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At least 865 records · Page 48

Lagrangian Characterization of Surface Transport From the Equatorial Atlantic to the Caribbean Sea Using Climatological Lagrangian Coherent Structures and Self‐Organizing Maps

Abstract This study presents an assessment of the transport of suspended material by surface ocean currents, which have a critical role in determining the connectivity and distribution of living and non‐living material. Lagrangian experiments reveal pathways from the Equatorial Atlantic to 10 strategic regions within the Caribbean Sea, determined by considering the space‐time variability of climatological Lagrangian Coherent Structures, which act as recurrent attracting pathways and transport barriers. Due to windage or Stokes drift, wind forcing is a significant factor in determining the spatial locations where particles cluster and the time needed to reach the Caribbean from the Equatorial Atlantic. Pathways shift westward within the Caribbean and take less time to arrive with increasing wind influence. Depending on the wind effect, the particles show higher confluence in different areas of the Caribbean. A case study is presented for the Mexican Caribbean nearshore area, isolated from ocean‐current trajectories. Here, wind weakens the transport barrier responsible for this isolation and causes particle confluence toward that region. Spatial patterns of the Eulerian velocity identified through Self‐Organizing Maps, with time dependence given by their best matching units, can reproduce the characteristic Lagrangian patterns of surface current climate variability. Our study demonstrates the application of tools from dynamical systems and unsupervised neural networks to understand Lagrangian patterns and identify the processes that drive them. These findings improve our understanding of transport mechanisms of suspended material by surface ocean currents in the Western Atlantic and the Caribbean Sea, which is essential for managing and conserving marine ecosystems.

Allende‐Arandía, Ma. Eugenia↗

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

Hydrology in the Age of Artificial Intelligence: From Fragmentation to Coherent Terrestrial Hydrosphere Science

The rapid rise of machine learning (ML) in hydrology has prompted debate about the discipline's scientific relevance. While ML often outperforms traditional models in streamflow prediction, we argue that this reflects a deeper limitation: persistent fragmentation of hydrological science itself. Narrow focus on isolated components has hindered the development of coherent, scale‐relevant understanding of the integrated terrestrial hydrosphere. This is illustrated, for example, by widely divergent estimates of groundwater–streamflow interactions and of water balance‐implied ongoing storage changes. We argue that hydrology's future lies not in choosing between ML and physics, but in integrating data‐driven and process‐based approaches to advance consistent, realistic, and societally relevant understanding of the terrestrial hydrosphere and its multifaceted roles in the Earth System.

Painter, Scott L. [Oak Ridge National Laboratory (↗

Coherent control of asymmetric spintronic terahertz emission from two-dimensional hybrid metal halides

Abstract Next-generation terahertz (THz) sources demand lightweight, low-cost, defect-tolerant, and robust components with synergistic, tunable capabilities. However, a paucity of materials systems simultaneously possessing these desirable attributes and functionalities has made device realization difficult. Here we report the observation of asymmetric spintronic-THz radiation in Two-Dimensional Hybrid Metal Halides (2D-HMH) interfaced with a ferromagnetic metal, produced by ultrafast spin current under femtosecond laser excitation. The generated THz radiation exhibits an asymmetric intensity toward forward and backward emission direction whose directionality can be mutually controlled by the direction of applied magnetic field and linear polarization of the laser pulse. Our work demonstrates the capability for the coherent control of THz emission from 2D-HMHs, enabling their promising applications on the ultrafast timescale as solution-processed material candidates for future THz emitters.

