Optics · Cross-listed from quant-ph
Classical and quantum electromagnetic momentum in anisotropic optical waveguides
Denis Kopylov and Manfred Hammer
The guided modes supported by dielectric channel waveguides act as individual carriers of momentum. We show this by proving that the modes satisfy an orthogonality condition which relates to the momentum of the optical electromagnetic field, with a link to the more familiar power (energy) orthogonality. This result forms the basis for a rigorous, self-consistent procedure for the quantization of broadband guided electromagnetic fields in the typical channels used in integrated photonic
circuits. Our work removes the existing theoretical gap between the classical solution of the Maxwell equations for guided fields and the intuitive understanding of
photons in waveguides. The presented approach is valid for straight, lossless, and potentially anisotropic, dielectric waveguides of general shape, in the linear regime, and including material dispersion. Examples for the hybrid modes of a thin film lithium niobate strip waveguide are briefly discussed.
Read the full abstract ▾
Optics
An epsilon-near-zero-based nonlinear platform for ultrafast re-writable holography
M. Zahirul Alam, Robert Fickler, Yiyu Zhou et al. (+3 more)
We re-examine real-time holography for all-optical structuring of light and optical computation using a contemporary material: a subwavelength-thick, spatially unstructured film of indium tin
oxide (ITO). When excited by spatially structured light at epsilon-near-zero frequencies, the film acts as an efficient and reconfigurable diffractive optical platform for all-optical modulation of light such as spatial structuring and optical computations. We demonstrate a few percent of absolute diffraction efficiency over greater than 300 nm bandwidth around telecom wavelengths using a film four orders of magnitude thinner than and up to six orders of magnitude faster than standard
holographic materials. Our findings highlight the potential of using epsilon-near-zero-based nanostructures for efficient modulation of spatially structured light and rapid prototyping without complex nanofabrication processes.
Read the full abstract ▾
All authors M. Zahirul Alam, Robert Fickler, Yiyu Zhou, Enno Giese, Jeremy Upham, Robert W. Boyd
Optics
Reconfigurable Silicon Photonics Extreme Learning Machine with Random Non-linearities as Neural Processor and Physical Unclonable Function
George Sarantoglou, Georgios Aias Karydis, Adonis Bogris et al. (+1 more)
An alternative extreme learning machine -ELM- paradigm is presented exploiting random non-linearities -RN, named RN-ELM, instead of a conventional fixed node non-linearity. This method is implemented on a hybrid neural engine, with the physical layer realized by an integrated silicon photonic mesh and the digital layer by a simple regression
algorithm. Non-linearities are intrinsically non-power depended and are generated through non-linear frequency to power mapping offered by optical filters. The numerical evaluation is based on an experimentally derived transfer function of an all-pass filter, implemented on a silicon reconfigurable photonic integrated chip -RPIC. RN-ELM is evaluated in a twofold manner; first as a machine learning scheme, where the expressivity offered by multiple, yet random, activation functions lead to a compact and highly simplified design with 5 optical filters, offering state-of-the-art performance in time-series prediction tasks with minimum hardware requirements. The second scenario entails its deployment as a physical unclonable function -PUF, for authentication applications directly in the physical layer. In this case, the random activation functions are associated with unavoidable, fabrication related waveguide imperfections that can act as hardware signatures. Numerical results reveal a probability of cloning as low as 10e-15, which corresponds to a highly secure authentication
token.
Read the full abstract ▾
All authors George Sarantoglou, Georgios Aias Karydis, Adonis Bogris, Charis Mesaritakis
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Atomic forces from correlation energy functionals based on the adiabatic-connection fluctuation-dissipation theorem
Damian Contant and Maria Hellgren
We extend the capabilities of correlation energy functionals based on the adiabatic-connection fluctuation-dissipation
theorem by implementing the analytical atomic forces within the random phase approximation (RPA), in the context of plane waves and pseudopotentials. Forces are calculated at self-consistency through the optimized effective potential method and the Hellmann-Feynman
theorem. In addition, non-self-consistent RPA forces, starting from the PBE generalized gradient approximation, are evaluated using density functional perturbation theory. In both cases, we find forces of excellent numerical quality. Furthermore, for most molecules and solids studied, self-consistency is found to have a negligible impact on the computed geometries and vibrational frequencies. The RPA is shown to systematically improve over PBE and, by including the exact-exchange kernel within RPA + exchange (RPAx), through finite-difference total energy calculations, we obtain an accuracy comparable to advanced wavefunction methods. Finally, we estimate the anharmonic shift and provide accurate theoretical references based on RPA and RPAx for the zone-center optical
phonon of diamond, silicon, and germanium.
Read the full abstract ▾
Applied Physics · Cross-listed from cs.LG
A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability
Jordan Abarca-Albores, Danna Cristina Gutiérrez Cabrera, Luis Antonio Salazar-Licea et al. (+5 more)
This paper presents a novel methodology for detecting faults in wind turbine blades using com-putational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data
segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure.
