Papers

Understanding the thermal response of optical and acoustic phonons is crucial for designing functional polymer nanocomposites. We study silicon nanoparticle (Si NP)-epoxy composites using combined Raman and Brillouin spectroscopy under local(laser-induced) and global(stage-controlled) heating. Raman spectra reveal THz longitudinal optical(LO) phonon softening and spectral broadening under local heating, indicating nanoscale hot-spots and interfacial scattering. Brillouin data track GHz longitudinal acoustic(LA) phonons, showing temperature- and concentration-dependent evolution of elasticity and damping. Contrasting heating methods unravels Si loading thresholds for isolated thermal absorbers, thermal percolation, acoustic attenuation and elastic homogenization. Local heating induces greater phonon softening and damping than global heating, with this disparity amplified at higher loadings by thermal gradients and interfacial dissipation. Global heating correlates with viscoelastic relaxation, showing intensified acoustic attenuation near the glass transition. Raman thermometry coupled with finite-element opto-thermal modeling allows evaluation of thermal conductivity of the composites characterized by increase from 0.09 to 0.46 W/(mK) for 0.07 up to 2 wt% of Si NPs, respectively, outperforming SiC nanowires at 2 wt% [D. Shen et al, Sci. Rep. 7, 2606 (2017)] despite bulk conductivity of Si being more than 3 times smaller than that of SiC. However, effective heat conductivity of our nanocomposites remain far below bulk Si, confirming that interfacial thermal resistance, not filler conductivity, governs heat transport.

A hallmark of aging is loss of information in gene regulatory networks. These networks are tightly connected, raising the question of whether information could be restored by perturbing single genes. We develop a simple theoretical framework for information transmission in gene regulatory networks that describes the information gained or lost when a gene is "knocked in" (exogenously expressed). Applying the framework to gene expression data from muscle cells in young and old mice, we find that single knock-ins can restore network information by up to 10%. Our work advances the study of information flow in networks and identifies potential gene targets for rejuvenation.

As global ocean general circulation models are run at eddy-permitting resolutions, reproducing accurate growth rates of baroclinic instabilities is a major concern when choosing a discretization of the equations of motion. From this viewpoint, we analyze discretizations on triangular and hexagonal grids with different types of variable staggering used in several ocean circulation models. By extending the linear baroclinic instability analysis in the Eady configuration to discretizations on more complex grids, several numerical subtleties are revealed. In comparison to discretizations on quadrilateral grids, the analyzed discretizations are less robust against unstable spurious modes, partly created by the mesh geometry. Some of the subtleties arise because spurious modes on staggered triangular and hexagonal grids do not adhere to Galilean invariance. As a consequence, their growth rates demonstrate a dependence on the alignment between the background flow and the grid, as well as the strength of a uniform background flow. The interactions with spurious modes become more significant on the axis of symmetric instabilities where the physical and spurious branches of instability are more difficult to separate in wavenumber space. Our analysis shows that in most cases moderate biharmonic viscosity and diffusion suppress spurious branches. However, one needs to carefully calibrate the viscosity and diffusivity parameters for each of the considered discretizations in order to achieve this.

The development of plasmonics and related applications in the terahertz range faces limitations due to the intrinsic high electron density of standard metals. All-dielectric systems are profitable alternatives, which allows for customized modulation of the optical response upon doping. Here we focus on plasmon-based hyperbolic metamaterials realized stacking doped III-V semiconductors that have been shown to be optically active in the terahertz spectral region. By using a multi-physics multi-scale theoretical approach, we unravel the role of doping and geometrical characteristics (e.g., thickness, composition, grating) in the modulation of high-k plasmon-polariton modes across the metamaterial.

