Papers

We investigate the impact of a non-negligible background temperature on relativistic plasma wake-fields generated when a beam of charged particles passes through a neutral plasma at rest. We focus on the blowout regime, wherein the plasma response is highly non-linear: plasma electrons are radially blown out and expelled away from the propagation axis of the beam particles, creating a region (bubble) of ions without electrons. Our study builds upon earlier investigations for non-linear models of plasma wakefields developed in the limit of zero background temperature (Lu et al., Phys. Rev. Lett. 96, 165002 (2006)). In the presence of a non-zero background temperature, we characterize the model and focus on its predictions for the bubble size and the electromagnetic fields inside the bubble. Model predictions are studied in combination with PIC simulations.

van der Waals stacking of two-dimensional (2D) materials offers a powerful platform for engineering material interfaces with tailored electronic and optical properties. While most van der Waals multilayers have featured inorganic monolayers, incorporating molecular monolayers introduces new degrees of tunability and functionality. Here, we investigate hybrid bilayers composed of atomically thin perylene-based molecular crystals interfaced with monolayer transition metal dichalcogenides (TMDs), specifically MoS2 and WS2. Using ab initio many-body perturbation theory within the GW approximation and the Bethe-Salpeter equation approach, we predict emergent properties beyond those of the isolated constituent systems. Notably, we find substantial renormalization of monolayer molecular crystal band gap due to TMD-induced polarization. Furthermore, by varying the TMD monolayer, we demonstrate tuning of the energy level alignment of the bilayer and subsequent control over a diversity of lowest-energy excitons, which include strongly bound hybrid excitons and long-lived charge-transfer excitons. These findings establish organic-inorganic van der Waals heterostructures as a promising class of materials for tunable optoelectronic devices and quantum excitonic phenomena, expanding the design space for low-dimensional systems.

Direct detection of bremsstrahlung photons, in principle, offers the most straightforward and most robust method of luminosity determination at the EIC, but requires an extraordinary performance of the photon detector. In this paper, we first discuss the extreme working conditions for such detectors at the EIC and the resulting technology choices. Then, we report the initial results of Monte Carlo simulations, using Geant4, of the proposed sampling calorimeter, which is made of a copper absorber with embedded quartz fibres read out by silicon photomultipliers. Finally, the tentative requirements for appropriate readout electronics are formulated.

Higher-order interactions that nonlinearly couple more than two nodes are important in many networked systems, and their effects on collective dynamics are increasingly being studied. Here we provide an overview of this rapidly growing field, and of the techniques that can be used to describe and analyze them. We focus in particular on new phenomena and challenges that emerge when nonpairwise interactions are considered. We conclude by discussing open questions and promising future directions on the collective dynamics of higher-order networks.

We present an all-fiber design for a Tm-based fiber amplifier that can tune over 1992-2065 nm with 300-350 W single-frequency (<100 kHz) output. Over 180 W is achieved out to 2085 nm with <10% ASE content without utilizing ASE spectral filters. The amplifier employs both Tm- and Tm/Ho-doped gain fibers in two preamplifier stages in addition to longer sections of Tm fiber to extend the bandwidth of the Tm-based high-power amplifier to longer wavelengths (>2050 nm). Efficiencies of 55% are realized across the full bandwidth. Roll-off occurs beyond 2085 nm where ASE becomes intractable. The amplifier has an average M^2 value of 1.39 at high-power due to the presence of light guided within the fiber pedestal. Estimates of the pedestal light and higher-order mode contents are provided.

We introduce TeMFpy, a Python library for converting fermionic mean-field states to finite or infinite matrix product state (MPS) form. TeMFpy includes new, efficient, and easy-to-understand algorithms for both Slater determinants and Pfaffian states. Together with Gutzwiller projection, these also allow the user to build variational wave functions for various strongly correlated electron systems, such as quantum spin liquids. We present all implemented algorithms in detail and describe how they can be accessed through TeMFpy, including full example workflows. TeMFpy is built on top of TeNPy and, therefore, integrates seamlessly with existing MPS-based algorithms.

