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Glasses are traditionally characterized by their rugged landscape of disordered low-energy states and their slow relaxation towards thermodynamic equilibrium. Far from equilibrium, dynamical forms of glassy behavior with anomalous algebraic relaxation have also been noted, for example, in networks of coupled oscillators. Due to their disordered and high-dimensional nature, such systems have been difficult to study theoretically, but data-driven methods are emerging as a promising alternative that may aid in their analysis. Here, we characterize glassy dynamics using the dynamic mode decomposition, a data-driven spectral computation that approximates the Koopman spectrum. We show that the gap between oscillatory and decaying modes in the Koopman spectrum vanishes in systems exhibiting algebraic relaxation, and thus, we propose a model-agnostic signature for robustly detecting and analyzing glassy dynamics. We demonstrate the utility of our approach through both a minimal example of a one-dimensional ODE and a high-dimensional example of coupled oscillators.

By the time of the first published issue of Physics in Medicine and Biology in 1956, the fundamentals of nuclear medicine were well established. The nature of radioactivity and its nuclear origins had been discovered, the tracer principle had been invented, radiation detectors had been developed and methods for generating diagnostic images and exploiting the therapeutic eTects of radionuclides were already in their infancy. Despite this, a practitioner in the 1950s would find it almost impossible to imagine the technology used in nuclear medicine today, the quality of the images produced, or the breadth of clinical and research applications it has enabled. Over the last 7 decades nuclear medicine has been transformed from a medical curiosity into a mainstream component of the modern healthcare system globally and an important tool in clinical research and therapeutic trials. This article highlights the landmark discoveries and technological advances since 1956 that have significantly shaped the field and got us to where we are today.

Accurate simulation of fluid flow in porous media is a challenging task due to the complexity of pore-space geometries and the computational cost of solving the Navier-Stokes equations. Traditional numerical solvers rely on carefully constructed meshes, often requiring manual intervention, and typically exhibit slow convergence. This difficulty is particularly pronounced in porous media, where the diffusive nature of momentum transport is hindered by intricate solid boundaries. These challenges limit the efficiency of numerical simulations, particularly when repeated evaluations are required. We present a neural-network-based framework for predicting pore-scale velocity fields directly from sample geometry. The method is based on a convolutional encoder-decoder architecture with skip connections, designed to preserve fine-grained structural information. Physical consistency is encouraged through a custom loss function composed of multiple terms: incompressibility, no-flow conditions within solids, periodicity constraints, and agreement with the global tortuosity index. We systematically analyze the influence of weight selection for these loss terms, quantifying their individual contributions to prediction accuracy. Several architectural variants inspired by computer vision are evaluated to identify one providing the best performance and robustness. The generalization ability of the trained network is assessed on samples outside the training distribution, including variations in boundary conditions, obstacle geometry, and porosity. Finally, we demonstrate additional practical applications in which network predictions are used to initialize the Lattice-Boltzmann simulations, a standard fluid dynamics solver, often used in complex boundary problems like porous media and used by us to train the network. We have used network-generated velocity field as a starting point and found that this significantly accelerates LBM solver convergence, achieving improvements in over 90% of cases.

Gravitational waves and ultralight dark matter are among the most compelling frontiers in fundamental physics, motivating proposals for very-long-baseline atom interferometerssuch as AION1, MAGIS2, AICE3 and AEDGE4 that aim to detect at frequencies at which ground-based5 and space-borne6 laser interferometers lose sensitivity. Very-long-baseline atom interferometers look for signals by comparing the quantum phase evolution of widely separated atomic ensembles interrogated by a common laser. However, their performance depends critically on suppressing noise sources, particularly laser phase noise. The experimental validation of such noise rejection remains an important challenge. Here we demonstrate a prototype differential atom interferometer based on the single-photon clock transition of fermionic 87Sr. Thus, we obtain a gradiometer configuration with a species intrinsically suited to kilometre-scale and space-baseline operation. The instrument operates at the standard quantum limit7 with no excess noise beyond atom shot noise. The differential configuration maintains quantum-limited sensitivity in the presence of several radians of artificially injected laser phase noise per shot, which emulates the conditions expected in a very-long-baseline atom interferometer. We also demonstrate the recovery of coherent oscillatory signals across a broad frequency range under fully phase-randomized conditions, a capability that is inaccessible to a single interferometer operating in the same regime. These results provide an experimental validation of the noise-immune measurement principle underlying very-long-baseline atom interferometers and mark an important step towards next-generation quantum sensors for gravitational-wave detection and searches for ultralight dark matter8,9.