Read the full abstract ▾
All authors Jordan Abarca-Albores, Danna Cristina Gutiérrez Cabrera, Luis Antonio Salazar-Licea, Dante Ruiz-Robles, Jesus Alejandro Franco, Alberto-Jesus Perea-Moreno, David Muñoz-Rodríguez, Quetzalcoatl Hernandez-Escobedo
Atomic Physics
Calculation of hyperfine structure in Tm II
Andrey I. Bondarev
The first measurements of the
magnetic dipole hyperfine structure constants A in singly ionized thulium revealed substantial discrepancies with the corresponding calculations [Mansour et al., NIMB 40, 252 (1989)]. More recent measurements [Kebapc{\i} et al., ApJ 970, 23 (2024)] expanded very limited
dataset of that work and demonstrated that two of the previously reported experimental A values were incorrect, motivating new theoretical calculations. In this work, we employ the configuration interaction method to calculate the A constants for several low-lying levels in Tm II, with the random-phase-approximation corrections also taken into account. Our results show good agreement with the new experimental data and provide reliable predictions for additional states where measurements are not yet available.
Read the full abstract ▾
Optics
Topological reorganization of near-field energy flow governing scattering transitions in subwavelength rectangular grooves
J. Sumaya-Martinez and J. Mulia-Rodriguez
The scattering of electromagnetic waves by subwavelength rectangular grooves has been extensively studied, yet its physical interpretation has largely relied on field-intensity distributions. Here we demonstrate that the transition from concave to convex scattering profiles observed as the groove width approaches the wavelength is governed by a topological reorganization of the near-field energy flow. Using a rigorous modal formulation for TM-polarized fields, we analyze the complex electromagnetic field and the associated time-averaged Poynting vector. We show that reducing the groove width induces the creation, migration, and annihilation of Poynting-vector singularities, including
vortices and saddle points, leading to a qualitative restructuring of electromagnetic energy transport. This topological transition redirects the local energy flux and manifests as a convex scattering profile in the far field. The results establish a direct link between near-field energy
topology and far-field scattering, providing a unified physical interpretation of subwavelength groove scattering.
Read the full abstract ▾
Optics
Photoacoustic model for laser-induced acoustic desorption of nanoparticles
Matthew Edmonds and James Bateman
Laser-induced acoustic desorption (LIAD) enables loading nanoparticles into optical traps under
vacuum for levitated optomechanics experiments. Current LIAD systems rely on empirical optimization using available laboratory
lasers rather than systematic theoretical design, resulting in large systems incompatible with portable or space-based applications. We develop a theoretical framework using the photoacoustic wave equation to model acoustic wave generation and propagation in metal substrates, enabling systematic optimization of
laser parameters. The model identifies key scaling relationships: surface acceleration scales as $\tau^{-2}$ with pulse duration, while acoustic diffraction sets fundamental limits on optimal spot size $w \gtrsim \sqrt{v\tau d}$. Material figures of merit combine thermal expansion and optical absorption properties, suggesting alternatives to traditional aluminum substrates. The framework validates well against experimental data and demonstrates that compact
laser systems with sub-nanosecond pulse durations can achieve performance competitive with existing laboratory-scale implementations despite orders-of-magnitude lower pulse energies. This enables rational design of minimal LIAD systems for practical applications.
Read the full abstract ▾
Physics and Society
What defines a group of friends? Rethinking community structure in signed, directed networks
Miguel A. González-Casado, Angel Sánchez and Santo Fortunato
We study the structure of personal relationships among 1068 high school students using a
dataset that contains the network of self-reported friendly and conflictive relationships, with information on their directionality and intensity. We analyse the resulting weighted, directed, and signed network using a
Bayesian stochastic block model framework, which enables the inference of group structure without imposing prior assumptions on the role of negative or asymmetric ties. While a full model incorporating all edge attributes yields statistically coherent clusters, these do not align with socially meaningful communities. To address this, we focus first on the network backbone of mutual affinities, and we characterize its group organization. Many communities display an assortative structure, often embedded within larger cohesive configurations, but we also observe more diverse patterns such as core-periphery structure and isolated nodes. We then examine how relationship intensity, directionality, and conflict shape group structure. Asymmetric ties, though often occurring between communities, are frequently present within them, revealing the stabilizing effect of group membership on non-mutual relationships. Furthermore, the presence of asymmetric ties does not inherently imply a hierarchical structure, given that all groups both receive and report significant levels of non-reciprocal ties. More intense ties play a disproportionate role in shaping community structure. Finally, negative ties tend to bridge communities, but we find that groups feature a significant level of internal conflict. Our research offers a new perspective on the study of group organization when rich information about the directionality, the intensity and the sign of ties is considered, with implications for identifying social vulnerability and designing targeted interventions.
