Papers

Polaron ground states and finite-temperature dynamics remain challenging to simulate because existing methods struggle to combine nonperturbative accuracy, systematic improvability, and scalability from models to materials-specific Hamiltonians. We introduce a translationally invariant variational coupled-cluster (CC) theory for polarons, termed delocalized CC (dCC), with closed-form energies at cost as low as $\mathcal{O}(N^3)$ and no phonon-number cutoff. dCC accurately describes the ground states of the one- and two-dimensional Holstein and Su–Schrieffer–Heeger (optical and bond) models and the Fröhlich model, in close agreement with density matrix renormalization group (DMRG) and diagrammatic Monte Carlo benchmarks. A projected tangent-space response formalism built on the same ansatz yields electron-addition spectral functions and optical conductivities at zero and finite temperature. The resulting spectra agree well with DMRG, Lanczos, and neural-network quantum-state benchmarks while retaining a physically interpretable excitation hierarchy, and extend to two-dimensional lattices at finite temperature beyond the practical reach of these methods. The same framework applies directly to ab initio electron–phonon matrix elements, yielding LiF electron- and hole-polaron binding energies that match state-of-the-art many-body calculations. These results establish dCC as a unified variational framework for polaron ground states and dynamics, from model systems to real materials.

Enhancing ray tracing in dissipative anisotropic media is challenging because the subsurface exhibits both geologic and rheological discontinuities and directionally dependent attenuation. Conventional real-ray tracing and complex energy-velocity formulations struggle with qSV cusps, wavefront triplications, and abrupt discontinuities. Recent conjugate and shortest-path extensions have been proposed to mitigate these issues, yet they rely on approximate Hamiltonian formulations that are typically tested in smooth background models. In addition, they rarely address first-arrival refraction or the coupled behavior of wave propagation and energy dissipation. Here, the g*-Hamiltonian-based formulation is incorporated into a shortest-path-based ray-tracing algorithm for layered dissipative anisotropic media. At the local scale, we first compute the complex energy velocities and corresponding ray quantities, which are ray velocity, ray attenuation, and ray quality factor, using three kernels. These kernels produce complex energy velocities for all body waves in different fashions, namely, the original complex energy velocity (OEV), conjugate real ray tracing (C-RRT), and g*-Hamiltonian (MEV). We then analyze their discrepancies and numerical behavior and examine several optimization strategies. The results demonstrate that all kernels agree for qSH and qP waves, whereas OEV generates pseudo-reflection or numerical instability and unphysical attenuation along ray paths in qSV modeling. C-RRT produces large errors, including non-physical negative attenuation in some qP and qSV traveltime values, and strongly biased ray quality factors under our parameter regime. Among the tested kernels, MEV-driven shortest-path ray tracing produces the most computationally stable first-arrival behavior.

Transmission through temporary groups does not necessarily preserve complete information about the group-size distribution in aggregate epidemic data. We show that, in finite-population SIS and SIR models, the group-size distribution enters the dynamics only through a finite set of moments selected by the order of the nonlinear transmission kernel. Consequently, markedly different distributions can generate identical stochastic dynamics when their relevant moments coincide. This equivalence extends to transient evolution, fluctuations, extinction-time statistics, and final outbreak sizes. In the deterministic limit, the first moment sets the invasion threshold, whereas the second controls the nature of the transition and the emergence of bistability and hysteresis. The two distributions become distinguishable only when a higher-order transmission mechanism activates their first unmatched moment; even a weak additional channel can shift the phase boundary and place the systems in different dynamical regimes. Solutions of the master equation and stochastic simulations support these analytical predictions. These results establish an intrinsic limit on epidemic inference: a single aggregate dynamical protocol can identify only an equivalence class of group-size distributions, rather than uniquely reconstructing the full distribution.

CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage. CheMLFlow provides modular workflow components, ready-to-run reference pipelines, standardized artifacts, and evaluation outputs that reduce orchestration overhead and support benchmarking across methods and datasets. The platform is designed to be extensible, reproducible, and automation friendly, with pluggable representations and models, deterministic splits, explicit run artifacts, batch execution, and report generation. As scientific software increasingly moves toward agent assisted experimentation, CheMLFlow's configuration driven workflows and structured outputs also provide a practical interface for coding agents to help users construct experiments, inspect results, and summarize findings under human supervision. This article describes the system architecture, core workflows, and benchmarks that reach literature performance for quantum mechanical, physicochemical and bioactivity property prediction, and use cases involving time series datasets demonstrating applications beyond molecular chemistry datasets.

Spin-polarized electron beams are essential tools for probing fundamental symmetries and for the search beyond the Standard Model. While plasma-based accelerators are a promising pathway towards higher-energy frontiers, they have so far failed to deliver a competitive polarized source: existing proposals are challenging to realize and achievable polarizations remain far below conventional sources. Here, we introduce a photocathode-like scheme, applied to a gas of pre-polarized hydrogen and halogen atoms. A VUV and a visible laser pulse excite the halogen atoms to create a two-component ionization medium, consisting of low-threshold excited halogen atoms and high-threshold polarized hydrogen. Particle-in-cell simulations show witness beams with tens of pC charge retaining up to 97% of the initial polarization, rivaling state-of-the-art conventional sources.

Sub-100-fs optical pulses and frequency comb sources have been revolutionizing a wide range of applications, from ultrafast optical science to optical frequency standard and measurement. To date, the leading techniques for generating such pulses in practical systems rely on tabletop mode-locked lasers, which inherently suffer from high system complexity, limited long-term reliability, and pronounced environmental sensitivity. Meanwhile, driven by advances in photonic integration, chip-scale approaches have sought to realize miniaturized pulse sources. However, simultaneously achieving sub-100-fs duration, ideal pulse shape, and a broadband flat-topped spectrum remains a significant challenge. Here, we address these challenges by combining two key photonic chip technologies: TFLN EO modulators for picosecond seed pulse generation, and highly nonlinear optical loop mirrors (NOLM) based on AlGaAsOI nanowaveguides for efficient temporal pulse cleaning and spectral broadening. In theoretical simulation and experiment, we show that for an input seed pulse centred at ~1550nm, a single-stage AlGaAs NOLM with a loop length of 1cm can produce flat-topped, nearly tenfold spectral broadening and over tenfold compression of pulse width, and more than 10dB suppression of pulse pedestals. Using initial EO comb pulses with ps-level durations at repetition rates of 10-20GHz, we demonstrate photonic-chip-enabled pulses with an unprecedented duration of 55fs and a flat-topped comb spectrum whose 10dB optical bandwidth exceeds 90nm. Our results highlight the remarkable potential of photonic chip technologies to realize high-repetition-rate, miniaturized sub-100-fs optical pulse generators with the prospect of superior stability and operability. The demonstrated photonic-chip-based sub-100-fs optical frequency comb sources may establish a new paradigm for both scientific research and practical applications.

Transport in fluids is generally reduced to continuum laws parametrized by bulk coefficients and effective interfacial parameters, such as viscosities, diffusivities, slip lengths, and interfacial resistances. This description becomes incomplete at the nanoscale, where spatial heterogeneity, molecular structure, and finite relaxation times are inseparable from the transport process. Here we formulate coupled transport in nanoconfined fluids as a space–time-resolved Onsager response matrix and extract it from equilibrium molecular dynamics simulations. Applied to a confined charged fluid, the framework resolves the nonlocal and transient pathways coupling particle, solute, heat, and charge transport. Momentum transport appears as a long-lived, nonlocal hydrodynamic mode, whereas charge transport relaxes rapidly through localized ionic friction. Off-diagonal responses reveal distinct projected dynamics, providing a microscopic basis for nonlocal, history-dependent transport laws.

