Papers

The solid electrolyte interphase (SEI) is essential for the long-term stability of batteries because it influences the reactions between the electrode and electrolyte. Despite previous studies, the SEI evolution from dense to porous phases remains incompletely understood. Here, we investigate the SoC-dependent evolution of the SEI using a phase-field framework under open-circuit conditions. Spatially correlated noise is introduced to describe stochastic transport perturbations, and the dense-to-porous transition time is evaluated from the evolution of interface roughness arising from the competition between noise-induced transport and surface relaxation. The simulations reveal three distinct roughness evolution regimes. The predicted transition time generally increases from approximately one month at 20% SoC to more than seven months at 80% SoC, with a pronounced change between 55% and 60%. This change corresponds to the graphite SoC-OCV relation, which controls the interfacial Li0 radical concentration through the Nernst condition. Under dynamic SoC conditions caused by irreversible capacity loss, the transition behavior changes as the SoC evolves during storage. These results show that the SoC-OCV relation is central to determining SEI phase stability and the dense-to-porous transition time under open-circuit storage.

Quantum sensing traditionally focuses on using quantum resources such as squeezing and entanglement to improve precision for sensing fixed signals. However, in many applications such as gravitational-wave detection and electromagnetic-field sensing, the signal evolves continuously and varies through time. Additionally, the learner is free to prepare, control, and measure the sensor at arbitrary times, possibly chosen adaptively. In this work, we establish exponential separation in sensing time for continuously evolving signals due to the available squeezing. The signals we study are characterized by a pattern size $T$, and we find that a sensor with squeezing at least $\omega(\sqrt{\log T})$ offers a $\mathrm{poly}(T)$ sensing time, whereas those with squeezing at most $o(\sqrt{\log T})$ must use an exponential sensing time in $T$.

In a bulk ferromagnet, magnetic energy is six orders of magnitude weaker than elastic energy. Slenderness removes this disparity. In a thin ferromagnetic rod, the two energies scale differently with aspect ratio, and an ordinary nickel wire deforms nonlinearly even under modest remote fields. We develop a three-dimensional variational theory of a ferromagnetic Kirchhoff rod under gravity and magnetic loading and study it through bifurcation analysis, computation, and experiment. A uniform field can stabilize a vertical rod against buckling under its own weight, the classical Greenhill instability. We give sharp criteria for clamped and pinned supports in terms of an Airy-type principal eigenvalue, and give a fine description of the character of the bifurcation landscape, which has qualitatively different features than its non-magnetic counterpart. When subject to the field generated by a permanent magnet, we identify a rich bifurcation landscape as the magnet is rotated quasistatically along a circle of radius slightly larger than the rest length of the rod. We prove that instability occurs only through a planar mode and that each snap is a nondegenerate saddle-node. We also locate the two folds that produce hysteresis. Experiments on $50\,\mu$m nickel wires confirm the Greenhill threshold, restabilization at ~$30$ mT for both clamped and pinned supports, as well as the salient features of the hysteresis loop such as switching angles, width, and geometry of folds accurately. Our analysis and experiments offer principled design rules for field-stabilized filaments and remotely switched bistable actuators. Our analysis also offers a well-conditioned numerical method is valid near the switching events, where the direct numerical minimization is strongly ill-conditioned.

A background-constrained silicon-telescope methodology has been developed to investigate recently observed internal electron-positron pair creation in low-energy deuteron-deuteron fusion. The decay of the near-threshold $0^+$ resonance in $^4$He produced in DD fusion results in an $e^-e^+$ pair sharing a total energy of 22.84 MeV. These high-energy leptons traverse thin silicon detectors and deposit only part of their kinetic energy. The resulting partial-absorption peak structure overlaps with the natural-radiation background of cosmic-ray secondaries and terrestrial gamma rays. The silicon detector telescope used in the present experiments is characterised against this background through a component-by-component model that combines a GEANT4 cosmic-ray simulation, a large-volume NaI(Tl)-constrained terrestrial-gamma propagation, and a beam-correlated neutron response. Long-duration measurements in multiple orientations and shielding configurations are used to quantify the run-to-run variability of the background and to translate it into an explicit systematic envelope on the extracted signal. The framework is then applied to the beam-on singles spectrum and the two-dimensional coincidence response, yielding a quantitative branching-ratio measurement under background-limited conditions. The methodology is directly transferable to other weak-channel searches in surface laboratories where continuum-like signals overlap with natural-radiation backgrounds.

