Papers

The deep ultraviolet (DUV) window (200-300 nm) is essential for the characterization of (bio)chemical and material systems through the UV signatures of nucleobases, amino acids, peptide bonds, many organic moieties, and wide bandgap transitions. However, extending ultrafast spectroscopy to the DUV to access the associated electronic and structural dynamics has largely remained elusive, due to the limited bandwidth and efficiency of common femtosecond DUV pulse sources. We now close this gap and demonstrate ultra-broadband femtosecond transient absorption (TA) spanning 200-800 nm, achieving unprecedented coverage of the entire DUV window. We generate supercontinuum probe pulses through soliton self-compression in a helium-filled hollow capillary fiber at a repetition rate of 20 kHz and fully suppress their high intrinsic intensity fluctuations via a correlation matrix referencing scheme. We thus achieve detector-noise-limited TA measurements with an exceptional resolution of 9 $\mu$OD in one second, and demonstrate these novel capabilities by resolving the ultrafast spin-crossover dynamics of a Fe(II) complex in the DUV. This work opens the path to unravel previously inaccessible photophysical and photochemical dynamics encoded in the DUV.

Exceptional points (EPs) are non–Hermitian degeneracies at which both eigenvalues and eigenvectors coalesce, usually engineered through balanced gain and loss or non–reciprocity. In this work, we show that the radial electrostatic problem of an anisotropic core–shell nanoparticle realizes an EP without any of the alluded conditons. Written in the logarithmic radial coordinate, the problem is a Cauchy–Euler equation that maps onto a damped oscillator and, equivalently, onto a two-site Hatano–Nelson Hamiltonian, so that the non-Hermiticity is emulated in space rather than in time and the sole control parameter is the degree of dielectric anisotropy, $\varepsilon_t/\varepsilon_r$. The shell is passive, reciprocal, and lossless, yet it hosts an EP and a non–Hermitian phase transition governed by hyperbolicity alone. Beyond a hyperbolicity threshold the electric field inside the shell turns from monotonic decay into spatial oscillation, producing hotspots at intermediate radii which can be exploited to tailor fluorescence, strong coupling, sensing, or cloaking. More broadly, this establishes hyperbolic media as a platform to simulate non–Hermitian physics without gain, loss, or non–reciprocity.

Automating prostate radiotherapy treatment planning is dosimetrically complex, particularly for extreme hypofractionated regimens. In this study, we introduce Dose-PlanNet, a physics-guided 3D deep learning architecture designed to predict dose distributions. This model's performance was evaluated on a cohort of patients treated in a prospective trial where two different dose fractionation regimens were employed. Dose-PlanNet achieved comparable target coverage ($D_{95}$), though statistical analysis revealed a marginal reduction in target homogeneity ($p<0.001$) offset. However the model achieved statistically significant improvements in high-dose organ-at-risk sparing ($p<0.001$). When evaluated against strict Prospective Randomized protocol volumetric constraints, automated plans met prespecified clinical acceptance criteria in $11$ out of $14$ Moderate Hypofraction Arm plans and $9$ out of $12$ Stereotactic Body Radiation Therapy Arm plans. This pipeline demonstrates that physics-informed deep learning can accelerate radiotherapy workflows while safely maintaining the stringent dosimetric quality required for high-precision clinical deployment.

At the nano- and microscale, various patterns influence and shape thermal and optical response, making them essential for the survival of various biological species. In addition, controlling thermal radiation is vital for a broad range of applications, such as thermal management, spectroscopy, optoelectronics, and energy conversion technologies. For this reason, there is strong pressure to elucidate the physics of thermal radiation at the nanoscale. In this article, we provide evidence that complex nanoscale geometries affect thermal management, leading to an unusual thermal response in heat-capacity measurements as a function of temperature. Beyond identifying the structural constraints associated with this unusual thermodynamic response, the current study introduces the possibility of shaping the apparent heat-capacity response through geometry without necessarily altering the system's chemistry.

