Papers

Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends critically on the quality of the sampling strategy used to identify chemically important configurations. Here we introduce the Handover Iterative Neural Quantum State (HI-NQS) algorithm, which embeds a classically trained autoregressive Transformer neural quantum state within the iterative sample–diagonalize–update framework of Sample-Based Quantum Diagonalization. A dual-channel Transformer architecture with explicit spin-up/spin-down cross-attention encodes fermionic spin structure as an architectural inductive bias, enabling expressive and physically informed wavefunction representations. After each subspace diagonalization, the resulting eigenvector is distilled back into the network through a factorized spin-marginal teacher signal, establishing a closed feedback loop between generative sampling and exact diagonalization. Benchmarks across a range of small molecules and a systematic nitrogen active-space series demonstrate that HI-NQS achieves chemical accuracy on all systems tested, with determinant-count scaling substantially more favorable than conventional CIPSI-based SCI for all but the smallest active spaces. All calculations are performed on GPU hardware without quantum computing resources, establishing HI-NQS as an efficient and scalable purely classical approach to the selected configuration interaction problem.

Nonlinear 2D materials such as 3R-phase molybdenum disulfide (3R-MoS2) offer strong second-order optical nonlinearities in an atomically thin platform, making them attractive for on-chip frequency conversion, quantum light generation, and integrated nonlinear nanophotonics. However, the second harmonic generation (SHG) efficiency of monolayer or few-layer 3R-MoS2 deposited on planar substrates remains fundamentally limited by weak light-matter interaction, poor phase matching, and small interaction volumes. Here, we introduce NanoPhotoNet-PINL, a physics informed AI-driven inverse design framework based on a hybrid one-dimensional convolutional neural network and deep neural network autoencoder, tailored for nonlinear MLMs metasurfaces. The model directly maps target dual-resonant reflection spectra at the fundamental and second-harmonic wavelengths to the required multi-layer geometries and material compositions that maximize the effective nonlinear overlap with an embedded 3R-MoS2 sheet. By integrating Maxwell-based nonlinear electrodynamics into the inverse design loop, we compute the second-harmonic conversion efficiency and modal overlap factors for each predicted MLMs design, enabling physics-guided training and evaluation. Our approach achieves an inverse-design prediction efficiency exceeding ~99.2 % along the linear spectral manifold, while the optimized dual-resonant MLMs yield more than three orders of magnitude enhancement in SHG intensity compared to a bare 3R-MoS2 flake on a planar substrate. NanoPhotoNet-PINL establishes a generalizable paradigm for intelligent inverse design of nonlinear multi-layer metasurfaces and phase-matched dual-resonant cavities for high-efficiency second-order processes.

We propose an array of Ramsey-type interferometers using $N$ superposition states, $(\vert 0 \rangle + \vert 1\rangle)^{\otimes N}$, as a sensor to detect wave-like dark matter. After exposure to the dark matter wave, which induces coherent qubit transitions, the signal is the imbalance between the numbers of 0 and 1 outcomes. The signal-to-noise ratio in this scheme is proportional to $N \alpha^2$, where $\alpha$ is the coupling of dark matter to the qubits, and thus the sensitivity to the coupling scales as $\delta \alpha \sim 1 / \sqrt{N}$. For comparison, in the detection scheme based on the Rabi-type transition, $\vert 0 \rangle \to \vert 1\rangle$, this scaling is achieved only when $N$ highly entangled qubits are used. Since the Ramsey-type measurement does not require entangled states, one can consider much larger $N$ by simply placing a large number of qubits within the de Broglie wavelength of the dark matter. We demonstrate that, using trapped-ion qubits in linear Paul traps as the sensor, the projected sensitivity to the coupling matches or surpasses existing laboratory, astrophysical, and cosmological bounds for $N \gtrsim 10^6$-$10^8$. We also evaluate its sensitivity to high-frequency gravitational waves. Our general framework should, in principle, be useful for other quantum sensing platforms.