Nature is imbued with mathematics, manifested through its stunning patterns, symmetries, and structures. Here, we unveil that in a multilayered framework of twisted birefringent optical components, a recursive number pattern of Pascal's triangle is naturally embedded in the structure of the Jones matrix which intuitively provide a generalized solution for pixel-to-pixel field transformation. The resulting standalone solution is universal across the electromagnetic spectrum, unifies N-layered metasurface and conventional bulk waveplates in a single framework, offers comprehensive insights about the bidirectional complex amplitude modulation and wavefront engineering in linear and circular polarization bases, and at the same time substantially reduces the computational cost. In essence, the discovery of number patterns in polarization optics/photonics will have broad impact across quantum optics, theory informed artificial intelligence model trainings, biomedical engineering and imaging, polarization information encryption, and advanced sensing applications.

We report a cavity-enabled solid-state magnetometer based on an NV ensemble coupled with a dielectric cavity, achieving 12 pT/$\sqrt{\rm{Hz}}$ sensitivity and a nearly threefold gain from multispectral features. The features originate from cavity-induced splitting of the NV hyperfine levels and leverages robust quantum coherence in the doubly dressed states of the system to achieve high sensitivity. We project simulated near-term sensitivities approaching 100 fT/$\sqrt{\rm{Hz}}$, close to the Johnson-Nyquist limit. Our results establish frequency multiplexing as a new operational paradigm, offering a robust and scalable quantum resource for metrology under ambient conditions.

Fromager and Lasorne [Electron. Struct. 6 025002 (2024)] have recently derived an in-principle exact Kohn-Sham density functional theory (KS-DFT) of electrons and nuclei, where the nuclear density and the (so-called conditional) electronic density are mapped onto a fictitious electronically non-interacting KS molecule. In this work, we apply the exact factorization formalism to the molecular KS wavefunction, thus leading to disentangled (but coupled) marginal and conditional KS equations. We show that, while being equivalent to the original theory, these equations open new perspectives in the practical extension of regular (electronic) KS-DFT beyond the Born-Oppenheimer approximation. The importance and treatment of correlations induced in this context by second-order geometrical derivatives is also discussed.

Accurately modeling quantum dissipative dynamics remains challenging due to environmental complexity and non-Markovian memory effects. Although machine learning provides a promising alternative to conventional simulation techniques, most existing models employ real-valued neural networks (RVNNs) that inherently mismatch the complex-valued nature of quantum mechanics. By decoupling the real and imaginary parts of the density matrix, RVNNs can obscure essential amplitude-phase correlations, compromising physical consistency. Here, we introduce complex-valued neural networks (CVNNs) as a physics-consistent framework for learning quantum dissipative dynamics. CVNNs operate directly on complex-valued inputs, preserve the algebraic structure of quantum states, and naturally encode quantum coherences. Through numerical benchmarks on the spin-boson model and several variants of the Fenna-Matthews-Olson complex, we demonstrate that CVNNs outperform RVNNs in convergence speed, training stability, and physical fidelity – including significantly improved trace conservation and Hermiticity. These advantages increase with system size and coherence complexity, establishing CVNNs as a robust, scalable, quantum-aware classical approach for simulating open quantum systems in the pre-fault-tolerant quantum era.

Quantum computing (QC) has the potential to revolutionise the future of scientific simulations. To harness the capabilities that QC offers, we can integrate it into hybrid quantum-classical simulations, which can boost the capabilities of supercomputing by leveraging quantum modules that offer speedups over classical counterparts. One example is quantum Monte Carlo integration, which is theorised to achieve a quadratic speedup over classical Monte Carlo, making it suitable for high-energy physics, strong-field QED, and multiple scientific and industrial applications. In this paper, we demonstrate that quantum Monte Carlo can be used to predict the number of pairs created when two photon beams collide head-on, a problem relevant to high-energy physics and intense laser-matter interactions. The results from the quantum simulations demonstrate high accuracy relative to theoretical predictions. The accuracy of the simulations is only constrained by the approximations required to embed polynomials and to initialise the quantum state. We also demonstrate that our algorithm can be used in current quantum hardware, providing up to 90 % accuracy relative to theoretical predictions. Furthermore, we propose pathways towards integrations with classical simulation codes.

