Papers

Flexible and accurate interpolation schemes using machine learning could be of great benefit for many use-cases in numerical simulations and post-processing, such as temporal upsampling or storage reduction. In this work, we adapt the physics-informed token transformer (PITT) network for multi-channel data and couple it with Fourier neural operator (FNO). The resulting PITT FNO network is trained for interpolation tasks on a dataset governed by the Euler equations. We compare the performance of our machine learning model with a linear interpolation baseline and show that it requires $\sim6-10$ times less data to achieve the same mean square error of the interpolated quantities. Additionally, PITT FNO has excellent mass and energy conservation as a result of its physics-informed nature. We further discuss the ability of the network to recover fine detail using a spectral analysis. Our results suggest that loss of fine details is related to the decreasing correlation time of the data with increasing Fourier mode which cannot be resolved by simply increasing Fourier mode truncation in FNO.

Molecules that violate Hund's rule by exhibiting an inverted singlet-triplet gap (STG), where the first excited singlet (S$_1$) lies below the triplet (T$_1$), are rare but hold great promise as efficient fifth-generation light emitters. Azaphenalenes (APs) represent one of the few known molecular classes capable of such inversion of the S$_1$/T$_1$ energy ordering, yet a systematic exploration of all unique APs is lacking. Here, we investigate 104 distinct APs and classify them based on their adherence to or deviation from Hund's rule using S$_1$-T$_1$ gaps computed with the second-order coupled-cluster method employing the Laplace transform (L-CC2). To capture substitution-dependent pseudo-Jahn-Teller distortions that are inadequately described by MP2 and DFT methods, we employ focal-point extrapolation scheme to obtain near-CCSD(T)/cc-pVTZ-quality geometries. We find three APs to undergo $D_{\rm 3h} \rightarrow C_{\rm 3h}$ and ten to show $C_{\rm 2v}\rightarrow C_{\rm s}$ symmetry lowering, leading to a total of 117 configurations of 104 unique APs. Our study identifies top candidates with inverted STGs, revealing how substitution and symmetry-lowering modulate these gaps to uncover new stable AP cores that provide promising targets for designing molecular light-emitters.

Two-dimensional turbulence with linear (Ekman) friction exhibits spectral properties that deviate from the classical Kraichnan prediction for the direct enstrophy cascade. In particular, for sufficiently small viscosity and large friction, the enstrophy flux is suppressed in the cascade and, as a consequence, the small-scale vorticity field becomes passively transported by the large-scale, chaotic flow. We numerically address this problem by investigating how the statistics of the Lagrangian Finite Time Lyapunov Exponent in 2D Ekman-Navier-Stokes simulations are affected by the friction coefficient and by the other parameters of the flow. We derive a simple phenomenological model that interpolates the dependence of the Lyapunov exponent on the flow statistics from the large friction limit, where analytical predictions are available, to the small friction region. We find that the distribution of the FTLE around this mean value is always close to a Gaussian, and this allows to make a simple prediction for the correction of the spectral slope of the direct cascade which is in very good agreement with the numerical results.

The Boltzmann wealth model provided by the Mesa framework is a classic example in agent-based modeling, yet its statistical properties are typically analyzed only through numerical simulation. This paper studies a mean-field version of the model, replacing local interactions with global random matching and adopting a synchronous update scheme. By establishing a mean-field master equation for the wealth distribution and employing the probability generating function method, we obtain a closed-form expression for the steady-state generating function, and analytically determine the model parameters. We further derive the variance, Gini coefficient, and tail asymptotic behavior of the steady-state wealth distribution. Numerical simulations of the corresponding mean-field agent-based model agree well with the theoretical predictions. This paper provides an analytical benchmark for this mean-field model, which can serve as a reference for theoretical analysis and result validation in related agent-based simulations.