α-Synuclein (α-syn) is an intrinsically disordered presynaptic protein. In synucleinopathies, it undergoes a structural transition into β-sheet-rich conformers that promote the formation of amyloid fibrils and pathological inclusions. Although fibrillar aggregates have been studied extensively, soluble oligomers, which may have the greatest neurotoxic potential, remain poorly understood because of their transient nature and structural heterogeneity. This review critically examines α-syn oligomeric species, with particular emphasis on advanced biophysical and surface characterization techniques and neurobiological models to elucidate oligomer formation, membrane interactions, and toxic mechanisms. Recent advances in spectroscopy, high-resolution microscopy, and mass spectrometry have significantly expanded the ability to characterize α-syn oligomers and their aggregation pathways. Nanopore-based and single-molecule approaches enable the investigation of transient and structurally heterogeneous oligomeric species at the level of individual particles. Interface science has further clarified direct interactions between oligomers and lipid membranes, providing mechanistic insight into neurotoxicity and the lipid-dependent modulation of α-syn conformation and stability. Neurobiological models have revealed multi-organelle disruption, prion-like propagation, and disease subtype-specific seeding. However, no single approach fully captures oligomer pathogenicity, which emerges from the interplay of structure, interfacial behavior, and cellular vulnerability. However, existing frameworks do not adequately address this complexity. Additional barriers include poor reproducibility and limited sensitivity across approaches. Future work should integrate these technologies with standardized biological protocols, advanced artificial intelligence algorithms, and biocompatible nanomaterials. Such an interdisciplinary approach could enable the development of multiscale platforms for real-time studies of α-syn soluble conformers, with clinical utility in the management of synucleinopathies.

We investigate the Reynolds-number dependence of the maximal Lyapunov exponent in fully developed turbulence, which quantifies the rate of chaotic divergence of nearby velocity fields. Using decorrelators constructed from infinitesimally perturbed flows, we find that the Lyapunov exponent scales with Reynolds number as λ∼Re^{α}, with an exponent α=0.59±0.04 exceeding the classical mean-field prediction. By explicitly separating the nonlinear strain and viscous contributions to decorrelator growth, we show that this departure is associated by intermittent fluctuations of the strain-rate tensor, which dominate the short-time growth of the infinitesimal perturbations over viscous damping. Direct numerical simulations of the Navier-Stokes equations and complementary tests using a reduced shell model yield consistent scaling behavior, indicating robustness within the frameworks considered. Our results show that the dynamical origin of chaotic divergence in turbulence is closely linked to intermittent strain-rate fluctuations.

Determining the nature of the optical excited state (excitons or free carriers) in nanostructured materials is crucial for device design, as optoelectronic and photovoltaic technologies require different considerations regarding the optimized excited state dynamics. Power-dependent photoluminescence is widely used to distinguish between excitons and free carriers, but the classical power-law analysis oversimplifies the underlying physics when the exponent lies between the linear (pure excitons) and quadratic (pure free carriers) limits. In this work, we present a complete study enabling a direct and quantitative analysis of the free-carrier fraction based on power-dependent peak photoluminescence and placing its analysis in the context of the Saha equation. We study Ruddlesden-Popper perovskites with varying thickness as a model system, as they cover a wide range of exciton binding energies and the full range of free carrier fractions. Our results agree with previously reported values for the exciton binding energies in these materials, confirming the reliability of this approach and providing a simple and effective tool for probing the nature of optically excited states in semiconductors with intermediate exciton binding energies. We demonstrate that our method allows probing spatial variations in the fraction of free charges near grain boundaries or edges at micrometer spatial resolution. Finally, our results highlight the importance of performing optical characterization under excitation densities relevant to realistic operating conditions, as higher fluences can artificially enhance exciton formation and distort excited-state interpretation under solar-fluence conditions.

