Papers

Skyrmions are particle-like topological textures that hold great promise for low-power electronics and wave-based functionalities. Yet their utility is hindered by the lack of robust and controllable transport. Here, we show that band topology can be harnessed to overcome this limitation. We experimentally realize an acoustic quantum skyrmion–valley Hall effect in a surface phononic crystal via engineered spin–orbit–momentum interaction. Skyrmions emerge as valley-locked topological edge states, robustly propagating along designed domain walls. Crucially, the skyrmion transport exhibits concurrent orbital angular momentum (OAM)–valley locking and spin–texture locking, enabling controllable propagation through selective excitation. Our results establish a direct correspondence between real-space and momentum-space topology, providing a general strategy for robust, controllable skyrmion transport.

We study a stochastic model of collective motion in which individuals update their orientation through pairwise aligning or anti-aligning copying interactions. We analyze both annealed dynamics, where interaction types are chosen probabilistically at each update, and quenched dynamics, where individuals are permanently assigned to aligning or anti-aligning subpopulations. Starting from the microscopic master equation on the circle, we derive an exact mesoscopic description via a Fourier-mode expansion and a systematic large $N$ expansion, obtaining closed Fokker-Planck equations and effective stochastic differential equations for the polarization. We show that competing alignment and anti-alignment suppress long-range polar order in the thermodynamic limit in both cases, while finite systems display nontrivial fluctuation-induced structure controlled by the interaction composition. Our results, validated by Gillespie simulations, establish an analytically tractable framework for collective dynamics characterized by competing copying rules and intrinsic noise.

Existing super-resolution microscopy is often constrained by inherent trade-offs between resolution, acquisition speed, phototoxicity, and hardware complexity. Computational post-processing approaches offer a promising alternative, but they typically suffer from linearity distortion, high computational cost, reliance on pre-training data, or reconstruction artifacts. Here, we present Source Localization (SoLo), a novel single-frame super-resolution algorithm for fluorescence imaging without these limitations. Built on the principle of inferring fluorescent source positions via sampling-detection strategy, SoLo achieves non-iterative, parallelizable computation, enabling real-time live-cell imaging with high spatiotemporal resolution. The intensity linearity preservation of SoLo makes it compatible with quantitative analysis such as calcium imaging and fluorescence resonance energy transfer. We further extended this framework to 3D-SoLo for volumetric imaging and nonlinear SoLo (NL-SoLo) for high-density fluorescence fluctuation imaging. With its ease of parameter tuning and compatibility with existing imaging systems, SoLo offers an accessible solution for ordinary labs, enabling diverse biomedical imaging applications.

Battery research is a rapidly growing and highly interdisciplinary field, making it increasingly difficult to track relevant expertise and identify potential collaborators across institutional boundaries. In this work, we present a pipeline for constructing an author-centric knowledge graph of battery research built on OpenAlex, a large-scale open bibliographic catalogue. For each author, we derive a weighted research descriptors vector that combines coarse-grained OpenAlex concepts with fine-grained keyphrases extracted from titles and abstracts using KeyBERT with ChatGPT (gpt-3.5-turbo) as the backend model, selected after evaluating multiple alternatives. Vector components are weighted by research descriptor origin, authorship position, and temporal recency. The framework is applied to a corpus of 189,581 battery-related works. The resulting vectors support author-author similarity computation, community detection, and exploratory search through a browser-based interface. The knowledge graph is then serialized in RDF and linked to Wikidata identifiers, making it interoperable with external linked open data sources and extensible beyond the battery domain. Unlike prior author-centric analyses confined to institutional repositories, our approach operates at cross-institutional scale and grounds similarity in domain semantics rather than citation or co-authorship structure alone.

(Abridge) On 13 September 2025 around 22 UTC, a localized tornado outbreak affected eastern Philippines, causing significant damage in Camarines Norte. The event developed within an atypical easterly severe weather regime characterized by warm, moist southeasterly flow and strong low-level wind shear associated with an easterly wave trough. A vorticity convergence zone along the inverted trough enhanced low-level rotation, while highly curved streamwise hodographs indicated a favorable environment for supercells and tornadogenesis. At least five vortices were identified, including three tornadic supercells. The Magang tornado was rated IF2.5 (EF3-equivalent) with $\sim$2 km damage path, while the Cahabaan and Napilihan tornadoes were rated IF1 (EF1-equivalent), with Cahabaan producing $\sim$3 km damage path. The remaining vortices were rated IF0 (EF0-equivalent). These tornadoes occurred simultaneously, indicating multiple discrete supercells within the same mesoscale environment and possible inflow-outflow interactions. Dual-polarization radar observations revealed Z$_\text{DR}$ and K$_\text{DP}$ columns, a debris signature in the Magang tornado, and a bounded weak echo region (BWER) in the Cahabaan supercell. This study documents the first known tornado outbreak and simultaneous tornadic supercells in the Philippines within an easterly severe weather regime.