36 MATERIALS SCIENCE↗

Au-activated N motifs in non-coherent cupric porphyrin metal organic frameworks for promoting and stabilizing ethylene production

Direct implementation of metal-organic frameworks as the catalyst for CO 2 electroreduction has been challenging due to issues such as poor conductivity, stability, and limited > 2e - products. In this study, Au nanoneedles are impregnated into a cupric porphyrin-based metal-organic framework by exploiting ligand carboxylates as the Au 3+ -reducing agent, simultaneously cleaving the ligand-node linkage. Surprisingly, despite the lack of a coherent structure, the Au-inserted framework affords a superb ethylene selectivity up to 52.5% in Faradaic efficiency, ranking among the best for metal-organic frameworks reported in the literature. Through operando X-ray, infrared spectroscopies and density functional theory calculations, the enhanced ethylene selectivity is attributed to Au-activated nitrogen motifs in coordination with the Cu centers for C-C coupling at the metalloporphyrin sites. Furthermore, the Au-inserted catalyst demonstrates both improved structural and catalytic stability, ascribed to the altered charge conduction path that bypasses the incoherent framework. This study underlines the modulation of reticular metalloporphyrin structure by metal impregnation for steering the CO 2 reduction reaction pathway.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Coherent helicity-dependent spin-phonon oscillations in the ferromagnetic van der Waals crystal CrI3

Abstract The discovery of two-dimensional systems hosting intrinsic magnetic order represents a seminal addition to the rich landscape of van der Waals materials. CrI 3 is an archetypal example, where the interdependence of structure and magnetism, along with strong light-matter interactions, provides a new platform to explore the optical control of magnetic and vibrational degrees of freedom at the nanoscale. However, the nature of magneto-structural coupling on its intrinsic ultrafast timescale remains a crucial open question. Here, we probe magnetic and vibrational dynamics in bulk CrI 3 using ultrafast optical spectroscopy, revealing spin-flip scattering-driven demagnetization and strong transient exchange-mediated interactions between lattice vibrations and spin oscillations. The latter yields a coherent spin-coupled phonon mode that is highly sensitive to the driving pulse’s helicity in the magnetically ordered phase. Our results elucidate the nature of ultrafast spin-lattice coupling in CrI 3 and highlight its potential for applications requiring high-speed control of magnetism at the nanoscale.

36 MATERIALS SCIENCE↗

Coherent terahertz radiation with 2.8-octave tunability through chip-scale photomixed microresonator optical parametric oscillation

High-spectral-purity frequency-agile room-temperature sources in the terahertz spectrum are foundational elements for imaging, sensing, metrology, and communications. Here we present a chip-scale optical parametric oscillator based on an integrated nonlinear microresonator that provides broadly tunable single-frequency and multi-frequency oscillators in the terahertz regime. Through optical-to-terahertz down-conversion using a plasmonic nanoantenna array, coherent terahertz radiation spanning 2.8-octaves is achieved from 330 GHz to 2.3 THz, with ≈20 GHz cavity-mode-limited frequency tuning step and ≈10 MHz intracavity-mode continuous frequency tuning range at each step. By controlling the microresonator intracavity power and pump-resonance detuning, tunable multi-frequency terahertz oscillators are also realized. Furthermore, by stabilizing the microresonator pump power and wavelength, sub-100 Hz linewidth of the terahertz radiation with 10 –15 residual frequency instability is demonstrated. The room-temperature generation of both single-frequency, frequency-agile terahertz radiation and multi-frequency terahertz oscillators in the chip-scale platform offers unique capabilities in metrology, sensing, imaging and communications.

42 ENGINEERING↗

Inter-valley coherent order and isospin fluctuation mediated superconductivity in rhombohedral trilayer graphene

Superconductivity was recently discovered in rhombohedral trilayer graphene (RTG) in the absence of a moiré potential. Superconductivity is observed proximate to a metallic state with reduced isospin symmetry, but it remains unknown whether this is a coincidence or a key ingredient for superconductivity. Using a Hartree-Fock analysis and constraints from experiments, we argue that the symmetry breaking is inter-valley coherent (IVC) in nature. We evaluate IVC fluctuations as a possible pairing glue, and find that they lead to chiral unconventional superconductivity when the fluctuations are strong. We further elucidate how the inter-valley Hund’s coupling determines the spin-structure of the IVC ground state and breaks the degeneracy between spin-singlet and triplet superconductivity. Remarkably, if the normal state is spin-unpolarized, we find that a ferromagnetic Hund’s coupling favors spin-singlet superconductivity, in agreement with experiments. Instead, if the normal state is spin-polarized, then IVC fluctuations lead to spin-triplet pairing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Coherent heteroepitaxial growth of I-III-VI 2 Ag(In,Ga)S 2 colloidal nanocrystals with near-unity quantum yield for use in luminescent solar concentrators