Read the full abstract ▾
Fluid Dynamics
Marangoni instabilities of cylindrical drops in a vertical Hele-Shaw cell immersed in stratified liquids
Li-Chen Huang and Yanshen Li
The Marangoni instability of cylindrical drops in vertical Hele-Shaw
cells immersed in stably stratified
liquids has been studied previously, yet the underlying mechanism has not been explored thoroughly. Here we study the onset of the Marangoni instability of such a system by experimentally explore the parameter space of the drop radius and concentration gradient. The concentration field is directly observed with
laser interferometry. The flow is found to become unstable when advection is too strong for
diffusion to maintain a stable concentration field. However, two different instability regimes are found depending on the drop radius. When the drop is small, the friction force caused by the two plates of the Hele-Shaw
cell is small so that it does not change much the velocity field. Marangoni advection in such a regime can be very strong so that the entire periphery of the drop can become unstable. When the drop is large, the friction becomes so large that the Marangoni velocity plateaus and the boundary layer thickness is also reduced. The modified velocity and concentration fields lead to another instability regime, where only
liquid close to the equator of the drop becomes unstable. A unifying scaling theory that includes both instability regimes is developed, which agrees well with the experimental results. Our findings may shed new light on the understandings of Marangoni flows in confined geometries.
Read the full abstract ▾
Optics · Cross-listed from quant-ph
Instantaneous velocity during quantum tunnelling
Xiao-Wen Shang, Jian-Peng Dou, Feng Lu et al. (+3 more)
Quantum tunnelling, a hallmark phenomenon of
quantum mechanics, allows particles to pass through the classically forbidden region. It underpins fundamental processes ranging from
nuclear fusion and photosynthesis to the operation of superconducting
qubits. Yet the underlying dynamics of particle motion during tunnelling remain subtle and are still the subject of active debate. Here, by analyzing the temporal
evolution of the tunnelling process, we show that the particle velocity inside the barrier continuously relaxes from a large initial value toward a smaller one, and may even approach zero in the evanescent regime. Meanwhile, the probability density within the barrier gradually builds up before reaching its stationary profile, in contrast to existing inherently. In addition, starting from the steady-state equations, we derive an explicit relation between the particle velocity and the barrier width, and show that the velocity in evanescent states approaches zero when the barrier is sufficiently wide. These findings resolve the apparent paradox of a vanishing steady-state velocity coexisting with a finite particle density. We point out that defining an effective speed from the probability density, rather than from the probability current, can lead to spuriously nonzero "stationary speed," as appears to be the case in Ref. [Nature 643, 67 (2025)]. Our work establishes a clear dynamical picture for the formation of tunnelling flow and provides a theoretical foundation for testing time-resolved tunnelling phenomena.
Read the full abstract ▾
All authors Xiao-Wen Shang, Jian-Peng Dou, Feng Lu, Sen Lin, Hao Tang, Xian-Min Jin
Atomic Physics · Cross-listed from quant-ph
Feedback Cooling and Thermometry of a Single Trapped Ion Using a Knife Edge
Hans Dang, Sebastian Luff, Martin Fischer et al. (+2 more)
We report on a simple and easy to implement method of feedback cooling trapped ions to temperatures below those achievable using only Doppler cooling. Additionally, the feedback cooling results in significantly shorter cooling times. For selected parameters, we demonstrate cooling to temperatures below $\hbar\Gamma/2 k_\mathrm{B}$. The motion of a single ion is monitored in real-time, allowing for the generation of a feedback signal that is applied to an auxiliary trap electrode. Motion detection is implemented by imaging the fluorescence
photons emitted by the ion onto a knife edge and detecting the transmitted light, a method used so far to cool trapped nanoparticles. The intensity modulation of the fluorescence resulting from the ion motion is used to generate and apply the feedback signal and also to determine the ion temperature. While the method benefits from a high rate of detected scattered
photons, which can be a challenge, and which we address by using a
parabolic mirror for collecting the fluorescence, we expect the method to also be applicable when using lenses with moderate numerical apertures.
Read the full abstract ▾
All authors Hans Dang, Sebastian Luff, Martin Fischer, Markus Sondermann, Gerd Leuchs
Medical Physics
Back with Weight: Revisiting Very Heavy Ions for Precision Radiotherapy
Jeannette Jansen, Olga Sokol, Yolanda Prezado et al. (+1 more)
Accelerated charged particles offer significant physical advantages over X-rays in radiotherapy. In addition to their superior depth-dose distribution, heavy ions provide notable biological benefits compared to
protons. Specifically, at therapeutic energies, very heavy ions are expected to exhibit high relative biological effectiveness and a low oxygen enhancement ratio, making them potentially ideal for treating radioresistant tumors. Over fifty years ago, the Lawrence Berkeley Laboratory started a clinical trial to treat cancer patients with particles ranging from helium to argon. However, treatments with ions of atomic number greater than 10 proved highly toxic to normal tissue. Most patients were ultimately treated with helium ions, whose biological effects closely resemble those of
protons. In recent years, novel strategies have emerged that can reduce normal tissue toxicity in radiotherapy while preserving tumor control. These advancements open the possibility of revisiting the clinical use of heavier ions. In this paper, we propose that neon ions may serve as an effective modality for treating resistant, hypoxic tumors. Their associated toxicity may be mitigated by employing approaches such as ultra-high dose rate (FLASH) or spatially fractionated radiotherapy. Current plans and prospects for the clinical application of neon ions will be discussed.