We study the effects of the intertube dipole-dipole interactions (DDI) in recent experiments with arrays of nearly integrable one-dimensional (1D) dipolar Bose gases of $^{162}$Dy atoms. An earlier theoretical modeling ignored those interactions, which we include here via a modification of the 1D confining potentials. We investigate the effects of the intertube DDI both during the state preparation and during the measurements of the rapidity distributions. We explore how the strength of the contact interactions and the magnetic field angles modify the intertube DDI corrections. We find that those corrections slightly change both the properties of the equilibrium state and the rapidity measurements. Remarkably, however, the changes nearly cancel each other, resulting in measured rapidity distributions that are very close to those predicted in the absence of the intertube DDI.

In preparation for the International Astronomical Union (IAU) General Assembly (GA) 2024, the first GA held in Africa, the African Network of Women in Astronomy (AfNWA) embarked on a visionary project: the creation of an inspiring storytelling book that showcases the remarkable journeys of professional female astronomers in Africa. This book is not merely a collection of biographies; it is a tapestry of resilience, passion, and scientific excellence woven through the lives of women who have ventured into the cosmos from the African continent. The primary aim of this book is twofold. Firstly, it seeks to bring greater visibility to women astronomers in Africa, highlighting their groundbreaking research and the personal stories that have shaped their careers. By shining a light on their achievements and awards, we hope to acknowledge their contributions to the field of astronomy and underscore the importance of diversity in science. Secondly, this book aspires to inspire and empower the next generation of scientists, particularly young women and girls across Africa. Through the personal narratives and professional achievements of these trailblazing astronomers and students in astronomy, we aim to spark curiosity, foster a love for science, and demonstrate that the sky is not the limit but just the beginning for those who dare to dream. As you delve into the stories within these pages, you will encounter a rich array of experiences and insights that reflect the unique challenges and triumphs women face in astronomy. From overcoming societal barriers to making groundbreaking discoveries, these women have carved paths that others can follow, proving that with determination and passion, the stars are within reach for everyone.

Understanding processes in porous media is fundamental to a broad spectrum of environmental, energy, and geoscience applications. These processes include multiphase fluid transport, interfacial dynamics, reactive transformations, and interactions with solids or microbial components, all governed by wettability, capillarity, and reactive transport at fluid-fluid and fluid-solid interfaces. Laboratory-based multiscale imaging provides critical insights into these phenomena, enabling direct visualization and quantitative characterization from the nanometer to meter scale. It is essential for advancing predictive models and optimizing the design of subsurface and engineered porous systems. This review presents an integrated overview of imaging techniques relevant to porous media research, emphasizing the type of information each method can provide, their applicability to porous media systems, and their inherent limitations. We highlight how imaging data are combined with quantitative analyses and modeling to bridge pore-scale mechanisms with continuum-scale behavior, and we critically discuss current challenges such as limited spatio-temporal resolution, sample representativity, and restricted data accessibility. We conduct an in-depth analysis on open-science trends in experimental and computational porous media research and find that, while open-access publishing has become widespread, the availability of imaging data and analysis code remains limited, often restricted to 'upon request'. Finally, we underscore the importance of open sharing of imaging datasets to enable reproducibility, foster cross-disciplinary integration, and support the development of robust predictive frameworks for porous media systems.

We present Rex and teaRex, C++17 libraries for efficient management of parton-level hard scattering event information and completely generic reweighting of such events, respectively. Rex is primarily an interfacing and I/O library for Les Houches Event format files and provides an internal event format designed with data parallelism in mind, and teaRex extends this format to provide full parton-level reweighting functionality with minimal code needing to be written by the end user. These libraries serve as the foundation for the MadtRex reweighting module for MadGraph5_aMC@NLO, extending the functionality of the CUDACPP plugin to allow for data-parallel model-generic leading order parameter reweighting on SIMD-enabled CPUs and SIMT GPUs, speeding up reweighting by more than two orders of magnitude compared to MadGraph5_aMC@NLO running on the exact same hardware while providing trivial scalability to larger and distributed systems.

