Papers

A mechanism by which the surface zonal flows of giant planets can be gradually attenuated with depth is explored. The zonal flow is driven by an imposed forcing in a thin layer near the surface. A meridional circulation is set up, analogous to the Ferrel-like cells observed in Jupiter's atmosphere. Acting on a stably stratified thin surface layer, the meridional flow induces a horizontal temperature anomaly which leads to a gradual reduction of the zonal winds with depth, governed by the thermal wind equation. Our model is a Boussinesq plane layer, with gravity acting parallel to the rotation axis. A suite of fully three-dimensional time-dependent numerical simulations has been performed to investigate the model behaviour. Below the forced stable layer, convection is occurring, typically in the form of tall thin cells. The fluctuating components of the three-dimensional flow can be driven by either the convection or the Reynolds stresses associated with the jet shear flow. When fluctuations are mainly driven by convection in the form of tall thin columns and the forcing is relatively weak, the horizontal temperature anomaly persists much deeper into the interior than when it is driven by shear flow. The zonal jets can therefore extend deep into the interior, consistent with the Juno gravity data.

Thermal scanning probe lithography offers high resolution and versatility, making it a promising alternative for fabricating photonic devices. Here, we introduce a new method that expands its applications by enabling direct fabrication of arbitrary perforated patterns on a silver film.

We review recent advances in the numerical modeling of turbulent flows and star formation. An overview of the most widely used simulation codes and their core capabilities is provided. We then examine methods for achieving the highest-resolution magnetohydrodynamical turbulence simulations to date, highlighting challenges related to numerical viscosity and resistivity. State-of-the-art approaches to modeling gravity and star formation are discussed in detail, including implementations of star particles and feedback from jets, winds, heating, ionization, and supernovae. We review the latest techniques for radiation hydrodynamics, including ray tracing, Monte Carlo, and moment methods, with comparisons between the flux-limited diffusion, moment-1, and variable Eddington tensor methods. The final chapter summarizes advances in cosmic-ray transport schemes, emphasizing their growing importance for connecting small-scale star formation physics with galaxy-scale evolution.

Platinum step edges dominate electrocatalytic activity in fuel cells and electrolysers, yet their atomistic electrochemical behaviour remains poorly understood. Here, we employ ab initio molecular dynamics under controlled electrode potentials to model a realistic stepped Pt–water interface incorporating experimentally observed (111)$\times$(111) and (111)$\times$(100) edge motifs. This allows us to resolve, for the first time, the site-specific structure, charge distribution, and electrostatics of the electric double layer at a nanostructured Pt surface. We find that differential capacitance near the potential of zero charge (PZC) arises almost entirely from potential-dependent chemisorption of water on flat (111) terraces. In contrast, step edges are saturated with chemisorbed water even below the PZC and thus do not contribute to the capacitance. Instead, edges accumulate excess positive charge and exhibit a locally elevated electrostatic potential, as revealed by spatially resolved macroscopic potential profiles. This electrostatic asymmetry implies a greater barrier for electron accumulation at step sites compared to terraces, consistent with enhanced charge localisation and reactivity. Finally, the higher-in-energy d-band centre and sharper projected density of states at edge atoms further support their role as active, positively charged centres. Together, these results provide a mechanistic explanation for the observed experimental shift of the PZC with step density and establish a predictive framework for understanding and optimising interfacial charging in nanostructured Pt electrocatalysts.

Optical interconnects are essential for data centers and AI systems. Given the limited energy production, ultra-low energy and dense optical interconnects are required to support the exponential growth of AI systems. Here we report the demonstration of a monolithically integrated optical transmitter where use of power efficient architecture and devices such as capacitive tuning of optical structures at zero static power consumption and efficient and wideband inverse designed grating couplers enable implementation of a 32-channel transmitter chip based on wavelength-division multiplexing achieving a record modulation energy efficiency of 32 fJ/b at 5 Gb/s/channel and 106 fJ/b at 32 Gb/s/channel, which includes the tuning of optical devices. Furthermore, a bit-error-rate of 1E-12 was achieved, while all channels are simultaneously operating with an aggregate data-rate of 1.024 Tb/s. The system utilizes 16 carrier wavelengths in the optical C band. The pseudo-random-bit-streams are electrically generated on-chip and used to drive individually wavelength-stabilized 2-section p-n-capacitive micro-ring modulators using integrated energy-efficient high-swing electrical drivers. The low-loss inverse designed grating couplers have -1-dB bandwidth of 25 nm. The chip concurrently achieves the highest aggregate data-rate, the highest energy efficiency and the highest bandwidth density for a multi-channel high date-rate optical transmitter chip reported to date.