Learned spectral models fail at symmetry forced degenerate sectors for two distinct reasons. Where symmetry forces levels to coincide exactly, the per level observable one would normally fit is not well defined, since every unit vector of that shared space is an eigenvector; and near a symmetry protected crossing, the eigenvector observable gradient carries a factor 1/(lambda_i - lambda_j) that is genuinely singular as the gap closes. The usual response is to regularize the divergence or threshold the gap, and both carry a real cost: a fixed gap cannot both protect a forced multiplet and keep two genuinely distinct levels apart. We show a different fix, on synthetic operator families with a known symmetry answer key. A gate reads the symmetry structure directly from the observed operators, as the linear commutant of the family, one singular value decomposition nullspace, following the simultaneous block diagonalization of Maehara and Murota. Block identity is read from the centre of that commutant rather than from eigenvalue clustering, which makes the forced versus accidental distinction structural rather than metric, and the objective switches between a projector trace through forced blocks and a per level target elsewhere. Under operator estimation noise the gate classifies correctly to epsilon of about 0.3, where energy clustering already fails by 0.02. Gated fitting reaches the truth at machine precision in both the symmetric and the symmetry breaking regime, in the latter converging to a truth that is a singularity of the ungated objective, and observable bias sits at the noise floor. Validation off the regular representation, where multiplicity and dimension separate, eliminated two defective estimators that all regular representation tests had passed. The demonstrated object is a gated estimator; a full parametric matrix model, with the matrices learned, is the next experiment.

We present a phase-space formulation of elastic full-waveform inversion (FWI) based on local-angle-domain (LAD) representations of seismic scattering. Instead of minimizing acquisition-domain waveform misfits, the method minimizes image-domain inconsistencies in a phase-space domain parameterized by position, propagation direction, scattering angle, azimuth, and time. The formulation is motivated by the observation that velocity and impedance perturbations occupy largely distinct regions of phase space. For anisotropic elastic media, we derive a generalized objective function together with the corresponding adjoint-state gradient and Hessian operators. Representing LAD images through directional and opening wavenumbers provides a unified description of propagation and scattering effects. The approach is independent of the forward-modeling engine and can be implemented with finite-difference, finite-element, spectral-element, ray-based, or beam-based methods. Under Born linearization, ray/beam formulations allow explicit Hessian computation. Owing to the near-orthogonality of directional and opening-angle sensitivities, the Hessian is more diagonally dominant than in conventional FWI, improving parameter separation, reducing velocity-reflectivity crosstalk, and enhancing inversion stability. By organizing seismic information in a physically meaningful phase-space representation, LAD-FWI supports adaptive, resolution-driven inversion and may reduce cycle skipping through attributes that vary more smoothly than waveform residuals. The framework naturally integrates directional focusing, reflector-consistency analysis, anisotropic parameter estimation, converted-wave imaging, and diffraction-based characterization, providing a unified workflow for elastic FWI, migration-velocity analysis, impedance inversion, and high-resolution subsurface imaging.

Modified Newtonian Dynamics (MOND) generally resolves the need for dark matter in galaxy rotation curves introducing a single new constant of acceleration $a_0$. It is well known that increasing $a_0$ by a factor of a few can alleviate the residual mass discrepancies that MOND leaves in galaxy clusters. Within a parameter-free Machian interpretation of MOND motivated by $a_0\sim GM_u/R_u^2$ arising from the scalar sum of inverse-square distance gravitational mass contributions in the universe, we promote $a_0$ to a variable in spherically symmetric mass distributions, which is sensitive to mass external to the locally enclosed spherical region. Instead of a boost of $a_0$ in terms of gravitational potentials as in EMOND, we show that a boost in terms of this directionless inverse-square field roughly amounts to the boost needed to accommodate the mass discrepancies of MOND in galaxy clusters, while retaining MOND predictions in galaxies. We begin to generalize this approach by defining a new $a'_0=G\sum_{i}m_i/|\mathbf{r}-\mathbf{r_i}|^2-a_N$, where $a_N$ is the magnitude of the usual Newtonian acceleration.

