As the field of Zr stable isotopes is rapidly expanding from the study of mass-independent to that of mass-dependent isotope effects, a variety of Zr standards have appeared in the literature. While several of these standards have been proposed as the ideal isotope reference material (iRM) against which all data should be reported, none of them have been shown to meet the compositional and/or conflict-of-interest-free distribution requirements put forth by the community. To remedy this situation, we report on a community-led effort to develop and calibrate a scale defining iRM for Zr isotopes: NIST RM 8299. Developed in partnership with the National Institute of Standards and Technology (NIST) from the widely used SRM 3169 Zr Standard Solution, the candidate RM 8299 was calibrated through an inter-laboratory study involving three laboratories. Our data show that RM 8299 meets all requirements of an ideal iRM. It is an isotopically homogeneous, high-purity reference material, that is free of isotope anomalies, and whose composition is identical to that of a major geological reservoir (Ocean Island Basalts). Furthermore, RM 8299 will be curated and distributed by NIST, a neutral, conflict-of-interest free organization, and was produced in sufficient quantities to last multiple decades. We recommend that all Zr isotope data to be reported against RM 8299. Our results also show that SRM 3169 lots #130920 and #071226 have identical composition to RM 8299. Therefore, using RM 8299 as the scale defining iRM will enable direct comparison of all future data with the vast majority of the existing literature data, both for mass-independent and mass-dependent isotope effects. To facilitate conversion of d94/90Zr values reported against other Zr standards, we provide high-precision conversion factors to the RM 8299 scale obtained using the double-spike method.

Climate change exacerbates extreme weather events like heavy rainfall and flooding. As these events cause severe losses of property and lives, accurate high-resolution simulation of precipitation is imperative. However, existing Earth System Models (ESMs) struggle with resolving small-scale dynamics and suffer from biases, especially for extreme events. Traditional statistical bias correction and downscaling methods fall short in improving spatial structure, while recent deep learning methods lack controllability over the output and suffer from unstable training. Here, we propose a novel machine learning framework for simultaneous bias correction and downscaling. We train a generative diffusion model in a supervised way purely on observational data. We map observational and ESM data to a shared embedding space, where both are unbiased towards each other and train a conditional diffusion model to reverse the mapping. Our method can be used to correct any ESM field, as the training is independent of the ESM. Our approach ensures statistical fidelity, preserves large-scale spatial patterns and outperforms existing methods especially regarding extreme events and small-scale spatial features that are crucial for impact assessments.

surfQuake is a new software designed to streamline the estimation of seismic source parameters. Its comprehensive set of toolboxes automate the determination of seismic arrival times, event association and locations, moment magnitude from P- or S- wave displacement spectra and moment tensor inversions within a Bayesian framework. surfQuake is programmed in Python 3 and offers the users the possibility of three programming levels for flexibility and customization. The core library allows users to integrate the core of surfQuake into their preexisting scripts, giving advanced users full control, while the Command Line Interface gives users access to an upper layer that simplifies the use of the core. Alternatively, surfQuake core is wrapped by a Graphical User Interface (GUI) and connected to a SQLite database making it accessible to users with little coding experience. The software has been fully tested with an earthquake cluster of more than 2000 events, that occurred in central Pyrenees in 2021-22. The source parameters retrieved from the cluster and the basic statistics associated with them are displayed using the surfQuake database toolbox. Additionally, we offer a web tutorial with the documentation of surfQuake and a set of usage examples for the three programming levels.

For rockfall hazard assessment on areas more than several km2 in size, the quantification of runout probability is usually done empirically. Classical methods use statistical distributions of reach or energy angles derived from rockfall databases. However, other topographic descriptors can be derived from the topographic profiles along the rockfall path. Using a database of more than 4,000 profiles of rockfall paths, we determine which topographic descriptors are most appropriate for reach probability estimation, by comparing their statistical distributions for rockfall stopping points, and for points the rocks overtook. We show that the curvilinear length of the propagation path, and the area under the propagation path can improve propagation estimations, especially when they are computed only along the final portion of the propagation path. This is illustrated by comparing an experimental distribution of rockfall stopping points to the estimated distributions.

Recognized as a not-an-option approach to mitigate the climate crisis, carbon dioxide capture and storage (CCS) has a potential as much as gigaton of CO2 to sequestrate permanently and securely. Recent attention has been paid to store highly concentrated point-source CO2 into saline formation, of which Thailand considers one onshore case in the north located in Lampang, the Mae Moh coal-fired power plant matched with its own coal mine of Mae Moh Basin. The current study is thus aimed to examine the influence of reservoir geomechanics on CO2 storage containment and trapping mechanisms, with co-contributions from geochemistry and reservoir heterogeneity, using reservoir simulator, CMG-GEM. With the injection rate designed for 30-year injection, reservoir pressure build-ups were 77% of fracture pressure but increased to 80% when geomechanics excluded. Such pressure responses imply that storage security is associated with the geomechanics. Dominated by viscous force, CO2 plume migrated more laterally while geomechanics clearly contributed to lesser migration due to reservoir rock strength constraint. Reservoir geomechanics contributed to less plume traveling into more constrained spaces while leakage was secured, highlighting a significant and neglected influence of geomechanical factor. Spatiotemporal development of CO2 plume also confirms the geomechanics-dominant storage containment. Reservoir geomechanics as attributed to its respective reservoir fluid pressure controls development of trapping mechanisms, especially into residual and solubility traps. More secured storage containment after the injection was found with higher pressure, while less development into solubility trap was observed with lower pressure.

Relating microscopic interactions to macroscopic observables is a central challenge in the study of complex systems. Addressing this question requires understanding both pairwise and higher-order interactions, but the latter are less well understood. Here, we show that the M\"obius inversion theorem provides a general mathematical formalism for deriving higher-order interactions from macroscopic observables, relative to a chosen decomposition of the system into parts. Applying this framework to a diverse range of systems, we demonstrate that many existing notions of higher-order interactions, from epistasis in genetics and many-body couplings in physics, to synergy in game theory and artificial intelligence, naturally arise from an appropriate mereological decomposition. By revealing the common mathematical structure underlying seemingly disparate phenomena, our work highlights the fundamental role of decomposition choice in the definition and estimation of higher-order interactions. We discuss how this unifying perspective can facilitate the transfer of insights between domains, guide the selection of appropriate system decompositions, and motivate the search for novel interaction types through creative decomposition strategies. More broadly, our results suggest that the M\"obius inversion theorem provides a powerful lens for understanding the emergence of complex behaviour from the interplay of microscopic parts, with applications across a wide range of disciplines.

Vibrational control (VC) of photochemistry through the optical stimulation of structural dynamics is a nascent concept only recently demonstrated for model molecules in solution. Extending VC to state-of-the-art materials may lead to new applications and improved performance for optoelectronic devices. Metal halide perovskites are promising targets for VC due to their mechanical softness and the rich array of vibrational motions of both their inorganic and organic sublattices. Here, we demonstrate the ultrafast VC of FAPbBr3 perovskite solar cells via intramolecular vibrations of the formamidinium cation using spectroscopic techniques based on vibrationally promoted electronic resonance. The observed short (~300 fs) time window of VC highlights the fast dynamics of coupling between the cation and inorganic sublattice. First-principles modelling reveals that this coupling is mediated by hydrogen bonds that modulate both lead halide lattice and electronic states. Cation dynamics modulating this coupling may suppress non-radiative recombination in perovskites, leading to photovoltaics with reduced voltage losses.

Bismuth oxide iodide (BiOI) has been viewed as a suitable environmentally-friendly alternative to lead-halide perovskites for low-cost (opto-)electronic applications such as photodetectors, phototransistors and sensors. To enable its incorporation in these devices in a convenient, scalable, and economical way, BiOI thin films were investigated as part of heterojunctions with various p-type organic semiconductors (OSCs) and tested in a field-effect transistor (FET) configuration. The hybrid heterojunctions, which combine the respective functionalities of BiOI and the OSCs were processed from solution under ambient atmosphere. The characteristics of each of these hybrid systems were correlated with the physical and chemical properties of the respective materials using a concept based on heteropolar chemical interactions at the interface. Systems suitable for application in lateral transport devices were identified and it was demonstrated how materials in the hybrids interact to provide improved and synergistic properties. These indentified heterojunction FETs are a first instance of successful incorporation of solution-processed BiOI thin films in a three-terminal device. They show a significant threshold voltage shift and retained carrier mobility compared to pristine OSC devices and open up possibilities for future optoelectronic applications.

Alkaline water electrolysis is considered a commercially viable option for large-scale hydrogen production. However, this process still faces challenges due to the high voltage (>1.65 V at 10 mA cm$^{-2}$) and its limited stability at higher current densities due to the inefficient electron transport kinetics. Herein, a novel cobalt based metallic heterostructure (Co$_3$Mo3N/Co$_4$N/Co/Co) is designed for application for water electrolysis. Operando Raman experiments reveal that the formation of Co$_3$Mo3N/Co$_4$N/Co heterointerface boosts the free water adsorption and dissociation, resulting in a surplus of protons available for subsequent hydrogen production. Furthermore, the altered electronic structure of Co$_3$Mo3N/Co$_4$N/Co heterointerface optimizes the {\Delta}GH of nitrogen atoms at the interface. This synergistic effect between interfacial nitrogen atoms and metal phase cobalt creates highly efficient hydrogen evolution reaction (HER) active sites, thereby enhancing the overall performance. Additionally, the heterostructure exhibits a rapid OH- adsorption rate, coupled with a strong adsorption strength, leading to improved oxygen evolution reaction (OER) performance. Crucially, the metallic heterojunction facilitates fast electron transport, expediting the aforementioned reaction steps and ultimately improving the overall efficiency of water splitting. The water electrolyzer with Co$_3$Mo3N/Co$_4$N/Co/Co as a catalyst exhibits outstanding performance, requiring an impressively low cell voltage of 1.58 V at 10 mA cm$^{-2}$ and maintaining approximately 100% retention over a remarkable 100 h duration at 200 mA cm$^{-2}$. This performance significantly exceeds that of the commercial Pt/C || RuO2 electrolyzer.