This study employs a neural network that represents the solution to a Schrödinger bridge problem to perform super-resolution of 2-m temperature in an urban area. Schrödinger bridges generally describe transformations between two data distributions based on diffusion processes. We use a specific Schrödinger-bridge model (SM) that directly transforms low-resolution data into high-resolution data, unlike denoising diffusion probabilistic models (simply, diffusion models; DMs) that generate high-resolution data from Gaussian noise. Low-resolution and high-resolution data were obtained from separate numerical simulations with a physics-based model under common initial and boundary conditions. Compared with a DM, the SM attains comparable accuracy at one-fifth the computational cost, requiring 50 neural-network evaluations per datum for the DM and only 10 for the SM. Furthermore, high-resolution samples generated by the SM exhibit larger variance, implying superior uncertainty quantification relative to the DM. Owing to the reduced computational cost of the SM, our results suggest the feasibility of real-time ensemble micrometeorological prediction using SM-based super-resolution.

Polar fields at the minimum of a sunspot cycle – which are a manifestation of the radial component of the Sun's poloidal field – are deemed to be the best indicator of the strength of the toroidal component, and hence the amplitude of the future sunspot cycle. However, the Sun's polar magnetic fields are difficult to constrain with ground-based or space-based observations from near the plane-of-ecliptic. In this context, polar filaments – dark, elongated structures that overlie polarity inversion lines – are known to offer critical insights into solar polar field dynamics. Through investigations of the long-term evolution of polar filament areas and length acquired from the Meudon Observatory and complimentary solar surface flux transport simulations, here we establish the common physical foundation connecting the Babcock-Leighton solar dynamo mechanism of solar polar field reversal and build-up with the origin and evolution of polar filaments. We discover a new relationship connecting the residual filament area of adjacent solar cycles with the amplitude of the next sunspot cycle – which can serve as a new tool for solar cycle forecasts – advancing the forecast window to earlier than polar field based precursors. We conclude that polar filament properties encapsulate the physics of interaction of the poloidal magnetic field of the previous and current sunspot cycles, the resultant of which is the net poloidal magnetic field at the end of the current cycle, thus encoding as a precursor the strength of the upcoming solar cycle.

This study presents a new strategy for achieving ultralow thermal conductivity in nonporous polymer/organic filler hybrids by suppressing heat capacity through tailored atomic vibrations to enhance thermal insulation. Unlike conventional polymer/inorganic filler hybrids, these hybrids exhibit interfacial thermal resistance one to three orders of magnitude lower. Combined experiments and simulations uncover thermal transport mechanisms. These hybrids demonstrate enhanced flame retardancy. Please see the abstract in the attached PDF.

Spectrum manipulation is central to photonic systems, where advanced computing and sensing applications often demand highly complex spectral responses to achieve high throughput. Conventional methods for enhancing spectral complexity typically rely on cascading discrete photonic components, resulting in a complexity that scales only linearly with the number of components. Here, we introduce hyper-spectral photonic integrated circuits (HS-PICs), in which spectral complexity scales exponentially with the number of components. This is achieved through recursive inverse design - a system-level inverse design strategy that exploits intricate inter-component interactions as design freedoms, thereby substantially expanding the design space for spectral engineering. Using this approach, we demonstrate that even a single waveguide structure can resolve spectra with sub-picometer resolution, surpassing the performance of current state-of-the-art spectrometers. This performance bridges optical and microwave frequencies in spectral analysis, enabling simultaneous monitoring of optical and radio signals within a single device. Our work establishes a transformative framework for next-generation computing and sensing technologies.