We developed high-purity vanadium-based nanoparticle targets for neutron scattering experiments aimed at exploring gravity-like short-range new interactions in the submicron regime. Vanadium and V-Ni nanoparticles were fabricated using top-down and bottom-up methods and quantitatively characterized by SEM-EDS, ICP-AES, NDIR and SAXS. Through the performance tests, an RF thermal plasma method was found to be the best from viewpoints of the reproducibility, dispersion of the radius, and contamination of metallic elements. The oxygen incorporation during fabrication was quantified, and its impact on the effective coherent scattering length was evaluated, leading to a minimum average coherent scattering length of $\mathrm{0.719(23)\,fm}$, comparable to that of natural vanadium. These results demonstrate that vanadium-based nanoparticle targets with controlled composition and nanostructure can be systematically designed and fabricated to suppress nuclear scattering backgrounds, thereby enabling experimentally viable coherent neutron scattering measurements for short-range interaction searches.

Cell shapes in confluent tissues range from nearly isotropic epithelial morphologies to highly elongated endothelial ones. In standard vertex models, tissue rigidity is controlled by a target shape index; increasing this index drives cell elongation and ultimate tissue fluidization. Here, we consider the case where cell elongation emerges autonomously by assigning an intrinsic, passive elastic preference for anisotropic shape. This distinction reverses the usual expectation: cell elongation does not fluidize the tissue, but drives a solid-to-solid transition from an ordered isotropic solid to a disordered anisotropic solid, with finite yield stress and shear rigidity on either side of the transition. These results decouple cell shape from tissue rheology and caution against inferring fluid-like mechanics from elongated cell morphologies alone.

The automation of scientific discovery has reached an inflection point. While AI systems now operate instruments, optimize parameters and generate hypotheses, most remain procedural: they execute workflows fixed by human designers. True autonomous science demands epistemic autonomy–the capacity to construct, challenge and revise physical explanations in response to evidence. Here we introduce AHOIS, a multi-agent AI scientist that embeds Socratic midwifery into closed-loop experimentation. A physics-critic agent interrogates hypotheses through causal questioning, constraint checking, counterexample generation and falsification-criteria formulation. We evaluate AHOIS on a real multimode-fibre optical platform, a high-dimensional system with complex wave transformations, indirect detection, environmental drift and multi-modal acquisition. Without prior encoding schemes, classifiers or speckle models, the system autonomously proposed and validated a random-interference encoding hypothesis, discovered task-adaptive sparse-measurement strategies, diagnosed distinct failure modes (encoding instability, fluorescence contamination and detector noise) and translated a published imaging protocol into an executable workflow on a non-original configuration. The discovered encoding yielded 16x16 measurements with effective rank 56.9 and classification accuracies of 76.97% on MNIST and 83.17% on Fashion-MNIST. Ablations show that Socratic interrogation improves physical consistency, hypothesis completeness, uncertainty calibration and experimental-plan validity. These results establish a route from workflow automation towards evidence-grounded, self-correcting autonomous discovery in complex physical environments.

Perfect absorption (PA) represents a fundamental limit of light-matter interaction and a means to maximize nanoscale energy conversion. While PA is now a well-established phenomenon, both the theoretical feasibility and a practical mechanism for achieving it under single-beam excitation within the strong coupling regime is unknown. Through rigorous solution of Maxwells equations for a compact photonic crystal (PhC) architecture incorporating a two-dimensional semiconductor, we present a general method based on degenerate critical coupling for single-port PA of exciton-polaritons. At the crossing of two polariton branches, we achieve near-unity absorption exceeding 99.8 % in a structure thinner than $100\,$nm. This effect is robust under realistic Gaussian beam excitation, and can be realized across different temperatures and excitonic materials by tailoring the PhC geometry. Our results establish a strategy for enabling efficient light-matter coupling, with direct implications for the development of metal-free, ultra-compact polaritonic logic devices, sensors, and energy-harvesting platforms.