Prior-based Multi-Voltage Threshold (MVT) sampling reconstructs pulse parameters from sparse threshold-crossing times rather than full waveforms, making parameter recovery inherently a model-dependent inverse problem. However, prior-based MVT has lacked a formal mathematical statement, leaving identifiability, stochastic error propagation, and threshold design without a unified theoretical foundation. We formalize prior-based MVT for strictly unimodal pulse families as a structured inverse problem. On that foundation, we develop the first unified theory of prior-based MVT, comprising deterministic identifiability conditions, a stochastic timing-error model with leading-order mismatch bias, and a nuisance-profiled threshold-design theory centered on an effective-information equation for robust single-event and partial-trigger multi-event operation. We instantiate the framework for the bi-exponential pulse model, derive executable design recipes, and validate the resulting predictions on a 10,000-pulse $^{22}$Na/LYSO/SiPM dataset. The experiments confirm that the framework yields useful threshold designs in the photopeak regime while also revealing the regime boundary at which partial triggering and model mismatch limit the predictive power of Fisher-guided optimization. These results provide the first unified mathematical foundation for prior-based MVT and recast it from an empirical threshold heuristic as a principled inferential framework.

Extreme flows realize the largest possible growth, either instantaneously or in finite time, of certain quantities of interest which is achieved by a suitable choice of the initial condition or the applied forcing. The quantities of interest usually measure some small-scale properties and therefore provide information about the regularity of the flow. Extreme behavior is at the heart of several open problems in fluid mechanics including the dissipation anomaly in turbulence and formation of singularities in various models of fluid flow. In this essay we describe a framework making it possible to study such extreme behavior systematically by combining mathematical analysis, scientific computation and physics. As a first step, one aims to deduce rigorous upper bounds on the growth of the quantities of interest in the solutions of a given model. These inequalities express fundamental limitations on the most extreme behavior possible among all admissible solutions. However, given how they are obtained, these bounds may be conservative and overestimate the growth actually realizable in the system. In order to probe this possibility, as the next step, we set up variational optimization problems where the growth of the quantity of interest is maximized under suitable constraints. Solution of such problems is enabled by modern methods of numerical optimization. When properties of the thus obtained maximizers match the bounds, the bounds are declared sharp and therefore cannot be fundamentally improved. Finally, properties of the solutions saturating the bounds reveal insights about the physical mechanisms realizing the extreme behavior. We survey problems where this research program has produced sharp bounds together with extreme flows saturating these bounds. A collection of open problems is then presented and we close the essay with a discussion of possible methodological improvements.

We present a statistical-mechanics framework for computing equilibrium binding constants $K$ in the dilute limit. From first principles, we derive a general expression relating $K$ to the relative populations of the bound and unbound states. Its transparency has twofold advantage: it makes the origin of the unbound-state volumetric term explicit, and it allows one to track exactly how an imposed volume restraint propagates through the expression. This makes $K$ directly computable, as restrained simulations can account for the volumetric contribution exactly, under the physically mild assumption of a homogeneous unbound state. The resulting estimators are computable from histograms of any suitably defined reaction coordinate, and determine unambiguously how the boundaries of the thermodynamic states of interest must be defined. We apply our framework to the cucurbit[7]uril/1-adamantanol host–guest complex and the galactonate–DgoT ligand–protein complex. Our results show that commonly used single-bin estimators depart from the theoretically correct one by $\approx 1$~kcal/mol in both systems. This shift originates in the definition of the bound state: by anchoring that definition to what state-of-the-art experiments resolve, the theory turns it from a hidden assumption into a controlled input, and provides a principled route to absolute binding affinities from molecular simulations.

Pipe flow at intermediate Reynolds numbers, between the laminar and fully turbulent regimes, takes the form of several spatially and temporally intermittent phases in which turbulent and laminar states coexist. In the lower range, $Re\in (1750,2300)$, turbulence appears in the form of localized traveling structures called "puffs", which form long-lived chaotic dynamical states, whose stochastic decays and splits control the steady state intermittency. At the other end, $Re\in (2300,3000)$, puffs are replaced by an extended turbulent state, with laminar pockets intermittently forming and disappearing within it. Using direct numerical simulations of pipe flow at $Re = 2400, 2450, 2500, 2550$, we provide evidence that these laminar gaps form a distinct dynamical state analogous to puffs: a traveling laminar pocket in a turbulent surrounding, stabilized by a shear-dependent self-tuning mechanism. We analyse the mean spatial profile of these gaps and show that their lifetimes are exponentially distributed, suggesting that gap closing corresponds to an escape from a chaotic saddle. Finally, we suggest these laminar gaps become unstable and disappear at a finite Reynolds number, $Re\sim 2900$, which can be interpreted as the onset point of spatially and temporally homogeneous turbulence.