Optical skyrmions have recently unlocked topological quasiparticle textures of light, rising in prominence for next-generation ultra-robust information processing. However, to date, their study has been mainly confined to coherent laser fields. Here we extend skyrmions to more general light sources of partially coherent, stochastic optical fields. We define stochastic optical skyrmions and uncover a hidden regime where spatial coherence acts as a primary determinant of topological stability. While environmental randomness typically degrades fully coherent states, we demonstrate that engineered partial coherence provides a self-healing mechanism that preserves topology under extreme turbulence. Moreover, we show that the coherence structure can be actively tailored to trigger on-demand topological phase transitions, such as skyrmion-to-skyrmionium conversion and skyrmion lattice splitting. These findings redefine the boundaries of topological photonics, paving the way for resilient and high-fidelity information platforms that remain operational in general, non-ideal, real-world environments.

Volatile methyl siloxanes (VMSs), widely present in consumer and industrial products, have attracted increasing concerns due to their persistence, bioaccumulation behavior, and adverse health effects. Beyond their environmental implications, VMSs also pose operational challenges for sensing technologies because they readily decompose on sensing materials to form silicon-based compounds (e.g., silica and silane) that irreversibly impair sensing performance, a phenomenon commonly known as siloxane poisoning. Despite its prevalence, the mechanistic basis of this deactivation remains poorly understood. Herein, we present the first comprehensive theoretical study of siloxane-induced poisoning in catalytic gas sensors. Guided by our self-developed AI Agent, Digital Sensor Platform (DigSen), we first identify siloxane poisoning as a previously overlooked yet high-impact research direction. Using hexamethyldisiloxane (HMDS) as a model compound, we then conducted first-principles calculations to uncover decomposition pathways across noble metal surfaces. Strikingly, a descriptor-based microkinetic volcano model is developed to capture the trade-off between sensing activity and resistance to poisoning, enabling predictive identification of anti-poisoning candidates. These insights not only elucidate the origin of siloxane poisoning but also demonstrate how AI-driven discovery, mechanistic theory, and experiments can be integrated into a closed-loop framework for catalytic sensor design. More broadly, this AI-guided paradigm represents a generalizable strategy for materials digital discovery, offering a transferable methodology that extends well beyond siloxane systems to diverse classes of materials challenges.

A graphene-optimized silicon carbide PIN detector was fabricated and its radiation tolerance under X-ray irradiation of 160 keV was evaluated. Its electrical properties, charge collection performance and time resolution of beta-particles (90Sr) are reported. After 1 MGy irradiation, the detector maintains an ultralow leakage current of approximately 2.2e-10 A @ 300 V and the C-V characteristics are basically consistent with full depletion at 120V. The time resolution of the graphene-optimized silicon carbide detector is 58.0 ps. The time resolution is comparable to that of state-of-the-art 4H-SiC low-gain avalanche detectors (LGADs). The G/RE 4H-SiC PIN detector exhibits outstanding time resolution performance. Compared with the time resolution of the RE 4H-SiC PIN detector, the time resolution of the G/RE 4H-SiC PIN detector has decreased by 39.6%. This demonstrates the significance of the graphene electrode design. The graphene detector exhibits a charge collection efficiency (CCE) of 99.24% after X-ray irradiation, along with excellent stability. The graphene-optimized silicon carbide detector maintains good timing resolution: 58.0ps before and 64.0ps after X-ray irradiation. Experimental results indicate that the CCE and time resolution performance exhibit good stability before and after irradiation. These results demonstrate stable performance under extreme X-ray exposure, highlighting the detectors potential for radiation-hard applications in high-energy physics, space missions, and nuclear reactor monitoring.

