In this work, we revisit the Generalised Navier Boundary Condition (GNBC) introduced by Qian et al. in the sharp interface volume-of-fluid context. We replace the singular uncompensated Young stress by a smooth function with a characteristic width $\varepsilon \gt 0$ that is understood as a physical parameter of the model. Therefore, we call the model the ‘contact region GNBC’ (CR-GNBC). We show that the model is consistent with the fundamental kinematics of the contact angle transport described by Fricke, Köhne and Bothe. We implement the model in the geometrical volume-of-fluid solver Basilisk using a ‘free angle’ approach. This means that the dynamic contact angle is not prescribed, but reconstructed from the interface geometry and subsequently applied as an input parameter to compute the uncompensated Young stress. We couple this approach to the two-phase Navier–Stokes solver and study the withdrawing tape problem with a receding contact line. It is shown that the model allows for grid-independent solutions and leads to a full regularisation of the singularity at the moving contact line, which is in accordance with the thin film equation subject to this boundary condition. In particular, it is shown that the curvature at the moving contact line is finite and mesh converging. As predicted by the fundamental kinematics, the parallel shear stress component vanishes at the moving contact line for quasi-stationary states (i.e. for $\dot \theta _d=0$ ), and the dynamic contact angle is determined by a balance between the uncompensated Young stress and an effective contact line friction. Furthermore, a nonlinear generalisation of the model is proposed, which aims at reproducing the molecular kinetic theory of Blake and Haynes for quasi-stationary states.

The kinetic theory of soliton gases (SG) is used to develop a solvable model for wave-mean field interaction in integrable turbulence. The waves are stochastic soliton ensembles that scatter off a critically dense SG or soliton condensate-the mean field. The derived two-fluid kinetic-hydrodynamic equations admit exact solutions predicting an induced mean field and SG filtering. The obtained SG statistical moments agree with ensemble averages of numerical simulations. The developed theory readily generalizes, with applications in fluids, nonlinear optics, and condensed matter.

Interferometric imaging is often accompanied by complex noise contamination, primarily manifested as Gaussian noise introduced by the thermal motion of detector electrons and speckle noise produced by the interference of coherent light and multipath scattering. However, most existing deep-learning-based denoising networks are typically designed to model and learn a single noise distribution. Consequently, when faced with real scenarios in which multiple noise sources are superimposed, these models often have limited generalization ability and struggle to suppress different noise components simultaneously. To address this problem, we propose SEVReNet, a self-supervised network for denoising interferometric images. On top of ordinary convolution, the proposed network introduces a scale-equivariant module and a rotation-equivariant module, forming a three-branch architecture that simultaneously leverages the advantages of translation, rotation, and scale equivariance. This design exploits the differing preferences of the three modules for different types of information and performs adaptive fusion through weighted integration, thereby achieving better separation of structural information and noise while preserving fine texture details, bringing into play the maximum advantages of the three modules for the denoising task. We conducted extensive experiments on both synthetic and real interferometric datasets contaminated by Gaussian and speckle noise. The results show that, compared with BM3D, U-Net, Restormer, and AdaReNet, SEVReNet achieves superior denoising performance, with an average PSNR of 31.48 dB and an SSIM of 0.924, significantly outperforming competing methods. These results verify the robustness and effectiveness of SEVReNet under complex noise conditions and provide new insights into noise modeling and image restoration in optical imaging.

Modulated oxidation pathways enabled by CoFe bimetallic alloy catalysts for effective elimination of antibiotics.

Physiological and pathological processes are governed by networks of genes called gene regulatory networks (GRNs). By reconstructing GRNs, we can accurately model how cells behave in their natural state and predict how genetic changes will affect them. Transcriptomic data of single cells are now available for a wide range of cellular processes in multiple species. Thus, a method building predictive GRNs from single-cell RNA sequencing (scRNA-seq) data, without any additional prior knowledge, could have a great impact on our understanding of biological processes and the genes playing a key role in them. To this aim, we developed IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework designed to infer directed, weighted, and signed GRNs directly from unperturbed single-cell RNA sequencing data. IGNITE uses the GRNs to generate gene expression data upon single and multiple genetic perturbations. IGNITE is based on the inverse problem for a kinetic Ising model, a model from statistical physics that has been successfully applied to biological networks. We tested IGNITE on two complementary systems of pluripotent stem cells (PSCs): murine PSCs transitioning from the naïve to formative states, and human PSCs differentiating toward definitive endoderm. These datasets differ in species, developmental trajectory, and single-cell technology (10X vs. Fluidigm C1), providing a stringent test of generalizability. Using only unperturbed scRNA-seq data, IGNITE simulated single and multiple gene knockouts (KOs) and produced predictions consistent with independent experimental observations. In mouse PSCs, IGNITE generated wild-type data highly correlated with experiments and accurately predicted the effects of Rbpj, Etv5, and triple KOs, while in human PSCs it correctly predicted differentiation-promoting and differentiation-blocking perturbations, in agreement with published studies. These results demonstrate that IGNITE robustly captures gene interaction logic across species and technologies, enabling robust in silico perturbation analyses directly from scRNA-seq data.