Battery thermal management systems (BTMSs) are critical for efficient and safe operation of lithium-ion batteries (LIBs), especially for fast charging/discharging applications that generate significant heating within the cell. Forced immersion cooling, where a dielectric fluid flows in direct contact with the LIB cells, is an effective cooling approach. But because of its complex nature, a thorough understanding of the underlying physics - including the coupled electrochemical, thermal, fluid, and mechanical effects - is required before immersion cooling will see wide adoption into commercial systems. In this work, to investigate the performance of a LIB subjected to forced immersion cooling, we develop a fully coupled modeling approach that solves the detailed electrochemical model in conjunction with the thermal-fluid transport models for both the cell and fluid domain. After calculating the electrochemical and thermal responses, we also estimate the mechanical stresses within the cell generated due to the ion diffusion and temperature rise that impact reliability. To assess the effectiveness of forced immersion cooling, we evaluate several different configurations for a cylindrical 18650 battery cell under varying cell discharge rates. We compare forced immersion cooling for two liquids (deionized water and mineral oil) at three different fluid mass flow rates. The results highlight the strong cross-coupling of the electrochemical and heat transfer phenomena. By comparing results across fluids and flow rates, we define a new metric that can be used to compare the cooling capacity considering different flow parameters. Overall, this study provides insights that will be useful in the design of immersion cooling-based BTMSs including, for example, the selection of forced immersion cooling specifications, such that the temperature is controlled without significant capacity loss.

We consider solenoidal space-periodic space-analytic solutions to the equations of magnetohydrodynamics. An elementary bound shows that due to the special structure of the nonlinear terms in the equations for modified solutions, effectively they lack a half of the spatial gradient, which appears to be a novel mechanism for depletion of nonlinearity. We present a two-phase iterative procedure yielding an expanded bound for the guaranteed time of the space analyticity of the hydrodynamic solutions. Each iteration involves two regimes: In phase 1, the enstrophy of the modified solution and the bound for the radius of the analyticity of the original solution simultaneously increase (the bound is proportional to the elapsed time since the beginning of phase 1). In phase 2, the enstrophy and bound simultaneously decrease. It is straightforward to generalize this construction for the equations of magnetohydrodynamics.

A new method recovers phase difference of interfering wavefronts from a pattern of interference fringes, avoiding discontinuity problem. The continuous phase is a solution of the first order differential equation of the interferogram function computed from the fringe intensity profile selected along the pathway over the interferogram.

The substantial threat of concurrent air pollutants to public health is increasingly severe under climate change. To identify the common drivers and extent of spatio-temporal similarity of PM2.5 and ozone, this paper proposed a log Gaussian-Gumbel Bayesian hierarchical model allowing for sharing a SPDE-AR(1) spatio-temporal interaction structure. The proposed model outperforms in terms of estimation accuracy and prediction capacity for its increased parsimony and reduced uncertainty, especially for the shared ozone sub-model. Besides the consistently significant influence of temperature (positive), extreme drought (positive), fire burnt area (positive), and wind speed (negative) on both PM2.5 and ozone, surface pressure and GDP per capita (precipitation) demonstrate only positive associations with PM2.5 (ozone), while population density relates to neither. In addition, our results show the distinct spatio-temporal interactions and different seasonal patterns of PM2.5 and ozone, with peaks of PM2.5 and ozone in cold and hot seasons, respectively. Finally, with the aid of the excursion function, we see that the areas around the intersection of San Luis Obispo and Santa Barbara counties are likely to exceed the unhealthy ozone level for sensitive groups throughout the year. Our findings provide new insights for regional and seasonal strategies in the co-control of PM2.5 and ozone. Our methodology is expected to be utilized when interest lies in multiple interrelated processes in the fields of environment and epidemiology.

Rabi oscillations have long been thought to be out of reach in simulations using time-dependent density functional theory (TDDFT), a prominent symptom of the failure of the adiabatic approximation for non-perturbative dynamics. We present a reformulation of TDDFT which requires response quantities only, thus enabling an adiabatic approximation to predict such dynamics accurately because the functional is evaluated on a domain much closer to the domain for which it was derived. Our reformulation applies to any real-time dynamics, redeeming TDDFT far from equilibrium. Examples of a resonantly-driven local excitation in a model He atom, and charge-transfer in the LiCN molecule are given.

Modeling chemical reactions with quantum chemical methods is challenging when the electronic structure varies significantly throughout the reaction, as well as when electronic excited states are involved. Multireference methods such as complete active space self-consistent field (CASSCF) can handle these multiconfigurational situations. However, even if the size of needed active space is affordable, in many cases the active space does not change consistently from reactant to product, causing discontinuities in the potential energy surface. The localized active space SCF (LASSCF) is a cheaper alternative to CASSCF for strongly correlated systems with weakly correlated fragments. The method is used for the first time to study a chemical reaction, namely the bond dissociation of a mono-, di-, and triphenylsulfonium cation. LASSCF calculations generate smooth potential energy scans more easily than the corresponding, more computationally expensive, CASSCF calculations, while predicting similar bond dissociation energies. Our calculations suggest a homolytic bond cleavage for di- and triphenylsulfonium, and a heterolytic pathway for monophenylsulfonium.

Using local nonlinear gyrokinetic simulations, we demonstrate that turbulent eddies can extend along magnetic field lines for hundreds of poloidal turns in tokamaks with weak or zero magnetic shear $\hat{s}$. We observe that this parallel eddy length scales inversely with magnetic shear and at $\hat{s}=0$ is limited by the thermal speed of electrons $v_{th,e}$. We examine the consequences of these "ultra long" eddies on turbulent transport, in particular, how field line topology mediates strong parallel self-interaction. Our investigation reveals that, through this process, field line topology can strongly affect transport. It can cause transitions between different turbulent instabilities and in some cases triple the logarithmic gradient needed to drive a given amount of heat flux. We also identify a novel "eddy squeezing" effect, which reduces the perpendicular size of eddies and their ability to transport energy, thus representing a novel approach to improve confinement. Finally, we investigate the triggering mechanism of Internal Transport Barriers (ITBs) using low magnetic shear simulations, shedding light on why ITBs are often easier to trigger where the safety factor has a low-order rational value.

In introductory physics laboratory instruction, students often expect to confirm or demonstrate textbook physics concepts (Wilcox & Lewandowski, 2017; Hu & Zwickl, 2017; Hu & Zwickl, 2018). This expectation is largely undesirable: labs that emphasize confirmation of textbook physics concepts are unsuccessful at teaching those concepts (Wieman & Holmes, 2015; Holmes et al., 2017) and even in contexts that don't emphasize confirmation, such expectations can lead to students disregarding or manipulating their data in order to obtain the expected result (Smith et al., 2020). In other words, when students expect their lab activities to confirm a known result, they may relinquish epistemic agency and violate disciplinary practices. We claim that, in other cases, confirmatory expectations can actually support productive disciplinary engagement. In particular, when an expected result is not confirmed, students may enter a productive "troubleshooting" mode (Smith et al., 2020). We analyze the complex dynamics of students' epistemological framing in a lab where student's confirmatory expectations support and even generate epistemic agency and disciplinary practices, including developing original ideas, measures, and apparatuses to apply to the material world.

Brillouin light scattering (BLS) spectroscopy is an effective method for detecting spin waves in magnetic thin films and nanostructures. While it provides extensive insight into the properties of spin waves, BLS spectroscopy is impeded in many practical cases by the limited range of detectable spin wave wavenumbers and its low sensitivity. Here, we present a generalized theoretical model describing plasmon-enhanced BLS spectroscopy. Three types of plasmonic nanoparticles in the shape of an ellipsoid of rotation are considered: a single plasmon resonator, a sandwiched plasmonic structure in which two nanoparticles are separated by a dielectric spacer, and an ensemble of metallic nanoparticles on the surface of a magnetic film. The effective susceptibilities for the plasmonic systems at the surface of the magnetic film are calculated using the electrodynamic Green functions method, and the enhancement coefficient is defined. It is analytically shown that the ratio of the plasmon resonator height to its radius plays the key role in the development of plasmon-enhanced BLS spectroscopy. The developed model serves as a basis for numerical engineering of the optimized plasmon nanoparticle morphology for BLS enhancement.

Brillouin light scattering (BLS) spectroscopy is a powerful tool for detecting spin waves in magnetic thin films and nanostructures. Despite comprehensive access to spin-wave properties, BLS spectroscopy suffers from the limited wavenumber of detectable spin waves and the typically relatively low sensitivity. In this work, we present the results of numerical simulations based on the recently developed analytical model describing plasmon-enhanced BLS. The effective susceptibility is defined for a single plasmonic nanoparticle in the shape of an ellipsoid of rotation, for the sandwiched plasmonic nanoparticles separated by a dielectric spacer, as well as for the array of plasmonic resonators on the surface of a magnetic film. It is shown that the eccentricity of the metal nanoparticles, which describes their shape, plays a key role in the enhancement of the BLS signal. The optimal conditions for BLS enhancement are numerically defined for gold and silver plasmon systems for photons of different energies. The presented results define the roadmap for the experimental realization of plasmon-enhanced BLS spectroscopy.

Small-angle scattering tensor tomography is a technique for studying anisotropic nanostructures of millimeter-sized samples in a volume-resolved manner. It requires the acquisition of data through repeated tomographic rotations about an axis which is subjected to a series of tilts. The tilt that can be achieved with a typical setup is geometrically constrained, which leads to limits in the set of directions from which the different parts of the reciprocal-space map can be probed. Here, we characterize the impact of this limitation on reconstructions in terms of the missing wedge problem of tomography, by treating the problem of tensor tomography as the reconstruction of a three-dimensional field of functions on the unit sphere, represented by a grid of Gaussian radial basis functions. We then devise an acquisition scheme to obtain complete data by remounting the sample, which we apply to a sample of human trabecular bone. Performing tensor tomographic reconstructions of limited data sets as well as the complete data set, we further investigate and validate the missing wedge understanding of data incompleteness by investigating reconstruction errors due to data incompleteness across both real and reciprocal space. Finally, we carry out an analysis of orientations and derived scalar quantities, to quantify the impact of this missing wedge problem on a typical tensor tomographic analysis. We conclude that the effects of data incompleteness are consistent with the predicted impact of the missing wedge problem, and that the impact on tensor tomographic analysis is appreciable but limited, especially if precautions are taken. In particular, there is only limited impact on the means and relative anisotropies of the reconstructed reciprocal space maps.

This article examines the critical role of fast Monte Carlo dose calculations in advancing proton therapy techniques, particularly in the context of increasing treatment customization and precision. As adaptive radiotherapy and other patient-specific approaches evolve, the need for accurate and precise dose calculations, essential for techniques like proton-based stereotactic radiosurgery, becomes more prominent. These calculations, however, are time-intensive, with the treatment planning/optimization process constrained by the achievable speed of dose computations. Thus, enhancing the speed of Monte Carlo methods is vital, as it not only facilitates the implementation of novel treatment modalities but also improves the optimality of treatment plans. Today, the state-of-the-art in Monte Carlo dose calculation speeds is 106 - 107 protons per second. This review highlights the latest advancements in fast Monte Carlo dose calculations that have led to such speeds, including emerging artificial intelligence-based techniques, and discusses their application in both current and emerging proton therapy strategies.