Surface bubbles are an abundant source of aerosols, with important implications for climate processes. In this context, we investigate the stability and thinning dynamics of soap films under effective gravity fields. Experiments are performed using a centrifugal thin-film balance capable of generating accelerations from 0.2 up to 100 times standard gravity, combined with thin-film interferometry to obtain time-resolved thickness maps. Across all experimental conditions, the drainage dynamics are shown to be governed by capillary suction and marginal regeneration-a mechanism in which thick regions of the film are continuously replaced by thin film elements (TFEs) formed at the meniscus. We consistently recover a thickness ratio of 0.8-0.9 between the TFEs and the adjacent film, in agreement with previous observations under standard gravity. The measured thinning rates also follow the predicted scaling laws. We identified that effective gravity has three distinct effects: (i) it induces a strong stretching of the initial film, extending well beyond the linear-elastic regime; (ii) it controls the meniscus size, and thereby the amplitude of the capillary suction and the drainage rate; and (iii) it reveals an inertia-to-viscous transition in the motion of TFEs within the film. These results are supported by theoretical modeling and highlight the robustness of marginal regeneration and capillary-driven drainage under extreme gravity conditions.

We present a compact design of dual-beam Zeeman slower optimized for efficient production of cold atom applications. Traditional single-beam configurations face challenges from substantial residual atomic flux impacting downstream optical windows, resulting in increased system size, atomic deposition contamination, and a reduced operational lifetime. Our approach employs two oblique laser beams and a capillary-array collimation system to address these challenges while maintaining efficient deceleration. For rubidium ($^{87}$Rb), simulations demonstrate a significant increase in the fraction of atoms captured by a two-dimensional magneto-optical trap (2D-MOT) and nearly eliminate atom-induced contamination probability at optical windows, all within a compact Zeeman slower length of 44 cm. Experimental validation with Rb and Yb demonstrates highly efficient atomic loading within the same compact design. This advancement represents a substantial improvement for high-flux cold atom applications, providing reliable performance for high-precision metrology, quantum computation and simulation.

Following the ultrafast photoexcitation of a semiconductor, it embodies competing dynamics among photocarriers, many-body transient states of highly energetic excitons, and electron-hole liquid. Here, we show that femtosecond optical pulse excitation induces transient excitonic shift current contributing to stronger THz emission from a single crystalline bulk MoS2 at low temperatures. The control of dominating excitonic shift current is elucidated from excitation density dependent experiments at varying temperatures. A strong decrease in the excitonic contribution beyond a critical fluence of 150microJ/cm^2 is observed at a very low temperature of 20K. This behavior suggests the formation of a new quantum condensate, i.e., the electron-hole liquid, in the regime when the exciton density is overwhelmingly large that the average spacing between exciton pairs is comparable to the exciton radius. Furthermore, the exciton density dependent THz emission at varying temperatures is consistent with the Varshni model and the crystal Debye temperature of 260K.

We demonstrate carrier-envelope-phase (CEP)-controlled photocurrents in mono-, bi-, and tri-layer MoS$_2$ driven by few-cycle laser pulses. The photocurrent in the two-terminal devices scales quadratically with the field amplitude, indicating perturbative carrier dynamics in the weak-field regime distinct from strong-field tunnelling. Our results extend light-field-sensitive current control from bulk dielectrics, semiconductors, and graphene to two-dimensional transition-metal dichalcogenides, highlighting their potential for electric-field sensitive optoelectronics.

We report experimental evidence of mechanical wave generation by magnetic nanoparticles (MNPs) under radiofrequency electromagnetic fields (EMFs, 65mT, 800 kHz, at 100ms bursts) in the ultrasonic (US) frequency range. We developed an experimental setup to detect the second harmonic (SH) signal under isothermal conditions. This SH-US signal was observed only in samples containing MNPs and consequently originated from the EMF interaction on samples containing MNPs. These findings suggest that US generation due to magneto-acoustic interaction in the MNPs could be responsible for the cell damage induced by EMF in cells containing MNPs without increasing the temperature. Experiments performed on magnetically aligned MNP clusters in gelatin significantly enhance the amplitude of the SH signal compared to non-aligned samples. This phenomenon could pave the way for new theoretical and practical approaches as for 'in vivo' magnetoacoustics theragnostic.

