Browse Articles

Linear active polymers in porous media: The type of activity matters

Active polymers represent a distinct class of non-equilibrium systems whose structure and movement are strongly influenced by the nature of activity and environmental complexity. In this work, we present a comparative study of an Active Brownian Polymer Chain (ABPC) and a Tangentially Active Polymer Chain (TAPC) in a two-dimensional porous environment using Brownian dynamics simulations. Our study on two different types of active polymers reveals distinct differences in their conformational and dynamical properties. We identify fundamentally different trapping mechanisms in the two systems: ABPC exhibits extensive folding in the porous confinement, whereas TAPC adapts stable spiral conformations accounting for persistent self-trapping at moderate and high activity. Furthermore, ABPC undergoes unrestraint motion at high activity, while TAPC displays reptation-like motion at low activity, leading to efficient escape from the traps. Our findings suggest that the nature and strength of activity coupled with environment complexity lead to emergent phenomena that dictate these unique conformational and dynamical properties of active polymers.

Facial expression discrimination emerges from partially overlapping neural subspaces of detection and identity

Comprehensive characterization of lithium carbonates recovered from different spent lithium-ion batteries black masses

Abstract The growing demand for electric vehicles has intensified the need for efficient recycling of lithium-ion batteries (LIBs) to recover critical materials. This work investigates the recovery and detailed characterization of Li 2 CO 3 samples recovered from two different types of black masses (BM) derived from spent NMC 622 batteries. Structural, morphological, and compositional analyses were carried out using X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Raman Spectroscopy (RS), and X-ray Microanalysis (EDX). The obtained results reveal that pretreatment of the starting black mass significantly influences the impurity content and crystalline structure of the recovered samples. The sample from thermally-pretreated BM exhibited higher purity and fewer secondary phases than those from mechanical-pretreated BM, where compounds such as Li 2 SO 4 , LiAl 2 (OH) 6 ·H 2 O, LiF, and LiOH were detected. Morphological differences correlated with the presence of impurities impacting crystal growth. These findings provide valuable insights for optimizing pretreatment and purification routes, thereby improving the quality of recycled Li 2 CO 3 for battery manufacturing and supporting circular economy strategies in the LIB sector.

TorchFF: A high-performance GPU-accelerated differentiable force field library

Molecular dynamics (MD) and the development of next-generation force fields increasingly rely on automatic differentiation for efficient simulated property prediction and parameter optimization. However, standard FFs and machine learning frameworks often suffer from significant performance bottlenecks—such as kernel launch overhead and memory bandwidth limitations—when executing the many-atom, small-kernel operations characteristic of MD simulations. Here, we present TorchFF, a high-performance, differentiable library that extends PyTorch with a suite of customized CUDA operators specifically engineered for molecular modeling. By implementing performance-critical routines—including bonded interactions, multipolar electrostatics, particle mesh Ewald, and neighbor list searches—as backend-optimized primitives, TorchFF bridges the gap between the flexible Python ecosystem and the execution speed of compiled MD engines.

Tailoring d–p orbital hybridization of single-atom Cu-N2S sites for enhanced photo-Fenton reaction

AI‑enabled resource management for 6G‑IoT hybrid systems: a unified simulation platform

Abstract The integration of Artificial Intelligence (AI) with sixth‑generation (6G) communication technologies is expected to transform resource management in Internet of Things (IoT) systems, where reliability, latency, and adaptability are critical. However, hybrid 6G–IoT environments combine two fundamentally different subsystems, resource‑constrained IoT devices and ultra‑high‑performance 6G infrastructure, creating a highly complex operational space with a multitude of interacting parameters. This results in severe heterogeneity across frequency bands, latency requirements, traffic behaviors, and computational capabilities. Such heterogeneity makes end‑to‑end modeling, resource management, and optimization extremely challenging when capturing the dynamics of both IoT endpoints and 6G networks. To address these challenges, this work presents 6G‑IoT‑Sim, a modular 6G‑enabled IoT simulator that provides a unified and extensible platform for implementing and analyzing 6G‑IoT networks. The platform incorporates 6G architectural capabilities, a configurable network‑design interface, a diverse dataset generator tool, and a real‑time monitoring dashboard. Additionally, it integrates a hybrid AI-based framework comprising different AI models and optimization techniques for intelligent resource management. A multidimensional performance evaluation demonstrates the effectiveness of the framework for intelligent resource management across diverse 6G‑IoT conditions. The results further highlight the potential of the proposed simulator as a flexible and extendable platform for advancing adaptive solutions in 6G‑enabled IoT systems.