Read the full abstract ▾
All authors Jeannette Jansen, Olga Sokol, Yolanda Prezado, Marco Durante
Fluid Dynamics
Survey of Mathematical Models of and Numerical Methods for Fluid Dynamics Water Engineering
Anshu Kumar, Kemi Olimba, Vyacheslav Kungurtsev et al. (+1 more)
Computational fluid dynamics (CFD) has become a cornerstone of modern water engineering, providing quantitative tools for the analysis, prediction, and management of complex hydraulic systems across a wide range of spatial and temporal scales. This survey reviews the mathematical models and numerical methods that underpin CFD applications in water engineering, from depth-averaged formulations such as the shallow water equations to fully three-dimensional Navier-Stokes models, as well as selected alternative modeling approaches. We examine the historical development of these models, their mathematical structure, and the numerical discretization and solution strategies commonly employed in practice, including finite difference, finite volume, and finite element methods. Beyond core solver technology, the survey addresses practical modeling issues such as source-term treatment, wetting and drying,
turbulence modeling, free-surface representation, and computational efficiency. The growing role of data integration is also discussed, encompassing data assimilation, uncertainty quantification, and emerging machine-learning-assisted approaches that complement physics-based solvers. To illustrate the impact of modeling and numerical choices on real-world applications, representative case studies from large-scale water management systems are reviewed. By integrating theory, numerical techniques, and applied perspectives, this survey provides a unified reference for researchers and practitioners seeking to understand both the foundational principles and contemporary challenges of CFD in water engineering.
Read the full abstract ▾
All authors Anshu Kumar, Kemi Olimba, Vyacheslav Kungurtsev, Fabio V. Difonzo
Optics
Three-Octave Supercontinuum Generation Spanning from Ultraviolet in Lithium Tantalate Waveguides
Lingfang Wang, Tianyou Tang, Huizong Zhu et al. (+1 more)
We demonstrate, for the first time, supercontinuum generation spanning more than three octaves in dispersion-engineered thin-film lithium tantalate (TFLT) waveguides. Pumped by a femtosecond
laser at 1560 nm, the waveguides yield a spectrum from 240 nm in the ultraviolet to beyond 2400 nm in the near-infrared. The spectral
evolution is mapped from low-power harmonic generation (second- and third-harmonic) to a high-power continuum driven by
soliton fission and dispersive wave emission. This first demonstration of ultrabroadband nonlinear optics in TFLT establishes it as a competitive, low-loss platform for integrated photonics, with significant potential for applications in frequency metrology and on-chip spectroscopy.
Read the full abstract ▾
All authors Lingfang Wang, Tianyou Tang, Huizong Zhu, Juanjuan Lu
Accelerator Physics
Bunch-by-Bunch Prediction of Beam Transverse Position, Phase, and Length in a Storage Ring Using Neural Networks
Can Liu, Xing Yang, Youming Deng et al. (+2 more)
Real-time, bunch-by-bunch monitoring of transverse position, longitudinal phase, and bunch length is crucial for beam control in diffraction-limited storage rings, where complex collective dynamics pose unprecedented diagnostic challenges. This study presents a neural network framework that simultaneously predicts these parameters directly from beam position monitor waveforms. The hybrid architecture integrates specialized Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term
Memory with
Attention (LSTM-
Attention) sub-networks, overcoming key limitations of traditional methods such as serial processing chains and batch-mode operation. Validated on experimental data from the Shanghai Synchrotron Radiation Facility and Hefei Light Source, the model achieves high prediction accuracy with a sub-millisecond latency of 0.042 ms per bunch. This performance demonstrates its potential as a core tool for real-time, multi-parameter diagnostics and active feedback in next-generation light sources.
Read the full abstract ▾
All authors Can Liu, Xing Yang, Youming Deng, Qingqing Duan, Yongbin Leng
Optics
Polygonal Spatiotemporal Optical Vortices Wavepackets with Prescribed Vortex Structure
Haifa Fan, Qian Cao, Andy Chong et al. (+1 more)
Optical
vortices carrying orbital angular momentum offer additional degrees of freedom. According to the orientation of orbital angular momentum, optical
vortices can be classified into spatial optical
vortex beam carrying longitudinalorbital angular momentum and spatiotemporal optical
vortices carrying transverse orbital angular momentum. As an emerging subset of optical
vortices, polygonal optical
vortices provide a unique platform for a wide range of frontier applications by introducing a new degree of freedom in the form of a customizable intensity structure. In the spatial domain, polygonal spatial optical
vortex beam carrying longitudinal orbital angular momentum have already demonstrated great potential in optical manipulation and two-
photon lithography. However, polygonal spatiotemporal optical
vortex wavepackets contain multiple sub spatiotemporal optical
vortices carrying transverse orbital angular momentum remains unrealized to date. In this work, we theoretically propose and experimentally demonstrate polygonal spatiotemporal optical
vortices wavepackets embedded with prescribed
vortex structures. Within the structure, a prescribed number of sub spatiotemporal optical
vortices carrying transverse orbital angular momentum is set along a designed polygonal spatiotemporal trajectory. Using the spatiotemporal
holographic shaping approach, we generate polygonal perfect spatiotemporal optical
vortex wavepacket and use the combination of multiple polygonal perfect spatiotemporal optical
vortex wavepacket to form polygonal spatiotemporal optical
vortex wavepacket with the prescribed
vortex structure. A full control over multiple key properties of the polygonal spatiotemporal optical
vortex wavepackets such as the geometry, number of phase singularities, and spatiotemporal distribution of sub spatiotemporal optical
vortices is also achieved.