Accurate and efficient simulation of ultrasound pressure fields and pulse-echo responses is essential for transducer design, beamforming optimization, and model-based imaging research. Conventional Spatial Impulse Response methods compute acoustic fields by explicitly sampling trapezoidal impulse responses for each rectangular aperture subdivision, which leads to high computational cost for large apertures, dense spatial grids, and high sampling frequencies. We introduce Sparse Delta Integration, a mathematical framework for the far-field SIR of rectangular apertures that expresses the trapezoidal spatial impulse response as the double integration of a sparse set of Dirac delta distributions. This formulation yields compact expressions for time-domain and frequency-domain SIRs and enables vectorized implementations whose computational cost is independent of the trapezoid duration. We further derive a spectral formulation for pulse-echo simulation that removes part of the conventional Fourier-domain convolution pipeline. Implemented in the open-source Python package eSDIva, these methods achieve speedups of up to 180x for temporal SIR computation and up to 20x for pulse-echo RF simulation relative to Field II, while maintaining high numerical accuracy with mean squared errors below 1e-5 and correlation coefficients close to unity.

In this essay I will make a non-exhaustive survey of the role of scientists, politicians, and the military in the development and use of the Bomb in the thirty years following the beginning of the United States Manhattan Project. I will be mainly focusing on facts or documents that have gradually fallen into oblivion, but that in my opinion are important to develop a critical view of the past responsibilities, and to consciously address the present dramatic situation of worldwide wars, that may yield again to the use of the Bomb.

The maximum energy dissipation principle is employed to nonlinear chemical thermodynamics in terms of distance variable (generalized displacement) from the global equilibrium, applying the optimal control interpretation to develop a variational formulation. The cost-like functional was chosen to support the suggestion that such a formulation corresponds to the maximum energy dissipation principle. Using this approach, the variational framework was proposed for a nonlinear chemical thermodynamics, including general cooperative kinetics model. The formulation is in good agreement with standard linear non-equilibrium chemical thermodynamics.

Muon acceleration is a key technology for producing low-emittance muon beams over a wide energy range. Various acceleration schemes have been proposed for applications ranging from low-energy $\mu$SR and precision particle-physics measurements to neutrino factories and muon colliders. Experimental demonstrations of muon acceleration, however, have so far been limited. At J-PARC, a positive-muon accelerator based on the production and acceleration of ultraslow muons is being developed, and in 2024 the first RF acceleration of positive muons was demonstrated. In this review, we provide a brief overview of muon-acceleration methods and related experiments, and then review the acceleration method, current status, and future prospects at J-PARC.

Within the Clifford algebra of spacetime, a covariant electrodynamics is formulated in which the derivative of the four-potential contains a scalar field (S) in addition to the electromagnetic bivector. A generalized system of Maxwell equations is derived that reduces to the classical Maxwell system when S=0. The energy-momentum tensor, the corresponding conservation laws, and a generalized Lorentz force density are obtained. It is shown that, in free space, the field S admits wave solutions, termed scalar S-waves. The structure and energy characteristics of a free S-wave, as well as its possible sources and the conditions for its experimental detection, are determined.

We clarify some mathematical features of the density of a dilute particle flow on a body surface in a two-fluid model, with a view toward its application to icing simulations. Inviscid solutions in the vicinity of a front stagnation point are obtained in exact and perturbed forms for small and large Stokes numbers, respectively, and the behavior of the density of the dispersed phase is clarified over the entire range of Stokes numbers. The viscous effect of the laminar boundary layer in the carrier phase on the particle-free thin layer appearing in the low-Stokes-number regime is also investigated based on the incompressible Navier-Stokes equations. An order-of-magnitude estimation of the velocity field of the dispersed phase reveals the cause of this layer and the criterion for its appearance. In particular, the latter result provides an improved form of Michael's criterion.

The European XFEL accelerator produces high-brightness X-ray pulses using relativistic electron beams. Beam characterization is performed at multiple diagnostic stations using scintillator screens, where beam images are analyzed to extract key parameters such as emittance, energy spread, and current profile. Accurate detection of the region of interest in these images is essential for reliable beam diagnostics and stable accelerator operation. Conventional methods, such as bounding-box-based beam localization, can become less reliable for complex or non-ideal beam profiles, such as low-intensity signals, tilted or streaked beams, and multiple beam structures. In this work, we develop machine learning-based approaches for region of interest detection directly from beam images. These ML methods adapt to variations in beam shape and intensity and enable more precise, pixel-level identification of beam regions. Experimental results demonstrate improved robustness and accuracy in challenging conditions compared to traditional techniques. The proposed methodology is validated at the European XFEL and is broadly applicable to image-based beam diagnostics across accelerator facilities.