Machine learning is emerging as a valuable tool for convection-related applications such as post-processing numerical weather prediction output, improving understanding of convective storm climatology and potentially improving existing convective parameterization schemes. In a rapidly developing field, it is vital to assess the strengths and limitations of machine learning approaches across different applications. Here, a probabilistic model is developed using a convective cell dataset as ground truth and predictors primarily from ERA5. The model's ability to reproduce the convective cell climatology at different regions relative to cold fronts (i.e. post-frontal and pre-frontal) is assessed during the warm-season in Germany. The optimal number of features (predictors) is selected using a feature elimination strategy. Overall, the optimised model exhibits high skill in reproducing the spatial and temporal cell frequency at different regions relative to the front. While the highest cell frequency is correctly identified near the surface front, the model underestimates the actual cell count in this region. Feature importance analysis shows that the model depends most heavily on CAPE to make its predictions. Additionally, the time of day predictor is key for accurately capturing the diurnal cycle of convective cells on both sides of the cold front. The study highlights both the advantages and the limitations of data-driven models, offering valuable insights for future data-driven climate and weather prediction models.

We present CHESS (CHEbyshev pSeudo Spectrum), a Wolfram Language package for high-precision one-dimensional transport of {\epsilon}-factorized differential equations for Feynman master integrals. The solver works with the matrix obtained by pulling a differential one-form to a chosen path. This matrix may be supplied directly, or assembled from constant matrices and precomputed scalar pullbacks of the one-forms. The program combines Chebyshev-Lobatto spectral collocation, sparse matrix assembly, sequential propagation in the {\epsilon}-expansion, and residue-based regularization of spurious regular singular endpoints. Benchmarks for large multi-scale integral families show rapid node convergence and agreement with independent reference data where such data are available. In the fixed local-series comparison used here, the Chebyshev transports also give shorter wall times; the reported process-tree memory usage is comparable for the smaller parallel runs and lower for the largest benchmark system in that comparison.

The magnetohydrodynamic (MHD) equilibrium $\beta$ limits in the Chinese First QuasiAxisymmetric Stellarator (CFQS) are investigated using the NTEC code, for both the standard and the magnetic island configurations. The equilibrium $\beta$ limit is identified upon the onset of the rapid destruction of nested flux surfaces by evaluating several numerical metrics, including the fractal dimension, weighted Birkhoff average, and effective volume of parallel diffusion. In the standard configuration, the net-current-free and the bootstrap-current-carrying equilibria can sustain well-ordered magnetic surfaces up to $\langle\beta\rangle\approx1.5\%$. The proliferation of stochastic field lines starts after the critical overlap between the internal major islands and the high-order island chains. Two types of divertor island configurations are studied based on net-current-free equilibria. It is found that the edge islands may transition into open field lines at low $\langle\beta\rangle$ values and lead to a drastic shrinkage of the last closed flux surface. Meanwhile, the threshold $\langle\beta\rangle$ value of the degradation of inner flux surfaces is similar to the standard configuration.

We develop a thermodynamically consistent phase-field model for soluble surfactants in two-phase flows, incorporating both interfacial and solid surface adsorption. The model is derived via variational principles consistent with the second law of thermodynamics, resulting in modified free energies and boundary conditions that capture surfactant transport, adsorption, and wetting dynamics. A key contribution of this work is the inclusion of surfactant adsorption on solid walls, which leads to qualitative agreement with experimental observations: unlike prior numerical studies that predicted hydrophilic surfaces becoming more hydrophilic and hydrophobic surfaces more hydrophobic, our model shows a shift toward increased hydrophilicity across all contact angles-consistent with experimental trends. Our results establish that solid adsorption provides the missing mechanism required for predictive modelling of surfactant-laden contact line dynamics.