This study focuses on discerning the role of structural parameters on the bifurcation characteristics and the underlying synchronization mechanism in an aeroelastic system undergoing nonlinear stall behaviour. To that end, wind tunnel experiments are performed on a NACA 0012 airfoil capable of undergoing bending (plunging) and torsional (pitching) oscillations under scenarios involving nonlinear aerodynamic loads, i.e., dynamic stall conditions. Flow conditions under both deterministic/sterile flows and fluctuating/stochastic flows are fostered. The structure possesses continuous or polynomial-type stiffness nonlinearities, and therefore, is an aeroelastic experiment involving both structural and aerodynamic nonlinearities. We discern the bifurcation routes for a range of key structural parameters such as frequency ratio, static imbalance, and the extent of structural nonlinearity. In addition to interesting and atypical routes to stall-induced instabilities, we systematically demonstrate the role of modal interactions - via a synchronization analysis - over the manifestation of these instabilities. To the best of the authors' knowledge, this is perhaps the first study to document the role of multiple structural parameters on a stall-induced aeroelastic system, and in turn, cast the physical mechanism behind these dynamical transitions from the vantage of synchronization.

The optical forces applied to a rare-earth doped optical dimer made of identical Yb3+:YAG nanospheres placed in the plane electromagnetic field propagating in vacuum has been theoretically considered. The electromagnetic fields at wavelengths 968 nm and 1030 nm have been normally and axially directed to the dimer axis. Wavelengths 968 nm and 1030 nm are resonant with the electron transitions of the Yb3+ ions and can generate the Stokes and anti-Stokes cycles, respectively. It has been shown that the electromagnetic field propagating at wavelength 1030 nm not only can be used to control optical forces applied to the dimer, but can cause cooling of the dimer, which is desirable in a number of applications.

We introduce a methodology to calibrate in situ a set of coils generating bi- or tri-axial magnetic fields, at frequencies where a calibration performed in static condition would be inaccurate. The coil constants are determined in a two-step procedure. Considering the presence of a static and of a time-dependent field, firstly, the static one is oriented perpendicularly to the polarization plane of a time dependent one; secondly, the polarization of the latter is made accurately circular. The methodology uses harmonic analysis of one component of the magnetization of an atomic sample whose spins adiabatically follow the time-dependent field.

We introduce a novel method to design and implement a tunable dynamical tissue phantom for laser speckle-based in-vivo blood flow imaging. This approach relies on Stochastic Differential Equations (SDE) to control a piezoelectric actuator which, upon illuminated with a laser source, generates speckles of pre-defined probability density function and auto-correlation. The validation experiments show that the phantom can generate dynamic speckles that closely replicate both surfaces as well as deep tissue blood flow for a reasonably wide range and accuracy.

X-ray grating interferometry allows for the simultaneous acquisition of attenuation, differential-phase contrast, and dark-field images, resulting from X-ray attenuation, refraction, and small-angle scattering, respectively. The modulated phase grating (MPG) interferometer is a recently developed grating interferometry system capable of generating a directly resolvable interference pattern using a relatively large period grating envelope function that is sampled at a pitch that allows for X-ray spatial coherence using a microfocus X-ray source or by use of a source G0 grating that follows the Lau condition. We present the theory of the MPG interferometry system for a 2-dimensional staggered grating, derived using Fourier optics, and we compare the theoretical predictions with experiments we have performed with a microfocus X-ray system at Pennington Biomedical Research Center, LSU. The theoretical and experimental fringe visibility is evaluated as a function of grating-to-detector distance. Quantitative experiments are performed with porous carbon and alumina samples, and qualitative analysis of attenuation and dark-field images of a dried anchovy are shown.

We used the monochromatic soft X-ray beamline P04 at the synchrotron-radiation facility PETRA III to resonantly excite the strongest $2p-3d$ transitions in neon-like Ni XIX ions, $[2p^6]_{J=0} \rightarrow [(2p^5)_{1/2}\,3d_{3/2}]_{J=1}$ and $[2p^6]_{J=0} \rightarrow [(2p^5)_{3/2}\,3d_{5/2}]_{J=1}$, respectively dubbed 3C and 3D, achieving a resolving power of 15,000 and signal-to-background ratio of 30. We obtain their natural linewidths, with an accuracy of better than 10\%, as well as the oscillator-strength ratio $f(3C)/f(3D)$ = 2.51(11) from analysis of the resonant fluorescence spectra. These results agree with those of previous experiments, earlier predictions, and our own advanced calculations.

Excited atomic nitrogen atoms play an important role in plasma formation in hypersonic shock-waves, as happens during spacecraft reentry and other high velocity vehicle applications. In this study, we have thoroughly studied collision induced excitation (CIE) associated with two colliding nitrogen atoms in the N(4S), N(2D) and N(2P) states at collisions energies up to 6 eV, using time-independent scattering calculations to determine cross sections and temperature-dependent rate coefficients. The calculations are based on potential curves and couplings determined in earlier MRCI calculations with large basis sets, and the results are in good agreement with experiment where comparisons are possible. To properly consider the spin-orbit coupling matrix, we have developed a scaling method for treating transitions between different fine-structure components with calculations that only require calculations with two coupled states, and with this we define accurate degeneracy factors for determining cross sections and rate coefficients that include all states. The results indicate that both spin-orbit and derivative coupling effects can play important roles in collisional excitation and quenching, and that although derivative coupling is always much stronger than spin-orbit, there are many transitions where only spin-orbit can contribute. As part of this, we identify two distinct pathways associated with N(2D) relaxation, including one Auger-like mechanism leading to 2N(2D) that could be important at high temperature.

Building on previous work, this paper extends the modeling of political structures from simplicial complexes to hypergraphs. This allows the analysis of more complex political dynamics where agents who are willing to form coalitions contain subsets that would not necessarily form coalitions themselves. We extend topological constructions such as wedge, cone, and collapse from simplicial complexes to hypergraphs and use them to study mergers, mediators, and power delegation in political structures. Concepts such as agent viability and system stability are generalized to the hypergraph context, alongside the introduction of the notion of local viability. Additionally, we use embedded homology of hypergraphs to analyze power concentration within political systems. Along the way, we introduce some new notions within the hypergraph framework that are of independent interest.

We present a Pressure-Oscillation-Free projection algorithm for large-density-ratio multiphase fluid-structure interaction simulations, implemented on a non-staggered Cartesian grid. The incompressible Navier-Stokes is decoupled with an improved five-step incremental pressure correction algorithm. Fluid-fluid interface is captured using the Cahn-Hilliard equation, and the surface tension model is coupled with a momentum-weighted interpolation scheme to suppress unphysical pressure oscillations, ensuring accurate evolution of multiphase interfaces. Interaction at the fluid-structure interface is obtained by implicitly solving for the feedback acceleration in the Eulerian-Lagrangian system. For validation of the present method, the comparison studies for Pressure-Oscillation-Free effect are systematically conducted using lid driving cavity and droplet deformation cases. Moreover, several challenging multiphase simulations are implemented and discussed. As a demonstrating example of fluid-structure interaction, a rising bubble bypassing an obstacle is tested.

Droplet impact behavior has attracted much attention recently due to its academic significance and diverse industrial applications. This study employs the lattice Boltzmann method to simulate the impact of a droplet on a hydrophobic plate featuring a square orifice. Unlike previous studies, the chemical property of the orifice considered in this work is not homogeneous but heterogeneous, and its cross-sectional wettability changes from hydrophobicity to hydrophilicity. The study first validates the numerical method against experimental data, and then investigates in detail the influences of the Weber number, wettability difference, and pore size. According to the numerical results, we observed that the evolutionary stages of the impinging droplet always include the spreading phase and the rebounding phase, while whether there exists the splitting phase, it depends on the combined effect of the wettability difference and the Weber number. The impact behavior of droplets is analyzed by evaluating the underlying mechanisms such as kinetic energy, surface energy, viscous dissipation energy, and pressure. It is interesting to note that the existence of wettability-patterned pore tends to promote adhesion of droplets on the plate, resulting in the droplet impact behaviors are largely different from that for the case of homogeneous pore. Additionally, a phase diagram is constructed for various Weber numbers and pore sizes, revealing that the dynamic behavior of droplets is determined by the competition among dynamic pressure, capillary pressure, and viscous pressure losses. These insights from numerical studies guide the development of innovative solid substrates capable of manipulating droplet motion.

Recently tellurium (Te) has attracted resurgent interests due to its p-type characteristics and outstanding ambient environmental stability. Here we present a substrate engineering based physical vapor deposition method to synthesize high-quality Te nanoflakes and achieved a field-effect hole mobility of 1500 cm2/Vs, which is, to the best of our knowledge, the highest among the existing synthesized van der Waals p-type semiconductors. The high mobility Te enables the fabrication of Te/MoS2 pn diodes with highly gate-tunable electronic and optoelectronic characteristics. The Te/MoS2 heterostructure can be used as a visible range photodetector with a current responsivity up to 630 A/W, which is about one order of magnitude higher than the one achieved using p-type Si-MoS2 PN photodiodes. The photo response of the Te/MoS2 heterojunction also exhibits strong gate tunability due to their ultrathin thickness and unique band structures. The successful synthesis of high mobility Te and the enabled Te/MoS2 photodiodes show promise for the development of highly tunable and ultrathin photodetectors.

Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision-transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 230 to 707 PFLOPS, with scaling efficiency maintained at 78% to 96% across 24,576 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.

Stabilization schemes in wall-bounded flows often invoke fluid transpiration through porous boundaries. While these have been extensively validated for external flows, their efficacy in channels, particularly from the standpoint of non-modal perturbations, is yet to be demonstrated. Here, we show that crossflow strengths previously considered ``ideal'' for optimizing stability in channels in fact admit strong non-modal energy amplification. We begin by supplementing existing modal calculations and then show via the resolvent that extremely strong and potentially unfeasible crossflows are required to suppress non-modal growth in linearly stable regimes. Investigation of unforced algebraic growth paints a similar picture. Here, a component-wise budget analysis reveals that energy redistribution through pressure-velocity correlations plays an important role in driving energy growth/decay. The superposition of a moving wall is also considered, and it is shown that while energy amplification generally worsens, it can potentially be suppressed beyond a regime shift in parameter space. However, these flows are marred by rapidly declining mass transport, rendering their ultimate utility questionable. Our results suggest that crossflow-based stabilization might not be useful in internal flows.