A time-domain analytic expression for chromatographic peak shapes is derived within a stochastic-diffusive framework that incorporates axial diffusion (molecular and multipath/Eddy), finite initial spatial variance, a retention mechanism characterized by a high rate of short-duration events, and an arbitrary number of independent slow retention mechanisms, each characterized by its own rate of infrequent, long-duration events. A highly efficient evaluation scheme is derived for this expression. In the single-slow-mechanism case, it is two to four orders of magnitude faster than the previously available analytic route. Analytical derivatives with respect to all model parameters are also obtained, and each can be evaluated at computational cost comparable to that of the peak-shape expression. Illustrative fits to three literature peaks yielded full-profile RMSE values lower than those of the exponentially modified Gaussian in all tested cases, with minima ranging from 0.03 to 0.14 percent of peak height, compared with 0.43 to 5.57 percent for the reference model. Relative to the one-slow-mechanism formulation, allowing more than one slow mechanism produced a data-dependent improvement that exceeded one order of magnitude for one of the peaks.

Advanced particle acceleration methods have produced high-peak-current ion beams with broad energy spread and complex spatial distribution. There is an urgent need to develop online spatial-resolved energy spectrometers for high-energy pulsed ions. This paper introduces a novel spectrometer based on a scintillation-fiber cube for online diagnosis of proton beams with broadband energy spread and complex spatial distribution. We present its working principles, experimental setup, and comprehensive calibration using monoenergetic and spatially uniform proton beams generated by a synchrotron accelerator. Calibration results confirm an energy measurement range of 6-93 MeV, a relative energy uncertainty of 0.6% at 80 MeV, and a pixel size of 0.5 mm for beam profile reconstruction. By exploiting a custom-designed energy degrader, we generated a complex proton beam and measured it with the scintillation-fiber cube spectrometer (SFICS). The results demonstrate the spectrometer's potential for online measurement of the energy spectrum and spatial distribution of complex proton beams.

This is our fourth work in the series on machine learning (ML) moment closure models for the radiative transfer equation (RTE). In the first three papers of this series, we considered the RTE in slab geometry in 1D1V (i.e. one dimension in physical space and one dimension in angular space), and introduced a gradient-based ML moment closure [1], then enforced the hyperbolicity through a symmetrizer [2], or together with physical characteristic speeds by learning the eigenvalues of the Jacobian matrix [3]. Here, we extend our framework to the RTE in 2D2V (i.e. two dimensions in physical space and two dimensions in angular space). The main idea is to preserve the leading part of the classical $P_N$ model and modify only the highest-order block row. By analyzing the structural properties of the $P_N$ model, we show that its coefficient matrices are symmetric and admit a block-tridiagonal structure. Then we use this property to introduce a block-diagonal symmetrizer for the ML moment model and derive explicit algebraic conditions on the closure blocks which guarantee the symmetrizable hyperbolicity of the resulting ML system. These conditions lead to a natural parametrization of the closure in terms of a symmetric positive definite matrix together with symmetric closure blocks, which can be learned from data while automatically enforcing symmetrizable hyperbolicity by construction. The numerical results show that the proposed framework improves upon the classical $P_N$ model while maintaining hyperbolicity.

Characterizing the full 6-dimensional phase-space distribution of beams from the LCLS-II photoinjector is essential for understanding and optimizing downstream accelerator performance. Long-term monitoring of this distribution is equally important for detecting drifts in machine state and implementing timely corrective actions. Continuous phase space characterization during routine operation demands reliable tomographic diagnostic measurements and fast, efficient reconstruction methods. In this work, we demonstrate the first fully autonomous 6-dimensional beam-tomography system deployed on the DIAG0 parasitic beamline at LCLS-II. Using machine-learning-based control algorithms, the system autonomously configures DIAG0 and executes tomographic manipulations within operational constraints, adaptively re-optimizing beamline parameters and scan ranges in response to changes in the incoming beam. Tomographic measurements are streamed to the S3DF computing cluster where generative analysis methods reconstruct the phase-space distribution. We demonstrate that this framework produces detailed 6-dimensional beam reconstructions at a cadence of one reconstruction every 5 to 10 minutes, enabling real-time, multi-hour monitoring of injector beam evolution with unprecedented fidelity. These results represent a significant step toward fully autonomous operation of accelerator beamlines with real-time beam diagnostics for current and next-generation accelerator facilities.