We introduce a novel and generic fusion scheme enabled by laser-plasma solitons, which promises to overcome several fundamental obstructions to reaching the breakeven condition. To demonstrate that, we invoke both deuterium-tritium (DT) and proton-boron (pB) as fuels. The intense electromagnetic field trapped inside the soliton significantly enhances the DT and pB fusion cross sections. Its ponderomotive potential evacuates electrons almost instantly, while the ions left behind are accelerated by the unshielded Coulomb field to kinetic energies suitable for fusion reaction on a longer time scale. Such a difference in time scales renders a time window for fusion to occur efficiently in an electron-free environment. We inject two consecutive lasers, where the first would excite plasma solitons and the second, much more intense and with a matched lower frequency, would fortify the soliton electromagnetic field resonantly. Solitons are known to expand during its lifetime, which significantly increases the number of ions to participate in the fusion process and the ion propagation distance. We show that the breakeven condition is attainable for both DT and pB cases. Invoking fiber laser and the iCAN laser technologies for high repetition rate and high intensity operation, gigawatt output may be conceivable.

Gas-radiation coupling critically influences hypersonic reentry flows, where extreme temperatures induce pronounced non-equilibrium gas and radiative heat transport. Accurate and efficient simulation of radiative gas dynamics is therefore indispensable for reliable design of thermal protection systems for atmospheric entry vehicles. In this study, a Boltzmann-type kinetic model for radiative gas flows is solved across a broad spectrum of flow and radiation transport regimes using the general synthetic iterative scheme (GSIS). The approach integrates an unstructured finite-volume discrete velocity method with a set of macroscopic synthetic equations. Within this framework, the kinetic model provides high-order closures for the constitutive relations in the synthetic equations. Simultaneously, the macroscopic synthetic equations drive the evolution of the mesoscopic kinetic system, significantly accelerating steady-state convergence in near-continuum regimes, as substantiated by linear Fourier stability analysis. Crucially, the algorithm is proven to be asymptotic-preserving, correctly recovering the continuum and optically thick limits, represented by the radiative Navier-Stokes-Fourier equations governing distinct translational, rotational, vibrational, and radiative temperatures, on coarse meshes independent of the mean free path. Numerical simulations of challenging benchmarks, including three-dimensional hypersonic flow over an Apollo reentry capsule, demonstrate that GSIS achieves orders-of-magnitude speedup over conventional iterative schemes in multiscale simulations of radiative gas flows while accurately capturing non-equilibrium effects and radiative heat transfer in hypersonic environments.

Finding constrained saddle points on embedded Riemannian submanifolds of Euclidean space is significant for analyzing energy landscapes arising in physics and chemistry. Existing works exploit explicit global/local regular level-set representations of manifolds, which may be unavailable or computationally inconvenient for manifolds represented through, e.g., projectors, factorizations, or rank constraints. In this paper, we develop a constrained saddle dynamic based on embedded-submanifold geometric primitives, completely avoiding the use of explicit representations. In particular, our dynamic is formulated compactly on the Grassmann bundle of the tangent bundle. By analyzing the Grassmann bundle geometry, we rigorously establish the local linear stability of the dynamic and the local linear convergence of the resulting algorithms. Remarkably, our analysis provides the first iterate convergence result for discretized algorithms to saddle points of prescribed indices in embedded-submanifold settings. Moreover, by virtue of the Grassmann bundle formulation, we remove unnecessary nondegeneracy assumptions on the eigenvalues of the Riemannian Hessian that are present in existing works. We also point out that locating saddle points can be more ill-conditioned than finding local minimizers, and requires using nonredundant parametrizations. Finally, numerical experiments on linear eigenvalue problems and electronic excited-state calculations showcase the effectiveness of the proposed algorithms and corroborate the established local theory.

We identify a polarization rotation systematic in the far field beams of refractive cosmic microwave background (CMB) telescopes caused by differential transmission in anti-reflection (AR) coatings of optical elements. This systematic was identified following the development of a hybrid physical optics method that incorporates full-wave electromagnetic simulations of AR coatings to model the full polarization response of refractive systems. Applying this method to a two-lens CMB telescope with non-ideal AR coating, we show that polarization-dependent transmission can produce a rotation of the far-field polarization angle that varies across the focal plane with a typical amplitude of 0.05-0.5 degrees. If ignored in analysis, this effect can produce temperature to polarization leakage and Stokes Q/U mixing.