In the realm of healthcare, label-free sensing is a vital component for various applications. Micro-photonic technology presents a promising avenue to pursue pivotal goals. With the use of this technology, healthcare professionals and researchers can harness the power of label-free sensing to develop effective diagnostic and therapeutic tools. The integration of label-free sensing and micro-photonic technology can lead to revolutionary advancements in the healthcare industry. Here, a hybrid multilayer, whose surface is decorated with micrometric features has been investigated to evaluate the application in this direction. Following the characterization of the plasmonic resonance, and upon demonstrating the surface plasmon polariton (SPP)/antenna mode hybridiza-tion, the optomechanical behavior of the system has been investigated. The hybrid system has a higher sensi-tivity due to its high-quality factor Q when compared to standard systems. The shift of the frequency is studied for a red dye, at different excitation wavelengths as well as in an aqueous environment. To validate the increased sensitivity, I conducted an analysis using bovine serum albumin (BSA). This protein is water-soluble and has an infrared absorption band (amide I), and it is also active in the Raman region. Moreover, it can consistently bind to Ag features. Through an image-based analysis, the surface-enhanced Raman scattering pattern of the BSA was recorded. The proposed sensing method is innovative and opens new perspectives on sensitive methods for biomolecule detection. Simultaneously, it shows promising results to exploit the optical resonance shift as a basic sensing approach for probing molecular patterns.

We introduce a novel time-division multiplexing differential saturated absorption spectroscopy (TDMDSAS) approach, providing superior accuracy and stability in Doppler-free spectroscopy. By distinguishing probe and reference fields in the temporal domain, TDMDSAS efficiently suppresses Doppler broadening and common-mode optical noise. We utilized this technology to determine the absolute frequency of diverse neutral Yb isotopes across its $6s^2\ ^{1}S_0\to 6s6p ^{1}P_1$ transitions. Furthermore, the first-ever observation of in-situ Doppler-free Zeeman sub-level spectra was accomplished, enabling the determination of magnetic field gradients. We stabilized a UV diode laser at 399 nm using an error signal derived from the spectral first-derivative demodulated signal of $^{174}\mathrm{Yb}$. This technique yielded a frequency stability of up to 15 kHz with a 40 s averaging time and a standard deviation of around 180 kHz over a half-hour period. Given its low cost, straightforward, and scalable nature, TDMDSAS holds excellent potential in metrology and quantum applications.

Transient luminous events (TLEs) is the collective name given to mesospheric electrical breakdown phenomena occurring in conjunction with strong lightning discharges in tropospheric thunderstorms. They include elves, sprites, haloes and jets, and are characterized by short lived optical emissions, mostly of red (665 nm) and blue (337 nm) wavelengths. Sprites are caused by the brief quasi-electrostatic field induced in the mesosphere, mostly after the removal of the upper positive charge of the thundercloud by a +CG, and they have been recorded above most of the lightning activity centers on Earth. In wintertime, there are just a few areas where lightning occurs, and of those, sprites have been observed over the Sea of Japan, the British Channel, and the Mediterranean Sea. Unlike their summer counterparts, winter thunderstorms tend to have weaker updrafts and as a result, reduced vertical dimensions and compact charge structures, whose positive and negative centers are located at lower altitudes. These storms are often susceptible to significant wind shear and as a result may exhibit a tilted dipole charge structure and a lateral offset of the upper positive charge relative to the main negative charge. We present results of numerical simulations using a three-dimensional explicit formulation of the mesospheric electrostatic electrical field following a lightning discharge from a typical mid-latitude winter thunderstorm exhibiting tilt due to wind shear and evaluate the regions of possible sprite inception. Our results show, as numerous observations suggest, that sprites can be shifted a large distance from the location of the parent +CG in the direction of the shear and will occur over a larger region compared with non-sheared storms.

Dynamic substructuring, especially the frequency-based variant (FBS) using frequency response functions (FRF), is gaining in popularity and importance, with countless successful applications, both numerically and experimentally. One drawback, however, is found when the responses to shocks are determined. Numerically, this might be especially expensive when a huge number of high-frequency modes have to be accounted for to correctly predict response amplitudes to shocks. In all cases, the initial response predicted using frequency-based substructuring might be erroneous, due to the forced periodization of the Fourier transform. This drawback can be eliminated by completely avoiding the frequency domain and remaining in the time domain, using the impulse-based substructuring method (IBS), which utilizes impulse response functions (IRF). While this method has already been utilized successfully for numerical test cases, none of the attempted experimental applications were successful. In this paper, an experimental application of IBS to rods considered as one-dimensional is tested in the context of shock analysis, with the goal of correctly predicting the maximum driving point response peak. The challenges related to experimental IBS applications are discussed and an improvement attempt is made by limiting the frequency content considered through low-pass filtering and downsampling. The combination of a purely time domain based estimation procedure for the IRFs and the application of low-pass filtering with downsampling to the measured responses enabled a correct prediction of the initial shock responses of the rods with IBS experimentally, using displacements, velocities and accelerations.

The increasing use of polymer composites in industry asks for the creation of better, faster and cost-effective methods to detect the damage state of such materials. This work presents the investigation of the phase decay , $\Delta{\Phi}$, as a new parameter to characterise crack growth in composites materials utilising an experimental framework of High Frequency Fatigue Testing (HFFT), a framework where the excitation occurs at vibration resonance. The proposed methodology empirically relates the crack growth measurements, from interrupted testing, with the structural phase decay response, distinctive of material strength degradation

Research of the nonlinear optical characteristics of transition metal dichalcogenides in the presence of photoactive particles, plasmonic nanocavities, waveguides, and metamaterials is still in its early stages. This investigation delves into the high-order harmonic generation (HHG) from laser induced plasma of MoS2 nanosheets in the presence of semiconductor photoactive medium such as CdSe and CdSe/V2O5 core/shell quantum dots. Our comprehensive findings shed light on the counteractive coupling impact of both bare and passivated quantum dots on MoS2 nanosheets, as evidenced by the emission of higher-order harmonics. Significantly, the intensity of harmonics and their cut-off were notably enhanced in the MoS2-CdSe and MoS2-V-CdSe configurations compared to pristine MoS2 nanosheets. These advancements hold promise for applications requiring the emission of coherent short-wavelength radiation.

This paper investigates the dynamics of crystalline clusters observed in Molecular Dynamics (MD) studies conducted earlier [Yadav, M., et al. Physical Review E, 107(5), 055214(2023)] for ultra-cold neutral plasmas. An external oscillatory forcing is applied for this purpose and the evolution is tracked with the help of MD simulations using the open source LAMMPS software. Interesting observations relating to cluster dynamics are presented. The formation of a pentagonal arrangement of particles is also reported.

Canalization is an optical phenomenon that enables unidirectional propagation of light in a natural way, i.e., without the need for predefined waveguiding designs. Predicted years ago, it was recently demonstrated using highly confined phonon polaritons (PhPs) in twisted layers of the van der Waals (vdW) crystal alpha-MoO3, offering unprecedented possibilities for controlling light-matter interactions at the nanoscale. However, despite this finding, applications based on polariton canalization have remained elusive so far, which can be explained by the complex sample fabrication of twisted stacks. In this work, we introduce a novel canalization phenomenon, arising in a single vdW thin layer (alpha-MoO3) when it is interfaced with a substrate exhibiting a given negative permittivity, that allows us to demonstrate a proof-of-concept application based on polariton canalization: super-resolution (up to ~{\lambda}0/220) nanoimaging. Importantly, we find that canalization-based imaging transcends conventional projection constraints, allowing the super-resolution images to be obtained at any desired location in the image plane. This versatility stems from the synergetic manipulation of three distinct parameters: incident frequency, rotation angle of the thin vdW layer, and thickness. These results provide valuable insights into the fundamental properties of canalization and constitute a seminal step towards multifaceted photonic applications, encompassing imaging, data transmission, and ultra-compact photonic integration.

In optics and photonics, a small number of building blocks, like resonators, waveguides, arbitrary couplings, and parametric interactions, allow the design of a broad variety of devices and functionalities, distinguished by their scattering properties. These include transducers, amplifiers, and nonreciprocal devices, like isolators or circulators. Usually, the design of such a system is handcrafted by an experienced scientist in a time-consuming process where it remains uncertain whether the simplest possibility has indeed been found. In our work, we develop a discovery algorithm that automates this challenge. By optimizing the continuous and discrete system properties our automated search identifies the minimal resources required to realize the requested scattering behavior. In the spirit of artificial scientific discovery, it produces a complete list of interpretable solutions and leads to generalizable insights, as we illustrate in several examples. This now opens the door to rapid design in areas like photonic and microwave architectures or optomechanics.

In orthodox Standard Quantum Mechanics (SQM) bases and factorizations are considered to define quantum states and entanglement in relativistic terms. While the choice of a basis (interpreted as a measurement context) defines a state incompatible to that same state in a different basis, the choice of a factorization (interpreted as the separability of systems into sub-systems) determines wether the same state is entangled or non-entangled. Of course, this perspectival relativism with respect to reference frames and factorizations precludes not only the widespread reference to quantum particles but more generally the possibility of any rational objective account of a state of affairs in general. In turn, this impossibility ends up justifying the instrumentalist (anti-realist) approach that contemporary quantum physics has followed since the establishment of SQM during the 1930s. In contraposition, in this work, taking as a standpoint the logos categorical approach to QM -- basically, Heisenberg's matrix formulation without Dirac's projection postulate -- we provide an invariant account of bases and factorizations which allows us to to build a conceptual-operational bridge between the mathematical formalism and quantum phenomena. In this context we are able to address the set of equivalence relations which allows us to determine what is actually the same in different bases and factorizations.

We propose a simple dissipative system with purely cubic defocusing nonlinearity and nonuniform linear gain that can support stable local-ized dissipative vortex solitons with high topological charges without the utilization of competing nonlinearities and nonlinear gain or losses. Localization of such solitons is achieved due to an intriguing mechanism when defocusing nonlinearity stimulates energy flow from the ring-like region with linear gain to the periphery of the medium where energy is absorbed due to linear background losses. Vortex solitons bifurcate from linear gain-guided vortical modes with eigenvalues depending on topological charges that become purely real only at specific gain amplitudes. Increasing gain amplitude leads to transverse expansion of vortex solitons, but simultaneously it usually also leads to stability enhance-ment. Increasing background losses allows creation of stable vortex solitons with high topological charges that are usually prone to instabilities in conservative and dissipative systems. Propagation of the perturbed unstable vortex solitons in this system reveals unusual dynamical re-gimes, when instead of decay or breakup, the initial state transforms into stable vortex soliton with lower or sometimes even with higher topo-logical charge. Our results suggest an efficient mechanism for the formation of nonlinear excited vortex-carrying states with suppressed destructive azimuthal modulational instabilities in a simple setting relevant to a wide class of systems, including polaritonic systems, structured microcavities, and lasers.