Dipolar quantum mixtures provide a route to many-body phases in which long-range anisotropic interactions couple with density, composition and spatial order. Here we realize a new quantum-degenerate dipolar mixture of $^{162}$Dy and $^{164}$Dy in a single-species-like apparatus. The mixture combines nearly matched single-particle Hamiltonians, tunable interactions and composition parameters, and isotope-resolved characterization. Tuning the interaction balance and relative composition reorganizes the coupled condensates from a miscible state into core–shell-like, side-by-side, and exchanged core–shell-like immiscible configurations. These results establish dysprosium isotope mixtures as a compact and versatile platform for multicomponent dipolar quantum matter, ranging from impurity physics to binary supersolidity.

Saturation curves, monotone responses that rise from zero to a plateau, recur across gas adsorption, enzyme kinetics, and self-limiting growth. Learning one from a few costly, noisy measurements is a common bottleneck. We present a Bayesian active-learning platform built on a penalized monotone I-spline. Non-negative weights make every posterior draw rise from zero and never fall, a survey measurement anchors the plateau in place of a flatness prior, and the input reaching any saturation level is read from the posterior by level crossing, assuming no kinetic form. Each measurement is placed where the surrogate is least certain, which beats random designs and edges a log-spaced grid of the same budget. One fixed configuration recovers five kinetically distinct regimes from twenty measurements to a saturation error of 0.037 to 0.041, below the output noise, and averages 0.045 on 250 curves from an unseen distribution. Running unconstrained Gaussian processes (GPs) and two unpenalized monotone bases through the same loop separates each ingredient's contribution. Logarithmic resolution and the roughness penalty carry the accuracy, while monotonicity guarantees a level crossing unique on every draw. The penalty keeps the surrogate the most accurate model at every noise level, and its second-order form centers the recommended input for saturation where a first-order penalty reads late (a bias of +0.32 s with 94% coverage, against +0.44 s and 64% for the matched unconstrained GP). Because the recommended input lies where the curve is nearly flat, no method can pin it down precisely, and we report it as a cost-indexed quantile with the probability of falling short.

The intrinsic coupling between magnetism and nontrivial band topology in magnetic topological insulators makes external magnetic fields a powerful tool for manipulating topological states. However, conventional magnetic control mechanisms, such as driving magnetic phase transitions or fully reversing magnetization, typically demand large magnetic fields and lack continuous tunability. Here, we establish a symmetry framework for the reversible switching of topological states via continuous in-plane spin rotation, governed by magnetic point group constraints on the Berry curvature distribution. Using a two-dimensional kagome ferromagnetic Chern insulator as a prototype, we demonstrate that a 60{\deg}in-plane magnetization rotation reverses the sign of the Chern number, transitioning through a topologically trivial state. Crucially, micromagnetic simulations confirm that this spin-reorientation-driven switching operates under exceptionally small magnetic fields and on ultrafast timescales. This work provides a highly efficient, low-energy paradigm for the manipulation of topological states.

Cavity-assisted photon scattering (CAPS) is a powerful mechanism for realizing strong interactions between the internal states of stationary qubits and flying photons, underpinning a broad range of hybrid atom-photon protocols including remote entanglement generation and heralded atom-photon gates. Recently, the motional quantum state has emerged as an important building block for quantum information processing with atomic qubits, both as a coherently controllable degree of freedom and as a fundamental error channel through undesired spin-motion coupling. For the resonant-coupling regime of cavity quantum electrodynamics relevant to CAPS operations, however, the analytical formulation of spin-motion-photon coupling has so far remained elusive. Here, we develop a complete analytical framework for CAPS that incorporates the coherent interaction between atomic motion and a reflected photon by extending scattering theory to include the motional degree of freedom. The resulting compact operator-based input-output relation applies uniformly across various cavity geometries, spin-dependent trapping potentials, and nonidentical multiple spins. As an exemplary application, we use the framework to elucidate how atomic motion affects CAPS-based atom-photon gates, identifying the parameter regimes that suppress motion-induced errors. Our framework provides a theoretical foundation both for mitigating motional errors in CAPS operations and for deliberately exploiting motion-photon interaction at the atom-photon interface.