We develop a kinetic-theory framework to investigate the steady rheology of a dilute gas interacting via a repulsive potential under uniform shear flow. Starting from the Boltzmann equation with a restitution coefficient that depends on the impact velocity and potential strength, we derive evolution equations for the stress tensor based on Grad's moment expansion. The resulting expressions for the collisional rates and transport coefficients are fitted with simple analytical functions that capture their temperature dependence over a wide range of shear rates. Comparison with direct simulation Monte Carlo (DSMC) results shows excellent quantitative agreement for the shear stress, temperature anisotropy, and shear viscosity. We also analyze the velocity distribution functions, revealing that the system remains nearly Maxwellian even under strong shear.

Kinetic theory derivation of a second-order continuum viscous traffic model for vehicles with adaptive cruise control system

The emergence of socio-economic structure: a first-principles kinetic theory

Abstract Optically levitated particles with that have their thermal motion suppressed / cooled down close to the quantum ground state promise to become a the key for upcoming quantum technology of macroscopic systems, such as ultra-precise nano-metrology. In order to employ such a quantum state in subsequent interactions with spatially varying light intensity profiles the particle state must be extended - amplified - to the dimensions comparable to the wavelength. Such an amplification may be achieved by the a precisely timed sequence of evolution in the trapping potential (rotation) and evolution in a stroboscopically changed potential. Here, we experimentally demonstrate the function of the nano-mechanical amplifier based on the nearly free expansion or the inverted unstable potential.

To improve the accuracy of turbulence identification in low-altitude flight safety monitoring, a turbulence intensity modeling and optimization method based on Turbulent Kinetic Energy theory and the XGBoost model is proposed. Firstly, atmospheric stability is determined using the Richardson number. Subsequently, turbulence intensity is calculated by combining different stability conditions and Turbulent Kinetic Energy theory. The data for constructing physical model a originates from observations obtained by wind profile radar and microwave radiometer. Considering that temperature and humidity detection equipment like microwave radiometers are not always available in real-world scenarios, and in observation scenarios relying solely on wind profile radar, a Gradient Boosting Decision Tree algorithm is further introduced to construct a turbulence inversion model based exclusively on wind profile radar data. This model fully exploits the nonlinear relationships between multi-dimensional observational features such as radial velocity, velocity spectrum width, and signal-to-noise ratio of the radar and turbulence intensity. It is trained using the output of the benchmark model. Experimental results indicate that after optimizing the turbulence intensity calculation model b (based solely on wind profile radar) with model a, the model’s MSE decreases by 0.11, MAE decreases by 0.13, and the R² value increases by 0.28. This optimization process reduces reliance on auxiliary temperature and humidity data and effectively addresses the challenge of turbulence identification under limited observational information.

The Rashba spin-orbit coupling (SOC) is a well-known mechanism for the spin-charge interconversion via the inverse and direct spin galvanic effects. The lack of a full inversion symmetry allows the coupling of the charge current and spin density. In this paper we investigate this phenomenon when the in-plane rotational symmetry is lowered to the C 2 v and C 3 v symmetry groups, whereby the electron spectrum becomes anisotropic. We find that in the C 2 v case, depending on the ratio between the Rashba SOC strengths along the principal axes, the nonequilibrium spin density deviates notably from the 90 ∘ rotation, with respect to the applied electric field, which is familiar in the isotropic case. In the C 3 v case, when a warping cubic in momentum term is present, whereas the standard 90 ∘ rotation of the spin density remains, the spin-charge interconversion depends on the intensity of the warping itself. The microscopic theory takes into account disorder including vertex corrections, via both the diagrammatic implementation of the Kubo formula and the quantum kinetic theory. We show that vertex corrections are crucial to capture the details of the inverse spin galvanic effect in contrast to previous treatments based on the constant broadening approximation.