Full Waveform Inversion (FWI) is a powerful technique for estimating high-resolution subsurface velocity models by minimizing the discrepancy between modeled and observed seismic data. However, the oscillatory nature of seismic waveforms makes point-wise discrepancy measures highly prone to cycle-skipping, especially when the initial velocity model is inadequate. To address this challenge, various alternative misfit functions have been proposed in the literature, each with unique strengths and limitations. Dynamic Time Warping (DTW) is a popular technique in signal processing for aligning time series using dynamic programming. While a differentiable variant of DTW has been recently proposed, its use in FWI is hindered by high-frequency artifacts in the adjoint source and the substantial computational cost of gradient evaluations. In this study, we propose a neural network-based approach to learn the time shifts that align two time series in a supervised manner. The trained network is then utilized to compare traces from observed and modeled seismic data, offering a stable and computationally efficient alternative to DTW. Furthermore, the inherent differentiability of neural networks via backpropagation enables seamless integration into the FWI framework as a misfit function. We validate this approach on two synthetic datasets, namely the Marmousi model and the Chevron blind test dataset, demonstrating in both cases a similar convergence behavior to that of SoftDWT whilst drastically reducing the computational time of the adjoint source calculation.

Shortly after the middle of the past century, a comprehensive presentation of Continuum Mechanics was written under supervision of Clifford Ambrose Truesdell III in two volumes of Siegfried Fluegge's Handbuch der Physik, a first in 1960 with Richard Toupin on The Classical Field Theories (the monster), including an Appendix on Tensor Analysis by Jerald LaVerne Ericksen, and a second volume in 1965 with Walter Noll on The Non-Linear Field Theories of Mechanics (the monsterino). Both nicknames are due to Truesdell. These contributions were gradually taken as turning points by the Mechanics Community worldwide, due to completeness of analysis and profoundness of documentation. Vastness of treatment acted however as a shield to careful reasoning on delicate but basilar notions which, in the wake of some scholars of the XIX century, were taken to be worthy of belief and incorporated in the presentation with a valuable historical background. Lack of engineering perspective didn't favour the necessary caution to be taken in facing a number of issues. Scholars in Continuum Mechanics, fascinated by the monumental work conceived and carried out by Truesdell and associates, did not dare any accurate revision. The analysis is here centred on unsatisfactory formulations presently disseminated in literature by followers of such authoritative treatment as Truesdell's opus magnum. The geometric approach in 4D Euclid spacetime adopted here is self-proposing even in classical context and provides clarity of notions, methods and results not achievable by the more familiar but less powerful and prone to confusing 3D treatment.

The two-loop electron self-energy correction is one of the most problematic QED effects and, for a long time, was the dominant source of uncertainty in the theoretical prediction of the bound-electron $g$ factor in hydrogen-like ions. A major breakthrough was recently achieved in [B. Sikora et al. Phys. Rev. Lett. 134, 123001 (2025)], where this effect was calculated without any expansion in the nuclear binding strength parameter $Z\alpha$ (where $Z$ is the nuclear charge number and $\alpha$ is the fine-structure constant). In this paper, we describe our calculations of one of the most difficult parts of the two-loop self-energy, represented by Feynman diagrams that are treated in the mixed momentum-coordinate representation.

Cold atomic ensembles offer precise tools for probing near-field interactions, yet experimental data linking atom dynamics to surface-induced forces remains limited. This study investigated the interaction between atoms and a dielectric surface using an atomic fountain measurement technique, in which cold rubidium atoms were released from a moving optical dipole trap. The launched cold atoms were irradiated with an evanescent light detuned from the D$_2$ transition by $-$20.2 to $+$20.2 MHz, after which they were recaptured by reactivating the optical dipole trap. Our measurements revealed that the number of recaptured atoms decreased with increasing flight time, and the decay was suppressed under blue-detuned conditions. We modeled the motion dynamics of the cold atomic ensemble, incorporating Casimir-Polder interactions between the dielectric surface and cold atoms, and observed that the rate of decrease in the number of residual atoms depended on the value of the van der Waals potential coefficient $C_3$. The calculation results demonstrated good agreement with the experimental results, allowing us to estimate $C_3 = 5.6^{+2.4}_{-1.9} \times 10^{-49}$ Jm$^3$ by comparing simulations with the experimental results across various $C_3$ values, accounting for experimental errors.