This report documents BBun (Blunt Body unsteady), a low-order code for the unsteady potential flow around one or more interacting three-dimensional bodies. The bodies are represented by vortex rings (piecewise-constant doublets) with a Neumann boundary condition; free vortex sheets are shed from the trailing edges and, optionally, from the tips, and convected in time, with an implicit unsteady Kutta condition. The report describes the choices that make the discretization consistent and convergent (a single desingularized kernel per wake line, a bordered factorization of the singular Neumann problem, interior velocity conditions for the nearly singular mode of ring-shaped bodies, the detection of the separation lines from the grid connectivity) and the extensions to several bodies, box wings, symmetric configurations (image method), non-lifting bodies, rigidly rotating bodies (propellers), the unsteady Bernoulli equation, a wake-body collision treatment and a strip-wise integral boundary layer. The verification and validation are reported in full: potential flow around a sphere, sensitivity and grid convergence, the two-dimensional limit, an independent vortex-lattice method, the NACA 0015 wing of McAlister and Takahashi (loads and chordwise pressures), the NACA 0012 profile drag, the biplane of Knight and Noyes, Prandtl's best wing system, Wagner's function and blade-element momentum theory for a propeller. A complete aircraft with counter-rotating tractor propellers illustrates the use of the code.

This study investigates how Raman O-H stretching signatures may be able to track latent-heat-related energetics during water's phase transitions. Prior work on latent heat and Raman analysis of phase change is reviewed to establish theoretical approach. Building on a framework that links O-H frequency shifts to hydrogen-bond strength, we relate entropy/enthalpy variations to temperature-dependent changes in peak position and integrated intensity. Experiments on pure water and impurity-doped samples, specifically CaCl$_2$, were performed under controlled temperatures, and spectra were deconvoluted and integrated over defined bands. The Raman metrics exhibit reproducible temperature dependence that captures the transition window, while CaCl$_2$ induces systematic shifts and measurable modifications in the apparent latent-heat signature. These results highlight Raman spectroscopy as a quantitative probe of thermodynamic evolution and impurity effects in water and related systems.

Physics time-series data are central to fundamental discoveries involving dark matter, neutrinos, and gravitational waves. Machine learning has shown strong potential to recover weak scientific signals buried in complex noise. Recent LLM-agent systems further automate ML development, but existing approaches are often driven primarily by one or a few numerical scores. In pursuit of a better score, agents may exploit shortcuts, trigger mode collapse, or game the evaluation procedure, producing models that achieve high scores but are scientifically invalid. We introduce SIDERIUS, an LLM agent system designed to move beyond a better score toward scientifically valid, long-horizon ML development. SIDERIUS combines contract-defined composable capabilities for flexible orchestration and human-in-the-loop, a multi-layer scientific evaluation protocol that distinguishes valid models from unhealthy or gaming models, and resource-aware exploration for computationally expensive scientific ML. In our TIDMAD benchmark, SIDERIUS with a GPT-based orchestrator achieves the highest Valid-model proposal rate of 63.8% and the best valid denoising score of 2.744, compared with 1.6% and -0.647, respectively, for the same GPT model using the general-purpose Codex coding agent. We further benchmark SIDERIUS on three additional physics time-series datasets, demonstrating its reuse and performance across different tasks. Together, these results suggest that progress in agentic scientific ML should be measured beyond a better score, by how an agent explores and by whether the models it produces can be trusted as science.

One of the key unresolved questions in modern cosmology is the Hubble tension, which arises from the inconsistency between determinations of the Hubble constant ($H_0$) based on nearby observations and those predicted by early-Universe data under the standard $\Lambda$CDM paradigm. Recent advances have identified the new Dark Energy Spectroscopic Instrument (DESI) survey as a promising observational tool for probing late-time cosmology. In this paper, we use the latest DESI DR2 baryon acoustic oscillation (BAO) measurements, combined with nonparametrically reconstructed Type Ia supernovae and cosmic chronometer data, to constrain the Hubble constant using three complementary machine learning methodologies: Gaussian process regression (GPR), artificial neural networks (ANNs), and long short-term memory (LSTM) networks. We perform individual and joint constraints on $H_0$ at the effective redshifts of DESI DR2 tracers, yielding $H_0 = 61.8 \pm 5.3\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ (GPR), $66.3 \pm 5.2\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ (ANN), and $68.7 \pm 6.0\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ (LSTM). Our results exhibit excellent agreement with the recent Planck measurements within $1\sigma$, which highlight the potential of data-driven, model-independent methods to address the Hubble tension. Finally, we further test the sensitivity of our results to potential systematics in the DESI data, emphasizing the importance of understanding systematic uncertainties in BAO surveys.