Nanofluidic memristors whose conductance evolves through history-dependent ionic transport and dynamic interfacial processes are promising building blocks for ionic neuromorphic applications. However, most existing designs rely on biological nanopores, polymers, and two-dimensional materials, which limit scalable fabrication and poses challenges to integration of ionic computing circuits and systems. Here, we report memristive behaviors of silicon-based solid-state nanopores (SSNPs) fabricated based on wafer-scale semiconductor processes. The SSNPs exhibit hysteretic current-voltage characteristics with a dependence on voltage sweeping frequency, electrolyte concentration, and nanopore geometry. To investigate the physical origin of their memory feature, the measured current of the SSNPs is decomposed into resistive, capacitive, and memristive components. An ion adsorption-desorption kinetics is developed to explain and predict the memristive behavior. A dynamical system analysis further reveals that the memristive behavior arises from delayed relaxation, thereby linking the measured hysteresis to the observed adaptive ionic response. Together, these findings establish native SSNPs as scalable ionic memristive elements and provide a generalized electrokinetic mechanism for memristive behavior under nanoconfinement. The resulting analytical framework connects device characterization with the underlying dynamics, deepens mechanism understanding, and guides the design of ionic neuromorphic devices.

Rydberg entangling gates in optical-tweezer arrays are commonly executed with the trapping light switched off, so every gate contains a release–and–recapture cycle that heats the atomic motion and can ultimately limit circuit depth. We develop a motional refocusing protocol that exactly removes this heating in the harmonic approximation using only programmable intensity switching of the trapping light. The protocol closes the release–and–recapture cycle for every matched harmonic mode, returning arbitrary motional populations and coherences exactly up to ordinary evolution under the static trap. We derive the recovery sequence in closed form for arbitrary catch depth and prove that, within the experimentally relevant regime, it is the unique globally time-optimal solution under bounded trap intensity. The harmonic theory is then extended in two directions. First, we construct exact common-intensity recovery sequences that simultaneously refocus several nondegenerate harmonic modes, including radial–axial and fully anisotropic three-dimensional traps. Second, we derive a composite sequence that suppresses the leading anharmonic correction of weakly anharmonic traps by canceling all first-order motional transitions induced by the quartic anharmonicity, changing the residual heating law from $U_0^{-2}$ to $U_0^{-4}$. Wave-packet simulations in realistic Gaussian tweezers validate the analytic theory and quantify the residual effects of anharmonicity, finite switching ramps, trap ellipticity, and control errors. Applied to representative cesium Rydberg gates, the protocol suppresses the dominant recapture heating to the anharmonic floor and prevents the associated motional Doppler contribution from increasing with circuit depth. The resulting framework provides a practical route toward heating-free trap-off neutral-atom gates using only trap-intensity modulation.

We propose a phase field model of a macrocracked single crystal in which the crack and the geometrically necessary dislocations descend from a single energy functional. Energy minimization alone then decides dislocation nucleation, through an integral criterion evaluated in closed form along slip chords. The criterion yields a size effect inaccessible to point-wise strength conditions: a grain-size-dependent yield stress. With slip suppressed the model reproduces Griffith fracture; with fracture suppressed, the nucleation load measured by the full non-smooth solver agrees with the closed-form nucleation criterion to four percent. The coupled computations produce a two-stage response: at loads an order of magnitude below cleavage, dislocation bands emitted from the notch tip blunt and shield it, raising the initiation load; once the crack grows, the bands heal; a compact cluster of like-signed dislocations travels with the tip, its canceling partner walls pinned at the grain boundary, and the dissipated fracture resistance equals the elastic one. In the purely energetic, dissipationless limit, emission shields the crack but does not toughen it; toughening requires dissipation, incorporated in the sequel through the threshold resistance to dislocation motion.