Scattering of non-classical light is enabling new ways to study and control photon transport. However, advances in this field often rely on simplifying assumptions regarding the quantum light's generation and its source. In this work, we relax some of these assumptions and probe the behavior of entangled photon pairs passing through a disordered layer after being generated by a randomly scattered pump via spontaneous parametric down conversion. We experimentally demonstrate that, even when both the pump and the down-converted photons propagate through a dynamic scattering medium, the pairs maintain a sharp peak in their correlations. A comprehensive theoretical and numerical analysis shows that these correlations persist regardless of when the pairs are generated, whether immediately after the pump is scattered or under other conditions. More specifically, we detail how the shape of the angular correlation depends on the pump's scattering and how it varies with the distance between the pair-generation region and the entrance of the disordered medium. These findings represent a crucial step toward understanding quantum light generation in complex media, and potentially exploiting it for quantum technologies.

Highly correlated photon sources can be realized through cooperative coupling among quantum systems, giving rise to superradiant collective emission. In solid-state ensembles, however, such collective behaviour is typically confined to subwavelength dimensions (the Dicke limit) and strongly suppressed at room temperature by inhomogeneous broadening and rapid dephasing, hindering practical implementations. Here, we show that molecular J-aggregates sustain room temperature superradiant emission and enter a highly collective regime when strongly coupled to the delocalized photonic modes of a silicon bound-state-in-the-continuum (BIC) metasurface. J-exciton polaritons exhibit markedly enhanced, excitation-density-dependent cooperativity, evidenced by an increase in the zero-delay second-order correlation to g^((2))(0)=3.77, well within the superbunched emission regime (g^((2))(0)>2), and by the growth and reorganization of spatially synchronized emissive domains over areas as large as 8.5 um^2, far exceeding the characteristic Dicke area ({\lambda}^2) at the emission wavelength of 589 nm. An interacting-emitter model identifies superradiant clustering as the mechanism by which metasurface-mediated interactions extend J-exciton cooperativity, establishing resonant dielectric metasurfaces as a route towards micrometre-scale superradiance and enhanced photon correlations in disordered materials.

Slow resonant extraction from synchrotrons via radio-frequency knock-out is a well-established technique to deliver charged particle beams for various applications. In this contribution, we present explicit analytical expressions for calculating the number of particles slowly extracted over time, commonly referred to as spills. The proposed formulation enables the semi-analytical determination of an amplitude modulation curve to be applied to the radio-frequency exciter, which flattens the spill macrostructure, a feature of high relevance to all users requiring uniform beam delivery.

To describe longitudinal fine structure like microbunching within a particle beam, a classical approach is to define a bunching factor which is the Fourier transform of the particle longitudinal density distribution. Such a 1D definition of bunching factor can be generalized to a 6D spectral form function (SFF) to describe more complicated structure in phase space. The complex SFF is another complete description of beam in spectral domain and can offer complementary and valuable insight in beam dynamics study which usually invokes the real particle density distribution. The basic property and Fokker-Planck equation of the SFF is presented, along with its solution in a general coupled linear lattice. The example applications of SFF in electron storage ring physics and laser-induced microbunching are presented.

The triple-flame system serves as the fundamental unit for understanding multi-flame interactions, revealing critical coupling mechanisms that scale to complex burner arrays. In this study, we investigated triple flame oscillators, consisting of three flickering laminar buoyant diffusion flames arranged in an isosceles triangular configuration, to construct a comparative regime diagram of dynamical modes. To overcome the limited experimental observability caused by the discretization of geometric parameters, we enabled continuous motion of the vertex flame at a controlled speed V, while independently varying the base length L and the fuel flow rate Q. We conducted a systematic investigation of the triple flame coupling behaviors by varying the triangle size, fuel flow rate, and vertex flame movement velocity. Based on the experimental observations, a comprehensive regime diagram was established to classify the dynamical modes of triple flickering buoyant diffusion flames. Notably, three previously unreported dynamical modes were identified for the first time. To interpret these complex flame interactions, a Stuart-Landau oscillator model with time-delay coupling was employed, which successfully reproduces the experimentally observed dynamical modes. Experimentally observed dynamical modes reveal a bifurcation diagram for the coupled triple Stuart-Landau system, elucidating the transitions between different synchronization modes.