We revisit the Reissner-Nordström (RN) geometry within the coherent quantum black hole framework, introducing a version that remains well-behaved at short distances while preserving the expected classical behavior at large scales. The key ingredient is a smooth Gaussian regularization, which replaces the usual sharp cutoff and {suppresses} the unphysical oscillations. This construction introduces a characteristic core radius $R_{\rm s}$, constrained relative to the mass $M$ and charge $Q$ to ensure either a {Ricci-regular} center or a softened integrable singularity. Near the origin, the spacetime exhibits distinct behaviors depending on the choice of parameters. When $R_{\rm s}$ satisfies the critical relation $R_{\rm s}^* = \sqrt{\pi}\,R_{\rm Q}^2 / R_{\rm M}$, the geometry is {Ricci-regulFar}, while deviations from this value produce a milder, integrable singularity. {We clarify that for generic parameters, the Kretschmann scalar retains a $1/r$ divergence even at $R_{\rm s}^*$, so the center is not fully regular in the sense of finite curvature invariants.} The effective stress-energy tensor can be interpreted as an anisotropic fluid, whose density and pressure smoothly interpolate between the quantum-dominated core and the classical RN region at large radii. The Misner-Sharp mass accumulates gradually from the center, recovering the ADM mass asymptotically. The quantum scale also modifies the horizon structure, giving rise to standard, extremal, or horizonless configurations depending on the ratios $\alpha=R_{\rm Q}/R_{\rm M}$ and $\beta=R_{\rm s}/R_{\rm M}$. Photon motion is correspondingly affected: the photon sphere moves inward, light deflection and Shapiro delay are slightly reduced, and the resulting shadow appears more compact and smoother, reflecting the quantum-corrected geometry.

The mechanical wave equation, derived from Newton's laws, takes its simplest form in the rest frame of the medium, and appears to lose that form entirely once expressed in the coordinates of another inertial frame — raising the question of whether, and how, it can be written consistently for an arbitrary inertial observer. This puzzle, often glossed over in undergraduate instruction, is typically set aside by invoking the rest frame of the medium as “preferred,” without clarifying what this means for the principle of relativity underlying physical law. We revisit the wave equation from first principles and demonstrate that this apparent non-invariance stems from its expression in Eulerian coordinates. We present two reformulations: a manifestly invariant form using Lagrangian derivatives applied at the level of microscopic particle force laws, and a factorized Eulerian form separating left and right-moving wave solutions. In this context, we explore limitations of Galilean transformation as the low-speed limit of Lorentz transformation, and discuss potential observational connections, particularly regarding wavelength changes in the Doppler effect. Our derivation provides both conceptual clarity and pedagogical value for intermediate and advanced undergraduate physics.

Soap films afford gentle production of air-water interfaces and underlie numerous practical applications. Recently these interfaces have been shown to exhibit unexpected redox capability, wherein Triton X soap films formed with water or HAuCl4 solutions (i) oxidize water up to 1.67 mM H2O2 and (ii) reduce aqueous Au3+ ions into gold nanoparticles, without external energy or catalyst. These reports tend to align with the broader claims of the redox nature of the air-water interface, which has been intensely debated. Here, we re-examine these claims via complementary application of a broad range of techniques, including NMR spectroscopy, fluorescence, colorimetric, TEM, DLS, ICP-OES. Experimental results reveal no evidence for H2O2 formation despite repeated formation of over 300 cycles of soap films, were probed via NMR spectroscopy and other analytical methods. Next, we interrogated soap films for their ability to spontaneously reduce Au3+ ions. TEM and time-resolved DLS revealed that the formation of Au nanoparticles was already formed spontaneous in bulk solutions, and casting soap films (300 cycles) had no measurable effect on particle size or density. This observation was confirmed via complementary quantitative ICP-OES. Our investigations showed that Au nanoparticles formation in bulk HAuCl4 solution without or with triton soap was accompanied by H2O2 generation, and we provide mechanistic insights into these processes. Collectively, these results demonstrate that amplification of the air-water interfacial area through soap-film formation is insufficient, by itself, to induce detectable H2O2 generation or enhance Au-ion reduction. More broadly, our findings highlight the need to distinguish reactions that are driven by purely air-water interfacial effects from those that originate from bulk-solution chemistry and at the solid-water interface

