Papers

We extend a recent physics-based matrix model for calculating PEM fuel cell impedance (doi:10.1149/2754-2734/ad6ce8) to cases of low air flow stoichiometry and functionally graded cathode catalyst layers (CCLs). We demonstrate that the matrix model produces accurate spectra and is almost three orders of magnitude faster than a model based on the standard boundary-value problem solver. The physics-based matrix model can compete with equivalent circuit models for fitting experimental EIS spectra, particularly those measured from cells with functionally graded CCL.

Synthetic frequency dimensions provide a powerful and highly reconfigurable platform for topological photonics. However, experimentally identifying their topological phases remains challenging because these systems do not naturally provide well-defined boundaries or readily accessible edge-state signatures. Here, we realize a reconfigurable Su-Schrieffer-Heeger (SSH) lattice in a synthetic frequency dimension using an integrated thin-film lithium-niobate photonic molecule and directly measure its topology. Electro-optic coupling between staggered resonator supermodes enables independent control of the effective intra-cell and inter-cell hopping strengths, enabling dynamic switching between trivial and non-trivial topological phases on the same chip. We validate the transition through two independently derived bulk topological invariants: direct retrieval of Zak phase from time-resolved synthetic-dimension band-structure spectroscopy; and extraction of the winding number using mean-chiral-displacement method from site-resolved steady-state measurements. Both approaches consistently identify the topological transition and agree closely with theoretical predictions. Our results demonstrate experimentally accessible, boundary-independent methods for characterizing topology in synthetic-frequency lattices. More broadly, the integrated and dynamically reconfigurable photonic platform provides a scalable framework for bulk topological characterization and programmable topological photonic systems.

Microwave breakdown driven by electron cyclotron (EC) waves provides a non-inductive route to plasma initiation in reactor-scale tokamaks. We introduce a three-dimensional Monte Carlo simulation that, for the first time, self-consistently treats nonlinear wave-particle interactions, atomic collisions, and guiding-center transport. The Monte Carlo simulation unveils the key role of parallel Brownian motion in the ionization avalanche mechanism. The predicted breakdown boundary is validated against KSTAR experiments. This work concludes that microwave gas breakdown will be successful under ITER-relevant conditions at a D$_2$ prefill pressure near 2 mPa with 1 MW of injected EC power.

While underground reservoirs emerge as essential elements to face global warming, these systems represent complex multi-physical and multiscale problems. The considered injection of fluids during hydrogen storage, carbon dioxide sequestration, or geothermal energy recovery involves a modification of the chemical equilibrium of the fluid in the porous reservoir. Chemical reactions can induce microstructural changes of the rock matrix, leading to a reduction of elastic properties of the material, and to potential settlement or stress redistribution. Consequently, it becomes pivotal to establish predictive behavior laws to describe the effect of chemical damage on elastic properties. Facing the difficulties to estimate experimentally the impact of chemical damage on mechanical properties, a Digital Rock Physics approach is proposed in this contribution. This numerical homogenization scheme is used to compare two distinct types of microstructure models: the first one consists in a Discrete Element Model, while the second one employs a continuous description. This continuous formulation is based on a Phase-Field description to predict the evolution of the microstructure subjected to chemical alterations and on the Fast Fourier Transform to estimate the macroscopic properties of the material. Finally, these frameworks establish different softening laws that can be used as constitutive ingredients for a cemented material during its weathering.

The relative velocity between a particle and the surrounding fluid is a key quantity that determines the various phenomena of particle-laden turbulence, such as particle clustering and turbulence modulation due to particles. We theoretically derive the expression for the relative velocity of a particle laden in turbulence by extending the argument of Balachandar (2009). This derivation provides a simplified form of the analytical model of Berk and Coletti (2024), and it makes the underlying physical assumptions explicit. Using direct numerical simulation data of turbulence in a periodic cube and turbulent channel flow, we demonstrate the validity of the derived expression, and show that it also holds for non-spherical particles and droplets.