Many matrices in computational chemistry are neither sparse nor low-rank, making the design of low-scaling algorithms difficult. In this Perspective, we point out that many important matrices in computational chemistry, such as the Coulomb matrix, the electronic repulsion integral tensor, the density matrix, the localized molecular orbital (LMO) coefficient matrix, the Fock matrix, and the nuclear Hessian matrix share the same property: when their basis functions are suitably ordered, their off-diagonal blocks have low numerical ranks (despite that they as a whole have high numerical ranks). This property, termed off-diagonal low rank (ODLR), has been extensively studied in the mathematics community, but has surprisingly found very little use in computational chemistry. This Perspective reviews the existing mathematical literature on how to use the ODLR property of matrices to compactly store, as well as efficiently calculate or use them. Subsequently, we review the use of the ODLR property in computational chemistry, and point out possible future opportunities of devising new low-scaling methods for dense, full-rank matrices, exploiting the ODLR property. In particular, we prove for the first time that Fock matrices and LMO coefficient matrices satisfy the ODLR property, even if the system is gapless (in which case the matrices are dense). This paves the way to linear scaling electronic structure calculations of gapless systems at zero electronic temperature.

The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separation is practical, we argue that combining short- and medium-range weather into a single prediction system would provide the public with a useful distillation of global weather and its impacts. To this end, we present Nested-EAGLE (Experimental Artificial intelligence Global and Limited-area Ensemble): a 0.25{\deg} global weather model with a 6 km refinement over the Contiguous United States (CONUS). The model achieves significantly lower mean-squared error in near-surface and low-level quantities over CONUS compared to NOAA's Global Forecast System and High-Resolution Rapid Refresh (HRRR), while remaining competitive throughout the rest of the global atmosphere. We show that the skill gains for near-surface fields stem from incorporating high-resolution regional analysis data into training through the nesting process. Forecasts of precipitation amounts are less skillful than those from HRRR, owing to deterministic training. However, we show that Nested-EAGLE provides the most accurate forecasts of storm locations at longer leads, despite blurred extrema. Our results motivate future work to extend the skill gains beyond CONUS and improve precipitation representation.

We introduce the Split Coaxial Cable Medium (SCCM), a mechanically tunable, capacitively loaded wire medium supporting artificial-dielectric and artificial-plasma Bloch regimes with high in-plane isotropy. Its unit cell comprises coaxial conductors interrupted by axial gaps. Relative axial displacement continuously varies their capacitive overlap and series capacitance while preserving the transverse lattice geometry and leaving the unit-cell inductance approximately unchanged. Equivalent RLC parameters are derived directly from the geometry and incorporated into complementary analytical models. A spatially dispersive local-field model predicts the Bloch dispersion and isofrequency contours, whereas a multilayer homogenization model provides the modal impedance, closed-form estimates of the low-frequency Bloch refractive index and plasma frequency, and loss-inclusive finite-slab scattering. Full-wave eigenmode and finite-slab simulations validate the predictions. Over the investigated displacement range, simulations yield tunabilities of $46\%$ in the low-frequency Bloch refractive index, from $n_{0,\min}=1.42$ to $n_{0,\max}=2.28$, and $21\%$ in the plasma frequency, from $f_{\mathrm{p},\min}=9.45~\mathrm{GHz}$ to $f_{\mathrm{p},\max}=11.61~\mathrm{GHz}$, while confirming high in-plane isotropy near the $\Gamma$ point in both regimes. These characteristics make the SCCM promising for gradient-index devices, directive antennas, and tunable plasma haloscopes.