Read the full abstract ▾
All authors Haifa Fan, Qian Cao, Andy Chong, Qiwen Zhan
Fluid Dynamics
Can Transformers overcome the lack of data in the simulation of history-dependent flows?
P. Urdeitx, I. Alfaro, D. Gonzalez et al. (+2 more)
It is well known that the lack of information about certain variables necessary for the description of a dynamical system leads to the introduction of historical dependence (lack of Markovian character of the model) and noise. Traditionally, scientists have made up for these shortcomings by designing phenomenological variables that take into account this historical dependence (typically, conformational tensors in fluids). Often, these phenomenological variables are not easily measurable experimentally. In this work, we study to what extent
Transformer architectures are able to cope with the lack of experimental data on these variables. The methodology is evaluated on three
benchmark problems: a cylinder flow with no history dependence, a viscoelastic Couette flow modeled via the Oldroyd-B formalism, and a non-linear polymeric fluid described by the FENE model. Our results show that the
Transformer outperforms a thermodynamically consistent, structure-preserving neural network with metriplectic bias in systems with missing experimental data, providing lower errors even in low-dimensional latent spaces. In contrast, for systems whose state variables can be fully known, the metriplectic model achieves superior performance.
Read the full abstract ▾
All authors P. Urdeitx, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto
Biological Physics · Cross-listed from cond-mat.soft
Explosive dispersal of non-motile microbes through metabolic buoyancy
Jimreeves David and Shashi Thutupalli
For non-motile microorganisms, spatial expansion in quiescent fluids is presumed to be limited by
diffusion. We report that microbial colonies can explosively circumvent this constraint through a self-amplifying physical process. As non-motile yeast and bacteria metabolize dense nutrients into lighter waste within their fluid environment, they generate buoyancy-driven Rayleigh-Bénard convection, an ubiquitous fluid-dynamical phenomenon that organizes material on scales from chemical reactors to planetary atmospheres. This robust, self-generated flow fragments and disperses cellular aggregates, which seed new growth sites, enhancing total metabolic activity and further strengthening the convective flow in an autocatalytic cycle. The resulting expansion follows accelerating power-law kinetics, quantitatively captured by a physical theory linking metabolic flux to flow velocity, and produces fractal patterns through a flow-focusing instability we term Circulation-Driven Aggregation, the hydrodynamic analogue of
Diffusion-Limited Aggregation. This `metabolic fireworks' mechanism establishes a canonical instance of proliferating active matter, where cellular metabolic activity self-organizes a physical transport engine–a living Rayleigh-Bénard convection–providing a fundamental, physics-based dispersal strategy.
Read the full abstract ▾
Medical Physics · Cross-listed from cs.CV
Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learning
Paloma Casteleiro Costa, Parnian Ghapandar Kashani, Xuhui Liu et al. (+5 more)
Fluorescence lifetime imaging microscopy (FLIM) is a powerful quantitative technique that provides metabolic and molecular contrast, offering strong translational potential for label-free, real-time diagnostics. However, its clinical adoption remains limited by long pixel dwell times and low signal-to-noise ratio (SNR), which impose a stricter resolution-speed trade-off than conventional optical imaging approaches. Here, we introduce FLIM_PSR_k, a deep learning-based multi-channel pixel super-resolution (PSR) framework that reconstructs high-resolution FLIM images from data acquired with up to a 5-fold increased pixel size. The model is trained using the conditional generative
adversarial network (cGAN) framework, which, compared to
diffusion model-based alternatives, delivers a more robust PSR reconstruction with substantially shorter inference times, a crucial advantage for practical deployment. FLIM_PSR_k not only enables faster image acquisition but can also alleviate SNR limitations in autofluorescence-based FLIM. Blind testing on held-out patient-derived tumor tissue samples demonstrates that FLIM_PSR_k reliably achieves a super-resolution factor of k = 5, resulting in a 25-fold increase in the space-bandwidth product of the output images and revealing fine architectural features lost in lower-resolution inputs, with statistically significant improvements across various image quality metrics. By increasing FLIM's effective spatial resolution, FLIM_PSR_k advances lifetime imaging toward faster, higher-resolution, and hardware-flexible implementations compatible with low-numerical-aperture and miniaturized platforms, better positioning FLIM for translational applications.
