internal Rydberg-like transitions of excitons in the magnetic semiconductor CrSBr, we reveal their binding energy and a dramatic anisotropy of their quasi-one-dimensional orbitals manifesting in strong fine-structure splitting. We switch the internal structure from strongly bound, monolayer-localized states to weakly bound, interlayer-delocalized states by pushing the system from antiferromagnetic to paramagnetic phases. Our analysis connects this transition to the exciton’s spin-controlled effective quantum confinement, supported by the exciton’s dynamics. In future applications, excitons or even condensates may be interfaced with spintronics; extrinsically switchable Coulomb correlations could shape phase transitions on demand.
Redox-active inverse crowns for small molecule activation
Cyclic crown ethers bind metal cations to form host–guest complexes. Lesser-known inverse crowns are rings of metal cations that encapsulate anionic entities, enabling multiple deprotonation reactions, often with unusual selectivity. Self-assembly of a cycle of metal cations around the multiply charged carbanion during the deprotonation reaction is the driving force for this reactivity. Here we report the synthesis of a pre-assembled inverse crown featuring Na+ cations and a redox-active Mg0 centre. Reduction of N2O followed by N2 release and subsequent encapsulation of O2− demonstrates its reduce-and-capture functionality. Calculations reveal that this essentially barrier-free process involves a rare N2O2− dianion, embedded in the metalla-cycle. The inverse crown can adapt itself for binding larger anions like N2O22− through a self-reorganization process involving ring expansion. The redox-active inverse crown combines the advantages of a strong reducing agent with anion stabilizing properties provided by the ring of metal cations, leading to high reactivity and selectivity.
An artificial market model for the forex market
As financial markets have transitioned toward electronic trading, there has been a corresponding increase in the number of algorithmic strategies and degree of transaction frequency. This move to high-frequency trading at the millisecond level, propelled by algorithmic strategies, has brought to the forefront short-term market reactions, like market impact, which were previously negligible in low-frequency trading scenarios. Such evolution necessitates a new framework for analyzing and developing algorithmic strategies in these rapidly evolving markets. Employing artificial markets stands out as a solution to this problem. This study aims to construct an artificial foreign exchange market referencing market microstructure theory, without relying on the assumption of information or technical traders. Furthermore, it endeavors to validate the model by replicating stylized facts, such as fat tails, which exhibit a higher degree of kurtosis in the return distribution than that predicted by normal distribution models. The validated artificial market model will be used to simulate market dynamics and algorithm strategies; its generated rates could also be applied to pricing and risk management for currency options and other foreign exchange derivatives. Moreover, this work explores the importance of order flow and the underlying factors of stylized facts within the artificial market model.
Metasurface enabled high-order differentiator
Metasurface-enabled optical analog differentiation has garnered significant attention due to its inherent capacity of parallel operation, compactness, and low power consumption. Most previous works focused on the first- and second-order operations, while several significant works have also achieved higher-order differentiation in both real space and k-space. However, how to construct the desired optical transfer function in a practical system to realize scalable and multi-order-parallel high-order differentiation of images in real space, and particularly how to leverage it to tackle practical problems, have not been fully explored. Here, drawing on the basic mathematical feature of the Fourier transform, we theoretically propose universal phase-gradient functions of the Pancharatnam-Berry-phase-based meta-device for performing arbitrary order differentiation. The fifth-order optical differentiations for both intensity and phase images are realized in the experiment. More importantly, by exploring this elaborately designed spatial differentiator, we construct another scheme for optical super-resolution and achieve the estimation of the distance between two incoherent point sources within 0.015 of the Rayleigh distance, which thereby provides a potential toolkit for optical alignment in high-precision semiconductor nano-fabrication. Our findings hold promise for image processing, microscopy imaging, and optical super-resolution imaging.
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