AlScN thin films were grown via metalorganic chemical vapor deposition (MOCVD), showing controllable incorporation of scandium (Sc) into the AlN lattices. Systematic variation of growth parameters demonstrated an obvious influence on Sc incorporation, with X-ray photoelectron spectroscopy (XPS) analysis indicating Sc composition up to $\sim$13% when (MCp)$_2$ScCl was used as the precursor. AlScN/AlN/GaN heterostructures grown on GaN templates exhibited the formation of a two-dimensional electron gas (2DEG) channel at the AlScN/AlN–GaN interface, confirming their potential use in high electron mobility transistor (HEMT) device technologies. Variation in AlScN/AlN barrier thickness within the heterostructures showed that thicker barriers yield higher sheet charge densities from both Hall and capacitance-voltage (C–V) measurements. With an AlScN/AlN barrier thickness of $\sim$30~nm, a sheet charge density of $5.22\times10^{12}$~cm$^{-2}$ was extracted from C–V. High-resolution scanning transmission electron microscopy (S/TEM) further confirmed Sc incorporation and revealed the wurtzite crystalline structure of the films and heterostructures. These results establish MOCVD growth of AlScN as a promising and compatible material for advancing III-nitride heterostructures in high-performance electronics and potentially ferroelectrics.

The coupled motion between the hydrodynamic flow and magnetic field introduces significant complexity into the structure of the magnetohydrodynamic (MHD) equations. A key factor contributing to this complexity is the presence of Alfvén waves, which critically influences the character of the flow and makes the problem considerably more challenging. Within the framework where the magnetic field is everywhere parallel to the flow velocity, we give an effective decomposition of the steady MHD equations in terms of the deformation tensor and the modified vorticity, where the modification in the vorticity is to record the effect of the Lorentz force on the velocity field. The existence and structural stability of the super-Alfvénic cylindrical transonic shock solutions for the steady MHD equations are established under three-dimensional perturbations of the incoming flow and the exit total pressure (kinetic plus magnetic).

Non-destructive characterization of lithium-ion batteries provides critical insights for optimizing performance and lifespan while preserving structural integrity. Optimizing electrolyte design in commercial LIBs requires consideration of composition, electrolyte-to-capacity ratio, spatial distribution, and associated degradation pathways. However, existing non-destructive methods for studying electrolyte infiltration, distribution, and degradation in LIBs lack the spatiotemporal resolution required for precise observation and quantification of the electrolyte. In this study, we employ neutron imaging with sufficient spatial resolution ~150 um and large field of view 20x20 cm2 to quantitatively resolve the electrolyte inventory and distribution within LiFePO4/graphite pouch cells under high-temperature accelerated aging. Quantitative standard curves based on neutron transmission attenuation reveal a clear electrolyte dry-out threshold at 3.18 g Ah-1 and the two stages evolutions of EI during cell aging were quantified. By integrating non-destructive electrochemical diagnostics, accelerated graphite material loss and liquid phase Li+ diffusion degradation is observed during pore-drying. Further analysis, including operando cyclic aging, reveals that the neutron transmission below the saturation reference is due to the enrichment of hydrogen nuclei within the solid-electrolyte interphase. Assumed pore-drying does not occur, the SEI signal of the electrodes can be quantitatively decoupled during ageing. Combined analyses with NI, TOF-SIMS, and SEM reveal that high EI cells exhibit uniform SEI growth and reduced degradation, while low EI cells show uneven SEI formation, accelerating capacity loss. This study unveils a dynamic electrolyte infiltration-consumption-dry-out process in LIBs, offering non-destructive and quantitative insights to guide sustainable and durable battery development.

Bilayer meta-gratings with asymmetric structural parameters could host unidirectional guide resonances. The distribution of unidirectional guide resonances in the space of structural parameters and synthetic parameters is identified. As the incident optical beam being resonant with the unidirectional guide resonance, the Goos-H$\ddot{a}$nchen shifts of the scattered beams exhibit two anomalous behaviors: the resonant peak of the Goos-H$\ddot{a}$nchen shift is accompanied by constant transmittance and reflectance; the magnitude of the Goos-H$\ddot{a}$nchen shift is not always proportional to the quality factor of the unidirectional guide resonance. The temporal coupled mode theory analysis reveals that the first anomalous behavior is due to interference between direct scattering and radiation from the unidirectional guide resonance; the Goos-H$\ddot{a}$nchen shifts are proportional to the group velocity as well as the quality factor of the unidirectional guide resonance. Numerical simulations of incidence of Gaussian beam with finite beam width provide intuitive visualization of the Goos-H$\ddot{a}$nchen shift.