Wakefield calculations are essential for analyzing beam-driven electromagnetic structures in accelerators. Although analytical wake functions are available for simple symmetric structures, complex geometries generally require mesh-based electromagnetic simulations, which provide finite-bunch wake potentials rather than point-charge wake functions directly. In this study, we present a systematic deconvolution-based method for extracting reliable wake functions from numerically calculated wake potentials using the prescribed drive-bunch distribution. The method is validated with a rectangular dielectric-lined waveguide (DLW), where the extracted longitudinal wake functions agree well with analytical solutions in both the short- and long-range regimes when the drive bunch is sufficiently short. The extracted wake function is further implemented in particle-tracking simulations, producing phase-space distributions consistent with those obtained using a built-in analytical wake-function model. The method is also applied to a modified rectangular DLW with a non-uniform horizontal dielectric distribution. The extracted longitudinal and transverse wake functions and corresponding beam impedances show that the dominant deflecting wakefield can be substantially reduced without significantly degrading the longitudinal wakefield. These results demonstrate the reliability and applicability of the proposed method for complex dielectric-loaded structures.

This paper reviews the experimental progress of negative triangularity (NT), a tokamak configuration where the poloidal cross-section is a reversed-D shape compared to the conventional positive triangularity (PT) shape. NT is a promising reactor scenario that addresses the fundamental tension between performance, exhaust, and robustness. NT studies have accelerated globally across these three pillars over the past several years. While tokamak pilot plants are typically designed for the standard PT H-mode regime, this approach faces significant challenges in balancing high core performance with manageable heat and particle exhaust as well as reliable robustness. In contrast, NT plasmas have achieved H-mode-level confinement while remaining robustly free of the deleterious edge localized mode (ELM) instability. Regarding exhaust, NT offers a larger divertor wetted area on the outboard side and demonstrates compatibility with detachment and operation at high core radiation fraction without the constraints of the L-H power threshold, while also exhibiting low core impurity retention. NT operates with high reproducibility over a wide operating space, demonstrated by robust discharge-to-discharge consistency, and has access to plasmas with very high Greenwald fractions and/or low edge safety factors compared to PT H-mode plasmas. Further research is required to answer outstanding questions related to reactor confinement extrapolation, the optimal triangularity for a reactor, and core-edge integration. NT studies in existing and planned tokamaks are increasing, as is interest in possible reactor concepts. The unique physics and engineering advantages of NT offer a robust and simplified foundation for a viable fusion power plant.

The nonlinear space-charge effect plays a significant role in high-intensity accelerators and has been extensively studied using multi-particle tracking methods. In this paper, we present a novel 2.5-dimensional symplectic space-charge solver specifically designed for long beam bunches. We begin by detailing its application to a transverse Gaussian density distribution under open boundary conditions in a straight system, where a semi-analytical expression is derived. We then demonstrate the solver's adaptation to arbitrary distributions in open space, as well as within rectangular and round conducting pipes. Finally, we discuss the extension of this solver to circular accelerator systems. This study shows that the fast 2.5-dimensional solver can be a good approximation to the fully three-dimensional solver for long bunches in large circular accelerators.

Bloch oscillations (BOs) represent a paradigmatic manifestation of force-driven band dynamics in lattices, conventionally characterized by continuous quasimomentum evolution and smooth periodic trajectories. Here we show that non-Hermiticity qualitatively changes this paradigm, introducing nonsmooth BOs characterized by cusp-like real-space trajectories and abrupt momentum jumps. Reversing the force switches the dynamics between smooth and nonsmooth BOs, enabling strongly nonreciprocal dynamics. We derive a universal relation for the critical force that governs the nonsmooth-to-smooth dynamical transition in general one-dimensional lattices, which is induced by a pitchfork bifurcation in momentum space. Our work establishes a unified framework for non-Hermitian BOs and uncovers a fundamental bifurcation mechanism that can generate nonsmooth wave transport in entirely linear systems.