The ionosphere is a highly complex plasma containing electron density structures with a wide range of spatial scales. Coupling of the ionosphere with the Earth's magnetosphere and the solar wind, as well as to the neutral atmosphere, makes the ionosphere highly dynamic and highly dependent on the driving processes. Thus, modelling the ionosphere and capturing its full dynamic range considering all spatiotemporal scales is challenging. Swarm is the European Space Agency's (ESA) first constellation mission for Earth Observation, comprising multiple satellites in low Earth orbit. During the Swarm-VIP-Dynamic project, a suite of statistical models has been developed using observations from Swarm and proxies for heliogeophysical processes. The statistical modelling technique of Generalised Linear Modelling was used to create models for both the electron density and the variability of the plasma structures at horizontal spatial scales between 7.5 km and 100 km. Separate models were created for low, middle, auroral and polar latitudes. The models make predictions based on explanatory variables, which act as proxies for the underlying physical processes. The performance of the models of the electron density approached the theoretical best values for some of the goodness-of-fit statistics. This suggests that the modelling method is appropriate for the task undertaken. The models of ionospheric variability at larger spatial scales (about 100 km) also perform well, however the model performance decreases at smaller spatial scales. This suggests that there are physical processes missing from the models. Possible candidates are instability processes or driving forces of the ionosphere by wave activity from below, neither of which are captured by the models.

These proceedings summarise a lecture on Digital Signal Processing (DSP) delivered at the CERN Accelerator School (CAS) on Beam Instrumentation (BI). In this context, a signal is any measurable quantity conveying information about the beam. A digital signal is obtained by discretising a continuous-time signal in both time (sampling) and amplitude (quantisation). Digital signal processing refers to the set of techniques used to analyse and manipulate such signals. Given the breadth of the field, this contribution focuses on fundamental DSP concepts and on techniques most commonly encountered in beam instrumentation applications.

Circular states of strontium are promising platforms for quantum technologies. They exhibit much longer lifetimes than laser-accessible Rydberg states and possess an optically active ionic core that can be manipulated with laser light. Here we trap circular states in optical tweezers by exploiting the dynamical polarizability of the ionic core. We measure the decay of the orbital component of a n=51 circular state and observe singlet-triplet spin oscillations arising from the interplay of vector light shifts induced by the optical tweezers, spin-orbit coupling associated with the Rydberg electron, and the difference in Landé $g$-factor between the ionic core and the Rydberg electrons. By observing the two electron spin dynamics, we measure the spin-orbit interaction and the correction $\delta g \sim$ 10^(-5) of the Landé $g$-factor of the Sr$^+$ 5s$_{1/2}$ ionic core electron with respect to that of a free electron. This provides a first high-precision measurement of $g_{\textrm{Sr}^+, 5s_{1/2}}$ = 2.002290(1).

We use a large database of direct numerical simulations to investigate the transition of the Rayleigh–Taylor instability to turbulence and its evolution toward a late-time self-similar regime. In addition to tracking the growth of the mixing layer through the mean heavy-fluid concentration profile, we analyze one-dimensional profiles of turbulent kinetic energy and dissipation, two key quantities in classical turbulent-mixing models. We consider two reduced-order modeling strategies that differ in where nonlinearity is introduced: either in the construction of the latent space or in the description of its temporal evolution. The first method uses a linear encoder–decoder obtained using Proper Orthogonal Decomposition (POD), with nonlinear reduced dynamics learned by a physics-informed neural network (PINN). The second uses a nonlinear encoder–decoder learned by an autoencoder, while constraining the latent dynamics to remain linear and satisfy physical constraints. Both approaches achieve satisfactory performance in reconstructing, interpolating, and extrapolating the dynamics of the Rayleigh–Taylor instability.

We develop a perturbative framework for constructing the fields of electromagnetic beams beyond the paraxial approximation, in which the vectorial correction is shared symmetrically between the electric and magnetic fields and separated from the isotropic scalar correction. The method is based on a direct iterative derivation from Maxwell equations and combines complementary electric-first and magnetic-first constructions, thereby removing the construction-dependent assignment of the vectorial correction to either the electric or magnetic field. In addition, the scalar envelope correction is obtained independently from the isotropic co-polar contribution to the solutions of the higher-order Helmholtz equation. The resulting corrected fields apply to an arbitrary paraxial envelope and input Jones polarization.