Read the full abstract ▾
All authors Paloma Casteleiro Costa, Parnian Ghapandar Kashani, Xuhui Liu, Alexander Chen, Ary Portes, Julien Bec, Laura Marcu, Aydogan Ozcan
Physics Education
Quantum Readiness in Latin American High Schools: Curriculum Compatibility and Enabling Conditions
Adriana Celeste Alvarado Leon, Osmar Denilson Herrera Cueva, Rosario Mercedes Morales Orvezo et al. (+2 more)
The accelerating global development of
quantum technologies strengthens the case for introducing
quantum computing concepts before university. Yet in Latin America, there is no consolidated, region wide integration of
quantum computing into secondary education, and the feasibility conditions for doing so remain largely unexamined. This paper proposes a qualitative, comparative framework to assess academic readiness for
quantum education across six countries - Peru, Bolivia, Chile, Argentina, Brazil, and Colombia - grounded in the relationship between curriculum compatibility and enabling conditions spanning institutional capacity, teacher preparation, infrastructure, and equity. Using official curricula, policy documents, national statistics, and educational reports, we apply structured qualitative coding and a 1-5 ordinal scoring system to generate a cross country diagnosis. The findings reveal substantial regional asymmetries: among the six countries studied, Chile emerges as the most institutionally prepared for progressive
quantum education integration, while the remaining countries exhibit varying combinations of curricular gaps and fragmented but promising enabling conditions. Building on this diagnosis, we propose a country sensitive, regionally coordinated roadmap for staged implementation, beginning with teacher development and pilot centers, leveraging open source platforms and local
language resources, and scaling toward gradual curricular integration. This work establishes a baseline for future quantitative and mixed method studies evaluating learning outcomes, motivation, and scalable models for
quantum education in Latin America.
Read the full abstract ▾
All authors Adriana Celeste Alvarado Leon, Osmar Denilson Herrera Cueva, Rosario Mercedes Morales Orvezo, Daniella Alexandra Crysti Vargas Saldana, Freddy Herrera Cueva
Physics Education · Cross-listed from stat.AP
An Open Workflow Model for Improving Educational Video Design: Tools, Data, and Insights
Mohamed Tolba, Olivia Kendall, Daniel Tudball Smith et al. (+3 more)
Educational
videos are widely used across various instructional models in higher education to support flexible and self-paced learning. However, student engagement with these
videos varies significantly depending on how they are designed. While several studies have identified potential influencing factors, there remains a lack of scalable tools and open
datasets to support large-scale, data-driven improvements in
video design. This study aims to advance data-driven approaches to educational
video design. Its core contributions include: (1) a workflow model for analysing educational
videos; (2) an open-source implementation for extracting
video metadata and features; (3) an accessible, community-driven database of
video attributes; (4) a case study applying the approach to two engineering courses; and (5) an initial machine learning-based analysis to explore the relative influence of various
video characteristics on student engagement. This work lays the groundwork for a shared, evidence-based approach to educational
video design.
Read the full abstract ▾
All authors Mohamed Tolba, Olivia Kendall, Daniel Tudball Smith, Alexander Gregg, Tony Vo, Scott Wordley
Computational Physics · Cross-listed from cond-mat.supr-con
Beyond dpa: an atomistic framework for a quantitative description of radiation damage in YBa2Cu3O7
Federico Ledda, Daniele Torsello, Davide Gambino et al. (+8 more)
Radiation damage in high-temperature cuprate
superconductors represents one of the main technological challenges for their deployment in harsh environments, such as fusion reactors and accelerator facilities. Their complex
crystal structure makes modeling irradiation effects in this class of materials a particularly demanding task, for which existing damage models remain inadequate. In this work, we develop an atomistic-based approach for describing primary radiation damage in YBa2Cu3O7, by coupling Molecular Dynamics and Binary Collision Approximation simulations in a way that makes them complementary. When integrated with Primary Knock-on Atom spectra obtained from Monte Carlo codes, our results establish a framework for multiscale modeling of radiation damage, enabling quantitative estimates of several damage descriptors, such as defect production, defect clustering, and the effective damaged volume for any specific irradiation conditions where collision cascades dominate. This computational approach is suitable for the prediction of irradiation effects in any complex functional
oxide, with applications ranging from aerospace to
nuclear fusion and high-energy physics.
Read the full abstract ▾
All authors Federico Ledda, Daniele Torsello, Davide Gambino, Flyura Djurabekova, Fabio Calzavara, Niccolò Di Eugenio, Ville Jantunen, Antonio Trotta, Erik Gallo, Kai Nordlund, Francesco Laviano
Fluid Dynamics
A unified MRT-LB framework for Navier-Stokes and nonlinear convection-diffusion equations and beyond: moment equations, auxiliary moments, multispeed lattices, and Hermite matrices
Baochang Shi, Xiaolei Yuan and Zhenhua Chai
We develop a unified multi-relaxation-time
lattice Boltzmann (MRT-LB) framework based on discrete Hermite
polynomials (Hermite
matrices) for the Navier-Stokes equations (NSEs) and nonlinear convection-
diffusion equations (NCDEs), using multispeed rectangular
lattice (rD$d$Q$b$) models. For NSEs, the proposed MRT-LB model simulates incompressible and compressible isothermal flows in both single-phase and multiphase systems. Macroscopic moment equations are derived from the MRT-LB model via the direct Taylor expansion method. By selecting appropriate fundamental moments, the target NSEs and NCDE are recovered from these moment equations. Critically, the elimination of spurious terms and/or the recovery of the desired terms relies on specific auxiliary moments: the second-order auxiliary moment ($\mathbf{M}_{2G}$) of the source term distribution function (SDF) and the third-order auxiliary moment ($\mathbf{M}_{30}$) of the
equilibrium distribution function (EDF) for NSEs, as well as the first-order auxiliary moment ($\mathbf{M}_{1G}$) of the SDF and the second-order auxiliary moment ($\mathbf{M}_{20}$) of the EDF for NCDE. Furthermore, using the weighted orthogonality of Hermite
matrices, we establish essential relations for weight coefficients and construct several multispeed rectangular
lattice models, including rD2Q25 and rD3Q53, with subgroup models rD2Q21, rD2Q17, rD2Q13, rD3Q45, and rD3Q33. A generalized third-order
equilibrium distribution function is derived. We emphasize that for rectangular
lattices, specific elements of the Hermite
matrix corresponding to third-order discrete Hermite
polynomials require correction to satisfy weighted orthogonality.