Quantum dot superparticles are a specific class of metamaterials created through the self-assembly of nanometer semiconductor quantum dots into organized micro-scale structures, such as microspheres. Superparticles exhibit unique optical, chemical, and electronic properties. These properties are not merely the sum of the constituent quantum dots but rather bear the signature of the collective behavior of the nanoscale building blocks. In particular, assembling an ensemble of quantum dots into a super-sphere allows them to function as a single, high-quality optical resonator. This structure efficiently confines the emission from the pump-excited quantum dots via whispering gallery modes. The emissive properties of such a superparticle resonator remain an area of active investigation. Using numerical simulation, we study the angular structure of the photoluminescence from superparticles of various sizes and architectures formed from CdS quantum dots. We show that, in general, the angular distribution of the SP emission is characterized by strong asymmetry, with a maximum in the backward direction relative to the incident pump beam. In contrast, this asymmetry is virtually absent in the forward and side-scattering directions. The excitation of resonant modes in the superparticle enhances the emission intensity and reduces the degree of its backward asymmetry. Furthermore, coating the CdS quantum dot particle with a silicon dioxide layer increases the probability of exciting field resonances in such a core-shell superparticle.

Understanding how molecular correlations give rise to mesoscale organization is central to the physics of complex fluids such as hydrogen-bonded mixtures. In this work, we develop a mesoscale bridge formalism that connects the site-site Ornstein-Zernike (SSOZ) framework to the field theoretical Teubner-Strey (TS) approach. This bridge highlights how local orientational correlations, typically lost in the SSOZ closure, reemerge as effective long-range components at the mesoscale. The resulting theory provides a unified description of density fluctuations spanning molecular to mesoscopic length scales. The approach is illustrated using X-ray scattering spectra from simulated and experimental hydrogen-bonded fluids, showing that the TS representation captures the essential features of the mesoscale structure. Beyond this specific application, the proposed formalism offers a general route to interpret the structural crossover between microscopic interactions and collective mesoscale organization in complex fluids, including aqueous and amphiphilic systems.

We study the polaritonic states and dynamics of multiple Jahn-Teller (JT) active molecules coupled to the modes of a Fabry-Perot cavity. We find that collective effects dramatically alter the interplay of electronic, vibrational and cavity angular momenta, giving rise to markedly different polaritonic spectra and dynamics even when going from one to two JT molecules. Starting from the ground vibronic state, we find that JT molecules collectively coupled to a common cavity can access a cascade of high-angular-momentum vibronic states in the presence of a single cavity photon, in sharp contrast to the single molecule case where the range of accessible vibronic angular momentum values are bounded. The observable consequences are a broadening of the cavity-molecular polariton spectrum and a suppression of photon polarization dynamics under broadband excitation of the system. Our results uncover new pathways for vibronic angular momentum transfer unique to collective molecular polaritonics with potential implications for cavity-assisted photo-physics and photo-chemistry.

When exposed to a time-periodic chemical signal, an E.~coli cell responds by modulating its receptor activity in a similar time-periodic manner. However, there exists a phase lag between the applied signal and the activity response. We study the variation of the activity amplitude and phase lag as a function of the applied frequency~$\omega$, using numerical simulations. The amplitude increases with~$\omega$, reaches a plateau, and then decreases again for large~$\omega$. The phase lag increases monotonically with~$\omega$ and finally saturates to $3\pi/2$ when~$\omega$ is large. The activity is no longer a single-valued function of the attractant signal, and plotting activity versus attractant concentration over one complete time period generates a loop. We monitor the loop area as a function of~$\omega$ and find two peaks for small and large~$\omega$, and a sharp minimum at intermediate~$\omega$ values. We explain these results as an interplay between the time scales associated with adaptation, activity switching, and applied signal variation. In particular, for very large~$\omega$, the quasi-equilibrium approximation for activity dynamics breaks down, a regime that has not been explored in earlier studies. We perform analytical calculations in this limit and find good agreement with our simulation results.