Pericyclic reactions provide stringent tests for quantum simulations because their mechanisms are governed by orbital symmetry and involve correlated transition states. In this work, we employ the variational quantum eigensolver (VQE) combined with a previously established symmetry-guided active-space selection protocol based on symmetry-matched fractions (SMF-VQE) to simulate Diel-Alder and Alder-ene reactions in complex systems involving extended pi-conjugation and multiple bonding. Although absolute electronic energies obtained from the current protocol exhibit significant deviations from the values computed using CCSD method, the symmetry-guided active spaces yield substantial cancellation of deviations in the energy differences. As a result, reaction energies are predicted with error (relative to CCSD) less than one kcal per mol, while activation energies are reproduced within about five to six kcal per mol. The symmetry-guided protocol also reduces the large combinatorial space of active-space choices to a single symmetry-consistent selection for each reaction.

We propose a mathematical model that represents the influence of an external actor (influencer) on a network of actors (influenced). The model is an adaptation of control systems within the family of formulations inspired by the Kuramoto Model of synchronisation. Capturing influence as a capacity to affect the character or behaviour of another, we study an external node's ability to influence the synchronisation of the Kuramoto system while its global behaviour is pulled to the external node's frequency and away from its natural mean frequency. In our work, stochastically generated dynamical weights assigned to the links between the network and the external influencer whereby links from one system to the other are assigned via one-sided heavy tail noise, generated by the Gamma distribution. We perform numerical experiments to examine transition points in the ability of the external node to alter the behaviour of the influenced network; either to disrupt synchronisation or to drive it to collective frequencies determined by the external node. We examine the dependence of transition points on the external node's frequency, where too ambitious a driving frequency fails to influence the system while retaining a synchronised state, and reducing achieves a state of synchronisation at a frequency shifted from the mean natural frequency. We also look at the analytic approximation for the system close to synchronisation and fragmentation to understand its behaviour around this limit.

We propose an improved mean-field analysis of cellular automata models of single-lane vehicular traffic. By combining aspects of the Car-Oriented-Mean-Field (COMF) theory and the 2-site cluster method, which have been previously successfully applied to similar models, we aim to capture both short- and long-range correlations more accurately. In contrast to classical mean-field theories, the improved method is well suited for models with inhomogeneous stationary states and able to capture the essential properties of phase separation, e.g. in models with slow-to-start rules. The improved accuracy and new physical insights are illustrated through an application to the VDR model with $v_{\text{max}}=1$.

Forecasting turbulent flow dynamics requires a balance between predictive fidelity and computational efficiency. Diffusion-based generative models can represent complex spatiotemporal dynamics, but their application to high-dimensional turbulent flows remains computationally expensive. In contrast, proper orthogonal decomposition (POD) provides compact, physically interpretable reduced-order representations, although aggressive modal truncation can remove relevant flow structures. This work introduces a hybrid reduced-order generative forecasting framework that combines POD with Generative Learning of Effective Dynamics (G-LED). The method performs temporal prediction in a physics-based modal space and uses diffusion-based reconstruction to recover physically meaningful flow-field representations. It is assessed using experimental measurements of the turbulent wake behind a circular cylinder. Three configurations are compared: full-field G-LED, global POD-G-LED, and localized POD-G-LED. Full-field G-LED provides the highest fidelity, preserving richer vorticity fluctuations and more consistent turbulent kinetic energy distributions, but requires approximately 17 h for diffusion-model training, 7 h for Transformer training, and 3 min to predict 100 future snapshots. By transferring prediction to a reduced POD space, global POD-G-LED reduces these costs to approximately 8 h, 2 h, and 50 s, respectively, while retaining dominant wake organization and coherent energetic structures. A localized POD-G-LED formulation assigns different modal resolutions to distinct wake regions and improves vorticity statistics and energetic distributions relative to the global reduced-order configuration. These results show that coupling physics-based modal representations with diffusion-based generative reconstruction offers an effective route to efficient turbulent-flow forecasting.