Read the full abstract ▾
Chemical Physics
PASPT2: a size-extensive and size-consistent partial-active-space multi-state multi-reference second-order perturbation theory for strongly correlated electrons
Chunzhang Liu, Ning Zhang and Wenjian Liu
A partial-active-space (PAS) multi-state (MS) multi-reference second-order perturbation theory (MRPT2) for the electronic structure of strongly correlated systems of electrons, dubbed PASPT2, is formulated by linearizing the intermediate normalization-based general-model-space state-universal coupled-cluster theory with singles and doubles [IN-GMS-SU-CCSD; J. Chem. Phys. 119, 5320 (2003)]. At variance with the existence of disconnected terms in the IN-GMS-SU-CCSD amplitude equations, the disconnected terms in the PASPT2 amplitude equations can be avoided completely by choosing a special reference-specific zeroth-order
Hamiltonian. The corresponding effective/intermediate
Hamiltonian can also be made connected and closed, so as to render the energies obtained by diagonalization fully connected. As such, PASPT2 is strictly size-extensive, in sharp contrast with the parent IN-GMS-SU-CCSD. It is also size-consistent when the PAS of a supermolecule is chosen to be the direct product of those of the physically separated, non-interacting fragments. Prototypical systems are taken as showcases to reveal the efficacy of PASPT2.
Read the full abstract ▾
Optics
Light matter interaction in van der Waals heterostructures with Mie voids
Zhuoyuan Lu, Kirill Koshelev, Pavel Tonkaev et al. (+5 more)
Recently introduced concept of Mie voids allows to enhance the field localization inside air cavities embedded in high-index materials. Mie voids provide an alternative approach to conventional dielectric resonators that confine optical fields within bulk high-index materials. Building on this concept, here we present a hybrid photonic platform that integrates monolayer WS2 with Mie void resonators patterned in a high-index Bi2Te3 substrate. By carefully aligning the dipolar void resonance with the excitonic transition of WS2, we achieve substantially enhanced photoluminescence and second-harmonic generation. Far-field imaging of the harmonic fields reveals spatially resolved hotspots that directly map localized resonant modes, with their positions tunable by cavity geometry and pump wavelength. This approach enables real-space control of nonlinear emission at the single-resonator level, offering a robust and reconfigurable platform for next-generation nonlinear photonics and surface-enhanced optical sensing.
Read the full abstract ▾
All authors Zhuoyuan Lu, Kirill Koshelev, Pavel Tonkaev, Ziyu Chen, Dawei Liu, Wenkai Yang, Yuri Kivshar, Yuerui Lu
Optics · Cross-listed from quant-ph
High-Performance Near-Infrared Quantum Emission from Color Centers in hBN
Sean Doan, Sahil D. Patel, Yilin Chen et al. (+9 more)
Color centers hosted in hexagonal boron nitride have emerged as a highly promising platform for single-
photon emission and
spin-
photon technologies relevant to
quantum communication and
quantum networking. As a wide-bandgap van der Waals material, hBN can host optically active
quantum defects across a broad spectral range. Here, we demonstrate a simple and scalable oxygen-
plasma process that reproducibly creates single
quantum emitters in hBN with blinking-free zero-
phonon lines spanning the near-infrared from 700 up to 971 nm. These emitters combine MHz-level brightness, single-
photon purity up to 99.9%, and ultranarrow cryogenic linewidths down to 2.7~GHz under quasi-resonant excitation, placing them in a particularly attractive regime for
quantum photonics. Photostability measurements further reveal resistance to photobleaching, sub-nm spectral stability over long timescales, and near-shot-noise-limited intensity fluctuations. Analysis of the
phonon sidebands shows weak vibronic coupling and ZPL-dominated emission, with Debye–Waller factors approaching 50%. Control experiments together with EDS elemental mapping support oxygen incorporation as a necessary ingredient in activating the NIR emitter population, while first-principles calculations identify O$_N$V$_N$ and O$_N$V$_N$H as the leading defect candidates. These results establish a high-performance NIR
quantum-emitter platform in hBN for free-space
quantum networking and future integrated
quantum-photonic architectures.