Photovoltaic materials are recognized for their potential as sustainable energy sources that enable the conversion between light and electrical energy. However, solar cells have been unable to surpass the theoretical limit of 32%, known as the Shockley-Queisser limit, and face challenges in effectively utilizing the broad spectrum of sunlight. To address this issue, extensive research is being conducted on multi-junction solar cells, which employ a layered structure comprising materials with varying bandgaps to more effectively harness the wide spectrum of sunlight. This study calculates the theoretical limit of these multi-junction solar cells and identifies optimal bandgap combinations, exploring new possibilities for photovoltaic devices and suggesting directions for technological advancement. This research presents new opportunities beyond the limitations of current solar cell technology and is expected to contribute to enhancing the economic viability and practicality of solar power as a sustainable energy source.

We use 62 electron density profiles collected by the Radio Occultation Science Experiment (ROSE), on MAVEN, when Mars was hit by CIRs and ICMEs close to aphelion (April 2021) and during two dust storms (June-July 2022) to examine the response of the Martian ionosphere to solar events and to solar events hitting during dust storms. We do so through three proxies - variation in total electron content between 80 and 300 km altitude, peak density, and peak altitude - of the aforementioned 62 ROSE electron density profiles, relative to a characterisation of the ionosphere through solar minimum leading to solar maximum, specific to local time sector and season, presented in Segale et al., (COMPANION). We observe an increased Total Electron Content (TEC) between 80 and 300 km altitude up to 2.5 x 10(15) m(-2) in April 2021 and up to 5 x 10(15) m(-2) in June-July 2022 compared to the baseline photochemically produced ionosphere. This increase in TEC corresponds mainly to increases in the solar energetic particles flux (detected by MAVEN SEP) and electron fluxes (detected by MAVEN SWEA). In addition to solar events, in June-July 2022, an A storm and a B storm were occurring and merging on the surface of Mars. We observe a raise in peak altitude in general lower than expected during dust storms, possibly due to high values of solar wind dynamic pressure (derived from MAVEN SWIA). From 31 ROSE profiles collected in this time period that showed both the M2 and M1 layer, we observe that, on average, M1 and M2 peak altitudes raise the same amount, suggesting that the thermosphere might loft as a unit during dust storms. During this time period, several proton aurora events of variable brightness were detected with MAVEN IUVS underlining the complex and multifaceted impact of dust activity and extreme solar activity on the Martian ionosphere.

The disruption and runaway electron analysis model code was extended to include tungsten impurities in disruption simulations with the aim of studying the runaway electron (RE) generation. This study investigates RE current sensitivity on the following plasma parameters and modelling choices: tungsten concentration, magnetic perturbation strength, electron modelling, thermal quench time and tokamak geometry: ITER-like or ASDEX-like. Our investigation shows that a tungsten concentration below 10-3 does not cause significant RE generation on its own. However, at higher concentrations it is possible to reach a very high RE current. Out of the two tested models of electrons in plasma: fluid and isotropic (kinetic), results from the fluid model are more conservative, which is useful when it comes to safety analysis. However, these results are overly pessimistic when compared to the isotropic model, which is based on a more reliable approach. Our results also show that the hot-tail RE generation mechanism is dominant as a primary source of RE in tungsten induced disruptions, usually providing orders of magnitude higher RE seed than Dreicer generation. We discuss best practices for simulations with tungsten-rich plasma, present the dependence of the safety limits on modelling choices and highlight the biggest shortcoming of the current simulation techniques. The obtained results pave the way for a wider analysis of tungsten impact on the disruption dynamics, including the mitigation techniques for ITER in the case of strong contamination of the plasma with tungsten.

In this Letter, we fill in the blanks in the theory of drops under shear flow by unifying analytical predictions for steady-state behavior proposed by Flumerfelt [R. W. Flumerfelt, J. Colloid Interface Sci. 76, 330 (1980)] for unconfined drops with interface viscosity with the one of Shapira & Haber [M. Shapira and S. Haber, Int. J. Multiph. Flow. 16, 305 (1990)] for confined drops without interface viscosity. Our predictions for both steady-state drop deformation and inclination angle are broadly valid for situations involving confined/unconfined drops, with/without interface viscosity and viscosity ratio, thus making our model so general that it can include any of the above conditions.

Soft material research has seen significant growth in recent years, with emerging applications in robotics, electronics, and healthcare diagnostics where understanding material mechanical response is crucial for precision design. Traditional methods for measuring nonlinear mechanical properties of soft materials require specially sized samples that are extracted from their natural environment to be mounted on the testing instrument. This has been shown to compromise data accuracy and precision in various soft and biological materials. To overcome this, the Volume Controlled Cavity Expansion (VCCE) method was developed. This technique tests soft materials by controlling the formation rate of a liquid cavity inside the materials at the tip of an injection needle, and simultaneously measuring the resisting pressure which describes the material response. Despite VCCE's early successes, expansion of its application beyond academia has been hindered by cost, size, and expertise. In response to this, the first portable, bench-top instrument utilizing VCCE is presented here. This device, built with affordable, readily available components and open-source software, streamlines VCCE experimentation without sacrificing performance or precision. It is especially suitable for space-limited settings and designed for use by non-experts, promoting widespread adoption. The instrument's efficacy was demonstrated through testing Polydimethylsiloxane (PDMS) samples of varying stiffness. This study not only validates instrument performance, but also sets the stage for further advancements and broader applications in soft material testing. All data, along with acquisition, control, and post-processing scripts, are made available on GitHub.

In this work a set of simulations that aim at the optimization of gaseous detectors for applications in X-ray fluorescence imaging in the energy range of 3 -- 30keV is presented. By studying the statistical distribution of the radiation interactions with gases, the energy resolution limits after charge multiplication for 6keV X-ray photons in Ar/CO$_2$(70/30) and Kr/CO$_2$(90/10) were calculated, obtaining energy resolutions of 15.4(4)% and 14.6(2)% respectively. The detector design was also studied to reduce the presence of escape peaks and complement a model to evaluate the inevitable X-ray fluorescence of copper generated by the conductive materials inside the detector.

superblockify is a Python package for partitioning an urban street network into Superblock-like neighborhoods and for visualizing and analyzing the partition results. A Superblock is a set of adjacent urban blocks where vehicular through traffic is prevented or pacified, giving priority to people walking and cycling. The Superblock blueprints and descriptive statistics generated by superblockify can be used by urban planners as a first step in a data-driven planning pipeline, or by urban data scientists as an efficient computational method to evaluate Superblock partitions. The software is licensed under AGPLv3 and is available at https://superblockify.city.

This paper presents the application, testing and first results of a new adaptive Bayesian inference analysis which utilises conventional and ultrafast spectroscopic measurements made in the divertor chamber to investigate the divertor physics during detachment. Validation of this software is performed prior and during analyses of results, demonstrated by compelling reproductions of ideal test cases and synthetic spectroscopic measurements. Application on real diagnostic data shows strong agreement with results from previous analysis methods. We identify unprecedented success in significant advances in time and computational efficiencies. We demonstrate a $\lesssim$1000$\times$ reduction in analysis time for spectroscopic measurements from simulated and real Super-X configurations, with the analysis technique presented in this report completing in <3 minutes. Analysis of synthetic and real diagnostic measurements identifies detachment physics in agreement with previous literature.

Recording and identifying faint objects through atmospheric scattering media by an optical system are fundamentally interesting and technologically important. In this work, we introduce a comprehensive model that incorporates contributions from target characteristics, atmospheric effects, imaging system, digital processing, and visual perception to assess the ultimate perceptible limit of geometrical imaging, specifically the angular resolution at the boundary of visible distance. The model allows to reevaluate the effectiveness of conventional imaging recording, processing, and perception and to analyze the limiting factors that constrain image recognition capabilities in atmospheric media. The simulations were compared with the experimental results measured in a fog chamber and outdoor settings. The results reveal general good agreement between analysis and experimental, pointing out the way to harnessing the physical limit for optical imaging in scattering media. An immediate application of the study is the extension of the image range by an amount of 1.2 times with noise reduction via multi-frame averaging, hence greatly enhancing the capability of optical imaging in the atmosphere.

We present super-resolved coherent anti-Stokes Raman scattering (CARS) microscopy by implementing phase-resolved image scanning microscopy (ISM), achieving up to two-fold resolution increase as compared with a conventional CARS microscope. Phase-sensitivity is required for the standard pixel-reassignment procedure since the scattered field is coherent, thus the point-spread function (PSF) is well-defined only for the field amplitude. We resolve the complex field by a simple add-on to the CARS setup enabling inline interferometry. Phase-sensitivity offers additional contrast which informs the spatial distribution of both resonant and nonresonant scatterers. As compared with alternative super-resolution schemes in coherent nonlinear microscopy, the proposed method is simple, requires only low-intensity excitation, and is compatible with any conventional forward-detected CARS imaging setup.

Optical trapping enables precise control of individual particles of different sizes, such as atoms, molecules, or nanospheres. Optical tweezers provide free-space omnidirectional optical trapping of objects in laboratories around the world. As an alternative to standard macroscopic setups based on lenses, which are inherently bound by the diffraction limit, plasmonic and photonic nanostructures promise trapping by near-field optical effects on the extreme nanoscale. However, the practical design of lossless waveguide-coupled nanostructures capable of trapping deeply sub-wavelength particles in all spatial directions using the gradient force has until now proven insurmountable. In this work, we demonstrate an omnidirectional optical trap realized by inverse-designing fabrication-ready integrated dielectric nanocavities. The sub-wavelength optical trap is designed to rely solely on the gradient force and is thus particle-size agnostic. In particular, we show how a nanometer-sized trapped particle experiences a force strong enough to overcome room-temperature thermal fluctuations. Furthermore, through the robust inverse design framework, we tailor manufacturable devices operating at near-infrared and optical frequencies. Our results open a new regime of levitated optical trapping by achieving a deep trapping potential capable of trapping single sub-wavelength particles in all directions using optical gradient forces. We anticipate potentially groundbreaking applications of the optimized optical trapping system for biomolecular analysis in aqueous environments, levitated cavity-optomechanics, and cold atom physics, constituting an important step towards realizing integrated bio-nanophotonics and mesoscopic quantum mechanical experiments.