Soft tactile sensors elevate robotic touch through enhanced flexibility and adaptability, yet most existing designs depend on embedded electronics that are susceptible to interference and environmental limitations. In this work, we leverage fluid-solid interactions to develop a class of soft tactile sensors that operate entirely without electronics at the sensing site. The sensor comprises a fluid-filled elastomeric channel connected to only two external pressure sensors. Touching different regions of the elastomeric surface displaces the viscous fluid, producing distinct pressure patterns that encode both touch position and force. These signals are decoded through a machine learning framework that integrates feature extraction, soft clustering, and adaptive neuro-fuzzy inference to achieve accurate localization and force estimation. We validate this concept through single-point touch localization and force estimation in a linear (1D) sensor and extend the same sensing principle to 2D tactile mapping by routing the channel across the surface using space-filling curves, while maintaining the same minimal hardware setup. This simple approach remains effective in environments where conventional electronic sensors often fail, such as underwater or in the presence of magnetic interference.

We examine the Casimir interaction between planar massive Dirac materials coupled to a Lorentz-violating Bumblebee-CFJ vector sector. The standard renormalized one-loop polarization tensor of the Dirac quasiparticles is used as the nonlocal surface response in the Lifshitz scattering formalism. For the normally oriented Lorentz-violating background, the surface response remains parity-even. Hence, the repulsive regimes do not originate from a Hall-like conductivity. Instead, they arise from the CFJ-induced mixing of TE and TM bulk modes, combined with the spatially dispersive response of the Dirac sheets. Furthermore, we show that the Dirac mass suppresses the interaction and changes its large-distance scaling, whereas the CFJ parameter drives transitions between attractive and repulsive regimes.

Chemical reaction-condition optimization – choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize yield and stereoselectivity – is a central, judgement-laden subtask of organic methodology research that large language models are increasingly expected to support. Yet existing chemistry benchmarks evaluate reaction-class labelling, retrosynthesis, or SMILES manipulation, and do not ask models to read a real condition-screening table and pick the best set. We introduce RxnOptBench, a benchmark whose every option and precedent is a real wet-lab entry mined from the optimization tables of organic-methodology papers published in 2025, graded by a continuous relative score derived from a declared headline utility that combines reported yield with enantiomeric excess (ee), diastereomeric ratio (dr), and regioisomeric ratio (rr), and equipped with a paired precedents-vs-no-precedents design that isolates in-context use of literature evidence from parametric memorization. Across nine frontier LLMs and three Chemistry LLMs, even the best models leave substantial headroom: chemistry-specialized models fall to the random-baseline floor on multi-axis selection, while open-weight models have closed most of the gap to proprietary frontier models. We release the final human-reviewed benchmark test set and evaluation code.

In a recent work [1] we demonstrated that the Fermi arcs in t-PtBi2 are indeed disconnected segments of the Fermi surface. We showed that the surface state band crosses EF between Weyl points and is has finite band gap at all other momenta until it merges with bulk bands. We did not find evidence of a superconducting gap in the spectra down to 3K. In recent Comment, Sergey Borisenko reanalyzed data from repository posted with our paper and concluded that a superconducting gap with i-wave symmetry is present in our data up to temperatures of 19K. We argue that this claim arises from incorrect identification of the location of Fermi arc and confusion between band gap and superconducting gap. This error was compounded by the fact that Author of the Comment analyzed data from interpolated images used for mapping the Fermi surface that are not suitable for extraction of energy gap on meV scale. This fact was previously communicated to the Author of the Comment during e-mail exchange. We maintain that our conclusion of absence of a superconducting gap above 3 K are valid and consistent with recent STM measurements performed on the same samples, which found surface superconductivity with Tc = 3K, below the temperature at which the data for our ARPES paper was measured.

Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally efficient flow measurements across complex vascular networks remains challenging. To address this, we first decompose the vascular network into a set of bifurcation units and then develop an operator network capable of mapping unit-specific parameters to the local solution fields of each bifurcation unit. By lumping the Windkessel-model outlet parameters and incorporating inlet boundary conditions from the solution of parent units, the flow and pressure fields can be rapidly approximated. Subsequently, operator-network-driven Schwarz waveform relaxation is applied across bifurcation units to correct discontinuities and improve numerical accuracy. On 7-segment and 55-segment arterial tree models, the proposed method achieves $13\times$ to $17\times$ wall-clock speedups over conventional 1D numerical simulation, with relative $L^2$ errors of 1% in both pressure and velocity. The resulting pulse wave velocity biomarkers agree with the conventional reference to within 1–2%, and the same trained operator generalizes to different tree-like 1D vascular network topologies.

In a binary alloy, spinodal decomposition has one composition direction in which the free energy can fall. A multicomponent alloy has more directions available, yet its elements usually separate together in a single, pseudobinary mode. Why does this happen, and what would allow two independent directions to become unstable? We examine these questions using binary interaction parameters and the composition-constrained curvature of the regular-solution free energy. Across equimolar BCC quaternaries, one unstable mode is the most common outcome at 500 K, while 15.2% have at least two. The tendency of unlike elements to mix helps explain the preference for one mode. We then show how particular arrangements of interactions, including a balanced repulsive triangle and an attractive-repulsive pair, guarantee two downhill directions. When those directions have similar strengths, simplified phase-field models produce a broad texture of locally different solid solutions rather than two chemical blocks. A phase-specific screen identifies five provisional alloys with nearly balanced modes, with Cu-In-Ir-Ti providing an example of this possibility. Thus, we establish a theoretical framework for engineering microstructures via spinodal decomposition derived from basic binary interaction tendencies.

We propose a BRST-invariant formulation of the Gribov–Zwanziger model in the Landau gauge, in which the Gribov scale is produced by the spontaneous breaking of a scalar quartet symmetry, rather than through the explicit breaking term of the conventional construction. We show that the resulting action preserves the ghost equation of the Landau gauge and reproduces the characteristic Gribov-type gauge propagator of the original Gribov scenario, a BRST-invariant mass term of the refined type being accommodated by the same mechanism. We establish the renormalizability of the model to all orders by means of algebraic renormalization techniques, no counterterm being required outside those already present in the classical action. We further show that the condensation of the quartet leaves unchanged the cohomology through which the observables are characterized, the operator realized in the displaced vacuum having the same cohomology as that of the symmetric phase, so that the mass scale is produced at no cost in the physical content of the theory, and the construction of genuine gauge-invariant observables in the Gribov–Zwanziger setting is thereby left open to the usual cohomological methods.

The quantum Hall effect, topological insulators and quantum capacitance are usually treated as distinct topics, yet they share a common thread. Bulk topology determines a protected boundary state, and quantum capacitance is one of the few electrostatic methods able to detect the density of states. This paper examines that connection. The integer quantum Hall effect showed that quantised boundary transport has a topological origin given by the Chern number, later extended to zero field in Z2 topological insulators with helical edge and Dirac surface states. Quantum capacitance, developed for two-dimensional electron gases, has since become a key technique for mapping the Landau level density of states underlying the quantum Hall effect and the linear Dirac density of states of topological surface states. This picture is complicated by self-consistent screening theory, which shows that the quantum Hall bulk splits into compressible and incompressible regions that shift with field. Local quantum capacitance measurements confirm that the bulk is not uniformly insulating across a quantised plateau, even though the plateau stays quantised. The paper argues that quantum capacitance is not a secondary feature of these phenomena but one of the clearest windows into the boundary density of states that topology imposes, and that it reveals the local, field-dependent nature of the insulating bulk assumption underlying the analogy between the quantum Hall effect and topological insulators.

Evaluating the drag coefficient Cd for a sphere moving in a fluid at finite Reynolds number Re has become a classical research subject in fluid dynamics, with publications appearing continuously by numerous authors over the years due to the unavailability of closed-form mathematical solutions. To date, mathematical formulas for calculating Cd as a function of Re can only be obtained by correlation curve fitting to experimental data. The consequence is the coexistence of various formulas with different mathematical fitting functions and an increasing number of fitting parameters. The present study focuses on examining the accuracy of Klyachko's 1934 formula for Cd with two common fractions as fitting parameters, which could enable closed form integration of a simplified equation governing rectilinear motion of aerosol spheres. The computed values of root-mean-square relative error from recently developed formulas show reasonable accuracy of the Klyachko formula for Re less than 1000 and its extension to Re less than 3e5 as a piecewise continuous function, with the possibility of significant improvement through a slight modification. Hence, a few demonstrative examples are presented using the extended Klyachko formula in the study of rectilinear acceleration, the calculation of the terminal velocity of aerosol spheres, and so forth, to shed fresh light on how this classical problem can be studied.