In this investigation, we analyzed individual interviews with six female undergraduate physics majors at a large, public, research university in the US to understand their progression at different transition points to becoming physicists. Following the frameworks of standpoint theory, Schlossberg's transition theory, and domains of power, we focused our analysis on how these women initially developed an interest in physics before coming to college and how important transition points in college impacted their physics self-efficacy and sense of belonging. We find that the transitions from high school to college introductory courses and then into the physics major were bottlenecks at which women faced new challenges. Our findings suggest that although women develop initial fascinations with physics in a myriad of unique and interesting ways, they tend to follow a similar trajectory that harms their physics self-efficacy and sense of belonging over time. Finally, although we focused on women who had persisted in pursuing a physics degree thus far, their accounts point to an unsupportive physics culture that could drive other women and students from marginalized demographic groups out of the discipline.

Contemporary perspectivism is viewed as a framework of scientific inquiry concerning the origin, generation and systematization of scientific knowledge of nature by focusing on the conditions under which such knowledge may arise in perspectivist terms and investigating the essential ramifications of these conditions. To this end, we develop the conceptual, methodological and semantic framework of contemporary perspectivism according to the norms of the proposed endo-theoretic approach. Implementation of the preceding three-fold scheme in quantum mechanics implies that the global structure of a quantum algebra of events can be consistently comprehended through a multilevel structure of locally variable Boolean perspectives, interconnected in a category-theoretic environment, yielding jointly all the information encoded in the former. In this respect, the proposed approach validates the perspectivist/contextual nature of quantum mechanics at a fundamental level of discourse. Furthermore, due to its general character, it may acquire the form of a theoretical pattern of scientific inquiry in the natural sciences, especially when dealing with complex trans-perspectival phenomena, the analysis of which requires the use of information resulting from more than one perspective. Finally, in the appendix, we provide a concise comparative assessment between our perspectivist framework of quantum theory and Rovelli's relational interpretation of quantum mechanics.

The metric gyro-potential of rotating distributions creates centripetal forces that can override Newtonian attraction on the inner and near-zone orbits. Einstein's geodesics in four metric potentials predict Zeeman-like shifts of Keplerian frequencies and measurable differences in the orbital speeds of direct and retrograde circulations.

Weather prediction is a critical task for human society, where impressive progress has been made by training artificial intelligence weather prediction (AIWP) methods with reanalysis data. However, reliance on reanalysis data limits the AIWPs with shortcomings, including data assimilation biases and temporal discrepancies. To liberate AIWPs from the reanalysis data, observation forecasting emerges as a transformative paradigm for weather prediction. One of the key challenges in observation forecasting is learning spatiotemporal dynamics across disparate measurement systems with irregular high-resolution observation data, which constrains the design and prediction of AIWPs. To this end, we propose our DAWP as an innovative framework to enable AIWPs to operate in a complete observation space by initialization with an artificial intelligence data assimilation (AIDA) module. Specifically, our AIDA module applies a mask multi-modality autoencoder(MMAE)for assimilating irregular satellite observation tokens encoded by mask ViT-VAEs. For AIWP, we introduce a spatiotemporal decoupling transformer with cross-regional boundary conditioning (CBC), learning the dynamics in observation space, to enable sub-image-based global observation forecasting. Comprehensive experiments demonstrate that AIDA initialization significantly improves the roll out and efficiency of AIWP. Additionally, we show that DAWP holds promising potential to be applied in global precipitation forecasting.

We present a unified framework that fully represents electromagnetic potentials, fields, and sources in vacuum, based on a reinterpretation of the classical Hertz-potential formalism. In this construction, $\phi$, $A$, $E$, $B$, $\rho$, and $J$ are systematically derived from a single vector wavefield $\Gamma(x, t)$ (called the "$\Gamma$-potential"), which is structurally aligned with the classical electric Hertz potential but of broader scope. A surjective mapping is established from such wavefields to all electromagnetic configurations in vacuum (that are sufficiently regular). This mapping induces a well-defined algebraic correspondence between the solution space of Maxwell's equations and the linear space of $C_t^{3} C_x^{3}$ vector wavefields (modulo the relevant symmetries), thereby enabling a framework for structural analysis of electromagnetic fields via their associated wavefields. Gauge freedom and the Lorenz gauge are naturally preserved; charge conservation and Maxwell's equations are inherently encoded in this representation. Building on this framework, we also introduce a transformation that provides a systematic method for generating new electromagnetic solutions from known ones. This transformation, called the "$\Gamma$-transformation", generalizes classical gauge transformations and may facilitate the exploration of hidden structures and symmetries in the solution space of Maxwell's equations.