The special nature of the fluorine atom imparts remarkable strength and unique physical properties to chemical bonds. Unlike man-made fluorochemicals, fluorinated natural products remain rare due to low bioavailability and toxicity of fluoride. Despite this, defluorinases have evolved in nature to cleave carbon-fluorine bonds, with the hydrolytic fluoroacetate dehalogenase being one of the most well-characterized examples. These enzymes are of fundamental interest and hold unrealized biotechnological potential, yet the scope of this unique chemistry remains underexplored in the biosphere. Here, we trained and applied a machine learning-based framework, termed latent generative landscapes (LGLs), to map the functional sequence space of the α/β-hydrolase superfamily. This approach identified 3014 putative defluorinases that were previously not annotated or plausibly misannotated. Experimental validation of selected candidates led to the reclassification of five novel defluorinases, all exhibiting high thermal stability (T m > 70 °C) and diverse catalytic efficiencies with conserved enantioselectivity on the model substrate 2-fluoro-2-phenylacetate. Notably, the enzyme A0A4Z0BVY8 exhibited 2.7-fold greater defluorination activity than the current state-of-the-art enzyme Q6NAM1. Our results establish that LGL modeling is a powerful strategy to decode cryptic carbon-fluorine bond chemistry in nature, enabling the future discovery and engineering of defluorination biocatalysts.

Active processes are omnipresent in the cell nucleus. From genome-based activities such as transcription and replication, to metabolic activity in the nucleoplasm and its liquid condensates, the nucleus is far from thermodynamic equilibrium. The nucleus' active nature leads to many emergent properties of its principal components - the genome and the nucleoplasm - ranging from genome's compartmentalization, coherent chromatin motions, emergent rheology, to non-equilibrium liquid-liquid phase separations of the nucleoplasm. Its rich phenomenology makes the nucleus well-suited to bring new non-equilibrium physics to light, which in turn reveals its complex underlying physiology. Understanding the role of active processes in the spatial organization and dynamics of the genome and the nucleoplasm is critical for new insights into the function of the human genome.

Magnetic nanoparticles (NPs) dispersed in dense ionic fluids represent promising stimulus-responsive materials with applications in emerging thermoelectric technologies. This study takes advantage of the additive-free surface of NPs produced by the Massart method, which allows modification of the solid/liquid interface to transfer the NPs into dense ionic fluids. The significant role of residual water is also analyzed. We investigate here (γ-Fe2O3) and core@shell ferrite@maghemite NPs dispersed in two media: the deep eutectic solvent choline chloride-urea 1 : 2 (Reline-ChU) and the ionic liquid 1-ethyl-3-methylimidazolium bistriflimide (EMIM-TFSI). Structure and transport properties are analyzed using a combination of small-angle X-ray scattering, dynamic light scattering (DLS) and forced Rayleigh scattering (FRS), where applicable. Exploring the influence of particle size reveals phase separation for the largest NPs. With nanoparticles typically 9 nm in diameter, the interparticle interactions can be tuned through the combined effects of the surface coating, counterions, and solvent, whereas the nature of the nanoparticle core has only a limited influence. The impact of water is studied using a combination of direct (Karl Fischer titration, KF) and indirect (SAXS, DLS, FRS) techniques on the final dispersions or after water addition. In Reline-Chu, which is miscible with water but degrades above 353 K, adding 5 wt% water either increases or decreases repulsion between NPs depending on the nature of the NP/solvent interface. In EMIM TFSI, which exhibits limited water miscibility, KF titrations enable quantification of the residual water and its localization between the bulk and the interface. Temperature-dependent FRS measurements enable the determination of an activation energy (Ea), related to the surface hydrophilicity, and confirm the direct titrations: Ea is close to the value for water in core@shell particles after preparation and shifts to the value of the ionic liquid after heating, indicating water displacement. A systematic control of NP interfacial chemistry, particularly residual water, is crucial and enables tunable colloidal stability and transport properties essential for advanced applications in ionic fluid systems.

