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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.

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.

High-frequency underwater acoustic field modeling is challenging for physics-informed neural networks (PINNs) due to the strongly oscillatory nature of acoustic pressure fields. Building upon the OceanPINN framework, this paper proposes a separable-variable OceanPINN with learnable spectral expansions for two-dimensional acoustic field prediction in high-frequency environments. The acoustic envelope is represented as a separable expansion of range-dependent and depth-dependent basis functions learned by one-dimensional spectral neural networks, with trainable expansion coefficients. Analytical derivatives of the trigonometric bases enable efficient and stable enforcement of the Helmholtz equation. Numerical results demonstrate improved accuracy and generalization over conventional OceanPINN under limited data conditions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The development of complex tissues relies on the precise assignment of cell identity. At the molecular scale, this process depends on the deposition of epigenetic modifications-such as methylation-that are regulated by complex biochemical networks and occur at specific regions on the DNA and chromatin. Here we show that despite the complexity of epigenetic regulation, dynamical scaling and self-similarity of DNA methylation marks emerge in embryonic development. Drawing on single-cell multi-omics experiments, super-resolution microscopy and statistical physics, we demonstrate that these phenomena originate in dynamical feedback between DNA methylation and the formation of nanoscale dynamic chromatin aggregates. These nanoscale processes lead to genome-wide increase in DNA methylation marks following a power law and self-similar correlation functions. Using this framework, we identify methylation patterns that precede gene expression changes in embryonic symmetry breaking. Our work identifies linear sequencing measurements as a laboratory to study mesoscopic biophysical processes in vivo.

Accurate prediction of nuclear magnetic resonance chemical shifts for transition-metal nuclei remains a challenging problem due to the high computational cost of quantum-chemical methods and the limited availability of experimental data. In this study, machine learning models were developed to predict chemical shifts of coordination compounds containing Mn, Fe, Nb, and Mo, using a curated data set of 1956 experimental measurements. Several approaches were evaluated, including descriptor-based models, graph neural networks, and transformer-based architectures. The Tabular Prior-Data Fitted Network model demonstrated the best performance across all nuclei, with prediction errors corresponding to 4.5-8% of the total chemical shift range. A comparison of molecular representations showed that two-dimensional descriptors provide accuracy comparable to three-dimensional approaches while requiring significantly lower computational cost. Model interpretation based on Shapley additive explanations revealed metal-specific structure-property relationships and enabled identification of the applicability domain. Attempts to construct unified models across different metals did not improve predictive performance, highlighting the element-specific nature of chemical shifts. External validation on an independent data set of 195Pt complexes confirmed the generalizability of our two-dimensional descriptor-based approach, achieving a holdout MAE of 159 ppm without any 3D structural information. These results demonstrate that machine learning models based on molecular descriptors provide an efficient and reliable alternative to quantum-chemical methods for predicting nuclear magnetic resonance chemical shifts of transition-metal compounds.

This work reports the synthesis, structural characterization, and photophysical and electrochemical evaluation of a novel series of mono-(acetyl-glycosylated) free-base corroles. The molecules were prepared using classical Gryko methodologies, yielding four new acetyl-glycosylated derivatives alongside a nonglycosylated reference compound. A complete spectroscopic and analytical characterization was performed, including absorption, steady-state, and time-resolved fluorescence, and electrochemical studies. Detailed analysis of the singlet and triplet excited states, supported by DFT calculations and natural transition orbital (NTO) analysis, confirmed the nature of the electronic transitions and provided insight into excited-state geometries. Furthermore, the derivatives were evaluated for key photobiological properties: all compounds demonstrated efficient singlet oxygen (1O2) generation, and, in preliminary membrane-model assays, an ability to interact with biological targets. The overall data confirm that these acetyl-glycosylated corroles retain potent photosensitizing capabilities, establishing their promising potential for applications such as photodynamic therapy (PDT).

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.