Optomechanical sensors enable exquisitely sensitive force measurements, with emerging applications across quantum technologies, standards, fundamental science, and engineering. Magnetometry is among the most promising applications, where chip-scale optomechanical sensors offer high sensitivity without the cryogenics or magnetic shielding required by competing technologies. However, lack of compatibility with integrated photonics and electronics has posed a major barrier. Here we introduce silicon-on-insulator optomechanical magnetometers to address this barrier. A new post-release lithography process enables high-quality metallisation of released mechanical structures, overcoming the incompatibility between silicon-on-insulator fabrication and functional magnetic films. This allows us to employ photonic-crystal cavities that enhance motion-to-optical signal transduction by over an order of magnitude. The resulting devices achieve magnetic field sensitivity of 800 pT Hz^-1/2, three orders of magnitude beyond previous waveguide-integrated designs. The advances we report provide a path towards high-performance, room temperature and chip-integrated magnetometers for applications ranging from biomedical imaging and navigation to resource exploration.

Developing chip-scale photonic platforms for the visible spectrum is essential for translating next-generation optical technologies. Here, we demonstrate that tantalum pentoxide Ta2O5 on insulator is a highly promising material framework for these applications, supporting high-Q resonator architectures across the visible spectral range. Utilizing routine and scalable thin-film deposition and fabrication methodologies, we realize Ta2O5 waveguide and resonator layouts compatible with photonic integrated circuit (PIC) architectures. Through a comparison with numerical simulations, we characterise compact racetrack resonators, evaluating their performance in both the green and blue spectral regions. In addition, we fabricate large-scale ring resonators exhibiting record-high quality factors for Ta2O5 in the visible spectrum, exceeding 7.0 x 10^4. These results highlight the immense potential of this platform for visible photonic systems and establish a robust, straightforward baseline for future visible integrated optics.

This survey describes a class of methods known as "fast direct solvers". These algorithms address the problem of solving a system of linear equations $\boldsymbol{Ax}=\boldsymbol{b}$ arising from the discretization of either an elliptic PDE or of an associated integral equation. The matrix $\boldsymbol{A}$ will be sparse when the PDE is discretized directly, and dense when an integral equation formulation is used. In either case, industry practice for large scale problems has for decades been to use iterative solvers such as multigrid, GMRES, or conjugate gradients. A direct solver, in contrast, builds an approximation to the inverse of $\boldsymbol{A}$, or alternatively, an easily invertible factorization (e.g. LU or Cholesky). A major development in numerical analysis in the last couple of decades has been the emergence of algorithms for constructing such factorizations or performing such inversions in linear or close to linear time. Such methods must necessarily exploit that the matrix $\boldsymbol{A}^{-1}$ is "data-sparse", typically in the sense that it can be tessellated into blocks that have low numerical rank. This survey provides a unifying context to both sparse and dense fast direct solvers, introduces key concepts with a minimum of notational overhead, and provides guidance to help a user determine the best method to use for a given application.

Accurate and efficient prediction of electronic wavefunctions is central to ab initio molecular dynamics (AIMD) and electronic structure theory. However, conventional ab initio methods require self-consistent optimization of electronic states at every nuclear configuration, leading to prohibitive computational costs, especially for large or strongly correlated systems. Here, we introduce a quantum-centric machine learning (QCML) model-a hybrid quantum-classical framework that integrates parameterized quantum circuits (PQCs) with Transformer-based machine learning to directly predict molecular wavefunctions and quantum observables. By pretraining the Transformer on a diverse dataset of molecules and ansatz types and subsequently fine-tuning it for specific systems, QCML learns transferable mappings between molecular descriptors and PQC parameters, eliminating the need for iterative variational optimization. The pretrained model achieves chemical accuracy in potential energy surfaces, atomic forces, and dipole moments across multiple molecules and ansatzes, and enables efficient AIMD simulations with infrared spectra prediction. This work establishes a scalable and transferable quantum-centric machine learning paradigm, bridging variational quantum algorithms and modern deep learning for next-generation molecular simulation and quantum chemistry applications.