Here we report the development of SPRAY, a massively parallel GPU accelerated, smoothed particle hydrodynamics (SPH)-based, radiation hydrodynamics (RHD) code designed specifically for simulating high intensity laser-plasma interactions. When a target is irradiated by an intense laser, highly complex fluid deformation occurs due to instabilities, which is challenging to study numerically. SPRAY is particle-based, mesh-free, and Lagrangian, which addresses numerical issues that posed difficulties to existing methods. Its SPH formulations for RHD governing equations are tailored toward accurate and reliable simulations of laser-target irradiation phenomena, and are solved via a time-dependent, flux-limited diffusion method. A new laser energy coupling module, which is based on the Wentzel-Kramers-Brillouin (WKB) approximation, is implemented with a totally mesh-free ray-tracing scheme that is applicable for arbitrary geometry and dimensions. The accuracy and reliability of the code are demonstrated with a series of benchmark problems. To the authors' knowledge, this is the first attempt to employ SPH method for simulations of laser-plasma interactions in high energy density physics research. Possible expansions to the code, such as laser beam-beam interaction modeling and more sophisticated multi-group radiation transport are left for future development.

Optical speckle patterns have been widely used for illumination in computational imaging, optical sectioning microscopy, and super-resolution imaging. However, commonly used speckles satisfy Rayleigh statistics, which are not ideal for diverse imaging applications. Here we tailor three-dimensional speckle intensity statistics for dynamic speckle illumination microscopy based on linear fluorescence. Optical sectioning is enhanced by axially varying speckle contrast, and image reconstruction noise is minimized with in-focus speckles of binary intensities. The customized speckle statistics are shown to tolerate sample-induced aberration and scattering. We apply tailored speckle illumination to mouse brain vascular imaging and demonstrate much improved image quality than optical-sectioning structured illumination. These results establish customization of speckle intensity statistics as a promising strategy for robust, high-throughput fluorescence imaging in thick, scattering biological specimens.

Higher-order networks with multiway interactions can exhibit collective dynamical phenomena that are absent in traditional pairwise network models. However, analyzing such dynamics becomes computationally prohibitive as their state space grows combinatorially in the multiway interaction order. Here we develop quantum algorithms for two central tasks – synchronization estimation and certification of the no-phase-locking regime – in the simplicial Kuramoto model. This model is a higher-order generalization of the celebrated Kuramoto model for coupled oscillators on graph-based networks. Under explicit assumptions on data access and types, and simplicial structure, we derive end-to-end quantum gate complexities and identify regimes with polynomial quantum advantage for synchronization estimation and super-polynomial quantum advantage for no-phase-locking certification over classical methods. More broadly, these results extend quantum algorithms for higher-order networks from structural analysis to nonlinear dynamical diagnostics, easing a major computational bottleneck and opening a route to quantum methods for probing higher-order phenomena beyond the reach of direct classical approaches.

Zonal flows and turbulence spreading play important roles in magnetic fusion plasma confinement, yet their coupling mechanisms remain elusive. Using global nonlinear gyrokinetic simulations, we show that turbulence spreading transports zonal flow radially, extending into the linearly stable regions after local nonlinear saturation of turbulence. Theoretical understanding has been gained by analyzing the simulation results in the context of a momentum theorem in toroidal plasmas [T.S. Hahm et al., Phys. Plasmas 31, 032310 (2024)] which is an extension of the Charney-Drazin non-acceleration theorem [J.G. Charney and P.G. Drazin, J. Geophys. Res. 66, 83 (1961)]. It indicates a direct relation between turbulence-driven enstrophy transport and perpendicular momentum generation.

Field-driven phenomena, from flash sintering to electromigration, exhibit threshold fields spanning six orders of magnitude. We show their product with the onset activation coherence length is a universal critical activation voltage, Vc =0.1-2.7 V. Vc represents the threshold electrical work required to resonantly couple to the universal phonon damping peak where lattice softening is maximized. This invariant unifies macroscopic thermal instabilities with the nanoscale Blech limit, establishing a universal phenomenological law for field-lattice coupling across 17 crystal families

A physics-informed neural network (PINN) is developed, for the first time, to learn the time-dependent quasi-static magnetohydrodynamic (MHD) equations in axisymmetric tokamak geometry, without any experimental or synthetic data. The initial study considered an ITER-like tokamak and found that a PINN, after careful treatment, was capable of learning the solution to the MHD system and predict a vertically displacing plasma, where general agreement with ground truth simulation was observed. The proof-of-principle demonstration highlights the potential of physics-constrained deep learning to learn complex plasma behavior.