The design of supersonic rocket nozzles by the Method of Characteristics is a mature discipline, yet the software that implements it is frequently either proprietary, heavyweight, or opaque, which limits reproducibility and classroom and field use. This paper presents AeroProp-X, an open-source, dependency-light toolkit that synthesizes shock-free minimum-length nozzle contours and evaluates their ideal aerothermodynamic performance entirely on commodity hardware without graphics acceleration or network access. The central contribution is methodological rather than physical: the planar characteristics solver is made self-verifying by exploiting an exact gas-dynamic invariant, namely that the computed exit wall height ratio must equal the isentropic area ratio at the design Mach number, so that every generated contour reports its own discretization error automatically. We describe the governing theory, the layered software architecture, and the synthesis algorithm, and we validate the implementation against analytic compressible-flow tables and against the expansion ratios of historical flight engines. The solver reproduces tabulated gas-dynamic quantities to table precision and converges to the exact area ratio with relative error below three parts in ten thousand, while remaining small enough to run interactively on an embedded-class processor. We argue that built-in verifiability, dependency minimalism, and explicit scoping of the ideal-flow assumptions together make the tool well suited to education, preliminary design, and reproducible research.

As climate change intensifies, extreme weather events (EWEs) increasingly disrupt the supply-demand balance of mass transit systems. Accurate and reliable prediction of origin-destination (OD) passenger flow is essential for timely emergency response, but remains difficult under abnormal conditions. Although existing physics-informed methods can enhance model robustness, they remain insufficient for transit systems under extreme weather disruptions, where passenger flow fluctuations are jointly shaped by the redistribution of latent demand as a direct result of EWEs (i.e., direct effects) and the increase in travel impedance due to EWE-induced supply contraction and congestion effects (i.e., indirect effects). To address these limitations, we first conceptualize the transit network as an electrical circuit, treating latent demand as voltage, travel impedance as resistance and passenger flow as current, and then propose the OhmicFlow framework to jointly predict these variables under disruptions through Ohm's law. Specifically, a future-aware spatiotemporal backbone is used as an ammeter to predict disrupted passenger flow, and its replica is reused as a bypass voltmeter with impedance controlled in the inputs to infer latent demand counterfactually and in parallel. Travel impedance under foreseeable EWE disruptions is further modeled using a thermistor analogy, enabling dynamic estimation by coupling supply contraction with congestion effects. A multi-objective loss is incorporated to fit observed data while enforcing the Ohmic constraint. Empirical experiments based on 10 years of Shenzhen Metro data covering 17 EWEs show that OhmicFlow consistently outperforms various baseline methods, achieving lower prediction errors across various chronological training settings while improving uncertainty calibration, robustness, transferability, and interpretability.

Perimeter-gated single-photon avalanche diodes (pg-SPADs) are known for their dynamic dark noise modulation capabilities. They are reported to trade noise for photon sensitivity under continuous illumination. However, the implications of this trade-off have not heretofore been studied with pulsed optical systems. This work bridges this gap. We demonstrate that pg-SPADs fabricated in a 0.35 $\mu$m standard CMOS process trade-off pulse detection efficiency for a reduction in the the spread of spurious events within a burst window. Consequently, herein, we propose guidelines for the optimal use of pg-SPADs in pulsed LIDAR applications in view of the observed trade-off.

Direct time of flight (dToF) measurements are susceptible to errors because of system-level and circuit-level timing jitters. In addition, device-level uncertainty stemming from the dark noise of single-photon avalanche diode (SPAD) contributes to the aggregated error. We demonstrate that perimeter gating can help reduce the device-level detection inaccuracy for SPAD devices by reducing the dark noise probability. Specifically, in this work, we developed a general framework to accurately estimate the dToF jitters stemming from different source levels and analyzed a counter-based time to digital converter (TDC) circuit that are commonly used in such systems. We have also measured dToFs using a perimeter-gated SPAD (pg-SPAD) detector fabricated in a 0.35 $\mu$m standard CMOS process. Experimental results show that pg-SPADs can improve measurement accuracy in both free-running and time-gated operations.