Scaling superconducting quantum computers to the fault-tolerant regime calls for a commensurate scaling of the classical control and readout stack. Today's systems largely rely on room-temperature, rack-based instrumentation connected to dilution-refrigerator cryostats through many coaxial cables. Looking ahead, superconducting fault-tolerant quantum computers (FTQCs) will likely adopt a heterogeneous quantum-classical architecture that places selected electronics at cryogenic stages – for example, cryo-CMOS at 4~K and superconducting digital logic at 4~K and/or mK stages – to curb wiring and thermal-load overheads. This review distills key requirements, surveys representative room-temperature and cryogenic approaches, and provides a transparent first-order accounting framework for cryoelectronics. Using an RSA-2048-scale benchmark as a concrete reference point, we illustrate how scaling targets motivate constraints on multiplexing and stage-wise cryogenic power, and discuss implications for functional partitioning across room-temperature electronics, cryo-CMOS, and superconducting logic.

A nearly half-cubic hectometer of deep Mediterranean-Sea waters is yearlong sampled with about 3000 high-resolution temperature sensors to study different sources of turbulent waterflows, which are vital for life. Although temperature differences are never larger than 0.01degrC, daily, weekly, and seasonal variations are observed. About half the time, relatively warm stratified waters are moved from 100's of meters higher levels to near the seafloor. These internal-wave and sub-mesoscale eddy-induced motions are half an order of magnitude more turbulent than those induced via general geothermal heating from below, and about one order of magnitude more turbulent than those from open-ocean processes. A rough estimate shows that eddy-induced stratified turbulence is likely more important for deep-sea life than rare, not observed, deep dense-water formation at the abyssal-plain mooring site. With a delay of about a week, the stratified turbulence tracks atmospheric disturbances, which are found 35% more energetic in winter than in summer. From comparison of turbulence-calculation methods, of band-pass filtering with vertical-displacement reordering, for data over one-four days, a generalization is proposed for the filter cut-offs under weakly stratified and near-homogeneous conditions in the deep Mediterranean.

The microscopic and fundamental origin of slide electrification, where droplets of water move across insulating surfaces accumulating and depositing electrical charges, is still debated. Charge transfer is often attributed to ion transfer at the receding contact line. However, it is still unclear whether ion transfer alone can fully account for the observed charge separation. We examined slide electrification of two polar, self-ionizing liquids (water, formamide) and two non-polar liquids (diiodomethane, bromonaphthalene). By cooling below the melting temperature, we were able to compare this process to tribocharging of the respective frozen components. Despite reduced ion mobility at sub-freezing temperatures, the frozen polar compounds continue to accumulate significant charge. Non-polar liquids exhibit lower charging (<25% of polar liquids) and nearly identical charging behaviour in both their liquid and frozen phases on five different substrates. Since non-polar liquids contain few free ions, these observations indicate an alternative charging mechanism, which could be electron transfer. Our findings suggest that slide electrification operates through at least two mechanisms, with the dominant charge transfer pathway shifting between ions and electron transfer depending on the electronegativity, phase, and temperature.

Many biological networks grow by elongation of filaments that can branch and fuse – typical examples include fungal mycelium or slime mold. These networks must simultaneously perform multiple tasks such as transport, exploration, and robustness under finite resources. Yet, how such multi-task architectures emerge from local growth processes remains poorly understood. Here, we introduce a minimal model of spatial network morphogenesis based solely on stochastic branching, fusion, and stopping, during elongation. Despite the absence of global optimization or feedback, the model generates a broad morphospace from tree-like, to loopy, as well as hybrid architectures. By quantifying multiple functional objectives, we show that (i) these synthetic structures occupy similar regions of performance space than evolved empirical fungal networks, and (ii) that their Pareto front of optimal trade-offs lies close to that of these same fungal networks. Our results show that biological architectures approaching multi-objective optimality can arise from simple local growth rules, and identify branching and fusion as fundamental ingredients shaping the architecture of living transport networks.