We present fully two-dimensional self-consistent calculations of the electron density, local filling factor and current distribution in a six-terminal GaAs/AlGaAs Hall bar of width W = 3 micrometers with two additional square contacts, A and B, placed on the channel axis and connected to the outside by air bridges, following the interior-contact experiment of Kendirlik et al. [Nat. Commun. 8, 14082 (2017)]. Within the screening theory of the integer quantized Hall effect, the Thomas-Fermi-Poisson equations are solved on the full device plane, including the probe arms and the depletion regions around the interior contacts, and the current is obtained from a local Ohm's law with a filling-factor dependent conductivity tensor, for source-drain and interior-contact excitation separately. Across the nu = 2 plateau the interior contacts pass through three regimes as the field is lowered: galvanically connected to the current path in a compressible bulk, floating in the Hall potential of a fully incompressible bulk, and isolated from both the bulk and the edge strips by closed incompressible rings encircling each contact. In the floating and isolated regimes the two-terminal resistance between the interior contacts rises by orders of magnitude. The isolation interval lies within the Hall plateau and ends before its low-field edge; its high-field end is sharply set by the disappearance of the incompressible bulk, while its low-field end shrinks with temperature and Landau-level broadening. The resistance between an interior contact and a perimeter probe approaches one half of the quantized Hall resistance across the plateau. The topology of the incompressible regions and the boundaries of the isolation interval are insensitive to the regularization of the local conductivity, to the grid and to moderate disorder, whereas the magnitude of the isolation resistance is not.

The $GW$ approximation is known to violate causality, yielding self-energies with unphysical poles in the upper half of the complex frequency plane. The combined $GW$+DMFT approach attempts to cure this by injecting the exact local self-energy. Using exact diagonalization on minimal Hubbard clusters, we rigorously benchmark the analytic structure of the $GW$ and $GW$+DMFT self-energies. We demonstrate a “dimer anomaly”: on the two-site dimer, inversion symmetry forces the non-local self-energy to be purely real, masking the causality violation. Moving to the non-bipartite three-site Hubbard ring, where hopping frustration yields a finite imaginary part in the non-local self-energy, we prove that both $GW$ and $GW$+DMFT violate the Matsubara causality condition. By analytically evaluating the exact Lehmann moments, we derive a closed-form expression for the high-frequency tail of the self-energy, $C = U^2\,\mathrm{Var}(d)\,I$, and show that standard methods fail to capture the exact equal-time two-particle correlators required to enforce it. The equal-time correlators that govern this tail are precisely those encoded in the high-frequency asymptotics of the fully irreducible vertex $\Lambda$; nevertheless, our intermediate-frequency analysis demonstrates that the local-only truncation of $\Lambda$ in $GW$+DMFT leaves non-local RPA resonances uncorrected. The two diagnostics are logically independent: $GW$+DMFT satisfies the necessary high-frequency condition $C\succeq0$ yet still violates the Matsubara sign condition at finite frequencies. Taking the interpolation-free real-axis evaluation of the exact self-energy as the primary causality certificate, with rational continuation as supporting evidence, we establish a rigorous benchmark demonstrating that local self-energy corrections are insufficient to guarantee a causal analytic structure on frustrated clusters.

Simplified optical systems often exhibit residual lens aberrations and Veiling Glare (VG), resulting in spatially varying blur and contrast reduction. Large-scale Lens Libraries (LensLib) enable reusable aberration correction models by covering diverse Point Spread Functions (PSFs), but their aberration-only training distribution does not include target-specific veiling glare. Extending such foundations to compound degradation is challenging because realistic target-system compound pairs are difficult to obtain. To address this challenge, we propose LensBridge, a two-stage framework that first establishes a reusable aberration correction foundation and then adapts it to compound optical degradation using only a few unpaired target observations. In Stage I, we build a PSF-aware one-step diffusion foundation by constructing discrete degradation priors from LensLib PSFs and learning to retrieve them directly from aberrated images, enabling PSF-aware correction without requiring explicit PSF at inference. In Stage II, we adapt this foundation to compound degradation through frequency-domain guidance. At the data level, Frequency-guided Degradation Completion (FDC) transfers target low-frequency characteristics to LensLib aberrated images while preserving aberration structures to synthesize compound training pairs; at the model level, Frequency-guided Pseudo Decomposition (FPD) forms aberration- and VG-dominant pseudo observations to condition separate adaptation branches. Extensive experiments across multiple optical systems demonstrate that LensBridge effectively extends reusable aberration correction foundations to joint aberration correction and veiling glare removal without target-system paired supervision. All code will be available at https://github.com/XiaolongQian/LensBridge.