We explore the impact of optical excitation using two interfering ultrashort optical pulses on ultrafast magnetization dynamics. Our investigation focuses on Pt/Co/Pt multilayers and TbCo alloy samples, employing a dual pump approach. We observe significant variations in the dynamics of magnetization suppression and subsequent recovery when triggered with two optical pulses of the same polarization-essentially meeting conditions for interference. Conversely, dynamics triggered with cross-polarized pump beams exhibit expected similarity to that triggered with a single pulse. Delving into the underlying physical processes contributing to laser-induced demagnetization and recovery dynamics, we find that our current understanding cannot elucidate the observed trends. Consequently, we propose that optical excitation with interfering light possesses not previously acknowledged capacity to induce long-lasting alterations in the dynamics of angular momentum.

To achieve decarbonization targets, wind turbines are growing in hub height, rotor diameter, and are being deployed in new locations with diverse atmospheric conditions not previously seen, such as offshore. Physics-based analytical wake models commonly used for design and control of wind farms simplify atmospheric boundary layer (ABL) and wake physics to achieve computational efficiency. This is done primarily through a simplified model form that neglects certain flow processes and through parameterization of ABL and wake turbulence through a wake spreading rate. In this study, we analyze the physical mechanisms that govern momentum and turbulence within a wind turbine wake in the stratified ABL. We use large eddy simulation and analysis of the streamwise momentum deficit and wake-added turbulence kinetic energy (TKE) budgets to study wind turbine wakes under neutral and stable conditions. To parse the wake from the turbulent, incident ABL flow, we decompose the flow into the base ABL flow and the deficit flow produced by the presence of a turbine. We analyze the decomposed flow field budgets to study the effects of changing stability on the streamwise momentum deficit and wake-added TKE. The results demonstrate that stability changes the importance of physical mechanisms for both quantities primarily through the nonlinear interactions of the base and deficit flows, with the stable case most affected by higher shear in the base flow and the neutral case by higher base flow TKE. Buoyancy forcing terms in the momentum deficit and wake-added TKE budgets are relatively less important compared to the aforementioned effects. While total TKE is higher in wakes in neutral ABL flows, the wake-added TKE is higher downwind of turbines in stable ABL conditions. The dependence of wake-added TKE on ABL stability is not represented in existing empirical models widely used for mean wake flow modeling.

Femtosecond high-intensity laser pulses at intensities surpassing $10^{14} \,\text{W}/\text{cm}^2$ can generate a diverse range of functional surface nanostructures. Achieving precise control over the production of these functional structures necessitates a thorough understanding of the surface morphology dynamics with nanometer-scale spatial resolution and picosecond-scale temporal resolution. In this study, we show that individual XFEL pulses can elucidate structural changes on surfaces induced by laser-generated plasmas, employing grazing-incidence small-angle x-ray scattering (GISAXS). Using aluminum-coated multilayer samples we can differentiate between ultrafast surface morphology dynamics and subsequent subsurface density dynamics, achieving nanometer-depth sensitivity and subpicosecond temporal resolution. The observed subsurface density dynamics serve to validate advanced simulation models depicting matter under extreme conditions. Our findings promise to unveil novel avenues for laser material nanoprocessing and high-energy-density science.

We present here a new geometry for laser amplifiers based on bulk gain media. The overlapped seed and pump beams are repetitively refocused into the gain medium with a Herriott-type multipass cell. Similar to a waveguide, this configuration allows for a confined propagation inside the gain medium over much longer lengths than in ordinary single pass bulk amplifiers. Inside the gain medium, the foci appear at separate locations. A proof-of-principle demonstration with Ti:sapphire indicates that this could lead to higher amplification due to a distribution of the thermal load.

Effective quantum communication requires room temperature (RT) operating single photon sensor with high photo detection efficiency (PDE) at 1550 nm wavelength. The leading class of devices in this segment is avalanche photo detectors operating particularly in the Geiger mode. Often the requirements for RT operation and for the high PDE are in conflict, resulting in a compromised solution. We have developed a device which employs a two-dimensional (2D) semiconductor material on a co-optimized dielectric photonic crystal substrate to simultaneously decrease the dark current by orders of magnitude and increase the PDE. The device is predicted to achieve RT operation with a PDE >99%. Harnessing the high carrier mobility of 2D materials, the device has ~ps jitter time and can be integrated into a large 2D array camera.

Optical imaging quality can be severely degraded by system and sample induced aberrations. Existing adaptive optics systems typically rely on iterative search algorithm to correct for aberrations and improve images. This study demonstrates the application of convolutional neural networks to characterise the optical aberration by directly predicting the Zernike coefficients from two to three phase-diverse optical images. We evaluated our network on 600,000 simulated Point Spread Function (PSF) datasets randomly generated within the range of -1 to 1 radians using the first 25 Zernike coefficients. The results show that using only three phase-diverse images captured above, below and at the focal plane with an amplitude of 1 achieves a low RMSE of 0.10 radians on the simulated PSF dataset. Furthermore, this approach directly predicts Zernike modes simulated extended 2D samples, while maintaining a comparable RMSE of 0.15 radians. We demonstrate that this approach is effective using only a single prediction step, or can be iterated a small number of times. This simple and straightforward technique provides rapid and accurate method for predicting the aberration correction using three or less phase-diverse images, paving the way for evaluation on real-world dataset.

Chiral engineering of TeraHertz (THz) light fields and the use of the handedness of light in THz light-matter interactions promise many novel opportunities for advanced sensing and control of matter in this frequency range. Unlike previously explored methods, this is achieved here by leveraging the chiral properties of highly confined THz surface plasmon modes. More specifically, we design ultrasmall surface plasmonic-based THz cavities and THz metasurfaces that display significant and adjustable chiral behavior under modest magnetic fields. For such a prototypical example of non-hermitian and dispersive photonic system, we demonstrate the capacity to magnetic field-tune both the poles and zeros of cavity resonances, the two fundamental parameters governing their resonance properties. Alongside the observed handedness-dependent cavity frequencies, this highlights the remarkable ability to engineer chiral and tunable radiative couplings for THz resonators and metasurfaces. The extensive tunability offered by the surface plasmonic approach paves the way for the development of agile and multifunctional THz metasurfaces as well as the realization of ultrastrong chiral light-matter interactions at low energy in matter with potential far-reaching applications for the design of material properties.

Silver bismuth sulfide (AgBiS$_2$) nanocrystals have emerged as a promising eco-friendly, low-cost solar cell absorber material. Their direct synthesis often relies on the hot-injection method, requiring the application of high temperatures and vacuum for prolonged times. Here, we demonstrate an alternative synthetic approach via a cation exchange reaction. In the first-step, bis(stearoyl)sulfide is used as an air-stable sulfur precursor for the synthesis of small, monodisperse Ag2S nanocrystals at room-temperature. In a second step, bismuth cations are incorporated into the nanocrystal lattice to form ternary AgBiS$_2$ nanocrystals, without altering their size and shape. When implemented into photovoltaic devices, AgBiS$_2$ nanocrystals obtained by cation exchange reach power conversion efficiencies of up to 7.35%, demonstrating the efficacy of the new synthetic approach for the formation of high-quality, ternary semiconducting nanocrystals.

There has been considerable interest and resulting progress in implementing machine learning (ML) models in hardware over the last several years from the particle and nuclear physics communities. A big driver has been the release of the Python package, hls4ml, which has enabled porting models specified and trained using Python ML libraries to register transfer level (RTL) code. So far, the primary end targets have been commercial FPGAs or synthesized custom blocks on ASICs. However, recent developments in open-source embedded FPGA (eFPGA) frameworks now provide an alternate, more flexible pathway for implementing ML models in hardware. These customized eFPGA fabrics can be integrated as part of an overall chip design. In general, the decision between a fully custom, eFPGA, or commercial FPGA ML implementation will depend on the details of the end-use application. In this work, we explored the parameter space for eFPGA implementations of fully-connected neural network (fcNN) and boosted decision tree (BDT) models using the task of neutron/gamma classification with a specific focus on resource efficiency. We used data collected using an AmBe sealed source incident on Stilbene, which was optically coupled to an OnSemi J-series SiPM to generate training and test data for this study. We investigated relevant input features and the effects of bit-resolution and sampling rate as well as trade-offs in hyperparameters for both ML architectures while tracking total resource usage. The performance metric used to track model performance was the calculated neutron efficiency at a gamma leakage of 10$^{-3}$. The results of the study will be used to aid the specification of an eFPGA fabric, which will be integrated as part of a test chip.

Currently, our understanding of magnetic fields in exoplanets remains limited compared to those within our solar system. Planets with magnetic fields emit radio signals primarily due to the Electron Cyclotron Maser Instability mechanism. In this study, we explore the feasibility of detecting radio emissions from exoplanets using the Square Kilometre Array (SKA) radio telescope. Utilizing data from the NASA Exoplanet Archive, we compile information on confirmed exoplanets and estimate their radio emissions using the RBL model. Our analysis reveals that three exoplanets- Qatar-4 b, TOI-1278 b, and WASP-173 A b- exhibit detectable radio signals suitable for observation with the SKA telescope.

We test the performance of the Polarizable Embedding Variational Quantum Eigensolver Self-Consistent-Field (PE-VQE-SCF) model for computing electric field gradients with comparisons to conventional complete active space self-consistent-field (CASSCF) calculations and experimental results. We compute quadrupole coupling constants for ice VIII and ice IX. We find that the inclusion of the environment is crucial for obtaining results that match the experimental data. The calculations for ice VIII are within the experimental uncertainty for both CASSCF and VQE-SCF for oxygen and lie close to the experimental value for ice IX as well. With the VQE-SCF, which is based on an Adaptive Derivative-Assembled Problem-Tailored (ADAPT) ansatz, we find that the inclusion of the environment and the size of the different basis sets do not directly affect the gate counts. However, by including an explicit environment, the wavefunction and, therefore, the optimization problem becomes more complicated, which usually results in the need to include more operators from the operator pool, thereby increasing the depth of the circuit.

We demonstrate the existence of photon traps within the framework of nonlinear electrodynamics. The trapping mechanism is based on the fact that, for null background fields, the optical metric reduces to the Kerr-Schild form, which plays a prominent role in the context of black hole physics. We then construct explicit examples where photons are confined in a region of spacetime, such that a distant observer cannot interact with them. Finally, we argue that the trapping scheme is quite universal, being entirely compatible with causality, energy conditions, and hyperbolicity.