Colloidal Ag(In,Ga)S 2 nanocrystals (AIGS NCs) with the band gap tunability by their size and composition within visible range have garnered surging interest. High absorption cross-section and narrow emission linewidth of AIGS NCs make them ideally suited to address the challenges of Cd-free NCs in wide-ranging photonic applications. However, AIGS NCs have shown relatively underwhelming photoluminescence quantum yield (PL QY) to date, primarily because coherent heteroepitaxy has not been realized. Here, we report the heteroepitaxy for AIGS-AgGaS 2 (AIGS-AGS) core-shell NCs bearing near-unity PL QYs in almost full visible range (460 to 620 nm) and enhanced photochemical stability. Key to the successful growth of AIGS-AGS NCs is the use of the Ag-S-Ga(OA) 2 complex, which complements the reactivities among cations for both homogeneous AIGS cores in various compositions and uniform AGS shell growth. The heteroepitaxy between AIGS and AGS results in the Type I heterojunction that effectively confines charge carriers within the emissive core without optically active interfacial defects. AIGS-AGS NCs show higher extinction coefficient and narrower spectral linewidth compared to state-of-the-art heavy metal-free NCs, prompting their immediate use in practicable applications including displays and luminescent solar concentrators (LSCs).

42 ENGINEERING↗

Establishing coherent momentum-space electronic states in locally ordered materials

Rich momentum-dependent electronic structure naturally arises in solids with long-range crystalline symmetry. Reliable and scalable quantum technologies rely on materials that are either not perfect crystals or non-crystalline, breaking translational symmetry. This poses the fundamental questions of whether coherent momentum-dependent electronic states can arise without long-range order, and how they can be characterized. Here we investigate Bi 2 Se 3 , which exists in crystalline, nanocrystalline, and amorphous forms, allowing direct comparisons between varying degrees of spatial ordering. Through angle-resolved photoemission spectroscopy, we show for the first time momentum-dependent band structure with Fermi surface repetitions in an amorphous solid. The experimental data is complemented by a model that accurately reproduces the vertical, dispersive features as well as the replication at higher momenta in the amorphous form. These results reveal that well-defined real-space length scales are sufficient to produce dispersive band structures, and that photoemission can expose the imprint of these length scales on the electronic structure.

36 MATERIALS SCIENCE↗

Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks

Abstract As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate ‘loss function’ alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging

Abstract The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore computationally expensive. Deep learning (DL) models have been developed to either provide learned priors or completely replace phase retrieval. However, such models require vast amounts of labeled data, which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets. Using 3D X-ray Bragg coherent diffraction imaging (BCDI) as a representative technique, we demonstrate AutoPhaseNN, a DL-based approach which learns to solve the phase problem without labeled data. By incorporating the imaging physics into the DL model during training, AutoPhaseNN learns to invert 3D BCDI data in a single shot without ever being shown real space images. Once trained, AutoPhaseNN can be effectively used in the 3D BCDI data inversion about 100× faster than iterative phase retrieval methods while providing comparable image quality.

36 MATERIALS SCIENCE↗

Concurrent multi-peak Bragg coherent x-ray diffraction imaging of 3D nanocrystal lattice displacement via global optimization

Abstract In this paper we demonstrated a method to reconstruct vector-valued lattice distortion fields within nanoscale crystals by optimization of a forward model of multi-reflection Bragg coherent diffraction imaging (MR-BCDI) data. The method flexibly accounts for geometric factors that arise when making BCDI measurements, is amenable to efficient inversion with modern optimization toolkits, and allows for globally constraining a single image reconstruction to multiple Bragg peak measurements. This is enabled by a forward model that emulates the multiple Bragg peaks of a MR-BCDI experiment from a single estimate of the 3D crystal sample. We present this forward model, we implement it within the stochastic gradient descent optimization framework, and we demonstrate it with simulated and experimental data of nanocrystals with inhomogeneous internal lattice displacement. We find that utilizing a global optimization approach to MR-BCDI affords a reliable path to convergence of data which is otherwise challenging to reconstruct.