How much initial-state precision is required to predict a nonlinear system to a prescribed accuracy over a finite horizon? We formulate this inverse prediction problem through a one-shot resource $B_N$, defined as the number of binary refinement bits required in the initial state by a specified local sensing architecture. Within a common scale-separable error-growth regime, changing a fixed tolerance changes $B_N$ only by an $N$-independent offset. An exactly solvable circle/Chebyshev map provides the chaotic calibration $B_N=N\log_2 m+O(1)$. We then apply the framework to the quasiperiodically forced logistic map under phase-only initial uncertainty. Over the resolved horizons, representative smooth-torus, strange-nonchaotic-attractor (SNA), and chaotic regimes exhibit a three-level precision-resource hierarchy: bounded, logarithmic-like, and linear-like growth, respectively. For the SNA, algebraic phase sensitivity $G_N^{\mathrm{op}}\sim N^\mu$ implies an admissible initial uncertainty of order $N^{-\mu}$, and therefore a precision cost proportional to $\log N$. Phase-grid refinement shows that the sampled worst-case maxima are numerically stable with increasing phase resolution. Near the torus–SNA fractalization point, derivative-based phase sensitivity can already grow while finite perturbations remain below a fixed operational tolerance for more than $10^6$ iterations, revealing exceptionally long activation horizons. Thus an SNA can occupy an intermediate finite-precision resource class between a smooth torus and ordinary chaos, while its operational manifestation can occur on a much longer timescale than its derivative-based sensitivity.

Tetrahymena calcium-binding protein (Tcb2) forms Ca$^{2+}$-responsive networks that exhibit contractile behavior, yet how Ca$^{2+}$ concentration controls their local mechanical response remains poorly understood. Here, we use optical tweezers to perform active microrheology on reconstituted Tcb2 networks inside a microfluidic device that enables precise control of Ca$^{2+}$ concentration, allowing systematic tuning of network structure and mechanics. We find that increasing Ca$^{2+}$ from 1 to 100~mM enhances the effective stiffness by nearly two orders of magnitude, from $\sim$1$\times$10$^{-3}$ to $\sim$7$\times$10$^{-2}$~pN/nm, corresponding to a transition from a viscosity-dominated to a more elastic and mechanically robust network. Recoil assays further reveal rapid release of stored elastic energy following deformation. After two orthogonal pulls, the bead recoils along the diagonal rather than retracing the loading path, indicating that stresses from different directions combine to produce a resultant restoring response. These results establish Tcb2 networks as a minimal, tunable system for probing chemomechanical coupling and viscoelasticity in Ca$^{2+}$-regulated protein networks.

Gas sensor arrays can estimate the composition of gas mixtures from their response patterns. However, when a sensor is used outside the laboratory, the true gas concentrations are unknown, so there is usually no direct way to determine whether the estimated result is reliable. Here, we develop a physics-constrained neural network that estimates time-varying gas concentrations and reconstructs the corresponding sensor signals at the same time. We tested the method with a twelve-channel membrane-type surface stress sensor array exposed to water-ethanol mixtures whose concentrations changed over time. The model reconstructed the concentration profiles of both components from the raw signals. Artificial offsets and sensitivity changes in one channel were detected from disagreement between the measured and reconstructed signals. Deletion or replacement of part of a measurement record was detected from violations of the sorption and mechanical equations. This approach enables time-resolved multicomponent gas analysis with self-verification from a single measurement.