We investigated strong-field dissociative ionization of $\mathrm{CF_2I_2}$ ensembles prepared in different initial rotational-state distributions using an electrostatic deflector. Pronounced changes in ion-channel branching ratios revealed a strong dependence of the fragmentation dynamics on the initial rotational excitation. Analysis of fragment yields and their laser-power dependences identifies resonance-enhanced multiphoton ionization through an intermediate excited state, $\mathrm{CF_2I_2^{\ast}}$. The measured branching behavior indicates competition between stabilization into bound ionic states and dissociative channels, driven by near-threshold non-adiabatic Coriolis-type coupling. Tuning the rotational energy by only a few $\mu$eV is sufficient to significantly alter the ionization dynamics and to redistribute the reaction products. These findings demonstrate the key role of rotational excitation in controlling non-adiabatic dynamics following strong-field ionization of $\mathrm{CF_2I_2}$.

The diffraction limit fundamentally constrains the spatial resolution of far-field imaging systems. While near-field techniques can circumvent this limit, their inherently short working distances (WD) severely restrict practical applications. Super-oscillatory lenses (SOLs) offer a far-field alternative; however, conventional SOLs are plagued by discrete operating wavelengths, low efficiencies (below 5%), and formidable trade-offs among numerical aperture, chromatic aberration, and depth of focus (DOF). Here, we introduce a nonlocal, nonlinear-curvature mechanism to design a freeform SOL that achieves ultrabroadband (0.3 to 1 THz), achromatic super-resolution focusing with an unprecedented efficiency of 44%. Operating at a 9 mm WD, the lens maintains a consistent sub-diffraction full-width at half-maximum (FWHM) of around 0.45 wavelength alongside an extended DOF of around 10 wavelengths. By integrating a compact 65-nm CMOS oscillator-radiator array, we establish an advanced imaging platform capable of resolving complex 2D and 3D sub-millimeter features (down to 0.15 mm). Readily scalable to the optical regime via two-photon lithography, this freeform SOL paradigm paves the way for next-generation, high-performance integrated photonics.

We propose a chirped-pulse injection scheme into a hard x-ray low-emittance synchrotron light source such as PETRA IV from a laser-plasma electron injector with active energy compression. The scheme enables delivering kA-scale short pulses with several tens of hertz repetition rate to any synchrotron beamline in the ring and allows producing femtosecond temporally coherent radiation pulses at target beamlines.

The free surface atop a turbulent liquid flow is deformed by the underlying fluid motion, with the turbulence imprinting its geometry on the surface. Here we develop a theoretical framework to model such deformations based on the Euler equation with gravity and surface tension, and evaluate it against simultaneous high-resolution measurements of surface topography and subsurface velocity fields in a zero-mean-flow turbulent water tank. We consider a range of Reynolds and Froude numbers, focusing on regimes in which the surface is unbroken. Over a range of spatial and temporal scales, we find close quantitative agreement between the measured surface curvature and that which is modeled based on the velocity field a few millimeters beneath the surface, both from the statistical and the local/instantaneous standpoints. Importantly, we verify a strong correlation between the magnitudes of the surface curvature and the divergence of the near-surface horizontal velocity, which in turn is directly related to gas and heat transfer at the air-water interface. We discuss how the sub-surface motion at increasing depths decorrelates from the surface shape, and does so more rapidly at smaller spatial scales. These findings demonstrate that measurements of surface deformations may be used to sense the state of the flow beneath the surface and provide a foundation to make optically-based inferences of processes controlled by near-surface turbulence.

Deviations from both the ideal linear Paul trap (LPT) geometry as well as the ideal quadrupole driving scheme introduce imperfections to ion trapping potentials. We investigate the effects of these imperfections in a LPT operated in a conventional dipole-drive configuration. We demonstrate the trapping of the Li$^+$ ions along the axial direction with zero and negative end-cap voltages. This occurs due to the modified axial $a-q$ space resulting from radial-to-axial coupling of the electric field. The dipole drive configuration lifts the degeneracy of the radial trapping potentials, resulting in unequal radial secular frequencies, and this is demonstrated experimentally. The combined effects of dipole drive and trap dimensions are summarized in a two-dimensional map that quantifies deviations from ideal behaviour. Based on this map, we propose a geometric modification that significantly reduces radial-to-axial coupling of the potential.