A critical review of the methodology used in F. Rathmann et al., Phys. Rev. Accel. Beams 29, 021001 (2026), to evaluate beam-induced depolarization of the Atomic Polarized Hydrogen Gas Jet (HJET) target at the Electron–Ion Collider (EIC) is presented. It is shown that several key assumptions underlying that analysis – including the introduction of a photon emission threshold, the application of Fermi's Golden Rule to coherent hyperfine transitions, the interpretation of power broadening as a physical linewidth increase, and the treatment of spatial magnetic fields – are either incorrect or internally inconsistent. As a consequence, the predicted large depolarization effects are demonstrated to be artifacts of the adopted methodology rather than genuine physical phenomena. A consistent quantum-mechanical treatment based on the time-dependent Schrödinger equation shows that beam-induced depolarization probabilities at the EIC are negligibly small.

Large spin polarization observed in chiral-induced spin selectivity (CISS) remains difficult to explain quantitatively. Experimental polarizations measured in chiral molecular systems are often substantially larger than expected from weak microscopic spin-orbit interaction in organic materials. We revisit the geometrical spin current introduced previously for electrons constrained to curved paths and propose a scale argument relevant to transport through double-stranded DNA (dsDNA). Estimation of the intrinsic geometrical spin-current scale using representative dsDNA parameters yields current magnitudes substantially larger than measured transport currents in many CISS experiments. We suggest that this hierarchy allows transport to be interpreted as weak leakage from underlying curved spin-current states. Within this picture, large CISS polarization emerges as transport selection of pre-existing spin-current branches.

We revisit Feynman's classic carousel problem (Feynman Lectures, Vol.~1, Sec.~19.4), in which a student walks radially inward on a platform driven at constant angular velocity $\omega_0$ by an external motor. Working consistently in the frame co-rotating with the platform, we show that the student's kinetic energy in that frame is exactly constant: the mechanical work done by the real radial friction force is exactly cancelled by the work done against the centrifugal inertial force. The Coriolis inertial force, and the equal-and-opposite tangential friction force required to cancel it and keep the student on a straight radial path, do zero mechanical work, because both are perpendicular to the radial velocity. Yet generating that tangential force costs metabolic energy – precisely the effect Feynman flagged with “one has to lean over and push sidewise.” The mechanical model adopted here (a point mass with kinematically imposed trajectory) is deliberately silent about internal physiology; the metabolic cost discussion of Sec.~\ref{sec:cost} is a separate phenomenological layer, explicitly flagged as such, not derived from the mechanics. We give an order-of-magnitude metabolic estimate, an entropy-production argument connecting the exercise to the second law, a feedback (PD-controller) reformulation of the same physics, and a playground experiment students can run with a phone and a heart-rate strap. Throughout, we are explicit about reference frames and about the limits of each model.

Label-free vibrational microscopy based on Raman scattering and mid-infrared (MIR) absorption has advanced biological imaging through the development of diverse high-sensitivity modalities. However, rigorous sensitivity comparisons across these techniques remain lacking. Here, we establish a unified theoretical framework for quantitatively comparing the shot-noise-limited sensitivities of stimulated Raman scattering (SRS), MIR photothermal (MIP), and stimulated Raman photothermal (SRP) microscopy. The framework is built on two quantities: an effective absorption coefficient, which places SRS on the same footing as linear absorption, and a photothermal (PT) factor, which isolates the contribution specific to PT readout. Using experimentally realistic parameters, we show that MIR absorption coefficients for representative polar vibrational modes are typically about two orders of magnitude larger than the effective absorption coefficients of SRS, even for strong off-resonant Raman bands. In MIP, however, this intrinsic advantage is partly offset by a small PT factor, limiting the net sensitivity gain over SRS to several-fold and, at most, about one order of magnitude. In SRP, by contrast, burst-pulse thermal accumulation substantially increases the PT factor. Under ideal shot-noise-limited conditions with matched per-pulse excitation-energy conditions in water, SRP is predicted to provide sensitivities several-fold higher than those of SRS and to approach those of MIP. This framework provides a common basis for sensitivity assessment across vibrational imaging modalities and clarifies how absorption strength, photothermal accumulation, and readout conditions determine the practical advantages of each modality.