We propose a method to overcome the difficulties encountered when applying reinforcement learning to fluid mixing processes. The proposed method has two main features: (i) it does not require detailed measurements of the flow state, and (ii) by effectively exploiting a stable limit cycle of a two-dimensional dynamical system (the Li'enard system), it can stably perform optimization without imposing explicit constraints on the control parameters. As an illustrative example, we optimize a process in which a fluid contained in a cylindrical vessel is mixed by periodically rotating the vessel. The resulting optimal vessel motion is physically reasonable: it reverses its direction of rotation before a solid-body rotation state is established. Furthermore, even when the fluid viscosity increases with time during the mixing process, the method can continuously adapt the control parameters to the changing viscosity.

Optical frequency combs (OFCs) are revolutionary light sources characterized by discrete and equally spaced spectral lines, and they have found widespread applications in metrology, spectroscopy, and communications. In the early stages, OFCs were realized using mode-locked lasers. With advancements in the fabrication of high-quality factor ($Q$) microresonators and the increasing demand for miniaturized and integrable photonic chips, microresonator-based OFCs, commonly referred to as microcombs, have been developed. Although early studies of microcombs primarily focused on single microresonators or a few resonators, a significant breakthrough occurred in 2021 when it was theoretically predicted that light propagating in the topological edge channel of an array of ring resonators could generate nested frequency combs known as topological OFCs. Since then, the field of topological OFCs has progressed rapidly, with experimental observations made in 2024. This Perspective will introduce the history of OFCs, placing particular emphasis on the emergence and development of topological OFCs, as well as exploring the research challenges and opportunities associated with them.

Using mid-infrared emission from semiconductor devices for covert communications remains a relatively unexplored and yet promising opportunity. The phenomenon of negative luminescence allows for a method of signatureless covert communications where the net infrared emission of an emitting optoelectronic device is balanced to be identical to the ambient thermal background. In this work we provide a practical demonstration of covert data transfer over a thermoradiative channel with data rates up to 100 kbps. In addition, we demonstrate several additional multiplexing techniques that make the proposed thermoradiative communications method more secure against interception by achieving zero instantaneous optical emission, while remaining detectable if a sufficiently spatially or spectrally discerning observation is utilised. Finally, we discuss various application scenarios in which the proposed methods can be used to achieve secure signatureless communications.

The orthogonal polarization states of vertical-cavity surface-emitting lasers (VCSELs) can be used to describe candidate solutions to the Ising Hamiltonian, which is useful for solving quadratic unconstrained binary optimization problems. However, the natural anisotropy of VCSELs tends to overly favor one polarization state, which impedes the system from working as desired. In this work, we have designed and fabricated a metasurface, which may lead to a VCSEL with reduced undesired anisotropy. By changing the geometric size of nano-structures in the metasurface, the polarization state of the output light can be altered. Based on the injection-locking theory and spin-flip model, we numerically show that VCSELs with lowered anisotropy are more easily affected by the injection locking needed in Ising systems. Additionally, we numerically study the evolution of a 3-bit VCSEL-based Ising system and verify that the computational accuracy of the photonic Ising machine can be improved to more than twice that of its counterpart with higher anisotropy.

We derive a thermodynamically-consistent model of fluid flow through a poroelastic medium. Starting from elastic and fluid free-energy densities, an energy-dissipation rate, and a kinematic constraint, the force-balance equations are derived using variational principles, with the pressure–density constitutive relation emerging as a direct consequence of the variational structure; the same kinematic constraint also supplies the total-flux transport structure. In the ideal-gas limit, the model linearization recovers the classical linear Biot equations. For power-law fluid energies, it yields isentropic pressure–density relations. A key advantage of the variational formulation is that extensions to richer physics, such as thermal effects, chemical reactions, or multi-component fluids, can be incorporated systematically by augmenting the energy and dissipation functionals without redesigning the force-balance or transport closure. We support the model with an energy-compatible two-field discretization and study consolidation under a surface load with three lateral-boundary treatments and three fluid-compressibility exponents.