The values of liquid odd-viscosity coefficients remain largely unknown, with only a single experimental measurement reported to date [Nature Physics 15, 1188 (2019)]. In this work, inspired by the well-known consequences of dispersion surfaces in classical liquids from the work of Lighthill, we theoretically determine the shapes of constant-phase wave crests formed around obstacles moving at constant velocity in two- and three-dimensional odd viscous liquids, which may or may not undergo rigid rotation. From this analysis, we derive parametric relations that the odd-viscosity coefficients must satisfy, providing a framework for their experimental determination.

Integrated photonic sensors have attracted significant attention recently for their potential for high-density integration. However, they face challenges in sensing gases with high sensitivity due to weak light-gas interaction. Slow light, which dramatically intensifies light-matter interaction through spatial compression of optical energy, provides a promising solution. Herein, we demonstrate a dual slow-light scheme for enhancing the sensitivity of photothermal spectroscopy (PTS) with a suspended photonic crystal waveguide (PhCW) on a CMOS-compatible silicon platform. By tailoring the dispersion of the PhCW to generate structural slow light to enhance pump absorption and probe phase modulation, we achieve a photothermal efficiency of 3.6x10-4 rad cm ppm-1 mW-1 m-1, over 1-3 orders of magnitude higher than the strip waveguides and optical fibers. With a 1-mm-long sensing PhCW incorporated in a stabilized on-chip Mach-Zehnder interferometer with a footprint of 0.6 mm2, we demonstrate acetylene detection with a sensitivity of 1.4x10-6 in terms of noise-equivalent absorption and length product (NEAL), the best among the reported photonic waveguide gas sensors to our knowledge. The dual slow-light enhanced PTS paves the way for integrated photonic gas sensors with high sensitivity, miniaturization, and cost-effective mass production.

Recent experiments reported a correlation between power law core temperature spectra and $D_\alpha$ emission, suggesting that heat avalanches penetrate the SOL. This paper derives a threshold criterion for avalanche penetration using a reduced model. Avalanches with $(\nabla\tilde T)_{rms}>\nabla\tilde T_{crit}$ at the separatrix are predicted to penetrate, and so broaden the SOL and heat load distribution. $\nabla\tilde T_{crit}$ is $\sim 1/\tau_\parallel$, where $\tau_\parallel$ is the parallel heat flow time through the SOL. Penetration occurs when avalanches are strong enough to steepen sufficiently to shock at the separatrix. A positive correlation is found between the nonlinear drive for steepening and the penetration depth. In particular, penetration depth exceeds that of the heuristic drift limit when shocks form. Implications for numerical and physical experiments are also discussed.

Intrathecal (IT) injection is an effective way to deliver drugs to the brain bypassing the blood-brain barrier. To evaluate and optimize IT drug delivery, it is necessary to understand the cerebrospinal fluid (CSF) dynamics in the central nervous system (CNS). In combination with experimental measurements, computational modeling plays an important role in reconstructing CSF flow in the CNS. Existing models have provided valuable insights into the CSF dynamics; however, most neglect the effects of tissue mechanics, focus on partial geometries, or rely on measured CSF flow rates under specific conditions, leaving full-CNS CSF flow field predictions across different physiological states underexplored. Here, we propose a comprehensive multiphysics computational model of the CNS with three key features: (1) it is implemented on a fully closed geometry of CNS; (2) it includes the interaction between CSF and poroelastic tissue as well as the compliant spinal dura mater; (3) it has potential for predictive simulations because it only needs data on cardiac blood pulsation into the brain. Our simulations under physiological conditions demonstrate that our model accurately reconstructs the CSF pulsation and captures both the craniocaudal attenuation and phase shift of CSF flow along the spinal subarachnoid space (SAS). When applied to the simulation of IT drug delivery, our model successfully captures the intracranial pressure (ICP) elevation during injection and subsequent recovery after injections. The proposed multiphysics model provides a unified and extensible framework that allows parametric studies of CSF flow dynamics and optimization of IT injections, serving as a strong foundation for integration of additional physiological mechanisms.