Pair density modulation is a phenomenon recently observed in exfoliated flakes of iron-based superconductors, in which the superconducting gap oscillates strongly with the same periodicity as the underlying crystalline lattice. We propose a model that explains this modulation in systems with broken intra-unit-cell symmetries through the emergence of nematic superconductivity, which further breaks the four-fold rotation symmetry. This results in a sublattice texture on the Fermi surface, aligned with the anisotropic superconducting gap of the nematic s± + d state. This gives rise to distinctive gap maxima and minima located on the two inequivalent iron sublattices while still being a zero-momentum pairing state. We discuss how further investigation of such modulations can give insight into the nature of the superconducting pairing, such as the signs of the order parameters and visualization of a phase transition to a mixed two-component state using local probes.

Novel phosphate glasses augmented with different amounts of the erbium (Er2O3) oxide were created in this work using the melt-quench process. The amorphous nature was verified by X-ray diffraction (XRD) investigation. The density for the resultant glass sample is increased when P2O5 is substituted with Er2O3. The structure of current glasses was investigated using Fourier transform infrared (FTIR) spectroscopy. Increased local ordering and the creation of Er-linked cross-bridges (Er-O-P interactions), which lower NBO concentration, are two structural changes brought about by Er2O3 doping up to 0.5 mol%. The dielectric properties were assessed throughout a wide frequency spectrum. Two distinct parts show the frequency dependence of the dielectric constant, ε': a falling portion during small frequencies and a plateau portion over large frequencies. It is clear that (ε') declines at 0.25 as well as 0.5 mol% Er2O3 and continuously increases at higher concentrations. Additionally, (σac) exhibits a similar trend, declining at Er2O3 values of 0.25 and 0.5 mol% and gradually increasing at higher concentrations. The computability of glass through cross linked at low Er doping is the main reason for reduction in ε', and σac. The Er-0.5 specimen with a minimum (ε') and ac conductivity is the ideal option for packing material because to its maximal propagation velocity. Phy-X/PSD software was used to calculate the mean free path (GMFP), equivalent atomic number (GZeq), and fast neutron removal cross-sections (GFNRCS). Furthermore, build up factors (GBF) were calculated for photon energies from 0.015 to 15 MeV and penetration depths from 0.5 to 40 mfp by using the G-P fitting technique. Er-1.0 sample has provided greater gamma-ray shielding than other samples, according to the results. Additionally, these findings highlight the potential of these glasses for radiation shielding in medical and industrial applications.

Antimicrobial resistance represents a critical public health challenge, driving the search for therapeutic strategies that bypass conventional resistance mechanisms. Antimicrobial photodynamic therapy (aPDT) offers a promising alternative that is based on light-triggered non-specific oxidative damage. Herein, we report four new bis-heteroleptic Ru(II) complexes, with the general formula [Ru(N-N)2(N-NX)]Cl2, where N-N are the ancillary ligands, 2,2'-bipyridine (bpy) or 4,7-diphenyl-1,10-phenanthroline (DIP), and N-NX is a polyether-functionalized phenanthroline ligand. This design preserves the favourable photophysical characteristics of the [Ru(bpy)3]2+ core while enabling lipophilicity modulation. Dynamic light scattering and emission lifetime studies support that the complexes bearing the DIP ligand (Ru-DIP-O3 and Ru-DIP-O4) self-assemble into nanoaggregates in aqueous media due to their amphiphilic nature, whereas their bpy analogues remain in their monomeric form. We propose a previously undescribed aggregate architecture in which the Ru(II) core is shielded within the hydrophobic interior, while the polyether chains remain solvent-exposed. Biological evaluation of the complexes against S. aureus strains reveals that Ru-DIP-O3 and Ru-DIP-O4 significantly inhibit bacterial growth, while the bpy derivatives exhibit negligible activity. Notably, Ru-DIP-O4 demonstrates at least a 16-fold enhancement in the bacteriostatic rate upon irradiation relative to dark conditions. Scanning electron microscopy studies provide evidence of membrane disruption in irradiated bacteria treated with Ru-DIP-O4. We attribute the enhanced photodynamic activity of the DIP-based complexes to aggregation-driven interactions with the bacterial membrane. Collectively, these findings underscore the therapeutic potential of rationally designed Ru(II) complexes for photodynamic applications and highlight the roles of amphiphilicity and nanoscale self-assembly as key parameters for the design of next generation aPDT agents.