It may be important to precisely know heights of moored oceanographic instrumentation. For example, moorings can be closely spaced or accidentally be located on small rocks or in small gullies. Height variations O(1 m) will yield registration of different values when conditions such as small-scale density stratification vary strongly. Such little height variations may prove difficult to measure in the deep sea, requiring high-accuracy pressure sensors preferably on all instruments in a mooring-array. In this paper, an alternative method for relative height determination is presented using high-resolution temperature sensors moored on multiple densely-spaced lines in the deep Western Mediterranean. While it was anticipated that height variations between lines could be detected under near-homogeneous conditions via adiabatic lapse rate O(0.0001degrC m-1) by the 0.00003degrC-noise-level sensors, such was prevented by the impossibility of properly correcting for short-term bias due to electronic drift. Instead, a satisfactory height determination was found during a period of relatively strong stratification and large turbulence activity. By band-pass filtering data of the highest-resolved turbulent motions across the strongest temperature gradient, significant height variations were detectable to within +/-0.2 m.

Two-frequency (two-color) laser fields provide a powerful and flexible means for steering molecular dynamics. However, quantitatively reliable and scalable theoretical tools for simulating laser-driven nonadiabatic processes under such fields remain limited. Here, we develop a two-mode Floquet fewest switches surface hopping (two-mode F-FSSH) approach for two-frequency driving within a mixed quantum-classical framework. We validate the algorithm on three driven one-dimensional two-state models: a Rabi model and two avoided-crossing scattering models. The electronic and nuclear dynamics are benchmarked against numerically exact results from split-operator calculations, showing good agreement across a broad range of field parameters and initial conditions. These results establish two-mode F-FSSH as a practical framework for simulating and designing two-frequency control protocols and motivate extensions to more realistic experimental settings.

Meteotsunamis are atmospherically driven sea-level oscillations that can trigger hazardous coastal flooding, particularly in resonant bays. This study assesses the GPU-based Meteo-HySEA model for meteotsunami simulation in the Adriatic Sea, benchmarking its performance against the CPU-based AdriSC-ADCIRC system. Three documented events (2014, 2017, 2020) were simulated using WRF downscaling of ERA reanalyses and validated with tide-gauge and microbarograph observations. Both models are limited by the underestimation of mesoscale pressure disturbances in the atmospheric forcing. Meteo-HySEA generally reproduces the timing and spatial variability of sea-level oscillations and often yields larger amplitudes than ADCIRC, but it tends to overestimate dominant wave periods, particularly in enclosed basins. Differences in oscillation persistence underscore the need for further validation against high-resolution tide-gauge data to assess whether Meteo-HySEA captures harbor seiches more realistically or ADCIRC better represents physical energy dissipation. Crucially, GPU acceleration provides order-of-magnitude gains in computational efficiency, enabling rapid high-resolution, multi-grid simulations including inundation, and thus offering strong potential for operational early warning.

Full-field vibration profilometry is essential for dynamic characterizing microelectromechanical systems (MEMS/MOEMS). Time-averaged interferometry (TAI) encodes spatial information about MEMS or MOEMS vibration amplitude in the interferogram's amplitude modulation using Bessel function (besselogram). Classical approaches for interferogram analysis are specialized for cosine function fringe patterns and therefore introduce reconstruction errors for besselogram decoding. This paper presents the DeepBessel: a deep learning-based approach for single-shot TAI interferogram analysis. A convolutional neural network (CNN) was trained using synthetic data, where the input consisted of besselograms, and the output corresponded to the underlying vibration amplitude distribution. Numerical validation and experimental testing demonstrated that DeepBessel significantly reduces reconstruction errors compared to the state-of-the-art approaches, e.g., Hilbert Spiral Transform (HST) method. The proposed network effectively mitigates errors caused by the mismatch between the Bessel and cosine functions. The results indicate that deep learning can improve the accuracy of full-field vibration measurements, offering new possibilities for optical metrology in MEMS or MOEMS applications.