Polarizability predicts how an object responds to an incident electromagnetic field, the interactions between small particles, or the optical forces exerted upon them. Polarizability is responsible for the effective-medium properties of artificial materials or metasurfaces. Despite significant progress in all these areas, it is unclear what the limits of the strength of such interactions are or, more specifically, what the upper bounds on the polarizability of a given spatial region that an unknown and designed particle would occupy are. This work connects the electromagnetic field description via an integral equation with a dual formulation of quadratic programming to derive fundamental bounds on components of all four polarizability tensors or on their specific combinations. In particular, the work establishes an intuitive visualization of what strong polarizability means and how strong it can be. The developed fundamental bound also answers which materials and domains are best for the given demands on polarizability. These findings establish a versatile platform that can accommodate a wide range of demands on polarizable bodies, providing an absolute measure of their performance against which the results of human-powered or automated design procedures can be compared.

At high Peclet number, the classical Taylor-Aris dispersion model becomes inadequate for early-time contrast-agent transport in arteries, while an explicit closure model and a clear physical interpretation of this regime remain lacking. In this study, we develop a novel explicit-closure one-dimensional (1-D) effective dispersion model for this regime, with its functional structures identified through symbolic regression. Analysis of the resulting model reveals that, in the high-Peclet-number regime, axial transport is redistributed between the effective convection flux and the dispersive flux, resulting in a reduction of the effective convective transport velocity in the dispersion model. This redistribution gives rise to a transition from the classical quadratic scaling to a linear scaling of the effective diffusivity with radial Peclet number. Numerical validation demonstrates close agreement with the convection-diffusion model over the investigated high-Peclet-number conditions, while the classical Taylor-Aris model exhibits substantial deviations. Application of the proposed model to averaged flow velocity inversion further demonstrates improved velocity estimation, particularly in the high-Peclet-number regime. These results highlight the importance of accounting for non-classical dispersion for reliable contrast-agent-based arterial blood flow velocimetry and provide new insight into high-Peclet-number mass transport.

We present a neural network (NN) basis-selection method for large-scale relativistic configuration interaction (RCI) calculations in GRASPG. The method employs configuration state function generators (CSFGs), each of which generates a set of configuration state functions (CSFs) with the same spin-angular couplings, as the basic selection units for the NN. A constant-orbital feature-elimination strategy removes feature channels whose values remain unchanged across the CSFG pool. The CSFG representation reduces the number of learning units processed by the NN by more than one order of magnitude, while constant-orbital feature elimination further reduces the dimensionality of the NN input. Combined with the high-performance GRASPG framework, the method improves the efficiency of both NN selection and subsequent RCI calculations, maintaining a balance between accuracy and computational cost. In a moderate Ni(12+) benchmark, where the corresponding full-space RCI calculation is still feasible, the CSFs generated by the retained CSFG sets reproduce the full-space RCI results at the few inverse-centimeter level for the target states. For the representative J = 0, even-parity block, the complete workflow reduces the wall time by 75.6 percent, and the peak memory required by a single RCI calculation is reduced by a factor of 10.1. In a larger-scale calculation with a full CSF expansion containing 1.27 x 10^9 CSFs, the method retains only 1.1-1.9 percent of the full-space CSFs and yields energy levels in good agreement with experimental data and other resource-intensive theoretical calculations.

Chirality-induced spin selectivity (CISS) is usually associated with molecular handedness. The possible contribution of chiral phonons is less established. We study a helical tight-binding model in which local phonon angular momentum modulates spin-dependent nearest-neighbor hopping. Fewest-switches surface hopping calculations give the transmitted spin polarization $\mathrm{SP}=aC+b\mathrm{PH}$. Here $C$ is the molecular chirality and $\mathrm{PH}$ is the phonon chirality. A mirror symmetry reverses $C$, $\mathrm{PH}$, and $\mathrm{SP}$ simultaneously. This symmetry excludes both a chirality-independent offset and a $C\cdot\mathrm{PH}$ term. The phonon contribution can therefore enhance, cancel, or reverse the molecular CISS signal.