The propagation of sustained Boussinesq gravity-current fronts along a heated wall is examined through laboratory experiments. The gravity currents are generated by continuously supplying a mixture of air and carbon dioxide at the inlet of a rectangular channel, while uniform wall heating is provided by resistive fabrics. In addition to the Froude number, defined either at the source (Fr_s) or at the current front (Fr), we show that the front velocity is governed by \Lambda_s, the ratio of the two buoyancy fluxes per unit area driving the flow: that generated by wall heating and that supplied at the inlet. As wall heating increases (i.e., as \Lambda_s increases), the current front propagates more slowly. This slowdown results from the direct heating of the current, which reduces the buoyancy of its head, and from the interaction between the front and the thermal plumes generated by natural convection downstream. In the most strongly heated experiments, the front eventually stops at a distance from the source that is inversely proportional to \Lambda_s. In these cases, the buoyancy of the head changes sign, and the head evolves into a positively buoyant plume whose vertical extent continues to increase. The experimental results are compared with predictions from a lumped analytical model describing the propagation and eventual arrest of the current.

We investigate the thermal structure of the Venusian middle atmosphere between 45 and 80 km using radio occultation (RO) measurements from Venus Express and Akatsuki spanning 2006-2024. Retrieved temperature profiles are compared with climatological predictions from the Venus-GRAM and the Venus Climate Database (VCD). Systematic deviations exceeding 10 K are observed between the RO temperatures and model climatologies, particularly at high latitudes in both hemispheres. The long-term dataset further reveals a possible decadal-scale temporal variability in temperatures across the low-to-mid latitude regions. This variability becomes less coherent at higher altitudes. A combination of post-stratification and bootstrap analysis on the temperature anomalies indicates that the observed temporal variability at low-to-mid and polar latitudes is not readily explained by the latitudinal sampling bias quantified using the adopted analysis, while the trends at mid-to-high latitudes are affected by sparse and uneven RO sounding. Sensitivity tests using modified cloud albedo inputs in VCD simulations show that adjusting cloud radiative forcing partially reconciles the discrepancies, especially at the lower altitudes, below 60 km. However, above the cloud top, at the lower latitudes, the divergence from the observations increases significantly, thereby failing to provide a general reconciliation between VCD and RO. These results highlight the need for updated empirical climatologies incorporating recent RO measurements and for improved physical parameterizations in Venus general circulation models to better capture the global variability in the planet's middle atmosphere.

We use perfect-model, large-ensemble nonlinear Recharge Oscillator (RO) simulations to quantify ENSO internal variability and the detectability of forced changes in ENSO characteristics. Fixed-parameter simulations show that linear trends in ENSO standard deviation, period, and skewness as large as those observed since 1955 can arise from internal variability in 10% of simulations. RO parameters estimated from single realizations exhibit spurious drifts even without forcing, underscoring the need for ensembles. Using 100-member ensembles, comparable to the largest climate-model ensembles, we identify detectable parameter trends. For ENSO amplitude, the detectability thresholds for forced changes in underlying processes are 15% per century for stochastic forcing, 25% per century for basin adjustment, and 50% per century for the Bjerknes feedback. For ENSO period, the detectability threshold for forced changes in the underlying recharge–discharge processes and delayed oceanic feedback is 15% per century. These results provide a testbed for interpreting RO-diagnosed ENSO changes in climate-model ensembles.

We demonstrate a physical mechanism for causal information filtering in a physical reservoir computing (PRC) by exploiting asymmetric coupling anisotropy. Using a network of coupled Duffing oscillators, we show that the directionality of internal coupling induces a spatial gradient in the effective potential, establishing a deterministic upstream-to-downstream information flow. This anisotropy allows for the selective amplification of semantic drifts, triggering a macroscopic saddle-node bifurcation as a physical interlock before global computational failure. Through spatiotemporal analysis of a 50-node system under traveling wave inputs, we confirm that local phase transitions effectively purge anomalous information while preserving the computational integrity of the remaining nodes. The results suggest that the intrinsic causality of the reservoir's topology provides a robust framework for autonomous reliability and fault-tolerant physical intelligence.