Electrospray thrusters are emerging as a leading propulsion technology for CubeSats, offering high specific impulse ($I_{sp} > 1000$ s) and low power requirements. However, the divergent ion plumes can impinge on spacecraft surfaces, particularly body-mounted solar arrays, causing contamination and thrust efficiency losses. This study presents a validated particle-tracking simulation to quantify the effects of thruster placement on thrust efficiency and surface contamination for 1U, 3U, and 6U CubeSats. The plume model employs a cosine power distribution ($k=1.8$) with half-angle $46^\circ$, validated against experimental data with errors below 7%. Results show that thrust efficiency ranges from 53.6% for rear-mounted thrusters on 3U body-mounted configurations to 100% for side-mounted configurations with deployable arrays. CubeSat size significantly affects impingement: 3U platforms experience 46.4% contamination with rear-mounted thrusters compared to 16.6% for 1U. Deployable solar arrays reduce contamination by 77% compared to body-mounted arrays, while side-mounted thrusters eliminate impingement entirely at the cost of only 1.6% efficiency loss. Corner-mounted configurations at $30^\circ$ cant provide intermediate performance with 88.9% efficiency and 11.1% contamination. These quantitative design guidelines enable mission planners to optimize thruster integration based on power budget and propellant mass constraints, with statistical uncertainty below 0.15% across all configurations.

The quantum control of spin-correlated radical pairs (SCRPs) holds promise for the targeted manipulation of magnetic field effects, with potential applications ranging from the design of noise-resilient quantum information processors to genetically encodable quantum sensors. However, achieving precise handles over the intricate interplay between coherent electron spin dynamics and incoherent relaxation processes in photoexcited radical-pair reactions requires tractable approaches for numerically obtaining controls for large, complex open quantum systems. Employing techniques relying on full Liouville-space propagators becomes computationally infeasible for large spin systems of realistic complexity. Here, we demonstrate how a control engineering approach based on the Pontryagin Maximum Principle (PMP) can offer a viable alternative by reporting on the successful application of PMP-optimal control to steer the coherent and incoherent spin dynamics of noisy radical pairs. This enables controls for prototypical radical-pair models that exhibit robustness in the face of relevant noise sources and paves the way to incoherent control of radical-pair spin dynamics.

In this work, an innovative scheme is proposed that exploits the response of magnetized plasmas to realize a refractive index exceeding unity for right circularly polarized (RCP) waves. Using two- and three-dimensional Particle-in-Cell (PIC) simulations with the OSIRIS 4.0 framework, it is shown that a shaped magnetized plasma lens (MPL) can act as a glass/solid-state-based convex lens, amplifying laser intensity via transverse focusing. Moreover, by integrating three key ingredients, a tailored plasma lens geometry, a spatially structured strong magnetic field, and a suitably chirped laser pulse, simultaneous focusing and compression of the pulse has been achieved. The simulations reveal up to a 100-fold increase in laser intensity, enabled by the combined action of the MPL and the chirped pulse profile. With recent advances in high-field magnet technology, shaped plasma targets, and controlled chirped laser systems, this approach offers a promising pathway toward experimentally reaching extreme intensities.

The formulation of a fluctuating hydrodynamic theory for interacting particles is a crucial step in the theoretical description of liquids. The microscopic mappings proposed decades ago by Dean and Kawasaki have played a central role in the analytical treatment of such problems. However, the singular mathematical nature of the density distributions used in these derivations raises concerns about the validity and practical utility of the resulting stochastic partial differential equations, particularly for direct numerical simulations. Recent efforts have centered on establishing a rigorous coarse-graining procedure to regularize the effective Dean-Kawasaki equation. Building on this foundation, we numerically investigate weakly interacting fluids within such a regularized framework for the first time. Our work reveals, at the level of structural correlations, the effects of regularization on the Dean-Kawasaki formalism and paves the way for improved numerical approaches to simulate fluctuating hydrodynamics in liquids.