The manuscript provides a description of a theoretical analysis of a non-Newtonian Prandtl fluid subject to peristaltic flow through an inclined asymmetric channel. We explore the effect of an inclined magnetic field on the peristaltic flow. This is relevant for applications involving fluid flow in narrow, inclined (tilted) tubes similar to blood vessels or the digestive system. The model also includes thermodynamic aspects such as heat diffusion (the Soret effect) and viscous dissipation resulting from wall-fluid slip conditions, which may help optimize medical devices such as lab-on-a-chip systems and dialysis machines. In this study, the concentration of a generic chemical, temperature, and fluid velocity are taken into account through mass, heat, and momentum balances, respectively. The solution is approximated using numerical techniques suitable for long wavelengths (low frequency) and low Reynolds numbers. The study also discusses trapping phenomena, which are crucial from a clinical point of view. The developed insights can improve the understanding of physiological flows in the gastrointestinal tract and blood vessels. By understanding how the fluid moves and how particles are trapped, these insights may contribute to the design of improved medical pumps and artificial organs. Graphical visualizations are provided for the fluid velocity profile, temperature distribution, and concentration of a generic chemical. Furthermore, the numerical results are validated through comparison with a closed-form solution from a benchmark problem.

Estimating a ground-state energy and preparing a ground state both split into a front-end that supplies an approximate state $|\chi\rangle$ and a back-end that reads or refines it. We argue, and measure at every step, that the only thing crossing that split is the front-end's spectral weight distribution $p_n=|\langle E_n|\chi\rangle|^2$ over the eigenstates of the problem Hamiltonian $H_P$. Everything downstream is priced in it, so the front-end is a swappable module, and we swap it seven times without altering anything to its right. We call the arrangement WARP. Four back-ends spend that distribution. A readout (A) samples $g(t)=\langle\chi|e^{-iH_Pt}|\chi\rangle$ on the device and extracts true eigenvalues by classical signal processing, returning an energy below the variational bound of the state it was handed, from a signal $50\times$ shorter than a Fourier transform needs. Three routes spend the same account on a state instead. An adiabatic schedule (B) has a minimum gap set by the overlap; a discrete amplification (C) is cheaper in depth but must halt at an index the overlap fixes, so it needs a measured value and not a bound; a recursion (D) of depth $\mathcal{O}(3^m)$ raises the question of whether it can be run at all. The interactions are the substance. On stretched N$_2$ a sample-based diagonalisation reports a variational bound 79 mHa high, fifty times outside chemical accuracy; reading the state it has already prepared returns $E_0$ to $1.4\times10^{-6}$ Ha, three orders of magnitude inside it, and needs no new front-end and no new circuit. And because the weights price everything, they must be measurable when scarce, so we beat a weak reference against a strong one and recover overlaps four orders of magnitude below the shot-noise floor. We map the operating envelope throughout, and state the limitations.

Dendritic silhouettes are cheap. Pond ice neurons, diffusion-limited aggregates, wiring-minimizing arbors, and biological neurons can share a branching statistic because they share a growth instability, not because they are functionally similar. We take that observation as a modeling constraint. The ice neuron only structurally resembles a biological neuron. A biological neuron carries an internal state and a field that it helps generate, letting geometry, dynamics, and field co-sculpt one another. In our construction, the ice neuron is the zero-gain limit of an interfacial system in which a FitzHugh–Nagumo state lives on the moving front and writes back into the field that drives it, with dimensionless gain $\gamma$. At $\gamma=0$, morphology and internal dynamics decouple into a Mullins–Sekerka growth band and a damped oscillator. Finite $\gamma$ deforms the spectrum, shifts the selected instability, and opens an oscillatory region below the isolated Hopf threshold. On a dendrite grown at $\gamma=0$ and then frozen, field closure increases distal persistence without changing the geometry; its first-order effect remains positive across repeated realizations of the same growth rule and localizes to a small set of non-cable field shortcuts. Nonlinear dynamics remain finite beyond the linear instability: below threshold, closure is almost the transmission; at spike amplitude, it is a correction to a regenerative cable event. A dendritic tree therefore is not intrinsically a structural spandrel. Shape alone is morphology without an internal state or a source term. That is the dendritic silhouette of the ice neuron. Excitability adds an active cable. Field closure is the biophysical mélange in which the neuron helps write the field that acts back on it, sculpting its dynamics and, during growth, its structure. That is a biological neuron.