We explore new methods for inducing the movement of water droplets on elastomeric substrates, such as polydimethylsiloxane (PDMS). Our approach builds upon the well-known phenomenon that water droplets move easily across these substrates when swelled w,ith silicone oil. While designing shallow open channels and traps on a PDMS film offers a simple way to control a droplet's trajectory by tilting the substrate in two orthogonal directions, this method is poorly suited for manipulating multiple droplets simultaneously. To address this limitation, we introduce two new methods. The first method utilizes a plano-concave PDMS platform with a radius of curvature significantly larger than the size of the droplet and its translation distance. When the system's configuration is altered, this curved substrate consistently guides the droplet along a curved geodesic. The second method incorporates diode-like gates on the PDMS. These gates are created on either flat or plano-concave PDMS films by embedding thin, rigid plates into the elastomer prior to crosslinking, followed by swelling in silicone oil. This process generates sigmoidal steps that restrict droplet motion to a single direction. Finally, we demonstrate how combining these two strategies enables various forms of directed motion for both single and multiple droplets.

We determine the quadrupolar logarithmic tidal response of electrically charged Einstein-Euler-Heisenberg black holes from the coupled static odd-parity gravitational and electromagnetic equations. Although the second electromagnetic invariant vanishes on the electric background, its quadratic interaction contributes to the axial fluctuation equations. At first order in the Euler-Heisenberg coupling $a$, a direct asymptotic recursion gives the canonical running matrix $C_{\rm can}^{\rm EH}=(9aQ^2/10)\left(\begin{smallmatrix}0&-Q\\-Q&M\end{smallmatrix}\right)$, defined as the coefficients multiplying the canonical source vector in the $r^{-2}\log(r_0/r)$ term. The resulting local coefficients retain the full dependence on the subextremal ratio $\frac{Q}{M}$ at this perturbative order. The two quartic invariants partially cancel in both nonzero channels, while the action-normalized fields make gravitoelectromagnetic reciprocity explicit. The local extremal limit of the running matrix reproduces the corresponding two-invariant effective-field-theory result after convention conversion. Allowing independent quartic couplings further shows that identical electric background geometries can have different axial running matrices and yields algebraic inverse relations within the specified two-operator model. The calculation determines local logarithmic running within minimally coupled quartic electrodynamics; the finite horizon-matched response requires an additional global matching calculation.

Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds a flow absent from existing automotive aerodynamics datasets such as DrivAerML and SHIFT-SUV: the shear layer leaving the cab roof passes over a recirculating bed flow before separating again at the tailgate. The resulting drag lowers fuel efficiency, raises emissions and limits the range of electric trucks. Scale-resolved Computational Fluid Dynamics (CFD) is too costly for broad design exploration; neural surrogates can predict flow features at a fraction of that cost, provided they are trained on large-scale, high-fidelity, domain-specific data. We introduce SHIFT-Truck, the first such dataset for pickup trucks. It comprises 1,000 Spalart-Allmaras delayed detached-eddy simulations (SA-DDES) of a reference pickup geometry morphed across 17 shape parameters. Each case is run on a mesh of about 100 million cells at a Reynolds number of $1.4 \times 10^7$ and released with time-averaged surface pressure, wall shear stress, volumetric pressure and velocity. The setup is verified by grid refinement and repeated runs, and checked against wind-tunnel measurements. We define geometry-grouped splits and benchmark four neural surrogates, DoMINO, GeoTransolver, AB-UPT and SMART, on surface and volume tracks. SHIFT-Truck also introduces controlled distribution shifts in the operating point, the input surface discretization and the vehicle archetype. Models with strong in-distribution performance can degrade substantially under these shifts: operating-condition changes expose failures to infer speed dependence, while tessellation and cross-vehicle shifts reveal markedly different robustness across architectures. SHIFT-Truck is thus a benchmark not only for surrogate accuracy but also for generalization across physical and numerical distributions.

We analyze a novel approach for quantum magnetic sensing via optical detection of nuclear spin precession in a noble gas. The detection is carried out with an ultraviolet frequency comb laser, so no alkali vapor is required to be mixed with the noble gas during the sensing stage, allowing for hours-long spin relaxation time at room temperature. Our analysis reveals that combining high laser power and low laser intensity noise is crucial to achieving and exceeding state-of-the-art magnetometric performance, including that of cryogenic magnetometers. We find operational regimes where either fundamental or technical noise dominate, which directs experimental effort towards increasing laser power or lowering laser intensity noise, respectively. We also show that optimizing beam size is key to achieving optimal sensitivity. Our calculations predict that a record-breaking sensitivity of $24~\mathrm{aT}/\sqrt{\mathrm{Hz}}$ is achievable in 10 minutes with a 10 W ultraviolet laser with a relative intensity noise of -100 dBc/Hz.