The generation of attosecond pulses has enabled real-time observation and manipulation of coherent electron dynamics, yet their low peak power has hindered the development of advanced attosecond pump-probe spectroscopy and attosecond nonlinear metrology. Here we overcome this limitation by generating a 2.8 uJ, 292 as isolated attosecond pulses with a peak power of 9.8GW,the highest pulse energy and peak power reported for a tabletop isolated attosecond source.This is achieved by combining a 2.4TW, few-cycle(8.3 fs) two-color synthesizer with a loose focusing geometry that enables macroscopic phase-matching. The synthesizer features a stabilized carrier-envelope phase and an actively synchronized relative time delay between the two-color channels, ensuring high stability and reproducibility. This high-power tabletop attosecond source will open up nonlinear effect experiments that were previously inaccessible with lower-power IAPs, establishing a foundation for advanced attosecond spectroscopy and nonlinear metrology.

Transverse thermophotovoltaics has been conceptually proposed as a paradigm distinct from conventional junction-based photovoltaics, but has so far lacked a theoretical foundation. In this Letter, we establish a microscopic formalism of this effect in which a transverse electric current emerges in a two-dimensional metal sheet via nonreciprocal surface plasmon polaritons driven by near-field thermal radiation. This theoretical formalism incorporates the electron-photon interaction by integrating electronic transition factor governed by energy-momentum conservation, the photon flux factor encoding the nonreciprocal surface modes, and their directional coupling. Our approach quantitatively confirms the plasmon-drag mechanism and reveals the role of impurity scattering. This work provides a rigorous theoretical foundation for transverse thermophotovoltaic devices and opens avenues for active nanoscale thermal energy conversion.

Form-finding of unilateral membrane structures is commonly addressed by solving equilibrium equations with Finite Element Methods (FEMs). This paper investigates Physics-Informed Neural Networks (PINNs) as an alternative, where the equilibrium equation is enforced by minimizing its residual at collocation points during neural-network training rather than by solving a mesh-based discretized system. This approach is well suited to form-finding problems based on Membrane Equilibrium Analysis (MEA), in which the unknown membrane surface is governed by a second-order elliptic Partial Differential Equation (PDE) with Dirichlet boundary conditions. Two PINN formulations are proposed and compared: a soft-Boundary Condition (soft-BC) approach, where the boundary conditions are imposed through a penalty term, and a hard-BC approach, where they are satisfied exactly by construction through distance and lift functions. The methods are assessed on three case studies with different geometrical complexity, including compression-only and tension-only stress states, and combined self-weight, concentrated vertical loads, and horizontal actions. Both formulations produce membrane surfaces in close agreement with solutions obtained using an FEM-based PDE solver. The hard-BC formulation gives smaller errors and a smoother residual distribution, especially near the boundary, showing that exact enforcement of the Dirichlet conditions improves overall accuracy. The soft-BC formulation still provides structurally meaningful solutions and remains attractive when simpler implementation is preferred and limited relaxation of the boundary data is acceptable. Overall, the results show that PINNs are a viable alternative for MEA-based form-finding.

The reconstruction of ocean subsurface temperature (OST) using satellite remote sensing data holds significant scientific value for advancing the understanding of ocean dynamics and climate variability. However, the scarcity of subsurface observations, combined with the high degree of nonlinearity and spatiotemporal heterogeneity in subsurface processes, poses substantial challenges to the accuracy and generalization capability of traditional reconstruction methods. To address these limitations, this study proposes an adaptive framework that could capture both vertical structural dependencies and temporal variation patterns of OST via spatio-temporal clustering. By incorporating this framework with various deep learning models, e.g., dual-path convolutional neural networks (DP-CNN), Attention U-Net, and Vision Transformer (ViT), the OST field can be accurately reconstructed at a global scale only using surface observations, i.e., sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). Experimental results demonstrate that multiple deep learning methods using the proposed framework largely outperform their original counterparts, yielding improvements in RMSE ranging from 12.4% to 27.2%. This study provides a reliable solution for subsurface temperature reconstruction, offering important implications for meteorological modeling and climate change assessment.