In the expanding landscape of two-dimensional (2D) materials, the investigation of systems beyond graphene is considered essential for the advancement of next-generation electronic and thermoelectric technologies. Monolayer honeycomb borophene oxide (h-B2O), a boron-based 2D material, has been identified as a promising candidate due to its unique topological features, such as nodal loops, and its potential superconducting behavior. In this study, the electronic properties of monolayer h-B2O are theoretically examined. Its band structure (BS) and density of states (DOS) are analyzed, revealing a metallic nature. To gain further insight, a tight-binding (TB) Hamiltonian is constructed incorporating the Py and Pz orbitals of boron, capturing the essential physics underlying the material's low-energy electronic behavior. For the first time, the electronic thermal conductivity (ETC) of monolayer h-B2O is calculated using the Kubo-Greenwood formalism within the diffusive transport regime, under both pristine and electrostatically gated conditions. The results reveal pronounced anisotropy (κyy ≫ κxx), with room-temperature ETC values of 5.9 × 10-2 mW m-1 K-1, 1 mW m-1 K-1, and 0.17 mW m-1 K-1 along the armchair (κxx), zigzag (κyy), and anomalous Righi-Leduc effect (κxy) directions, respectively. Furthermore, charge transport is found to be predominantly governed by the Pz orbital of boron, owing to its higher carrier occupancy compared to the Py orbital. The effect of a perpendicular electric field (PEF) with varying strengths (V = 0.5, 0.75, and 1 eV) is also investigated. The applied field induces bandgap openings at the Dirac cones located along the X-Γ path, with the gap magnitude following the relation Eg = 2 V, and causes noticeable shifts in the Van Hove singularities in the DOS. As the field strength increases, the ETC in all directions exhibits a consistent decreasing trend, with approximately equal relative reductions. These results underscore the tunability of ETC in h-B2O, highlighting its potential for advanced thermal management applications, including thermal cloaking.

The green synthesis of magnesium oxide nanoparticles was achieved using Sonneratia ovata leaf extract. Several analytical and spectroscopic methods were used to characterise the crystallite size, structural behaviour, and optical and morphological properties. The high crystalline nature and the estimated average crystallite size of 22.8 nm after calcination at 500 °C were explained by the X-ray diffraction pattern. The aqueous extract of S. ovata and the metallic precursor both had distinctive absorption bands in the FTIR spectrum that were consistent with functional groups. The spherical form of MgO NPs was seen in the FESEM images. The purity of MgO, with Mg and O present, was confirmed by EDAX spectra and mapping. Particle size analysis revealed that the MgO NPs mediated by S. ovata had an average size and range of 18.82 nm. The green synthesis of MgO NPs was found to be a promising antimicrobial agent against pathogens such as Escherichia coli and Staphylococcus aureus and to exhibit antioxidant activity. The maximum average removal of Congo Red (CR) dye was 94.2% at low pH, and around 90% for Rhodamine B (RhB) under optimal conditions. The results demonstrated that the synthesised MgO NPs show promising potential as effective nanocatalysts for wastewater treatment, thereby advancing nanotechnology and contributing to the Sustainable Development Goals (SDGs) 6, 7, 9, and 14.

Although ice polymorphs commonly feature orientational order and disorder, it is difficult to grasp the nature of partial order. In this study, we report on the hydrogen ordering of ice V using calorimetry at ambient pressure with an isothermal annealing approach. H2O/D2O isotopic substitution underlines the existence of the partially ordered intermediate state between ice XIII (below 113 K) and ice V (above 120 K), which exhibits a large isotope effect on the enthalpy of hydrogen disordering. Combined with the observation of two-staged time evolution of hydrogen order and the significant deuteration-induced slowdown of the ordering kinetics by a factor of 15-60, we propose that this intermediate state bears dynamic disorder. This reflects mutual conversions of ordered configurations taking place, i.e., domain fluctuations between differently ordered configurations. This finding raises a new perspective to characterize partial order, leading to the potential application toward frustrated functional materials.