Direct Air Capture remains highly energy intensive, with most systems relying on high-temperature regeneration of amines or metal oxides. Here we present the first comprehensive evaluation of a low-temperature DAC process based on a moisture-swing mechanism that reversibly captures and releases CO2 using commercial ion exchange resins. The proposed vacuum moisture swing, VMS, process replaces thermal regeneration with a low-temperature water vapor stripping step driven by vacuum evaporation. A cyclic model, informed by experimentally measured water and CO2 sorption kinetics, was optimized across air relative humidity of 20 to 80 percent and kinetic regimes of 0.5 to 2.0x baseline. Optimized VMS operation at 20 percent relative humidity achieves CO2 productivities of 0.2 to 0.6 kg CO2 per kg sorbent per day, comparable to or exceeding high-temperature amine systems without external heat input. Electrical energy required for gas and vapor flow and CO2 compression to 0.1 MPa ranges from 1 to 15 MJ per kg CO2, driven primarily by vapor flow in the stripping step. At a representative productivity of 0.5 kg CO2 per kg sorbent per day, energy demand is about 2.5 MJ per kg CO2, surpassing typical productivity, energy tradeoffs, but with water losses of 1.4 to 3.5 kg water per kg CO2 due to evaporation, similar to liquid-based systems. Water loss scales with productivity and decreases under higher humidity and faster kinetics. The VMS process manages water through vacuum-driven evaporation and condensation, enabling the use of non-fresh or saline water sources. This work establishes a low-temperature DAC pathway that integrates realistic CO2 and water transport with built-in water management.

Well-documented research on physics graduate education has demonstrated long-standing issues that hinder equitable student access and participation. Addressing these challenges can be particularly difficult because they are often rooted in entrenched disciplinary and departmental cultures that tend to be rigid and resistant to change. In this work, we aim to cultivate a data-driven culture of cyclic self-reflection and action to proactively identify and address issues that affect student well-being and success in both a new physics and a long-standing astrophysics graduate program within a single institution. Drawing primarily on qualitative data (open-ended survey responses and focus-group interview data) from 15 students in both programs, we collaborated with the program leadership to identify actionable steps for improvement. In this paper, we present findings on student experiences across the two programs and discuss implications for research and practice. More broadly, this work provides a framework for graduate programs seeking to build a data-driven culture that improves student experiences.

Variational quantum eigensolvers have been extensively studied, yet there are still no methods that offer black-box applicability with consistent performance. Separable pair approximations promise to be candidates for such methods: they compile to shallow constant-depth quantum circuits with linear gate count and parameter dependence and circumvent most bottlenecks of variational quantum algorithms through their classical simulability. At the same time, they seamlessly integrate into prominent more general circuit designs and subspace strategies. So far, their capability as a consistent method has only been indicated and demonstrations have been restricted to manually designed model systems. In this work, we extensively evaluate the consistency of SPA states for hydrogen chains, alkanes, and small molecules within an orbital-optimized VQE framework. Our benchmarks demonstrate consistent approximations with classical complexity comparable to Hartree-Fock. Our open-source implementation within the Tequila framework allows convenient use of the algorithms as a standalone method or as a subpart of more extensive procedures. Our results underpin the potential of SPA circuits as scalable, chemically motivated low-depth circuits with various applications and validate their usage as a chemically consistent method.