Self-phoretic oscillatory motion in a one-dimensional channel

We study a simple model for a particle that is active due to repulsive self-phoresis and that has been proposed to model symmetric camphor grains. The particle generates a concentration field through the continuous emission of a chemical substance and its motion is driven by gradients of this field as it diffuses within a confined channel whose ends perfectly reflect the chemical. The reflection of the chemical field leads to an effective confinement of the particle, which itself is reflected before encountering the channel ends. The system displays a transition from a passive state, where the particle rests at the channel midpoint, to an active state characterized by highly regular, non-chaotic oscillations. We analytically construct the phase diagram and derive the oscillation frequency and amplitude in the vicinity of the transition. A perturbative analysis perfectly describes the dynamics of the particle, even for oscillations as large as half the channel size. Furthermore, we develop an analysis which explains the mechanism of particle reflection close to the channel edges in the regime of large activity.

Polarization contrast neutron imaging enables operando visualization of iron plating in redox flow batteries

Trends and predictors of pediatric cardiovascular mortality: a 2014–2024 multicenter retrospective cohort study in Hawassa, Ethiopia

Limits of the non-linear generalized Langevin equation: Cross-correlations, irreversibility, and desynchronization

The generalized Langevin equation (GLE) is widely used to model complex soft-matter systems, including biomolecular dynamics, by incorporating memory effects and colored noise into coarse-grained descriptions. However, recent results suggest that combining memory with non-linear forces, which are ubiquitous in soft matter, introduces fundamental analytical inconsistencies. Here, using a simplified model, we investigate the practical numerical consequences of these analytical results in equilibrium systems. We show that non-linear forces generate cross-correlations with the noise, modifying the fluctuation–dissipation theorem and rendering the noise position-dependent and irreversible. This implies that the commonly assumed reversible Gaussian noise in GLE simulations fails to capture essential features of the microscopic fluctuations. For weak non-linearities, these issues can be partially resolved either by using an iterative optimization of memory or by using microscopically consistent noise, which unexpectedly synchronizes GLE trajectories with the underlying microscopic dynamics. For stronger non-linearities, such as high barriers or shoulders in the external potential, however, iterative reconstruction fails and we observe desynchronization, indicating that the non-linear GLE no longer correctly reproduces the microscopic dynamics. Our results show in which situations non-linear GLEs can be accurately applied and when they fail, thus providing practical guidance for their application to coarse-grain soft-matter systems.

Fast-growing intracellular Mycobacterium tuberculosis populations evade antibiotic treatment

Abstract Slow growth rates are considered a hallmark of Mycobacterium tuberculosis (Mtb) and have been historically associated with persistence and subsequent drug tolerance. Despite the fundamental role intracellular growth plays in the pathogenesis of tuberculosis (TB), approaches that define intracellular growth rates have remained challenging. Here, we developed a high-throughput, live-cell imaging approach to quantify Mtb replication within human macrophage populations at high spatiotemporal, single-cell resolution. Unexpectedly, we discovered fast-growing intracellular Mtb populations with doubling times below 10 hours. Importantly, when treated with first-line antibiotics, these intracellular fast-growing subpopulations remained. To validate this in vivo, we developed a mouse model of tuberculosis featuring a Mtb-Timer fluorophore that reports replication activity. Single-cell analysis revealed a strong correlation between heavily burdened host cells and Mtb populations with heightened replicative activity. These data identify fast-growing intracellular bacteria as a mechanism of antibiotic evasion and challenges the prevailing idea that attributes antibiotic tolerance to bacterial dormancy.