Nature access is increasingly recognised as a public health and equity imperative, yet cities lack standardised ways to measure who benefits from green infrastructure. We present the first national, building-level assessment of the 3-30-300 urban greening rule across England, integrating high-performance computing with open LiDAR and geospatial datasets. Our framework quantifies proximity, availability, and accessibility of greenery, linking each to socioeconomic deprivation through Gini-based inequality metrics. Results reveal that while most English residents meet the tree visibility criterion, only 0.1% of urban areas meet all three thresholds; canopy cover and park access sharply diverge along deprivation lines. Wealthier areas enjoy greater ambient greenness, whereas deprived urban cores often have better proximity to parks but lower vegetation density. The study offers a scalable computational blueprint for assessing nature equity and demonstrates that green accessibility represents a new dimension of socioeconomic inequality. These findings call for policy approaches that move beyond proximity metrics toward equitable, quality-based standards for nature access.

Biomass-derived hard carbon has become the most promising anode material for sodium-ion batteries (SIBs) due to its high capacity and excellent cycling stability. However, the effects of synthesis parameters and structural features on hard carbon's (HC) electrochemical performance are still unclear, requiring time-consuming and resource-intensive experimental investigations. Machine learning (ML) offers a promising solution by training on large datasets to predict hard carbon performance more efficiently, saving time and resources. In this study, four ML models were used to predict the capacity and initial Coulombic efficiency (ICE) of HC. Data augmentation based on the TabPFN technique was employed to improve model robustness under limited data conditions, and the relationships between features and electrochemical performance were examined. Notably, the XGBoost model achieved an R2 of 0.854 and an RMSE of 23.290 mAh g-1 for capacity prediction, and an R2 of 0.868 and an RMSE of 3.813% for ICE prediction. Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses identified carbonization temperature (Temperature_2) as the most important factor influencing both capacity and ICE. Furthermore, we used bamboo as the precursor to synthesize four hard carbons based on the predictive approach. The electrochemical performance of these samples closely matched our predictions. By leveraging machine-learning approach, this study provides an efficient framework for accelerating the screening process of biomass-derived hard carbon candidates.

This work attempts to understand the mechanism of simultaneous electrodeformation and electroporation in Giant Unilamellar Vesicles (GUVs) using a minimal analytical model. In the small deformation limit, the coupled electroporation, electrohydrodynamics and membrane mechanics are solved. The excess membrane area generated by electroporation manifests as amplitudes of the second, fourth, and sixth Legendre modes, P2(cos{\theta}), P4(cos{\theta}), and P6(cos{\theta}), respectively, which serves as the shape function. The proposed model reveals that accentuated deformation in GUVs under strong pulsed DC fields arises from the additional surface area introduced by membrane poration. Thus, the resulting GUV deformation, obtained as a result of a balance of electric stresses and the membrane and hydrodynamic stresses, is prolate or oblate cylindrical or square shaped instead of prolate or oblate ellipsoids, as otherwise seen under weak AC/DC fields. The origin of higher modes is essentially due to electropore-generated membrane conductance, which is approximated to angularly vary as 2/3(1/2+P2(cos{\theta})), to keep the calculations analytically tractable, whereby the electric potential varies as P3(cos{\theta}) in addition to P1(cos{\theta}) seen for unporated vesicles. The vesicle correspondingly admits P4(cos{\theta}) and P6(cos{\theta}) shape deformation modes, besides P2(cos{\theta}) observed for unporated vesicles, on account of the quadratic dependence of Maxwell stresses on the electric field. The model qualitatively and semiquantitatively, with a correction factor (fitting parameter), captures the square shape modes for e̱ta} = 1, prolate ellipsoids (cylinders) for e̱ta} >1, and oblate cylinders for e̱ta} < 1, where e̱ta} ={\sigma}i/{\sigma}e is the ratio of the electrical conductivity of the inner fluid ({\sigma}i) to the outer fluid ({\sigma}e).