Assigning individuals with hearing impairment to auditory profiles can support a better understanding of the causes and consequences of hearing loss and facilitate profile-based hearing-aid fitting. However, the factors influencing auditory profile generation remain insufficiently understood, and existing profiling frameworks have rarely been compared systematically. This study therefore investigated the impact of two key factors - the clustering method and the number of profiles - on auditory profile generation. In addition, eight established auditory profiling frameworks were systematically reviewed and compared using intrinsic statistical measures and manifold learning techniques. Frameworks were evaluated with respect to internal consistency (i.e., grouping similar individuals) and cluster separation (i.e., clear differentiation between groups). To ensure comparability, all analyses were conducted on a common open-access dataset, the extended Oldenburg Hearing Health Record (OHHR), comprising 1,127 participants (mean age = 67.2 years, SD = 12.0). Results showed that both the clustering method and the chosen number of profiles substantially influenced the resulting auditory profiles. Among purely audiogram-based approaches, the Bisgaard auditory profiles demonstrated the strongest clustering performance, whereas audiometric phenotypes performed worst. Among frameworks incorporating supra-threshold information in addition to the audiogram, the Hearing4All auditory profiles were advantageous, combining a near-optimal number of profile classes (N = 13) with high clustering quality, as indicated by a low Davies-Bouldin index. In conclusion, manifold learning and intrinsic measures enable systematic comparison of auditory profiling frameworks and identify the Hearing4All auditory profile as a promising approach for future research.

Thermal electron measurements in space plasmas typically suffer at low energies from spacecraft emissions of photo- and secondary electrons and from charging of the spacecraft body. We examine these effects by use of numerical simulations in the context of electron measurements acquired by the Electron Analyser System (SWA-EAS) on board the Solar Orbiter mission. We employed the Spacecraft Plasma Interaction Software to model the interaction of the Solar Orbiter spacecraft with solar wind plasma and we implemented a virtual detector to simulate the measured electron energy spectra as observed in situ by the SWA-EAS experiment. Numerical simulations were set according to the measured plasma conditions at 0.3~AU. We derived the simulated electron energy spectra as detected by the virtual SWA-EAS experiment for different electron populations and compared these with both the initial plasma conditions and the corresponding real SWA-EAS data samples. We found qualitative agreement between the simulated and real data observed in situ by the SWA-EAS detector. Contrary to other space missions, the contamination by cold electrons emitted from the spacecraft is seen well above the spacecraft potential energy threshold. A detailed analysis of the simulated electron energy spectra demonstrates that contamination above the threshold is a result of cold electron fluxes emitted from distant spacecraft surfaces. The relative position of the break in the simulated spectrum with respect to the spacecraft potential slightly deviates from that in the real observations. This may indicate that the real potential of the SWA-EAS detector with respect to ambient plasma differs from the spacecraft potential value measured on board. The overall contamination is shown to be composed of emissions from a number of different sources and their relative contribution varies with the ambient plasma conditions.

Fluctuating lattice Boltzmann solvers are widely employed to model mesoscopic fluid behavior in soft-matter systems, including colloidal suspensions and dilute polymer solutions. Despite their utility, these methods can lose accuracy and stability when non-hydrodynamic modes interfere with the dynamics, especially in single–relaxation-time schemes. Here, we introduce a ghost-mode filtered fluctuating lattice Boltzmann method (GMF-FLBM) for the D3Q27 lattice, obtained by selectively eliminating the propagation of the ghost deterministic content while preserving the necessary stochastic forcing. We show, over a broad range of relaxation times, that GMF-FLBM recovers the amplitudes of equilibrium fluctuations with a comparable accuracy as a fully regularized high-order formulation, while requiring only minor adjustments to the conventional BGK collision framework.