With an increasing number of prospective geological CO$_2$ storage projects and potential pressure communication between different projects, risk sharing under uncertain geological conditions are relevant to many project operators. In this work, the project operators are modeled as agents in a stochastic cooperative game. The agents can have different risk attitudes, here defined as being willing to accept more or less uncertainty in the predicted storage of CO$_2$. This uncertainty stems from lack of knowledge of geological parameters as well as unknown future actions of competing agents, and the corresponding probability distributions need to be estimated by numerical simulation. The agents can choose to share commercial risk if collaboration is preferable to a baseline scenario of individual injection. If their operations affect each other by means of, e.g., pressure communication, there may be no unique natural definition of a baseline scenario. As a remedy, we suggest belief distributions that combine uncertainty in physical data with maximum entropy prior distributions over the sets of viable injection actions. For a realistic storage site, exemplified by the Utsira Formation in the North Sea, we present numerical results for both cases of pressure competition, and no hydraulic connections between different project operations. It is shown that risk averse agents benefit from collaboration when there is no pressure communication or other interference between agents. It is also demonstrated that pressure communication leads to large variability in the feasible injection rates, but the resulting belief distributions are nevertheless informative and useful for decision making about collaboration.

We study the of motion of a spinning, dilating particle with hadronic properties moving on a generic geometric background including curvature, torsion, and nonmetricity. In particular, we discuss generalized spin supplementary conditions and also introduce the concept of a shear supplementary condition. Using these, we investigate the evolution of the dynamical mass of the microstructured test body and the cases where the latter is a constant of motion. In general, we find novel Regge-like trajectories relating the mass to the dilation and/or the shear currents of hypermomentum. This means that for particles with hadronic properties, the rest mass is not a constant of motion in general.

We develop a thermodynamic theory of algorithmic catalysis within the watts per intelligence framework, identifying reusable computational structures that reduce irreversible operations for a task class while satisfying bounded restoration and structural selectivity constraints. We prove that any class specific speed-up is upper-bounded by the algorithmic mutual information between the substrate and the class descriptor, and that encoding this information incurs a minimum thermodynamic cost via Landauer erasure. Combining these results yields a coupling theorem that lower-bounds the deployment horizon required for an algorithmic catalyst to be energetically favourable. The framework is illustrated on an affine SAT class and situates contemporary learned systems within an information thermodynamic constraint on intelligent computation.

Recent work on the Loeb Scale has provided astronomy a structured framework for assessing anomalous interstellar objects, including a quantitative mapping of a classification ranking, its evolution with the addition of data, and a broader observational strategy for firming its verdict. What remains unclear is the epistemic and methodological meaning of the threshold built into that framework. Here we argue that the central philosophical issue is no longer whether astronomy can define such a threshold, but how a threshold already in place should regulate scientific inquiry under uncertainty. We suggest that candidate technosignature status, such as Level 4 on the Loeb Scale, should be understood as an intermediate epistemic status: stronger than permissive openness, weaker than confirmation, yet sufficient to justify methodological escalation. The argument proceeds in three steps. First, it reconstructs the recent philosophical debate through the work of Lomas, Lane, and Cowie. Second, it turns to historical cases discussed by Kaplan (2026) to show that important discoveries are often delayed not only by weak evidence, but also by paradigms, prestige, and institutional filtering. Third, it interprets candidate status as a form of structured scientific commitment under uncertainty, one that justifies intensified observation, broader hypothesis management, and more deliberate allocation of attention and resources without licensing belief in artificial origin. The paper concludes by arguing that AI should not be the arbitrator in deducing an extraterrestrial origin, but can support the detection, comparison, and prioritization of anomalies once a candidate status has been formally recognized.

The realization of on-chip polarization beam splitters robust to fabrication imperfections remains a key challenge for polarization-sensitive photonic integration. We demonstrate a topologically protected polarization beam splitter based on a Floquet-engineered microring lattice implemented on a CMOS-compatible silicon nitride platform. By tailoring the dispersion of inter-ring coupling, the lattice supports complementary trivial and topological band gaps for orthogonal eigenpolarizations. At telecom wavelengths, TE modes propagate via a topological edge state while TM modes are suppressed by a trivial gap; this behavior reverses at shorter wavelengths. We measure extinction ratios of 16-20 dB for the protected port and 10-20 dB for the non-protected port, with insertion loss of 2 dB at long wavelengths. Reduced TM extinction at shorter wavelengths is attributed to suboptimal input coupling. We further identify spectral regions where both polarizations exhibit nontrivial band gaps, enabling polarization-independent edge transport and establishing a Floquet dual-polarization topological regime. Because operation is governed by band topology rather than geometric fine-tuning, the device shows intrinsic robustness to defects. These results establish polarization-tailored topological lattices as a scalable platform for robust beam splitting, routing, and interconnects in classical and quantum photonic systems.