Molecular aggregates are at the basis of a broad range of functional systems, including photosynthetic light-harvesting complexes, organic photovoltaics, optoelectronic devices, and molecular sensors. Their structural organization, however, is often difficult to resolve because intrinsic heterogeneity. In this context, circular dichroism (CD) spectroscopy is particularly powerful because of its sensitivity to the three-dimensional organization of molecular building blocks. Extending CD measurements from ensembles to individual objects therefore offers a unique approach to probing supramolecular architecture in heterogeneous, non-crystallizable systems. However, without spatial and orientational averaging, measurements are highly sensitive to imbalances between left- and right-circularly polarized excitation, deviations from ideal circular polarization, and polarization imperfections coupled to linear dichroism, which creates spurious CD signals. Here, we present a systematic theoretical and experimental framework for understanding, quantifying, and controlling artifacts in single-object CD spectroscopy. We develop a mathematical description of the principal artifact mechanisms, identify their origins in practical implementations, including Pockels-cell-based polarization modulation, and establish strategies for their minimization. We validate this framework using an experimental implementation capable of acquiring CD spectra from individual molecular aggregates over 690-785 nm. Our results demonstrate that reliable single-object CD measurements are achievable when polarization-related artifacts are rigorously characterized and controlled. This Tutorial Review provides a unified theoretical framework and practical guide for single-object CD spectroscopy and its application to single-particle chirality, supramolecular photonics, and biophotonics.

Reconstructing pressure and turbulence quantities from experimental velocity measurements is challenging, especially without time-resolved data. Furthermore, limitations such as low seeding density, finite resolution, and measurement noise severely hinder the reconstruction of accurate flow fields. We introduce SnapPINN, a two-stage physics-informed neural network (PINN) that successfully reconstructs 3D velocity, their spatial gradients, pressure fields and estimates turbulent kinetic energy dissipation from a single snapshot of sparse, noisy velocity data. Evaluated here on 3D DNS turbulent pipe flow data, SnapPINN uses a sine-activated architecture with sequentially trained, decoupled velocity and pressure sub-networks. In stage 1, the velocity network fits particle data while enforcing incompressibility, serving as a physically consistent smoothing operator that regularises velocity gradients against noise. In stage 2, the velocity network is frozen, and the pressure network is trained using the pressure Poisson equation and the pretrained velocity gradients. We systematically map reconstruction performance of SnapPINN across 100 test cases to mimic challenging experimental, such as adding significant position noise, linearization of velocity field and seeding sparsity as low as $0.07\%$ of the fully resolved DNS grid. Quantitatively, bulk velocity was reconstructed within $0.5\%$, while errors remained below $50\%$ for the gradient-sensitive energy dissipation rate and within $4$–$24\%$ for the a~posteriori inferred $\mathrm{Re}_{\tau}$, even under extremely sparse and noisy conditions. Finally, we establish a practical reliability map that shows which experimental conditions are likely to yield reliable SnapPINN reconstructions in the absence of ground truth.

Direct magnetic field sensors can address integration drift commonly observed in conventional inductive magnetic diagnostics used in fusion systems. In this work, an AlGaN/GaN Hall-effect sensor was fabricated, packaged, and deployed inside the Helically Symmetric eXperiment (HSX)—the first quasi-helically symmetric stellarator, operating with a 1 T on-axis magnetic field and up to 200 kW of launched electron cyclotron resonance heating (ECRH) power—for in-situ magnetic field monitoring near the plasma edge. The sensor leverages the high-mobility two-dimensional electron gas (2DEG) formed in the AlGaN/GaN heterostructure for sensitive magnetic field measurement, while the wide-bandgap GaN material system provides thermal robustness for harsh-environment operation. During 68 consecutive plasma discharge shots, the sensor remained functional and produced clear transient responses associated with plasma ignition and discharge dynamics. Comparisons between biased and unbiased operation, as well as plasma-discharge and coil-only shots, confirmed that the response originated from the biased Hall-effect sensor element. Furthermore, the sensor output exhibited temporal correlation with the plasma stored energy measured by the HSX diamagnetic loop across high-energy, late-breakdown, and failed-breakdown discharges.