36 MATERIALS SCIENCE↗

Ultrafast Bragg coherent diffraction imaging of epitaxial thin films using deep complex-valued neural networks

Abstract Domain wall structures form spontaneously due to epitaxial misfit during thin film growth. Imaging the dynamics of domains and domain walls at ultrafast timescales can provide fundamental clues to features that impact electrical transport in electronic devices. Recently, deep learning based methods showed promising phase retrieval (PR) performance, allowing intensity-only measurements to be transformed into snapshot real space images. While the Fourier imaging model involves complex-valued quantities, most existing deep learning based methods solve the PR problem with real-valued based models, where the connection between amplitude and phase is ignored. To this end, we involve complex numbers operation in the neural network to preserve the amplitude and phase connection. Therefore, we employ the complex-valued neural network for solving the PR problem and evaluate it on Bragg coherent diffraction data streams collected from an epitaxial La 2-x Sr x CuO 4 (LSCO) thin film using an X-ray Free Electron Laser (XFEL). Our proposed complex-valued neural network based approach outperforms the traditional real-valued neural network methods in both supervised and unsupervised learning manner. Phase domains are also observed from the LSCO thin film at an ultrafast timescale using the complex-valued neural network.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

36 MATERIALS SCIENCE↗

Three-dimensional strain imaging of irradiated chromium using multi-reflection Bragg coherent diffraction

Abstract Radiation-induced materials degradation is a key concern in limiting the performance of nuclear materials. The formation of nanoscale void and gas bubble superlattices in metals and alloys under radiation environments can effectively mitigate radiation-induced damage, such as swelling and aid the development of next generation radiation tolerant materials. To effectively manage radiation-induced damage via superlattice formation, it is critical to understand the microstructural changes and strain induced by such superlattices. We utilize multi-reflection Bragg coherent diffraction imaging to quantify the full strain tensor induced by void superlattices in iron irradiated chromium substrate. Our approach provides a quantitative estimation of radiation-induced three-dimensional (3D) strain generated at the microscopic level and predicts the number density of defects with a high degree of sensitivity. Such quantitative evaluation of 3D strain in nuclear materials can have a major impact on predicting materials behavior in radiation environments and can revolutionize design of radiation tolerant materials.

36 MATERIALS SCIENCE↗

High-efficiency microwave-optical quantum transduction based on a cavity electro-optic superconducting system with long coherence time

Abstract Frequency conversion between microwave and optical photons is a key enabling technology to create links between superconducting quantum processors and to realize distributed quantum networks. We propose a microwave-optical transduction platform based on long-coherence time superconducting radio-frequency (SRF) cavities coupled to electro-optic optical cavities to mitigate the loss mechanisms that limit the attainment of high conversion efficiency. We optimize the microwave-optical field overlap and optical coupling losses in the design while achieving long microwave and optical photon lifetime at milli-Kelvin temperatures. This represents a significant enhancement of the transduction efficiency up to 50% under incoming pump power of 140 μW, which allows the conversion of few-photon quantum signals. Furthermore, this scheme exhibits high resolution for optically reading out the dispersive shift induced by a superconducting transmon qubit coupled to the SRF cavity. We also show that low microwave losses enhance the fidelity of heralded entanglement generation between two remote quantum systems. Finally, high precision in quantum sensing can be reached below the standard quantum limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Black-hole powered quantum coherent amplifier

Abstract Atoms falling into a black hole (BH) through a cavity are shown to enable coherent amplification of light quanta powered by the BH-gravitational vacuum energy. This process can harness the BH energy towards useful purposes, such as propelling a spaceship trapped by the BH. The process can occur via transient amplification of a signal field by falling atoms that are partly excited by Hawking radiation reflected by an orbiting mirror. In the steady-state regime of thermally equilibrated atoms that weakly couple to the field, this amplifier constitutes a BH-powered quantum heat engine. The envisaged effects substantiate the thermodynamic approach to BH acceleration radiation.

Physics↗