The atmospheric boundary layer undergoes significant changes throughout a diurnal cycle, affecting wind turbine performance and wakes in wind farms. Wind farm Large Eddy Simulations (LES) under such conditions provide rich datasets to study the underlying dynamics and identify important trends. Here, we describe a comprehensive open dataset generated using LES of an 8-turbine wind farm consisting of four rows of two turbines. To avoid specifying either prescribed surface temperature or heat flux, a local 1D soil heat conduction model is used with time-periodic solar surface heating, coupled to LES. After several days of low-resolution LES, an approximately time periodic behavior is achieved, after which high-resolution LES is continued during a 24-hour period. Analysis of the LES data reveals that wind turbine wakes have a significant impact on the temperature field and spatial surface heat flux patterns and exhibiting increased surface temperature behind the wind farm at night under the specific conditions of the simulation (dry unvegetated soil, clear sky). We observe that for a few morning hours the first row of wind turbines generates less power compared to the last row. Detailed analyses of the data using innovative web-services facilitated data access tools reveal that during the morning transition, the presence of a low-level jet and the wind farm blockage effect combine to cause cooling and a reduction in wind speed at hub height upstream of the wind farm. In addition, larger turbulence levels exist downstream in the wind farm, explaining the larger power production of downstream turbines.

Non-conventional beyond-the-state-of-the-art signal processing schemes require parallelism, scalability, robustness and energy efficiency to meet the demands of complex data-driven applications. With further research, magnonic and spintronic circuits can potentially help to fulfill these requirements. We present an experimental proof-of-concept of a hybrid device that can employ broad deteriorated microwave signals to excite and detect low energy propagating spin waves (SWs). For this, we use the output signal of a spin-transfer torque nano-oscillator (STNO) and connect it to a RF filter based on a magnonic delay-line. The STNO serves as a tunable nano-scaled signal generator with a broad output linewidth. Its RF output is fed as input into the magnonic delay-line circuit. Tuning the magnetic field solely at the magnonic circuit, we demonstrate the capability to selectively filter a broad RF input, obtaining a spin-wave output signal with a much narrower linewidth. This allows to tune the frequency of the RF signal at the output simply by tuning the magnetic field. Our findings are a first step towards a versatile, energy-efficient and compact wave-based filter with high sensitivity. Such a device can use even low-power, degraded signals and convert them into tunable SW outputs, effectively reducing the need for charge-based signal processing.

An influential argument for scientific realism posits that, if scientific theories were not true, their empirical success would be a coincidence. Here, I show that the false Drude's theory succeeds in explaining the Wiedemann-Franz law by coincidence–a fortuitous compensation of errors.

Surface Acoustic Wave gas sensors have garnered increasing attention as highly sensitive, miniaturized, and wireless compatible platforms for molecular detection. Their unique ability to convert surface perturbations into measurable acoustic shifts makes them ideal for gas sensing across diverse environments. This review synthesizes reported SAW platforms across substrates and modes Rayleigh, SH-SAW, Love links transduction pathways to material choice, and benchmarks performance for key analytes, e.g., NO2, NH3, VOCs, CO2, etc. We catalogue nanostructured oxides, polymers, carbon based films, and hybrid heterojunction coatings, highlighting attributes such as porosity, surface chemistry, and interfacial charge transfer that govern sensitivity and reversibility. We also highlight the emerging use of SAW devices to probe adsorption desorption dynamics, offering analyte specific interaction signatures beyond equilibrium, offering a new perspective into analyte specific interaction pathways. Additionally, the integration of machine learning is discussed as a transformative tool for signal decoding, environmental compensation, and adaptive calibration. We also identify key challenges, cross sensitivity, signal drift, material degradation, and deployment at the edge and review recent strategies to address them. Looking ahead, we envision the evolution of SAW platforms into intelligent, autonomous sensing systems with applications in environmental monitoring, industrial process control, and healthcare diagnostics.

Quantum entanglement remains a challenging concept to teach and visualise due to its microscopic and non-classical nature. We present innovative educational demonstration material consisting of electronic dice that simulate the properties of quantum entanglement through haptic interaction. The system uses displays, orientation sensors, and wireless communication to visualise key quantum mechanical principles such as superposition, measurement, and entanglement correlations. This analogy enables students to experience quantum phenomena through familiar objects, making abstract concepts more tangible. The dice support various educational scenarios, from basic entanglement demonstrations to more complex quantum key distribution experiments, and can be adapted for different educational levels from secondary school to undergraduate physics courses. Initial implementations demonstrate that the interactive nature of the Quantum Dice can help users develop an intuitive understanding of quantum mechanical principles. The low-cost, open source, and robust design makes Quantum Dice accessible to a wider range of educational institutions.