Research on single photon sources in layered materials has been limited so far to transition metal dichalcogenides (TMDs) and hexagonal boron nitride (hBN) as hosting platforms. These quantum emitters exhibit advantages due to the distinct semiconducting and insulating characteristics of the two classes of materials, which enable their integration with van der Waals heterostructures and devices. Here, we report single photon emission in ZnPS3, which belongs to the MPX3 family characterized by stronger electronic correlations than those observed intrinsically in TMDs or hBN. We provide a comprehensive characterization of the vibrational and optical properties of nonmagnetic ZnPS3 crystals, focusing on unraveling the mechanisms responsible for the single photon emission. Using polarization-resolved Raman scattering spectroscopy, we identify key phonon modes and uncover strong metal-ligand interactions that influence both phonon dynamics and defect-bound excitonic states. Low-temperature photoluminescence spectroscopy reveals stable and narrow optical transitions localized at defect sites, while second-order correlation measurements confirm the quantum nature of the emission. We complement our experimental analysis with ab initio density functional and GW many-body perturbation theory calculations to investigate the characteristics of the bulk and defect-related electronic structure. Our theoretical analysis reveals that phosphorus vacancies introduce midgap states, enabling optical transitions occurring at the energy range consistent with the experimentally observed emission lines. This joint approach identifies P-vacancies as the likely origin of single photon emitters in ZnPS3. Furthermore, we anticipate that similar behavior should be present in other MPX3 compounds, offering a framework for exploring defect-based quantum emitters with intrinsic magnetic tunability.


OMEGA V2 is open-source software for GPU-accelerated image reconstruction in positron emission tomography (PET), single photon emission computed tomography (SPECT), and computed tomography (CT). The software offers flexible GPU accelerated image reconstruction methods and tools for imaging algorithm development which are accessible from Python, MATLAB, and GNU Octave. This paper presents the software architecture, projector models, algorithms, and demonstrates its performance with realistic high-resolution 3D examples from PET, SPECT and cone-beam CT.

Approach:
OMEGA V2 is based on OpenCL and CUDA allowing wide GPU support. The software provides modular forward/backprojector operators and a broad suite of built-in iterative algorithms and regularization models. It supports features such as time-of-flight imaging, list-mode reconstruction, multi-resolution approaches, and various physical corrections including attenuation, scatter, and normalization. 

Main results:
OMEGA V2 provides a unified open-source reconstruction framework for PET, SPECT and CT, including hybrid workflows such as PET/CT and SPECT/CT. It provides cross-vendor GPU acceleration via OpenCL, supporting AMD and Intel devices alongside CUDA-capable GPUs, and introduces a new Python interface that complements and mirrors the existing MATLAB/GNU Octave workflow. The software substantially extends prior OMEGA releases with SPECT functionality, extensive CT functionality, and a Python implementation with wide interoperability such as with PyTorch. High-resolution 3D examples in PET, SPECT, and CBCT demonstrate high-quality reconstructions and fast runtimes on modern consumer GPUs. 

Significance:
Combination of PET, SPECT, and CT in an open-source, GPU-optimized framework with broad algorithmic and projector coverage offers a unified suite for computational imaging, method development and translation of methods in CT, PET and SPECT, and their hybrid combinations (PET/CT, SPECT/CT). Its open-source nature, extensive algorithm library, and flexible programming interfaces enable users to develop custom reconstruction methods with access to GPU-accelerated projectors.

The CROSSBRAIN EU project aims to address the heterogeneous nature of brain pathologies by developing wireless implantable microbots (µBots, planned dimensions 100 × 100 × 100 μm3) for highly localized neuromodulation. These devices are designed to precisely modulate brain activity with minimal invasiveness, enabling targeted resolution of specific spatiotemporal events, capabilities not currently achieved by existing neuromodulation technologies. A crucial step involves visualizing and ensuring the optimal placement of the µBots in the brain tissue, to study their functionality after implantation. In this preliminary ex vivo study, we used non-functional µBot silicon (Si) dummies matching the lateral dimensions of the intended µBots, with reduced thickness (100 × 100 × 50 μm3) to simplify fabrication and handling. Due to the intrinsic MRI incompatibility of the µBot platform, encompassing both the dummies used in this study and the future functional devices under development, and the limitations of standard histological approaches in reliably identifying and preserving the implant site during processing, we developed an integrated imaging workflow combining 2D and 3D techniques. While standard histological methods and tissue clearing presented substantial limitations in preserving the position of the dummies within the brain tissue, combining histological techniques with 3D X-ray tomography provided a robust strategy. In particular, synchrotron radiation-based X-ray Phase Contrast Tomography (XPCT), with its intrinsic high contrast and resolution, enabled detailed visualization of dummies within the surrounding vascular and cellular architecture. In contrast, conventional micro-Computed Tomography (micro-CT), although more widely accessible, enabled non-destructive guidance for targeted sectioning. Importantly, and in line with the scope of a Brief Research Report, this study presents a preliminary but technically robust investigation conducted within the CROSSBRAIN project, aimed at identifying and establishing an optimized imaging strategy for the visualization of implanted µBots in brain tissue. This methodological framework is intended as an initial step toward future in vivo studies, in which the validated imaging pipeline will be applied to track both dummy and functional devices and to enable subsequent evaluation of foreign body response under physiologically relevant conditions. This ex vivo workflow therefore provides the essential technical foundation for such future investigations and supports the clear positioning of this work as a feasibility and optimization study. This approach could be particularly valuable for new generations of implantable technologies incompatible with MRI and could support future development of personalized neuromodulation therapies by enabling precise device localization and structural tissue assessment.