Electrospray ionic liquid ion sources operating in the pure ion regime emit metastable, low solvation number ion clusters whose post-emission dissociation modifies plume composition, energy distributions, and diagnostic observables. While field-free measurements support first-order, thermally activated decay on microsecond timescales, the acceleration region is characterized by rapidly varying electric fields and nanosecond residence times such that dissociation becomes strongly field-enhanced. In this work, microcanonical ensemble molecular dynamics (MD) trajectories are used to infer dissociation kinetics of EMI-BF4 ion clusters under controlled internal energy and applied electric field. Positive and negative dimers, trimers, and tetramers are simulated at temperatures between 600 to 1000 K and uniform fields of 10^6 to 10^9 V/m. Dissociation lifetimes are extracted using a connectivity-based fragmentation criterion, and product channels are classified to obtain pathway-resolved branching probabilities. Across all solvation numbers, lifetimes are field independent in the weak field limit, but collapse by orders of magnitude above 10^8 V/m. Cluster polarity dependence is most prominent in the temperature-controlled regime and diminish as electrostatic work dominates. The MD simulations further reveal that field-driven changes in dissociation topology, including a transition in trimer breakup from neutral pair evaporation to charged core ion ejection, and three competing tetramer pathways involving single neutral loss, double neutral emission, and core ion ejection. The MD dataset is reduced to compact parameterizations of {\tau_n}(E,T), yielding transferable kinetic inputs for multiscale plume transport models.

Gravitational evidence firmly establishes the existence of dark matter (DM), yet its non-gravitational interactions and fundamental nature remain unresolved. In this work, we demonstrate that a chirality boost-spin gauge field, which emerges naturally in the General Standard Model formulated within gravitational quantum field theory, offers a vector DM candidate. We construct the corresponding low-energy effective field theory for this DM with axial-vector couplings to Standard Model fermions and systematically investigate its signatures in direct detection, indirect detection, and collider experiments. Comparing our predictions against current experimental data places stringent constraints on the DM parameter space, with the viable regions within the further reach of future searches.

Exact periodic solutions of the incompressible Navier-Stokes equations arise when the convective field is a pure gradient and can be absorbed into the pressure. We construct solutions outside this class, in arbitrary spatial dimension, by an iteration in which each step solves a linear diffusion problem forced by the transverse part of the preceding convective field. The construction is dimension-independent, and for a cyclic family of initial fields the first two iterates are available in closed form. Carried out numerically in three dimensions, the iteration converges: at Re=10 successive iterates contract with an observed factor 0.57 and the limit satisfies the mild equation with relative defect 1.0 x 10^-3. The observed factor exceeds unity by Re=30, which identifies the practical range of the iteration rather than a proved convergence boundary. We also show what goes wrong when the projection is omitted, as it was in an earlier iteration of the author's: the iterates cease to be divergence-free and an apparent growth appears that belongs to the expansion and not to the flow. For an n-dimensional generalisation of the initial field the fraction of the convective field not absorbed by pressure is 2sqrt(2)/3, independently of n over the range examined. Divergence and cell kinetic energy provide simple diagnostics that detect failure at the step where it occurs.

A general framework for inferring the computational role of spike-timing-dependent plasticity (STDP) does not currently exist. Here, we develop a game-theoretic description for a canonical, convergent neural circuit with general STDP and postsynaptic-potential (PSP) kernels. Parity-matched STDP-PSP interactions induce a potential game among presynaptic neurons, whereas parity-mismatched interactions induce a zero-sum game. These components, respectively, implement contrastive PCA and flow selection; their weighted interplay determines learning dynamics and computation for arbitrary STDP rules.

The 30 and 40 GHz instruments of the QUIJOTE radio astronomy experiment use very sensitive receivers which need to be characterized both to ensure their proper functioning and for their calibration. Given the age of the PXI-1031DC device used up until now for this purpose, the design of a new device, the Calibrator TFGI QUIJOTE, has been proposed. This new device has been designed with the idea of being simple and modular, easy to use and upgrade. It is built on a Raspberry Pi 5 system, using an MCC 118 as analog reading module. This system, widely known among both makers and professionals, not only meets the requirements of modularity and ease of use, but also ensures that the device is much more economically competitive than the PXI. A first version of the device has been built and tested, obtaining good results in accuracy and ease of use.