Yield stability of groundnut genotypes across contrasting environments in Niger using GGE biplot analysis

Coherent dynamics of the spin-boson model in the ultra-strong coupling regime

Quantum mechanics describes the unitary time evolution of closed systems. In practice, every quantum system interacts with the environment, leading to an irreversible loss of coherence. The spin-boson model (SBM) is central to the understanding of the fundamental process of decoherence of a two-state quantum system interacting with a bosonic heat bath, but the nature of transient dynamics in the presence of hybrid diagonal and off-diagonal system–bath interactions remains much less explored. Here, we investigate how the hybrid system–bath interactions of an Ohmic environment induce localization in the bias-free SBM. For strong coupling to the environment, localization is strongly affected by a dynamically generated bias via the renormalization of the tunneling amplitude. We find that counteracting effects of Hamiltonian parameters on non-exponential short-time dynamics and long-time population equilibration can lead to a separation of timescales and transient quantum coherent dynamics that can persist even for ultra-strong system–bath interaction. The findings offer novel insight into the equilibration behavior of quantum devices operating in the ultra-strong coupling regime.

Molecular velcro for precision genome repair

The new apicomplexan species Monocystis cantonensis from the earthworm Amynthas aspergillum retains a micropore but lacks an apicoplast

Assessing melting points from machine learning interatomic potentials using PBE and PBEsol exchange–correlation functionals

Accurate prediction of a material’s melting temperature is critical for materials design and high-temperature applications. In this work, we investigate melting behavior across a deliberately selected set of elemental metals spanning systems where cohesive-energy trends suggest that PBE and PBEsol are expected to perform differently, as well as cases where their performance is ambiguous. Melting temperatures are computed using the two-phase coexistence (TPC) approach in conjunction with a machine-learned interatomic potential based on the moment tensor potential (MTP) framework, enabling large-scale simulations that minimize finite-size effects and ensure sufficient equilibration. The TPC-MTP results reveal a clear functional dependence in the predicted melting temperatures. PBE provides good agreement for several lighter elements, whereas PBEsol gives the best overall agreement across the full dataset. However, the element-resolved trends are not governed by cohesive energy alone, indicating that liquid-phase energetics, anharmonicity, and finite-temperature phase stability also contribute to the observed functional dependence. For intermediate and structurally complex systems, both functionals exhibit less systematic performance. Overall, this study provides a systematic assessment of functional-dependent melting temperature predictions, highlighting both the strengths and limitations of the combined TPC–MTP methodology and underscoring the need for carefully selected exchange–correlation treatments in high-accuracy melting-point simulations.

Clonal memory of cell division in humans diverges between healthy haematopoiesis and acute myeloid leukaemia

Serum per- and polyfluoroalkyl substances levels among young adults at risk for chronic kidney disease of unknown etiology in Nicaragua

Thermodynamics of quantum oscillators

In this work, we present a compact analytical approximation for the quantum partition function of systems composed of quantum oscillators. The proposed formula is general and applicable to an arbitrary number of oscillators described by a rather general class of potential energy functions (not necessarily polynomials). Starting from the exact path-integral expression of the partition function, we introduce a temperature-dependent Gaussian approximation for the high-temperature propagator and then invoke a principle of minimal sensitivity to minimize the error. This leads to a system of coupled nonlinear equations whose solution yields the optimal parameters of the Gaussian approximation. The resulting approximate partition function accurately reproduces thermodynamic quantities such as the free energy, average energy, and specific heat—even at zero temperature—with typical relative errors in the range of about 1%–5%. The accuracy deteriorates only moderately when the anharmonicity and coupling strengths are increased. We illustrate the performance of our analytical formula with numerical results for systems of up to ten coupled anharmonic oscillators. These results are compared with “exact” numerical results obtained via Hamiltonian diagonalization for small systems and path-integral Monte Carlo simulations for larger ones.

Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations

Abstract Plants recruit antagonistic microbes to defend against phytopathogens, offering a route to rational biocontrol beyond empirical screening. Here, using six generations of leaf-microbiome data from natural Arabidopsis populations infected by the oomycete Albugo laibachii , we show that microbial diversity is driven by infection, site, and host genotype, and that infected plants form modular networks with increased inter-kingdom antagonism. We train four machine-learning models to discriminate infected from uninfected plants by microbiota composition and identify microbes enriched in diseased (disease-associated) or healthy (health-associated) plants. Testing the most predictive bacteria, fungi, and cercozoa in planta , we find all confer varying protection against Albugo , with health-associated microbes outperforming disease-associated taxa. The best candidate, a Cystofilobasidium fungus, is validated in a synthetic community, where genomic and community assays indicate biocontrol acts mainly through microbe-microbe interactions rather than plant immune activation. This work shows that pairing microbiome data with machine learning identifies effective biocontrol agents.