Here, we propose a systematic first-principles study of K-based perovskite hydrides KM3H9 (M = Fe/Co/Ni/Cu) in the framework of density functional theory. The calculated formation energies indicate the thermodynamic stability of all the studied compounds, which is further confirmed by the absence of imaginary frequencies in the phonon dispersion spectra. The electronic structure analysis shows metallic nature for all the systems, and the magnetic computations show that the compounds have a stable antiferromagnetic ground state. The mechanical properties, particularly elastic constants, indicate that the materials are mechanically stable and have ductile behavior. Moreover, the thermodynamic analysis verifies the stability across a wide range of temperatures. Significantly, the hydrogen storage performance is studied, indicating that these materials have potential gravimetric and volumetric hydrogen storage capacities as well as suitable desorption temperatures. These results show the potential of KM3H9 (M = Fe/Co/Ni/Cu) hydrides as promising candidates for solid state hydrogen storage applications.

The propane-CO2 coupling reaction (CO2-PDH) enables propylene production with simultaneous CO2 utilization. Elucidating the nature of active sites and the role of CO2 remains challenging due to catalyst heterogeneity and parallel pathways. Herein, we construct a well-defined catalytic platform comprising isolated metal sites embedded in the Beta zeolite framework. Among the metals examined (V, Nb, Ta, Cr, Mo, and W), Cr-Beta exhibits the best performance, achieving 66.9% propane conversion and 51.2% propylene yield with syngas coproduction (H2/CO = 0.82). Characterizations identify the active site as a pseudotetrahedral framework Cr species, {(≡SiO)3Cr···(HO-Si≡)}. Framework Cr═O species, generated during air pretreatment, serve as the active centers for initial oxidative dehydrogenation and are subsequently reduced to tetrahedral Cr sites. These reduced Cr sites catalyze CO2-PDH by coupling propane dehydrogenation with the reverse water-gas shift reaction, while CO2 simultaneously promotes propane conversion through a formate-mediated surface hydrogen consumption pathway that shifts thermodynamic equilibrium and suppresses coke formation via the reverse Boudouard reaction. This work provides mechanistic insight into active-site evolution and CO2 participation in alkane-CO2 co-conversion, offering guidance for the rational design of efficient catalysts for simultaneous fossil-resource valorization and CO2 utilization.

Metal-support interactions (MSIs) play a pivotal role in boosting electrocatalytic performance by optimizing the electronic state of metal active sites and stabilizing them in different supports, thereby optimizing electron transfer kinetics and adsorption/desorption behavior of reaction intermediates. Hence, this review systematically elaborates on the MSI regulatory mechanisms of diverse support types and their microstates, along with its typical electrocatalytic applications. For support microstates: different crystal phases tune MSI strength via lattice arrangement differences; specific exposed crystal facets strengthen metal-support electronic coupling through lattice matching and surface coordination; vacancy defects in supports serve as a key means for electronic state regulation to finely adjust MSI strength; atom doping in supports significantly modulates MSI nature by altering interfacial electron transfer efficiency and constructing stable coordination structures. In electrocatalytic applications, MSI exerts critical regulatory effects and is widely applied in key reactions including catalytic water splitting (HER/OER), fuel cell-related reactions (HOR/ORR), carbon dioxide reduction reaction (CO2RR), nitrogen reduction reaction (NRR), and small organic molecule oxidation. Therefore, this review systematically clarifies the multi-dimensional regulatory rules of support properties (type, crystal phase, facet, vacancies, doping) on MSI, and provides theoretical and practical guidance for the design and performance optimization of atomically dispersed catalysts.