This work reports a novel plasma wave observation in the near-Sun solar wind: frequency-dispersed ion acoustic waves. Similar waves were previously reported in association with interplanetary shocks or planetary bow shocks, but the waves reported here occur throughout the solar wind sunward of $\sim 60$ solar radii, far from any identified shocks. The waves reported here vary their central frequency by factors of 3 to 10 over tens of milliseconds, with frequencies that chirp up or down in time. Using a semi-automated identification algorithm, thousands of wave instances are recorded during each near-Sun orbit of the Parker Solar Probe spacecraft. Wave statistical properties are determined and used to estimate their plasma frame frequency and the energies of protons most likely to be resonant with these waves. Proton velocity distribution functions are explored for one wave interval, and proton enhancements that may be consistent with proton beams are observed. A conclusion from this analysis is that properties of the observed frequency-dispersed ion acoustic waves are consistent with driving by cold, impulsively accelerated proton beams near the ambient proton thermal speed. Based on the large number of observed waves and their properties, it is likely that the impulsive proton beam acceleration mechanism generating these waves is active throughout the inner heliosphere. This may have implications for acceleration of the solar wind.

Precision measurements of space and time, like those made by the detectors of the Laser Interferometer Gravitational-wave Observatory (LIGO), are often confronted with fundamental limitations imposed by quantum mechanics. The Heisenberg uncertainty principle dictates that the position and momentum of an object cannot both be precisely measured, giving rise to an apparent limitation called the Standard Quantum Limit (SQL). Reducing quantum noise below the SQL in gravitational-wave detectors, where photons are used to continuously measure the positions of freely falling mirrors, has been an active area of research for decades. Here we show how the LIGO A+ upgrade reduced the detectors' quantum noise below the SQL by up to 3 dB while achieving a broadband sensitivity improvement, more than two decades after this possibility was first presented.

Representation learning for the electronic structure problem is a major challenge of machine learning in computational condensed matter and materials physics. Within quantum mechanical first principles approaches, Kohn-Sham density functional theory (DFT) is the preeminent tool for understanding electronic structure, and the high-dimensional wavefunctions calculated in this approach serve as the building block for downstream calculations of correlated many-body excitations and related physical observables. Here, we use variational autoencoders (VAE) for the unsupervised learning of high-dimensional DFT wavefunctions and show that these wavefunctions lie in a low-dimensional manifold within the latent space. Our model autonomously determines the optimal representation of the electronic structure, avoiding limitations due to manual feature engineering and selection in prior work. To demonstrate the utility of the latent space representation of the DFT wavefunction, we use it for the supervised training of neural networks (NN) for downstream prediction of the quasiparticle bandstructures within the GW formalism, which includes many-electron correlations beyond DFT. The GW prediction achieves a low error of 0.11 eV for a combined test set of metals and semiconductors drawn from the Computational 2D Materials Database (C2DB), suggesting that latent space representation captures key physical information from the original data. Finally, we explore the interpretability of the VAE representation and show that the successful representation learning and downstream prediction by our model is derived from the smoothness of the VAE latent space, which also enables the generation of wavefunctions on arbitrary points in latent space. Our work provides a novel and general machine-learning framework for investigating electronic structure and many-body physics.

High-dimensional quantum systems have been used to reveal interesting fundamental physics and to improve information capacity and noise resilience in quantum information processing. However, it remains a significant challenge to realize universal two-photon quantum gates in high dimensions with high success probability. Here, by considering an ion-cavity QED system, we theoretically propose, to the best of our knowledge, the first high-dimensional, deterministic and universal two-photon quantum gate. By using an optical cavity embedded with a single trapped 40Ca+ ion, we achieve a high average fidelity larger than 98% for a quantum controlled phase-flip gate in four-dimensional space, spanned by photonic spin angular momenta and orbital angular momenta. Our proposed system can be an essential building block for high-dimensional quantum information processing, and also provides a platform for studying high-dimensional cavity QED.

Community search (CS) aims to identify a set of nodes based on a specified query, leveraging structural cohesiveness and attribute homogeneity. This task enjoys various applications, ranging from fraud detection to recommender systems. In contrast to algorithm-based approaches, graph neural network (GNN) based methods define communities using ground truth labels, leveraging prior knowledge to explore patterns from graph structures and node features. However, existing solutions face three major limitations: 1) GNN-based models primarily focus on the disjoint community structure, disregarding the nature of nodes belonging to multiple communities. 2) These model structures suffer from low-order awareness and severe efficiency issues. 3) The identified community is subject to the free-rider and boundary effects. In this paper, we propose Simplified Multi-hop Attention Networks (SMN), which consist of three designs. First, we introduce a subspace community embedding technique called Sparse Subspace Filter (SSF). SSF enables the projection of community embeddings into distinct vector subspaces, accommodating the nature of overlapping and nesting community structures. In addition, we propose a lightweight model structure and a hop-wise attention mechanism to capture high-order patterns while improving model efficiency. Furthermore, two search algorithms are developed to minimize the latent space's community radius, addressing the challenges of free-rider and boundary effects. To the best of our knowledge, this is the first learning-based study of overlapping community search. Extensive experiments validate the superior performance of SMN compared with the state-of-the-art approaches. SMN achieves 14.73% improvements in F1-Score and up to 3 orders of magnitude acceleration in model efficiency.

Modeling of time profiles of solar energetic particle (SEP) observations often considers transport along a large-scale magnetic field with a fixed path length from the source to the observer. Here we point out that variability in the turbulent field line path length can affect the fits to SEP data and the inferred mean free path and injection profile. To explore such variability, we perform Monte Carlo simulations in representations of homogeneous 2D MHD + slab turbulence adapted to spherical geometry and trace trajectories of field lines and full particle orbits, considering proton injection from a narrow or wide angular region near the Sun, corresponding to an impulsive or gradual solar event, respectively. We analyze our simulation results in terms of field line and particle path length statistics for $1^\circ\times 1^\circ$ pixels in heliolatitude and heliolongitude at 0.35 and 1 AU from the Sun, for different values of the turbulence amplitude $b/B_0$ and turbulence geometry as expressed by the slab fraction $f_s$. Maps of the most probable path lengths of field lines and particles at each pixel exhibit systematic patterns that reflect the fluctuation amplitudes experienced by the field lines, which in turn relate to the local topology of 2D turbulence. We describe the effects of such path length variations on SEP time profiles, both in terms of path length variability at specific locations and motion of the observer with respect to turbulence topology during the course of the observations.

Non-Hermitian physics has received great attention recently. In particular, band structures in non-Hermitian systems can be engineered to exhibit various topological effects. Among them, one of the most intriguing phenomena is the non-Hermitian skin effect (NHSE). Here, we investigate NHSE in systems featuring directed chains or directed graphs, where the arrows denote the directions of the non-reciprocal hopping between neighbouring nodes. We show that the systems exhibit pure skin modes with non-oscillatory wavefunctions, in contrast to previously studied NHSE. Interestingly, the sum of the decay constants along different directions for each skin mode obeys a power partition rule, i.e. their sum is a fixed value and the value of each constant only depends on the ratio between the non-reciprocal hopping parameters and is independent of detailed graph configurations. Such Pure Skin Effect (PSE) can be explained by using a generalized method for solving the Generalized Brillouin-zone with multiple bulk states.

I propose a general quantitative framework to evaluate the quality, track the historical development, and guide future optimization of photovoltaic (PV) absorbers at any development level, including both experimentally synthesized and computer-simulated materials. The framework is based on a PV figure of merit designed to include efficiency limitations due to imperfect photocarrier collection that are not included in classic detailed balance methods. Figure-of-merit-driven efficiency limits including collection losses are calculated for 28 experimentally synthesized PV absorbers and 9 PV absorbers simulated by electronic structure methods. Among emerging absorbers for which no working solar cells have yet been reported, there are very large differences in their likelihood to achieve high PV efficiencies.

Polariton condensation relies on the massive occupation of the lowest-energy polariton state beyond a required critical density. The mechanisms driving both the occupation and depopulation of the lowest-energy polariton state all rely on multi-particle scattering, and its dynamics determine the extent to which condensates can form spontaneously. To pinpoint many-body processes hindering polariton condensation in two-dimensional metal-halide semiconductors, we examine the exciton-polariton dynamics in a Fabry-P\'erot microcavity over timescales involving the dynamics of multi-particle polariton ($\ll 1$\,ps) and exciton scattering processes ($\gg 1$\,ps). We find evidence of enhanced nonlinear exciton-exciton scattering in the microcavity compared to that in the semiconductor, and that the exciton reservoir mediates polariton scattering. We posit that the complex scattering landscape between the exciton reservoir and polaritons limits the formation of polariton condensates in two-dimensional metal-halide semiconductors, and we discuss the generality of our conclusions for materials systems in which the lattice mediates strong multi-particle correlations.

Lead halide perovskite nanocrystals (LHP-NCs) embedded in polymer matrices are gaining traction for next-generation radiation detectors. While progress has been made on green-emitting CsPbBr3 NCs, scant attention has been given to the scintillation properties of CsPbCl3 NCs, which emit size-tunable UV-blue light matching the peak efficiency of ultrafast photodetectors. In this study, we explore the scintillation characteristics of CsPbCl3 NCs produced through a scalable method and treated with CdCl2. Spectroscopic, radiometric and theoretical analysis on both untreated and treated NCs uncover deep hole trap states due to surface undercoordinated chloride ions, eliminated by Pb to Cd substitution. This yields near-perfect efficiency and resistance to polyacrylate mass-polymerization. Radiation hardness tests demonstrate stability to high gamma doses while time-resolved experiments reveal ultrafast radioluminescence with an average lifetime as short as 210 ps. These findings enhance our comprehension of LHP NCs' scintillation properties, positioning CsPbCl3 as a promising alternative to conventional fast scintillators.