Whispering gallery mode (WGM) resonators with broken rotational symmetry exhibit phenomena that are generally absent in conventional symmetric cavities, including directional emission. While such effects have been extensively investigated in solid and planar resonators, they remain largely unexplored in hollow, three-dimensional cavities, where optical confinement is fundamentally altered by the thin wall geometry. Here, we demonstrate controlled fabrication of asymmetric silica microbubble resonators through anisotropic expansion during the microbubble formation process. X-ray tomography confirms the resulting three-dimensional geometry and allows us to quantitatively characterize the cavity deformation. Despite the broken rotational symmetry, the resonators maintain whispering gallery modes with high loaded Q-factors exceeding $10^5$ for moderate deformations. Optical characterization reveals that only certain resonances exhibit directional emission, whereas neighboring modes retain conventional isotropic behavior. Two-dimensional numerical simulations reproduce the observed emission characteristics and indicate that deformation-induced leakage of higher-order modes provides a plausible mechanism for the directional radiation. These results establish asymmetric hollow microbubble resonators as a platform for investigating light transport in three-dimensional asymmetric whispering gallery cavities while preserving the high-Q performance required for photonics applications.

We show a finite-time Otto-like quantum thermodynamic cycle that preserves the adiabatic population structure of a time-dependent harmonic oscillator in the non-quasi-static regime. In the conventional energy representation, finite-rate driving induces non-adiabatic population redistribution and leaves residual excitations after the Hamiltonian has returned to its initial value. We show that this difficulty can be avoided by formulating the dynamics in the Lewis-Riesenfeld invariant representation, without modifying the physical Hamiltonian through auxiliary counterdiabatic driving. For a parametric Mathieu protocol, quantum inertia produces a mismatch between the spatial width of the working mode and its transient dressed energy scale. We propose an experimental implementation of this scheme in a trapped-ion Paul trap using stimulated Raman interactions, with independent control of the laser detuning and beam intersection angle. This provides a finite-time implementation in which the invariant population structure is preserved while the physical trap frequency evolves non-quasi-statically. Our results establish a clear distinction between adiabatic operation and quasi-static driving, providing a route toward finite-time quantum thermal cycles that retain the adiabatic energy structure without requiring the quasi-static limit.

The ability to generate ultranarrow-band perfect absorption and bound states in the continuum (BICs) in the visible spectrum is highly useful and desired for many advanced optical and nanophotonic applications. However, achieving perfect absorption and BICs with a high quality-factor in the visible region remains challenging due to the material background absorption, radiative losses, and fabrication constraints. Here, we propose and demonstrate a dielectric nanophotonic platform that simultaneously achieves perfect absorption (99.0% at 603 nm) and multiple symmetry-protected BICs in the visible by integrating a dielectric metasurface with a distributed Bragg reflector (DBR). The coupling between the metasurface's localized dielectric resonances and the DBR-assisted photonic mode suppresses radiative leakage and enables ultranarrow-band resonances (full-width at half-maximum ~ 0.65 nm) with strong electromagnetic field (|E|) enhancement (up to 16.4 folds) within the DBR stopband. In addition, the metasurface's asymmetric design enables a magnetic multipole-assisted quasi-BIC resonance with polarization-dependent mode degeneracies, and the periodic structure enables Mie resonance-assisted multiple BICs. Furthermore, the tunability of design parameters provides additional degrees of freedom to control the resonances and their spectral positions. The coexistence of perfect narrowband absorption and BICs within a compact dielectric nanophotonic platform in the visible establishes a promising route toward next-generation low-loss, high-performance meta-optical devices and technologies.

Fano resonances, characterized by a unique, sharp, and asymmetric spectral line shape arising from the interference between a narrowband discrete resonant state and a broadband continuum state, provide rich opportunities to control and manipulate light-matter interactions at the nanoscale. Here, we propose and demonstrate a nanophotonic architecture that integrates a multi-layered dielectric cavity into a one-dimensional grating to achieve an outstanding range of Fano resonance tunability by design, spanning 194 nm across the visible (from 500 to 694 nm), even with an ultra-thin layer of cavity (80 nm thick), offering the potential of highly compact photonic devices. The precise tailoring of the Fano spectral position through the adjustment of the cavity's structural parameters enables strongly localized field accumulation (at a maximum of 26 folds) within the cavity region. Additionally, the proposed architecture allows for bright and saturated structural colors in the visible, promising a route toward advanced digital display and printing technologies. Importantly, the experimental performance achieved with a simple, etch-free fabrication process results in a significant reduction in fabrication complexity and process-induced optical degradation. Our work showcases a robust platform for possible applications in Fano resonance-based low-loss nanolasers, on-chip photonic devices, optical communication components, and next-generation quantum technologies in the visible range.