From the onset of fundamental statistical mechanical constructs formulated in the late 19th century, alchemical free-energy methods slowly emerged and transitioned to become operational tools of biomolecular simulation applicable to a wide range of problems including protein-ligand binding for drug discovery research. This article reconstructs how statistical mechanical approaches such as thermodynamic integration and free-energy perturbation were reconfigured in the early 1980's to address the complexities of increasingly heterogeneous biomolecular systems. Drawing on oral history interviews and primary literature, the study examines the technical, institutional, theoretical, and infrastructural conditions under which these methods were implemented, and became progressively operational. These conditions encompassed the consolidation of lab-specific software infrastructures, the formulation of practical simulation protocols, as well as essential statistical mechanical clarifications. From this perspective, the progress of free-energy methods proceeded less from a unified convergence than from an iterative troubleshooting process of alignment involving practical and theoretical considerations. The aim of the present article is to offer a historically grounded account of how free-energy techniques acquired practical and functional reliability.

This research explores several critical factors affecting CFD-based prediction accuracy of submarine hydrodynamics and builds upon previous work on preliminary mesh and solver benchmarking. A scaled submarine model is analyzed numerically using the Reynolds-Averaged Navier-Stokes (RANS) turbulence model at a Reynold number (Re) of 3.6x106 with wall-resolved meshes targeting y+ <5. Based on prior work using a 13x106 cells mesh generated with SnappyHexMesh, a mesh convergence study was performed with increasing resolution of fine (15x106 cells), finer (18x106 cells), and finest (22x106 cells). Subsequently, five turbulence closure models which are k-$\omega$ SST, k-$\omega$ 2006, Lien Cubic, k-$\epsilon$ Launder-Sharma, and Spalart-Allmaras were assessed for predictive performance and computational efficiency. Boundary layer fidelity was examined by comparing velocity profiles extracted from Cadence and SnappyHexMesh meshes against the theoretical law of the wall. In the final phase, the influence of appendages on local and global resistance was analyzed. The results presented that the finest mesh (22x106 cells) achieved a near mesh-independent solution with an error of 1.16% and extrapolated to less than 0.11% showing strong agreement with lab-scale data. Among the turbulence models, the k-$\omega$ SST model demonstrated the most reliable performance, with resistance predictions less than 1.2% of error. In addition, the law-of-the-wall analysis illustrated that the Cadence allowed full resolution of the viscous sublayer, while SnappyHexMesh performance was constrained by wall-normal cell sizing. Appendage analysis revealed that the sail alone contributed up to 7.1% of the pressure drag and 7.7% of viscous drag, while the rudder added 3.7% and 5.6% respectively, indicating a substantial impact on overall resistance. These findings contribute to developing robust and accurate CFD strategies for submarine hydrodynamics and offer a reliable framework for future resistance evaluation and design assessment in complex maritime applications.

Submarines are vital for maritime defense, requiring optimized hydrodynamic performance to minimize resistance. Advancements in Computational Fluid Dynamics (CFD) enable accurate predictions of submarine hydrodynamics for optimal design. This study compared the meshing capabilities of OpenFOAM and commercial software as well as the performance of High-Performance Computing (HPC) and standard PC resources upon hydrodynamic characteristics. The RANS turbulence model with was employed to analyze the resistances of the MARIN's BB2-class submarine. CFD simulations were conducted at a model scale (1:35.1) at a speed of 1.8235 m/s ( of 21 knots) upon various mesh densities from 1 to 97 million cells. Empirical equations were initialized for turbulence parameters. Mesh sensitivity and iteration convergence ensured validated results. The findings showed that the results were validated with errors ranging from 0.3% to 10% across different mesh densities. The lowest error (0.3%) was achieved with 97 million cells generated by the commercial meshing tool with HPC, while 13 million cells by OpenFOAM with a standard PC resulted in a 3.4% error. Accuracy improved with precise initialization of turbulence parameters, mesh strategy, numerical schemes, and computing resources. The application of a standard PC with the OpenFOAM meshing tool was able to produce an acceptable accuracy, with less than 5% error for lower mesh densities. Thus, it can be suggested that using a standard PC was beneficial for preliminary hydrodynamic simulations. However, HPC with commercial software was essential for detailed industrial analyses, such as full-scale resistance and propulsion simulations.