The determination of quartic force fields for use in vibrational second-order perturbation (VPT2) calculations, currently available in numerous electronic structure packages, becomes very expensive as the size of the molecule increases, especially if high-level coupled cluster theory is used. Machine-learned potentials (MLPs) for large molecules and clusters offer a viable alternative to obtain the quartic force field (QFF). Here, we report Fortran and Python software to determine the QFF and perform VPT2 calculations of energies from MLPs. We describe this software briefly and then apply it to \ce{H2O} and protonated oxalate as test cases. The Fortran software is applied to 21-atom aspirin, using a fast MLP reported by us. Despite the fact that there are 32,509 unique cubic force constants for aspirin, the computer time to calculate them using this MLP is trivial, i.e., around one minute. These results are the first quantum anharmonic ones for such a large molecule. The present protocol offers an efficient way to study quantum anharmonic effects for vibrational energies in large molecules. Currently, these are obtained overwhelmingly from classical molecular dynamics simulations, which cannot describe strong anharmonicity.

Compressible multiphase and multicomponent solvers require accurate interface representation without spurious pressure oscillations. At material interfaces, pressure and velocity are continuous while density and the equation of state exhibit abrupt discontinuities. Standard approaches reconstruct primitive or characteristic variables to capture these properties, but do not clarify the failure mechanisms of conservative reconstruction or fully leverage the wave-decoupling advantages of characteristic decomposition. This work derives the complete eigenstructure of the Allaire five-equation model for two variable sets. In the fully conservative~(FC) formulation, $\mathbf{U} = [\alpha_1\rho_1,\,\alpha_2\rho_2,\,\rho u,\,\rho v,\,\rho E,\,\alpha_1]^T$, eigenvectors contain a thermodynamic jump term~$\Psi$ that enforces $dp=0$ and $du=0$ at material contacts by compensating for compressibility mismatches. In the semi-conservative~(SC) formulation, $\mathbf{V} = [\alpha_1\rho_1,\,\alpha_2\rho_2,\,\rho u,\,\rho v,\,p,\,\alpha_1]^T$, the volume-fraction eigenvector carries a structural zero in the pressure slot, enforcing equilibrium without thermodynamic correction. Explicit left and right eigenvectors are derived for one- and two-dimensional stiffened-gas flows. Both formulations satisfy Abgrall's equilibrium condition when reconstruction is performed in characteristic space; reconstruction in physical space yields $\mathcal{O}(1)$ pressure and velocity errors at interfaces regardless of the variable set. The eigenvector structure further reveals that the shear wave is decoupled from all thermodynamic and interface fields in both formulations, extending this result from single-species to compressible multiphase flows including gas-liquid configurations. One- and two-dimensional gas-gas and gas-liquid test cases confirm oscillation-free, accurate results.

Due to attractive inter-particle forces, cohesive particles suspended in turbulence undergo a complex process of aggregation, breakup, and restructuring. Despite a growing body of knowledge on the “flocculation” of cohesive granular materials suspended in homogeneous isotropic turbulence, little focus has so far been placed on wall-bounded flows where turbulence and shear are inhomogeneous. This study presents a first investigation of a fully developed wall-bounded flow of resolved cohesive particles. Five direct numerical simulations of turbulent channel flows laden with finite-sized particles at successively increasing cohesive strength are performed. A population balance equation (PBE) framework is used to analyze aggregate dynamics. When integrated over the full domain, the PBE is closed by aggregation and breakup alone. However, this balance is found to not hold locally in the wall-normal direction, where regions of net aggregate production and depletion are identified. This imbalance is shown to be compensated by the size-dependent wall-normal transport of aggregates, revealing a mean circulation: larger aggregates are preferentially produced in the channel center and migrate toward the wall where they break, while smaller aggregates are transported away from the wall, grow, and reenter the cycle.