Crash data objectively characterize road safety but are rare and often unsuitable for proactive safety management. Traffic conflict indicators such as time-to-collision (TTC) provide continuous measures of collision proximity but require thresholds to distinguish routine from safety-critical interactions. Extreme Value Theory (EVT) offers statistically defined thresholds, yet these do not necessarily represent how drivers perceive and respond to conflict. This study introduces a behavioural modelling framework that identifies candidate behavioural thresholds (CBTs) by explicitly modelling how drivers adjust their movements under conflict conditions. The framework is based on a Latent Class Logit Kernel (LC-LK) model that captures inter-class heterogeneity (routine vs. defensive driving) and intra-class correlation between overlapping spatial alternatives. This yields probability curves showing how the likelihood of defensive manoeuvres varies with conflict indicators, from which CBTs such as inflection points and crossovers can be extracted. The framework tests four hypotheses: (1) drivers exhibit varying degrees of membership in both low- and high-risk classes; (2) membership shifts systematically with conflict values, revealing behavioural thresholds; (3) this relationship follows a logistic shape, with stable behaviour at safe levels and rapid transitions near critical points; and (4) even in free flow, drivers maintain a baseline caution level. Application to naturalistic roundabout trajectories revealed stable TTC thresholds (0.8-1.1 s) but unstable MTTC2 estimates (e.g., 34 s), suggesting cognitive limits in processing complex indicators. Overall, the framework complements EVT by offering a structured, behaviourally grounded method for identifying and validating thresholds in surrogate safety analysis.

High-finesse planar Fabry-Pérot (FP) cavities spectrally filter the incident field at discrete resonances, and thus cannot be utilized to resonantly enhance the field of ultrashort pulses. Introducing judicious angular dispersion into a pulse can give rise to `omni-resonance', whereby the entire bandwidth of a spatiotemporally structured ultrafast pulse couples to a single longitudinal cavity resonance, even when the pulse bandwidth far exceeds the resonant linewidth. Here we show that omni-resonance increases the intra-cavity peak intensity above that of a pulse having equal energy and bandwidth when tightly focused in free space – maintained across its entire bandwidth and along a cavity longer than the Rayleigh length of the focused pulse. This paves the way towards broadband resonant enhancement of nonlinear optical effects, thereby bridging the gap between ultrafast optics and resonant photonics.

The dynamics of political opinion are a critical component of modern society with large-scale implications for the evolution of intra- and international political discourse and policy. Here we utilize recent high-resolution survey data to quantitatively capture leading-order psychological and information-environmental patterns. We then inform simulations of a theoretical dynamical framework with several different models for how populations' ideology evolves over time, including a model which reproduces current macro-scale ideological distributions given the empirical micro-scale data gathered. This effort represents an attempt to discover true underlying trends of political reasoning in general audiences, and to extrapolate the long-term implications of those trends as they interact with the political exposure landscape. Accurate modeling of this ecosystem has the potential to predict catastrophic outcomes such as hyperpolarization, and to inform effective intervention strategies aimed at preserving and rebuilding constructive political communication.

We investigate the quantum dynamics of Coulomb potential systems in thermal baths. We study these systems within the framework of open quantum dynamics theory, focusing on preserving the rotational symmetry of the entire system, including the baths. Thus, we employ a three-dimensional rotationally invariant system-bath (3D-RISB) model to derive numerically “exact” hierarchical equations of motion for atomic orbitals (AO-HEOM) that enable a non-perturbative and non-Markovian treatment of system-bath interactions at finite temperatures. To assess the formalism, we calculated the linear absorption spectrum of an atomic system under isotropic thermal environment, with systematic variation of system-bath coupling strength and temperature.