A comprehensive numerical solution is developed for the transient thermo-elasto-hydrodynamic (TEHD) characteristics of mechanical face seals. Transient lubrication features of the fluid film, transient thermal deformation features of the seal rings, dynamic behavior, and rough faces contacting are coupled. The finite volume method is utilized for the fluid film solution, and the Duhamel's principle contributes to calculation of the time-varying solid properties. An overall flowchart for the numerical solution is established, with an approach of Parallel Dual Time Steps (PDTS approach) proposed and utilized for the explicit time solver. Both of the efficiency and accuracy of the PDTS approach are evaluated by comparing with the reference. An outer-herringbone-grooved face seal in a start-up stage is studied. The simultaneously existing physical effects of the face expansion and the seal ring movement are successfully simulated with the proposed method. Neglecting viscosity-temperature effect and convergent gap forming could underestimate the load-carrying capacity of the fluid film; smaller contacting force but larger maximum contacting pressure are found comparing with the THD and HD results; performance keeps varying at steady speed due to thermal lag effect. The proposed numerical solution could be impactful for mechanism analyzing of the undesirable running of mechanical face seals related to the transient TEHD effects.

Terahertz time-domain spectroscopy (THz-TDS) provides a non-invasive and label-free method for probing the internal structure and electromagnetic response of materials. Numerical simulation of THz-TDS can help understanding wave-matter interactions, guiding experimental design, and interpreting complex measurement data. However, existing simulation techniques face challenges in accurately modeling THz wave propagation with low computational cost. Additionally, conventional simulation solvers often require dense spatial-temporal discretization, which limits their applicability to large-scale and real-time scenarios. Simplified analytical models may neglect dispersion, multiple scattering, and boundary effects. To address these limitations, we establish a novel computational framework that integrates frequency-domain physics-informed neural networks (FD-PINNs) with less data-driven. To validate our proposed FD-PINNs, simulation results from finite-difference time-domain (FDTD) and time-domain (TD)-PINNs were used to compare with FD-PINNs. Finally, experimental results from THz-TDS systems were employed to further exhibit accurate reconstruction ability of FD-PINNs.

Optical microscopy is one of the most widely used techniques in research studies for life sciences and biomedicine. These applications require reliable experimental pipelines to extract valuable knowledge from the measured samples and must be supported by image quality assessment (IQA) to ensure correct processing and analysis of the image data. IQA methods are implemented with variable complexity. However, while most quality metrics have a straightforward implementation, they might be time consuming and computationally expensive when evaluating a large dataset. In addition, quality metrics are often designed for well-defined image features and may be unstable for images out of the ideal domain. To overcome these limitations, recent works have proposed deep learning-based IQA methods, which can provide superior performance, increased generalizability and fast prediction. Our method, named $\mathrm{\mu}$DeepIQA, is inspired by previous studies and applies a deep convolutional neural network designed for IQA on natural images to optical microscopy measurements. We retrained the same architecture to predict individual quality metrics and global quality scores for optical microscopy data. The resulting models provide fast and stable predictions of image quality by generalizing quality estimation even outside the ideal range of standard methods. In addition, $\mathrm{\mu}$DeepIQA provides patch-wise prediction of image quality and can be used to visualize spatially varying quality in a single image. Our study demonstrates that optical microscopy-based studies can benefit from the generalizability of deep learning models due to their stable performance in the presence of outliers, the ability to assess small image patches, and rapid predictions.