The emerging amino acid nanostructures provide a new type of smart biomaterial for biomedical applications. However, the lack of mechanistic understanding of their temperature-dependent self-assembly on inert solid surfaces limits the rational design of the desired nanostructures. Herein, we report different monolayer patterns of valine, leucine, and isoleucine molecules on a Au surface and uncover the influence of temperature on their self-assembly behavior. The randomly distributed amino acid molecules self-assemble into long-range periodic ordered monolayer assembly structures at a temperature of 600 K, which exhibit a characteristic periodic molecular arrangement but not the strict crystallographic features of 2D crystals. Temperature controls the kinetic accessibility and molecular diffusion, enabling the balance between intermolecular interactions and thermal motion to yield ordered assemblies at 600 K. l-Valine molecules on an inert Au (111) confinement surface exhibit the coexistence of two different motif arrangements: the antiparallel and parallel structures, whereas l-leucine and l-isoleucine molecules show only the antiparallel structure. The noncovalent interaction, which competes with the thermal motion of molecules, is closely associated with the formation of periodic dipole moment distributions and periodic molecular structures, thereby giving rise to the highly ordered monolayer structure of amino acid molecules.

We describe the single-crystal X-ray structures of the highly air-sensitive atomic layer deposition (ALD) precursor tris(dimethylamido)(pentamethylcyclopentadienyl)zirconium(IV) (Cp*Zr(NMe2)3). The crystals of Cp*Zr(NMe2)3 undergo a phase transition between −80 and −173 °C. At −80 °C, Cp*Zr(NMe2)3 crystallizes in the space group Cmce with 3/4 molecules in an asymmetric unit; at −173 °C, the thermal motions become frozen, and the structure can be solved instead in the space group Pbcm with 3/2 molecules in the asymmetric unit. Structural refinements for both crystals were complicated by heavily disordered molecules, which were resolved by a comparative study between the high- and low-temperature structural models. Reliable bond distances and angles for Cp*Zr(NMe2)3 were subsequently obtained. The structural information obtained for Cp*Zr(NMe2)3 serves as important benchmark data for the study of the analogue liquid ALD precursor CpZr(NMe2)3, which currently plays a critical role in microelectronics industries.

Selecting an appropriate divergence measure is a critical aspect of machine learning, as it directly impacts model performance. Among the most widely used, we find the Kullback–Leibler (KL) divergence, originally introduced in kinetic theory as a measure of relative entropy between probability distributions. Just as in machine learning, the ability to quantify the proximity of probability distributions plays a central role in kinetic theory. In this paper, we present a comparative review of divergence measures rooted in kinetic theory, highlighting their theoretical foundations and exploring their potential applications in machine learning and artificial intelligence.

A definitive answer to the existence and magnitude of the negative thermal expansion (NTE) and the 13C nuclear magnetic resonance (NMR) signature in C60 fullerene has been previously demonstrated using quantum-mechanical treatments of thermal rovibrational motion. This approach, while accurate, is computationally expensive, lacks the implementation of dispersion corrections, and is fundamentally limited to systems with well-defined equilibrium geometries and sufficiently strong restoring forces, making it inapplicable to weakly bound van der Waals complexes. Alternative methods, such as ab initio path integral molecular dynamics (PIMD), are more flexible but remain computationally expensive, especially when combined with calculations of the 13C NMR parameters. To overcome these limitations, we introduce an accurate and efficient neural network-based approach that combines machine learning interatomic potentials (MLIPs) with an NMR machine learning (NMR-ML) model. MLIPs enable machine learning PIMD (MLPIMD) simulations, while the NMR-ML model computes 13C isotropic magnetic shielding, σiso, directly from MLPIMD snapshots. We perform temperature-dependent MLPIMD simulations with MLIPs trained at different levels of theory. In all cases, NTE is observed, and the results reveal how both dispersion effects and atomic basis set choices influence its magnitude. Furthermore, we confirm that NTE is a quantum-mechanical phenomenon, and hence, classical MD simulations cannot reproduce it. To further test our approach, we investigate fully quantum-mechanical secondary isotope shifts of 13C NMR magnetic shielding due to the isotope change from 12C to 13C of the immediate neighbor with hexagon-hexagon or hexagon-pentagon bonds with the observed nucleus. The results show good agreement with the experimental data, highlighting the accuracy of our approach. This work demonstrates that ML-accelerated simulations enable accurate and efficient modeling of thermally activated quantum mechanical phenomena.