Single-photon emitters in hexagonal boron nitride (hBN) combine bright optical emission with optically addressable spin states, offering a promising platform for integrated quantum photonics. However, their stochastic creation and spectral variability have prevented deterministic integration with photonic cavities. Here we demonstrate a fabrication protocol that enables precise, deterministic coupling of pre-selected visible emitters to circular Bragg grating (CBG) cavities in hBN. By patterning etched alignment markers and performing prefabrication confocal mapping, we locate emitters with sub-micron accuracy and design cavity geometries matched to their zero-phonon line wavelengths. The resulting devices show enhanced emission and reliable spectral alignment between emitter and cavity mode. This work establishes a deterministic cavity-emitter integration scheme in a van der Waals material and provides a scalable route towards on-chip quantum photonic and spin-based platforms using hBN.

Van der Waals materials are offering intriguing opportunities as building blocks for advanced quantum information technologies and integrated quantum photonic systems. Critical to their development, is robust and high quality light-matter interactions which can be delivered through the fabrication of optical resonators. Here we demonstrate a robust fabrication of one dimensional photonic crystal cavities (1D PCC) and microdisk resonators from hexagonal boron nitride, exhibiting Quality factors of ~ 4300 and ~ 8300, respectively. With these two classes of devices we demonstrated cavity mode tuning via atomic layer deposition and gas condensation. Cavity resonances were shifted by and ~9 nm for the 1D PCCs and ~16 nm in the microdisk resonators, respectively. Our work opening a promising pathway for a realisation emitter cavity coupling in hBN and eventually to a fully integrated quantum photonic circuitry with hBN.

Bioresponsive optical materials that transduce nanoscale biointerface events into measurable spectral signals are of growing interest for sensing and antiviral technologies Here we show that a Tamm plasmon TP device consisting of a SiOTiO distributed Bragg reflector capped with a nanostructured silver layer exhibits a selective and structure dependent response to SARSCoV2 virus like particles VLPs Upon VLP exposure the conventional wavelength shift delta lambda of the TP resonance is minimal whereas the resonance depth undergoes a systematic attenuation To capture both spectral and amplitude variations we introduce a displacement angle alpha defined from the translation vector of the Tamm dip before and after exposure The angle alpha increases monotonically with VLP concentration and enables a limit of detection of 13 ng per mL Control experiments with polystyrene nanoparticles of similar size and with heat denatured VLPs yield negligible changes indicating sensitivity to the native conformational state of viral surface proteins rather than to generic nanoparticle morphology or bulk refractive index effects These results establish Tamm plasmon structures as promising bioresponsive platforms for label free detection and for probing structure dependent virus material interactions.

Determining whether two graphs are structurally identical is a fundamental problem with applications spanning mathematics, computer science, chemistry, and network science. Despite decades of study, graph isomorphism remains a challenging algorithmic task, particularly for highly regular structures. Here we introduce a new algorithmic approach based on ideas from spectral graph theory and geometry that constructs candidate correspondences between vertices using their curvatures. Any correspondence produced by the algorithm is explicitly verified, ensuring that non-isomorphic graphs are never incorrectly identified as isomorphic. Although the method does not yet guarantee success on all inputs, we find that it correctly resolves every instance tested in deterministic polynomial time, including a broad collection of graphs known to be difficult for classical techniques. These results demonstrate that enriched spectral methods can be far more powerful than previously understood, and suggest a promising direction for the practical resolution of the complexity of the graph isomorphism problem.

Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck. To overcome this, we establish the systematically designed MolLR25 ({Mol}ecules with {L}ong-{R}ange effect) benchmark up to 1200 atoms, generated using high-fidelity DFT, and introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR exhibits stable error scaling, achieves superior fidelity in capturing non-covalent decay, and maintains precision on complex protein conformations. Crucially, its efficient design provides up to 30% speedup compared to purely local models. This work validates the necessity of non-local architectures for generalizable MLFFs, enabling high-fidelity molecular dynamics for large-scale chemical and biological systems.

We present a 19-day joint measurement of optical turbulence and coherent data communications over a 4.6 km urban FSO link, providing empirical insights into turbulence effects on the performance of fiber-coupled coherent communication systems.