Read the full abstract ▾
All authors Sean Doan, Sahil D. Patel, Yilin Chen, Jordan A. Gusdorff. Mark E. Turiansky, Luis Villagomez, Luka Jevremovic, Nicholas Lewis, Kenji Watanabe, Takashi Taniguchi, Lee C. Bassett, Chris Van de Walle, Galan Moody
Plasma Physics
Toward the Origins of Binding Energy Shifts and Satellites Formation During Plasma-XPS Measurements
J. Trey Diulus, Ashley R. Head, Jorge Anibal Boscoboinik et al. (+3 more)
In
plasma X ray photoelectron spectroscopy emerges as a powerful platform for real time, in situ chemical analysis under conditions relevant to
semiconductor processing and other
plasma enabled technologies. This study investigates the origins of binding energy shifts and satellite peaks formation observed during
plasma XPS measurements across conductive, dielectric, and gas phase systems. Using a standard laboratory based ambient pressure XPS apparatus coupled with an alternating current driven capacitively coupled
plasma source, we show that metastable surface species, such as transient Au
oxides, can be detected during
plasma exposure, revealing chemical states hardly accessible using conventional ultrahigh
vacuum XPS. In dielectric samples, we observe pressure- and
plasma type dependent BE shifts up to 50 eV, attributed to X ray induced and
plasma mediated surface charging. These shifts are mitigated at higher pressures
plasmas or in electronegative
plasmas, the latter due to enhanced charge compensation mechanisms involving slow negative ions. For gas phase species, AC
plasma excitation leads to spectral broadening and the emergence of satellite peaks with a few eV energy separations, linked to oscillating local
plasma potentials in the probing volume. These findings highlight the important and complex interplay of
plasma parameters, surface charging, and local electric fields in shaping XPS spectra. Overall,
plasma XPS emerges as a critical metrological tool for probing transient surface chemistry, with implications for
semiconductor processing, material synthesis, and
plasma diagnostics.
Read the full abstract ▾
All authors J. Trey Diulus, Ashley R. Head, Jorge Anibal Boscoboinik, Carles Corbella Roca, Alexander Tselev, Andrei Kolmakov
Space Physics · Cross-listed from astro-ph.EP
Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere
Jiawei Gao, Chuanfei Dong, Chi Zhang et al. (+6 more)
Understanding the
magnetic field environment around Mars and its response to upstream
solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have been exclusively physics-based, relying on computationally intensive simulations. For the first time, we develop a data-driven model of the Martian induced magnetospheric
magnetic field using Physics-Informed Neural Network (PINN) combined with MAVEN observations and physical laws. Trained under varying
solar wind conditions, including B_IMF, P_SW, and {\theta}_cone, the data-driven model accurately reconstructs the three-dimensional
magnetic field configuration and its variability in response to upstream
solar wind drivers. Based on the PINN results, we identify key dependencies of
magnetic field configuration on
solar wind parameters, including the hemispheric asymmetries of the draped field line strength in the Mars-
Solar-Electric coordinates. These findings demonstrate the capability of PINNs to reconstruct complex
magnetic field structures in the Martian induced magnetosphere, thereby offering a promising tool for advancing studies of
solar wind-Mars interactions.
Read the full abstract ▾
All authors Jiawei Gao, Chuanfei Dong, Chi Zhang, Yilan Qin, Simin Shekarpaz, Xinmin Li, Liang Wang, Hongyang Zhou, Abigail Tadlock
Biological Physics
Predicting the Interfacial Energy and Morphology of DNA Condensates
Sihan Liu and Andrej Košmrlj
The physics and morphology of biomolecular condensates formed through
liquid-
liquid phase separation underpin diverse biological processes, exemplified by the nested organization of nucleoli that facilitates ribosome biogenesis. Here, we develop a theoretical and computational framework to understand and predict multiphase morphologies in DNA-nanostar solutions. Because morphology is governed by interfacial energies between coexisting phases, we combine Flory-Huggins theory with coarse-grained molecular dynamics simulations to examine how these energies depend on key microscopic features of DNA nanostars, including size, valence, bending
rigidity, Debye screening length, binding strength, and sticky-end distribution. The phase behavior of DNA nanostars is quantitatively captured by a generalized
lattice model, in which the interplay between sticky-end binding energy and conformational
entropy determines the effective interactions. Focusing on condensates comprising two dense phases, we find that Janus-like morphologies are ubiquitous because the interfacial energies between the dense and dilute phases, $\gamma_{i\in\{1,2\}}$, are typically comparable. In contrast, nested morphologies are rare as they require a large asymmetry in $\gamma_i$, which arises only for highly dissimilar nanostars such as those differing markedly in valence or size. Moreover, the interfacial energy between the two dense phases, $\gamma_{12}$, can be modulated either discretely, by varying sticky-end distribution, or continuously, by tuning the crosslinker ratio; the former may even eliminate nested configurations. These findings establish physical design principles for constructing complex condensate architectures directly from microscopic molecular parameters.
Read the full abstract ▾