Introduction: Free radical polymerization plays a vital role in the preparation of polymer materials. A great number of ethylene-based polymer materials are produced by this method. The kinetics of free radical polymerization is one of the most important theories in the field of polymer science. However, this theory, based on the steady-state assumption, is not perfect and is full of controversy. Methods: In this paper, we propose a new approach to calculate the kinetic parameters of free radical polymerization and analyze the characteristics of the polymerization process. Results: The research indicates that the concentration of free radicals increases rapidly over time in the early stage of polymerization and then decreases approximately exponentially. The concentration of free radicals does not remain constant during this process, which suggests the steady-state assumption is not true. Discussion: The rate constant for monomer chain propagation significantly affects the variations in the monomer concentration and kinetic chain length over time. In general, a larger value of the rate constant for chain propagation can cause the monomer concentration and kinetic chain length to decrease more rapidly over time. Similarly, the initiator activity also affects the variations in the monomer concentration and kinetic chain length over time. Higher initiator activity generally results in a faster decrease in the monomer concentration and kinetic chain length. Conclusion: The above discussion comprehensively describes the kinetic characteristics of free radical polymerization, which enables teachers to teach the kinetic theory of free radical polymerization more effectively and improve their teaching level.

Excitons in organic semiconductors exhibit an energy distribution due to molecular thermal motion and disordered molecular packing. In our previous work, we modeled the exciton energy distribution using a simple Gaussian function centered at the optical bandgap. By comparing the overlap area (Aex) between the model and the solution absorption spectra of the emitters, we demonstrated that emitters with a high density of band-tail states are conducive to achieving high device efficiency. Herein, we develop two new blue emitters, TCPN and NCPN, featuring hybridized local and charge transfer (HLCT) and localized excited (LE) states, respectively. We optimize the initial model, including replacing the solution absorption spectra with thin-film excitation spectra, keeping the total number of excitons constant while varying the degree of exciton dispersion, and using the overlap integral (Jex) instead of Aex. This work provides novel insights into designing high-efficiency emitters through the lens of the exciton energy distribution.

General Background: Oxidation–reduction reactions are fundamental processes in chemistry and play essential roles in energy systems, biological processes, environmental chemistry, and metallurgical operations. Specific Background: Thermal energy is a critical factor that governs both thermodynamic feasibility and kinetic behavior in redox reactions, affecting reaction rates, equilibrium conditions, and electron transfer pathways. Knowledge Gap: Although the influence of temperature on chemical reactions has been widely recognized, a unified explanation connecting thermodynamic principles, kinetic theories, and practical redox applications across multiple scientific fields remains limited. Aims: This review summarizes how heat governs oxidation–reduction reactions by examining its effects on reaction thermodynamics, reaction kinetics, mechanistic pathways, and overall process performance. Results: The analysis of classical theories and published studies shows that temperature alters equilibrium constants, modifies activation energies, accelerates reaction rates, and affects electron transfer mechanisms. These thermal effects play significant roles in chemical systems including metallurgy, biological redox processes, environmental reactions, and emerging energy technologies. Novelty: The article synthesizes theoretical and applied perspectives to present an integrated view of thermal control in oxidation–reduction chemistry. Implications: Understanding the role of heat in redox reactions provides a conceptual foundation for improving chemical process design, optimizing reaction conditions, and guiding future research in electrochemistry, materials science, and energy conversion technologies. Keywords: Oxidation Reduction Reactions, Reaction Kinetics, Electron Transfer, Thermodynamic Equilibrium, Thermal Energy Key Findings Highlights Temperature modifies equilibrium behavior and activation barriers in redox systems Thermal conditions regulate electron exchange pathways during chemical transformations Multiple scientific fields apply temperature-controlled redox chemistry principles

A novel approach to constructing kinetic models is proposed on the basis of the separation of local and nonlocal contributions to particle interaction. The method results in a generic kinetic equation for complex fluid systems, amenable to efficient numerical realization. The main obstruction to causality caused by a coupling between the nonuniform temperature field and the nonideal equation of state is resolved based on gauge invariance of the hydrodynamic limit. A new lattice Boltzmann model is derived for compressible nonideal fluid simulation based on the Enskog-Vlasov kinetic theory as a case of practical importance.

White dwarf models require accurate equationsof state and ionic transport coefficients in the warm dense-matter regime, where kinetic theory models and tabulated equationsof state are often inaccurate. Here spectral-partitioned density functional theory and machine-learned interatomic potentials are combined to perform large-scale, first-principles quantum molecular dynamics simulations of deuterated water (D_{2}O) near the principal Hugoniot. This approach retains Kohn-Sham accuracy while achieving orders-of-magnitude speedup, yielding converged equationof state and transport properties over a broad pressure and temperature range. The results reveal the thermodynamic conditions under which ionic transport models for interdiffusivity and shear viscosity converge and identify those in closest agreement with density functional theory benchmarks at temperatures in the warm dense-matter regime. The present framework extends first-principles transport calculations to higher temperatures than previously achieved and provides an efficient, scalable, and general approach for studying transport properties in complex multicomponent mixtures.