The relaxation behaviour of isolated quantum systems taken out of equilibrium is among the most intriguing questions in many-body physics. Quantum systems out of equilibrium typically relax to thermal equilibrium states by scrambling local information and building up entanglement entropy. However, kinetic constraints in the Hamiltonian can lead to a breakdown of this fundamental paradigm due to a fragmentation of the underlying Hilbert space into dynamically decoupled subsectors in which thermalisation can be strongly suppressed. Here, we experimentally observe Hilbert space fragmentation (HSF) in a two-dimensional tilted Bose-Hubbard model. Using quantum gas microscopy, we engineer a wide variety of initial states and find a rich set of manifestations of HSF involving bulk states, interfaces and defects, i.e., d = 2, 1 and 0 dimensional objects. Specifically, uniform initial states with equal particle number and energy differ strikingly in their relaxation dynamics. Inserting controlled defects on top of a global, non-thermalising chequerboard state, we observe highly anisotropic, sub-dimensional dynamics, an immediate signature of their fractonic nature. An interface between localized and thermalising states in turn displays dynamics depending on its orientation. Our results mark the first observation of HSF beyond one dimension, as well as the concomitant direct observation of fractons, and pave the way for in-depth studies of microscopic transport phenomena in constrained systems

Metal-organic frameworks (MOFs) are promising candidates for the advanced photocatalytic active materials. These porous crystalline compounds have large active surface area and structural tunability, highly competitive with oxides, the well-established material class for the photocatalysis. However, due to their complex organic and coordination chemistry composition, photophysical mechanisms involved in the photocatalytic process in MOFs are still not well understood. Employing electron paramagnetic resonance (EPR) spectroscopy, we investigate the fundamental processes of electron and hole generation, as well as capture events that lead to the formation of various radical species in UiO-66, an archetypical MOF photocatalyst. As a result, we detected a manifold of photoinduced electron spin centers, which we subsequently analyzed and identified with the help of density-functional theory (DFT) calculations. Our findings provide new insights into the photo-induced charge transfer processes, which are the basis of photocatalytic activity in UiO-66. This sets the stage for further studies on photogenerated spin centers in this and similar MOF materials.

We consider the two-dimensional Rayeigh-B\'enard convection problem between Navier-slip fixed-temperature boundary conditions and present a new upper bound for the Nusselt number. The result, based on a localization principle for the Nusselt number and an interpolation bound, exploits the regularity of the flow. On one hand our method yields a shorter proof of the celebrated result in Whitehead & Doering (2011) in the case of free-slip boundary conditions. On the other hand, its combination with a new, refined estimate for the pressure gives a substantial improvement of the interpolation bounds in Drivas et al. (2022) for slippery boundaries. A rich description of the scaling behaviour arises from our result: depending on the magnitude of the slip-length and on the Prandtl number, our upper bounds indicate five possible scaling laws: $\textit{Nu} \sim (L_s^{-1}\textit{Ra})^{\frac{1}{3}}$, $\textit{Nu} \sim (L_s^{-\frac{2}{5}}\textit{Ra})^{\frac{5}{13}}$, $\textit{Nu} \sim \textit{Ra}^{\frac{5}{12}}$, $\textit{Nu} \sim \textit{Pr}^{-\frac{1}{6}} (L_s^{-\frac{4}{3}}\textit{Ra})^{\frac{1}{2}}$ and $\textit{Nu} \sim \textit{Pr}^{-\frac{1}{6}} (L_s^{-\frac{1}{3}}\textit{Ra})^{\frac{1}{2}}$

A caustic is a mathematical concept describing the beam formation when the beam envelope is reflected or refracted by a manifold. While caustics are common in a wide range of physical systems, caustics typically exhibit a reciprocal wave propagation and are challenging to control. Here, we utilize the highly anisotropic dispersion and inherent non-reciprocity of a magnonic system to shape non-reciprocal emission of caustic-like spin wave beams in an extended 200 nm thick yttrium iron garnet (YIG) film from a nano-constricted rf waveguide. We introduce a near-field diffraction model to study spin-wave beamforming in homogeneous in-plane magnetized thin films, and reveal the propagation of non-reciprocal spin-wave beams directly emitted from the nanoconstriction by spatially resolved micro-focused Brillouin light spectroscopy (BLS). The experimental results agree well with both micromagnetic simulation, and the near-field diffraction model. The proposed method can be readily implemented to study spin-wave interference at the sub-micron scale, which is central to the development of wave-based computing applications and magnonic devices.

CH$_3^+$, a cornerstone intermediate in interstellar chemistry, has recently been detected for the first time by the James Webb Space Telescope. The photodissociation of this ion is studied here. Accurate explicitly correlated multi-reference configuration interaction {\it ab initio} calculations are done, and full dimensional potential energy surfaces are developed for the three lower electronic states, with a fundamental invariant neural network method. The photodissociation cross section is calculated using a full dimensional quantum wave packet method, in heliocentric Radau coordinates. The wave packet is represented in angular and radial grids allowing to reduce the number of points physically accessible, requiring to push up the spurious states appearing when evaluating the angular kinetic terms, through a projection technique. The photodissociation spectra, when employed in astrochemical models to simulate the conditions of the Orion Bar, results in a lesser destruction of CH$_3^+$ compared to that obtained when utilizing the recommended values in the kinetic database for astrochemistry (KIDA).

The ability to control forces between sub-micron-scale building blocks offers considerable potential for designing new materials through self-assembly. A typical paradigm is to first identify a particular (crystal) structure that has some desired property, and then design building-block interactions so that this structure assembles spontaneously. While significant theoretical and experimental progress has been made in assembling complicated structures in a variety of systems, this two-step paradigm fundamentally fails for structurally disordered solids, which lack a well-defined structure to use as a target. Here we show that disordered solids can still be treated from an inverse self-assembly perspective by targeting material properties directly. Using the Poisson's ratio, $\nu$, as a primary example, we show how differentiable programming connects experimentally relevant interaction parameters with emergent behavior, allowing us to iteratively "train" the system until we find the set of interactions that leads to the Poisson's ratio we desire. Beyond the Poisson's ratio, we also tune the pressure and a measure of local 8-fold structural order, as well as multiple properties simultaneously, demonstrating the potential for nontrivial design in disordered solids. This approach is highly robust, transferable, and scalable, can handle a wide variety of model systems, properties of interest, and preparation dynamics, and can optimize over 100s or even 1000s of parameters. This result connects the fields of disordered solids and inverse self-assembly, indicating that many of the tools and ideas that have been developed to understand the assembly of crystals can also be used to control the properties of disordered solids.

The use of machine learning algorithms to investigate phase transitions in physical systems is a valuable way to better understand the characteristics of these systems. Neural networks have been used to extract information of phases and phase transitions directly from many-body configurations. However, one limitation of neural networks is that they require the definition of the model architecture and parameters previous to their application, and such determination is itself a difficult problem. In this paper, we investigate for the first time the relationship between the accuracy of neural networks for information of phases and the network configuration (that comprises the architecture and hyperparameters). We formulate the phase analysis as a regression task, address the question of generating data that reflects the different states of the physical system, and evaluate the performance of neural architecture search for this task. After obtaining the optimized architectures, we further implement smart data processing and analytics by means of neuron coverage metrics, assessing the capability of these metrics to estimate phase transitions. Our results identify the neuron coverage metric as promising for detecting phase transitions in physical systems.

Motional narrowing implies narrowing induced by motion, for example, in nuclear resonance, the thermally induced random motion of the nuclei in an inhomogeneous environment leads to counter-intuitive narrowing of the resonance line. Similarly, the excitons in monolayer semiconductors experience magnetic inhomogeneity: the electron-hole spin-exchange interaction manifests as an in-plane pseudo-magnetic field with a periodically varying orientation inside the exciton band. The excitons undergo random momentum scattering and pseudospin precession repeatedly in this inhomogeneous magnetic environment - typically resulting in fast exciton depolarization. On the contrary, we show that such magnetic inhomogeneity averages out at high scattering rate due to motional narrowing. Physically, a faster exciton scattering leads to a narrower pseudospin distribution on the Bloch sphere, implying a nontrivial improvement in exciton polarization. The in-plane nature of the pseudo-magnetic field enforces a contrasting scattering dependence between the circularly and linearly polarized excitons - providing a spectroscopic way to gauge the sample quality.

In this letter, a multi-wave quasi-resonance framework is established to analyze energy diffusion in classical lattices, uncovering that it is fundamentally determined by the characteristics of eigenmodes. Namely, based on the presence and the absence of extended modes, lattices fall into two universality classes with qualitatively different thermalization behavior. In particular, we find that while the one with extended modes can be thermalized under arbitrarily weak perturbations in the thermodynamic limit, the other class can be thermalized only when perturbations exceed a certain threshold, revealing for the first time the possibility that a lattice cannot be thermalized, violating the hypothesis of statistical mechanics. Our study addresses conclusively the renowned Fermi-Pasta-Ulam-Tsingou problem for large systems under weak perturbations, underscoring the pivotal roles of both extended and localized modes in facilitating energy diffusion and thermalization processes.

Generative AI, such as OpenAI's GPT-4V large-language model, has rapidly entered mainstream discourse. Novel capabilities in image processing and natural-language communication may augment existing forecasting methods. Large language models further display potential to better communicate weather hazards in a style honed for diverse communities and different languages. This study evaluates GPT-4V's ability to interpret meteorological charts and communicate weather hazards appropriately to the user, despite challenges of hallucinations, where generative AI delivers coherent, confident, but incorrect responses. We assess GPT-4V's competence via its web interface ChatGPT in two tasks: (1) generating a severe-weather outlook from weather-chart analysis and conducting self-evaluation, revealing an outlook that corresponds well with a Storm Prediction Center human-issued forecast; and (2) producing hazard summaries in Spanish and English from weather charts. Responses in Spanish, however, resemble direct (not idiomatic) translations from English to Spanish, yielding poorly translated summaries that lose critical idiomatic precision required for optimal communication. Our findings advocate for cautious integration of tools like GPT-4V in meteorology, underscoring the necessity of human oversight and development of trustworthy, explainable AI.

In this article, a framework for defining and analysing a family of graphs or networks from symbolic music information is discussed. Such graphs concern different types of elements, such as pitches, chords and rhythms, and the relations among them, and are built from quantitative or categorical data contained in digital music scores. They are helpful in visualizing musical features at once, thus leading to a computational tool for understanding the general structural elements of a music fragment. Data obtained from a digital score undergoes different analytical procedures from graph and network theory, such as computing their centrality measures and entropy, and detecting their communities. We analyze pieces of music coming from different styles, and compare some of our results with conclusions from traditional music analysis techniques.

We present a quantum optical pattern recognition method for binary classification tasks. Without direct image reconstruction, it classifies an object in terms of the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the same behaviour of a classical neuron of unit depth. Once trained, it shows a constant $\mathcal{O}(1)$ complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical neuron (that is at least linear in the image resolution). We provide simulations and analytical comparisons with analogous neural network architectures.

Recent advancements in machine learning have led to novel imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an important and open problem. We propose a method that leverages conformal prediction to retrieve upper/lower bounds and statistical inliers/outliers of reconstructions based on the prediction intervals of downstream metrics. We apply our method to sparse-view CT for downstream radiotherapy planning and show 1) that metric-guided bounds have valid coverage for downstream metrics while conventional pixel-wise bounds do not and 2) anatomical differences of upper/lower bounds between metric-guided and pixel-wise methods. Our work paves the way for more meaningful reconstruction bounds. Code available at https://github.com/matthewyccheung/conformal-metric