The electrostatic potential in certain types of boundary value problems can be found by solving Laplace's Equation (LE). To develop students' ability for solving problems that can be solved effectively using Laplace's equation in an upper-level electricity and magnetism course, we developed and validated a tutorial focused on finding electrostatic potential in a Cartesian coordinate system. The tutorial was implemented across three instructors' classes, accompanied by scaffolded pretest and posttest. We also conducted think-aloud interviews with advanced students using both unscaffolded and scaffolded versions of the pretest and posttest. Findings reveal common student difficulties that were included in the tutorial as a guide to help address them. The difference in the performance of students from the pretest to the posttest was similar on the scaffolded version of the tests for all three instructors' classes and interviewed students. Equally importantly, interviewed students demonstrated greater differences in scores from the pretest and posttest on the unscaffolded versions, suggesting that the scaffolded version of the tests may have obscured evidence of actual learning from the tutorial. While a scaffolded test is typically intended to guide students through complex reasoning by breaking a problem into sub-problems and offering structured support, it can limit opportunities to demonstrate independent problem-solving and evidence of learning from the tutorial. Additionally, one instructor's class underperformed relative to others even on the pretest. This instructor had mentioned that the tests and tutorial were not relevant to their current course syllabus and offered a small amount of extra credit for attempting to help education researchers, highlighting how this type of instructor framing of instructional tasks can negatively impact student engagement and performance.

Recent advances in optical simulation and computational techniques have renewed interest in high-finesse optical cavities for applications such as enhancing light-matter interactions, engineering complex photonic band structures, and storing quantum information. However, the extended interaction times enabled by these cavities often come at the cost of slow optical read-out protocols and limited control over system transients. To address this challenge, we demonstrate an ultrafast intra-cavity optical gating scheme in a high-finesse, second-order nonlinear microcavity incorporating a thin-film of lithium niobate. A femtosecond optical gate pulse – tuned to the transparency region of the cavity's dielectric mirrors – achieves instantaneous up-conversion of the intra-cavity field via sum-frequency generation. The resulting upconverted signal exits the cavity as a short pulse, providing space- and time-resolved, on-demand access to the intra-cavity state. We validate this approach by tracking the dynamics of multiple resonant modes excited in a plano-concave distributed Bragg reflector microcavity, showing close agreement with analytical models. Additionally, we demonstrate that stimulated intra-cavity difference-frequency generation can efficiently instantiate cavity modes on femtosecond timescales. This gating scheme is fully compatible with low-temperature microcavity experiments, paving the way for advanced quantum state storage, retrieval, and real-time control of light-matter interactions.

The LCLS began operations in 2009, utilizing SLAC's normal-conducting (NC) LINAC, which features control equipment dating back to the 1960s and 1980s. The Linac Electronics Modernization Plan (LEMP) aims to replace the legacy control equipment with a system based on the open-source Marble carrier board and Zest+ digitizer board, both of which are used in the LCLS-II HE LLRF system. Adaptation of the LLRF system developed for the continuous-wave (CW) superconducting RF (SRF) LCLS-II to the short-RF pulse NC LCLS includes leveraging the knowledge and experience gained from recent LLRF projects at SLAC and efficiently reusing the core functionality of the hardware and code base developed for previous projects, in collaboration with LBNL, FNAL and JLAB. A prototype has been deployed and tested at station 26-3, demonstrating RF generation/control, interlocks, triggers, and waveform capture. Here, we describe the hardware, firmware and software infrastructure, highlight key features, and present initial test results.

We report on the synthesis and spectroscopic characterization of Sm$^{3+}$-doped K$_2$YF$_5$ microparticles. The particles were synthesized via the hydrothermal technique, yielding a particle size of approximately 20 $\mu$m in length. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses confirmed their orthorhombic crystal structure. A combination of absorption and laser excited fluorescence performed on samples cooled to 10~K, allow for the determination of {56} experimental crystal-field levels. A parametrised crystal-field analysis for Sm$^{3+}$ in the C$_{\rm s}$ point group symmetry centres of K$_2$YF$_5$ yields good approximation to the data.

Lyme disease, caused by the bacterium Borrelia burgdorferi, is a significant public health concern in North America, with approximately 500,000 cases reported annually in the United States. The dissemination of B. burgdorferi from the initial tick bite site to various tissues is facilitated by surface adhesins that bind to extracellular matrix (ECM) proteins such as fibronectin (Fn). This study investigates the binding dynamics of B. burgdorferi surface proteins RevA, BBK32, BmpA, OspA, and OspC to Fn using atomic force microscopy-based single-molecule force spectroscopy (AFM-SMFS). Our results demonstrate that RevA and BBK32 form strong, stable bonds with Fn, highlighting their roles as key mediators of host-cell attachment. By quantifying the rupture forces and kinetic parameters of these interactions, we provide a deeper understanding of B. burgdorferi adhesion mechanics and offer insights into potential therapeutic strategies targeting early bacterial attachment.