Relativistic magnetic reconnection, a prime mechanism for particle acceleration, has been extensively studied in electron–ion and pair plasmas, but how the dissipated energy is shared among species in electron–positron–ion plasmas, and in particular how ions are energized within a pair-dominated layer, remains poorly understood. We develop an analytic theory that couples single-particle orbit dynamics to the collective plasma response, and we validate it with particle-in-cell simulations spanning pair-dominated to electron–ion compositions. Particles are directly energized in electric-dominated regions until the reconnected magnetic field deflects them into the outflow; the characteristic escape length is in turn set by the relativistic skin depth of the reconnection-heated mixture, including the ion contribution. Writing $\sigma_e^*$ for the magnetic energy available per particle in units of $m_ec^2$ and $\mu=m_i/m_e$, the mean lepton energy scales as $\sigma_e^*$, whereas the mean ion energy scales as $\mu^{1/3}(\sigma_e^*)^{2/3}$ when ions escape non-relativistically from the electric-dominated regions and approaches the lepton energy when their escape is ultrarelativistic. The predicted energy fractions agree with simulations across composition, mass ratio, and magnetization. The ion share of the dissipated energy can fall far below the commonly assumed ion–lepton equipartition, reducing the energy available for hadronic and neutrino emission in reconnection-powered astrophysical sources.

Firing neurons generate action potentials that propagate along axons to transmit signals supporting cognitive functions. These electrical currents generate magnetic fields, yet direct detection of these neuronal magnetic fields by MRI remains elusive. This Mini Review investigates why this goal has been proven difficult to achieve and whether an emerging approach, quantum sensing MRI, can overcome the challenge.

Mask-based lensless imagers capture measurements using simple, compact hardware and recover images using a reconstruction algorithm. The reconstruction algorithm affects the image quality, inference speed, and computational requirements of the imaging system, often trading off image quality against computational efficiency. Advancing toward integrated lensless imagers, where encoding and reconstruction occur within a single device (e.g., a mobile phone), requires a fast, high-quality, and practical reconstruction method. We introduce the Real-Time Lensless Vision Transformer (RTLViT), a lightweight, purely data-driven reconstruction architecture for high-quality and real-time lensless reconstruction. With only 1.09 million learnable parameters, RTLViT provides higher-quality reconstructions than both real-time baselines and substantially larger attention-based architectures, including improvements of up to 3.46 dB in peak signal-to-noise ratio over architectures with $15 \times$ more learnable parameters. We demonstrate that RTLViT maintains high-quality reconstructions across a wide range of training dataset sizes and two different mask designs (lenslets and a diffuser). Finally, we implement real-time reconstruction with RTLViT on laptops and smartphones, supporting future integrated lensless imagers for real-time applications.

Freestanding thin films of quantum materials naturally develop sub-micrometer, nonuniform strain fields that strongly affect their electronic, magnetic, and structural properties in both equilibrium and nonequilibrium conditions. However, current methods to quantitatively resolve these mesoscopic features are primarily restricted to scanning probes, making it challenging for dynamical measurements such as single-shot imaging and femtosecond microscopy. Here, we present a new computational framework which we denote as bend contour electron tomography (BCET), which efficiently converts bend contours in transmission electron microscope images into quantitative two-dimensional maps of strain and topography. By iteratively minimizing a designed loss function between experimental and simulated bend contour images, BCET retrieves both surface morphology and in-plane strain tensor fields without requiring scanning or diffraction mapping. We applied BCET to freestanding SrTiO$_3$ thin films, demonstrating the successful reconstruction of the local strain distribution and curvature field with high fidelity. Our approach provides a quantitative framework for characterizing mesoscale structures in freestanding films using wide-field imaging, opening new avenues to investigate how spatial inhomogeneity governs phase transitions and nonequilibrium dynamics in two-dimensional quantum materials.