The XLZD observatory is a next-generation experiment designed to search for weakly interacting massive particles (WIMPs) and other rare events using a 60-80 tonne liquid xenon time projection chamber (TPC). This detector aims to achieve sensitivity across the full WIMP parameter space down to the neutrino fog, establishing the ultimate sensitivity for this dark matter search paradigm. This unprecedented scale introduces substantial engineering challenges and pushes operation into largely unexplored regimes: the interplay between high-voltage (HV) systems, liquid xenon, and conducting materials in ultra-pure environments. To systematically investigate these challenges, we have built MOTION, a 70 kg LXe detector dedicated to understanding HV performance and electrostatic phenomena up to 200 kV (negative polarity). We describe the design and construction of the experimental infrastructure, including the cryogenic system, xenon purification and storage. MOTION enables controlled studies of dielectric breakdown in LXe, permitting systematic characterization of discharge mechanisms and their dependence on electrode geometry, surface condition, and applied voltage. The detector also facilitates investigations of field emission and photoemission from electrodes following various surface treatments, and provides a platform for validating the design of an HV feedthrough constructed from radiopure materials. The insights from these studies are essential for ensuring the operational stability, radiopurity, and scalability required for next-generation dark matter detectors.

Stark spectroscopy is a powerful method for probing molecular dipole moment changes, charge transfer dynamics, and polarizability under applied electric fields. Time-Resolved Terahertz Stark Spectroscopy (TRTSS), which employs intense single-cycle terahertz (THz) pulses to induce transient Stark shifts, overcomes key limitations of conventional approaches. Unlike static or low-frequency fields, THz pulses oscillate much faster than typical molecular rotation times, effectively preventing dipole reorientation and enabling measurements in solutions at ambient conditions. Here, we extend TRTSS to molecules dissolved in water, the most important polar solvent for chemical and biological systems and report the first demonstration of Stark spectroscopy in water at room temperature. Using Malachite Green and Methyl Orange as model systems, we observe clear THz-induced spectral modulations, demonstrating that TRTSS can successfully reveal THz Stark responses even in highly polar, hydrogen-bonded environments. Measured signals exhibit a combination of linear (dipole-driven) and quadratic (polarizability-driven) Stark effects in both systems, consistent with time-dependent density functional theory (TD-DFT) calculations. Comparison with TD-DFT further suggests that conformational effects can influence the extracted Stark parameters in solvated molecules.

Neurtal atoms in optical tweezer arrays constitute a highly promising platform for quantum computing and quantum simulation. Their operation relies on precise control of the Rydberg excitation, which is highly sensitive to background electric fields. Here, we identify a previously overlooked source of stray electric fields arising from trapped charges within the antireflection coating layers of glass vacuum cells. In contrast to the conventional approach of removing surface charges through ultraviolet-light-induced desorption, we compensate these clamped charges by generating additional charges via photo-ionization of a cold atomic ensemble. We verify the resulting suppression of stray electric fields through Rydberg excitation spectroscopy in an atomic array and further confirm that the residual electric field inside the vacuum cell is effectively eradicated with the aid of external electrodes. Our work identifies and mitigates a previously unrecognized source of residual electric field, providing a practical solution for improving the performance of neutral atomic quantum processors and other Rydberg-based quantum technologies, as well as surface-sensitive atomic systems.

The mechanism of turbulence modulation in particle-laden flow over erodible beds remains an open question. Using particle-resolved direct numerical simulations, this study realises a longitudinal-to-transverse bedform transition by varying the Shields number, revealing non-monotonic modulation of near-wall turbulence. At low Shields numbers, streamwise sediment ridges generate form-induced streaks that produce a distinct secondary peak in the premultiplied energy spectra, exceeding the conventional near-wall turbulent peak and enhancing the turbulent kinetic energy. As the Shields number increases, saltation intensifies and disrupts these structures, causing the secondary peak to vanish in the streamwise direction and weaken in the spanwise direction, thereby suppressing turbulence. Proper orthogonal decomposition of the bed surface reveals a redistribution of modal contribution from a single dominant mode to higher-order modes, with longitudinal features persisting as remnants, directly linking bedform evolution to turbulence modulation.