The gain measurements of several 1 mm-thick, 10$\times$10 cm$^2$ Thick Gaseous Electron Multipliers (ThGEMs), operated in pure argon at 3.3 bar and room temperature are presented. Electrostatic simulations, performed with the COMSOL MultiPhysics software, were employed to guide the design of the detectors, and the field configurations are discussed. A modified ThGEM design, incorporating two embedded internal electrodes in addition to the conventional top and bottom electrodes, was developed to mitigate discharge-induced instabilities and to enable operation at higher gains. Several such structures were produced and compared to the standard 1 mm-thick two-electrode ThGEM. Gain measurements were conducted for every design described here over a wide range of applied electric fields and up to large values. The experimental setup and measurement methodology are described, alongside a comparative analysis of the performance of the different detector geometries.

We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focusing on anthracene and naphthalene. By combining machine learning models for interatomic potentials and polarizability tensors, we enable efficient, large-scale simulations that capture temperature-dependent vibrational dynamics beyond the harmonic approximation. Our approach reproduces key qualitative features observed experimentally. We show, systematically, what are the fingerprints of anharmonic lattice dynamics, thermal expansion, and Raman tensor symmetries on PO-Raman intensities. However, we find that the simulated polarization dependence of Raman intensities shows only subtle deviations from quasi-harmonic predictions, failing to capture the pronounced temperature-dependent changes that have been reported experimentally in anthracene. We propose that part of these inconsistencies stem from the impossibility to deconvolute certain vibrational peaks when only experimental data is available. This work therefore provides a foundation to improve the interpretation of PO-Raman experiments in complex molecular crystals with the aid of theoretical simulations.

Recently, Liao and Qin [J. Fluid Mech. 1008, R2 (2025)] claimed that numerical noise in direct numerical simulation of turbulence using the deterministic Navier-Stokes equations is "approximately equivalent" to the physical noise arising from random molecular motion (thermal fluctuations). We show here that it this claim not supported by their results and that it contradicts other results in the literature. Furthermore, we demonstrate that the numerical implementation of thermal fluctuations in their so-called "clean numerical simulations" is incorrect.

Plasmonic resonances enable strong electromagnetic field confinement and have been widely exploited in plasmonic nanolasers, particularly through surface plasmon polaritons and localized surface plasmons. However, their performance is often limited by bidirectional output coupling and multimode far-field emission, primarily due to higher-order diffraction arising from these modes. In this work, we utilize the Tamm plasmon resonance to realize lasing in the NIR region with wide tunability. The optical Tamm states are excited at the metal-DBR interface by an incident pump pulse and their emission intensity is significantly enhanced via extraordinary optical transmission through a metallic nanohole array. The subwavelength periodicity of the nanohole array restricts the emission to the zeroth order, resulting in a highly directional far-field pattern with a full width at half maximum of approximately 0.631 degrees. To further improve performance, a second DBR is incorporated beneath the pump side, which substantially suppresses backward emission around the lasing wavelength and enhances forward lasing intensity by around 1.3 X 10^4 times, thus increasing the integrated emission power. The combination of Tamm plasmon excitation and dual-DBR feedback significantly improves the cavity's optical response and overall lasing efficiency. Additionally, we have demonstrated lasing at 870 nm with a reduced pump threshold of 2.8 X 10^7 V/m (energy of 0.0031 mJ/cm^2). Moreover, a broad tunability in lasing wavelength, spanning from 850 nm to 944.5 nm, is achieved. These results demonstrate a cost-effective and versatile strategy for plasmonic nanolasers with enhanced output power, low reflection-side loss, wide tunability, and strong integration potential for on-chip photonic and quantum technologies.

A new electromagnetic calorimeter (ECAL) consisting of 1596 lead tungstate PbWO$_{\rm 4}$ scintillating crystals has been fabricated and installed in the experimental Hall D at Jefferson Lab (JLab). The high-granularity, high-resolution calorimeter is required by the JLab Eta Factory experiment, whose main physics goal is to study rare decays of eta mesons. The ECAL replaced the inner part of the forward lead glass calorimeter of the GlueX detector. Signals from the detector will be digitized using twelve-bit flash analog-to-digital converters operated at a sampling rate of 250 MHz. The ECAL is integrated into the trigger system of the GlueX detector using electronics modules designed at JLab. The ECAL is currently at the commissioning stage and should be ready for the physics run in January 2025. We will give an overview of the JEF experiment, the design and construction of the ECAL, and the integration of the detector and its infrastructure into the Hall D experimental setup.