Induced pluripotent stem cell (iPSC) culture increasingly relies on segmentation foundation models, yet deployment on laboratory CPUs and edge hardware requires compression schemes that are both efficient and auditable. We present a deployment-oriented evaluation of compressed Cellpose-SAM using a pre-specified retention criterion: the 95% cluster-bootstrap interval of mean change from FP32 must remain above a fixed -0.02 margin for every imaging modality. On a stratified 176-field panel spanning BBBC038 nuclei, BBBC039 U2OS fluorescence, and NIST iPSC images across density regimes, weight-only W8A16 preserves instance F1 across all modalities. A sensitivity-guided mixed W4/W8 scheme, using four INT8 exceptions, achieves a 6.76x reduction in weight storage with no observed catastrophic failures (0/176 fields), matching W8A16 at this sample size. In contrast, ternary weight-only quantization achieves 12.08x compression but fails catastrophically on 169/176 fields. These results demonstrate that compression should be evaluated by modality-stratified downstream retention rather than single-number accuracy, and establish a reproducible protocol for auditing compressed foundation models in regulated stem-cell imaging.

The recent emergence of large language models (LLMs) has produced research focusing on the ability of these tools to solve physics problems, evaluate student work, or otherwise impact the problem-solving process of students. However, studies exploring how physics students perceive LLMs (in terms of capabilities, educational impacts, usage, and role in problem solving) remain limited. This study evaluates the first-year physics students' perceptions toward LLMs and further explores how these perceptions change after practicing problem-solving with and without LLMs and engaging in a reflective lesson on the functioning and educational impacts of LLMs. We find that student opinions toward LLMs vary, with generally favorable perceptions of their capabilities but greater skepticism regarding their value for learning. Despite this skepticism, a majority of students self-report regularly using LLMs to obtain help, commonly reporting deadlines and convenience as motivating factors. Following the lesson, students expressed greater skepticism toward LLMs in several areas, with 88% of students believing that LLMs can leave them with a false sense of confidence about their understanding, up from 58% before the lesson.

From interstitial space to luminal cavities, fluid pressure and flow can remodel, reshape and even redefine a biological tissue. Fluids can either govern or react to mechanical interactions between cells. However, measuring flows at cellular scales is difficult, which makes it challenging to understand tissue hydraulics. Here, we develop a theoretical approach that captures cellular mechanics and fluid flow in one framework. We find that hydraulics can drastically influence tissue behavior. Hydraulic coupling between cell shape and size governs a tissue's response to osmotic shock, while tuning a tissue's permeabilities can channel fluid either between or across cell membranes. In active tissues, hydraulics can suppress cell mobility to the point of fracture, where we discover a hydraulic ratchet that drives fluid out of cells to generate small luminal spaces. We find experimental evidence that hydraulics can suppress cell motion in early stage zebrafish embryos injected with a thickening agent, which indicates that hydraulics may generally govern the behaviors of many multicellular systems.

Fiber lasers and amplifiers offer attractive alternatives to conventional solid-state systems. However, generation of high-energy ultrashort laser pulses in fibers faces challenges due to the complex interplay of multiple nonlinear effects arising due to pulse confinement within a small fiber core and also limitations imposed by the gain bandwidth of the available active fibers. The discovery of self-similar amplification and gain-managed nonlinear amplification (GMNA) pulse propagation regimes in fibers with normal dispersion suggests that these challenges can be turned into an advantage. Here we show that pulses generated in the GMNA regime are, in fact, the realization of the idealized similariton-type pulses in realistic fibers with limited gain bandwidth. Our analytical and numerical results show how one should shape the fiber gain as a function of propagation length to achieve constant peak power similariton-like pulses with steadily increasing energy, the pulse bandwidth exceeding the gain bandwidth, and the nearly linear frequency chirp allowing for efficient pulse compression to its Fourier limit. Absent Raman nonlinearities, these pulses can reach $\mu$J level energies in standard single-mode fibers, representing a tenfold increase in pulse energy compared to the best currently available nonlinear amplifiers. Our results have significant implications for the fundamental understanding of nonlinear wave dynamics and for the advancement of fiber laser technology, supporting the reliable generation of high-energy pulses for practical use in areas such as micromachining, metrology, and bioimaging.