Clay's solid-fluid phase transition, a key cause of geohazards like landslides and debris flows, remains notoriously difficult to model due to its coupled frictional yielding and strain-rate-dependent fluidization. Its complexity poses a substantial challenge to constitutive modeling. For the first time, this study proposes a novel critical-state hydrodynamic model (CSHM), which efficiently captures clay's nonlinear solid-fluid phase transition by integrating quasi-static and viscous stress components in a unified framework. The quasi-static stress is described by a critical-state-based elastoplastic model, representing the solid-like behavior. In contrast, the viscous stress is described using a novel hydrodynamics-based rheological model that captures the fluid-like behavior by introducing a state variable termed “clay temperature”. The quasi-static component captures key aspects including nonlinear elasticity, stress dilatancy, and critical state, whereas the proposed viscous component describes shear-heating or shear-cooling rheology. Subsequently, extensive element simulations are employed to evaluate the new CSHM. Finally, validation against experimental data demonstrates that the CSHM accurately captures the clay's solid-to-fluid phase transition. The analyses reveal that: (i) While sand undergoes a shear-induced heating phase transition and is well described by the existing kinetic theory, clay exhibits shear-cooling, which our novel model accurately captures. (ii) Clay's phase transition is characterized by two transitional points (critical-state point and viscous-stress-dominant point) and three different regimes (solid-like, transitional, and fluid-like). (iii) Unlike the traditional HB model, a 2D model describing stress in the fluid-like state, the CSHM is a 3D full-range phase transition model that captures evolution from initial to critical state, and eventually fluid-like state. • Proposes critical-state hydrodynamic model for clay's solid-fluid phase transition. • CSHM integrates critical-state elastoplasticity (solid) and hydronhamics (fluid). • Proposes novel hydrodynamic model with ‘clay temperature’ for viscous stress. • Seamlessly bridges solid and fluid states via critical-state and clay temperature. • Comparison with experimental results confirm the model's accuracy.

Newtons laws of motion (NLM) and Einsteins Special Theory of Relativity (STR) form the conceptual backbone of classical and modern physics, respectively. Despite their extensive empirical success, both frameworks are typically formulated without explicit consideration of thermodynamic constraints such as temperature evolution, system openness, and energy dissipation. This work investigates the thermodynamic consistency of NLM and STR by analytically examining their foundational equations under closed, open, and adiabatic system conditions using established principles from classical mechanics, kinetic theory of gases, and thermodynamics. The analysis demonstrates that Newtons equations of motion implicitly assume constant acceleration and unbounded time evolution, which, when applied to open systems, violate energy conservation and imply behaviour akin to perpetual motion. By explicitly incorporating temperature as a dynamical variable and recognising its intrinsic coupling to time, modified equations of motion are derived for closed thermodynamic systems. These equations retain the functional form of Newtonian relations but introduce a bounded temperature increment, deltaT, thereby ensuring compliance with the first and second laws of thermodynamics and preventing divergence in velocity, displacement, or work. A similar thermodynamic examination of STR is conducted, focusing on relativistic length contraction, time dilation, and the mass–energy relation E = mc2. When interpreted in terms of macroscopic or open systems, these relations imply the simultaneous divergence of mass and energy at high velocities, thereby contradicting conservation principles. However, when reformulated for isolated or adiabatic ideal-gas systems, analogous relativistic relationships emerge naturally from mechanical compression and temperature variation, without requiring inertial-frame abstractions or unphysical infinities. The study further demonstrates that the traditional interpretation of E = mc2 as unrestricted mass–energy interconvertibility is thermodynamically inconsistent. Instead, the equation is shown to represent the mechanical work required to accelerate a mass toward relativistic speeds within a finite time, thereby highlighting the physical impossibility of reaching the speed of light for finite-energy systems. Overall, this work establishes that both NLM and STR remain conditionally valid only within restricted thermodynamic domains. By explicitly incorporating temperature, system boundaries, and energy conservation, the analysis clarifies the physical limits of these foundational theories and provides a thermodynamically consistent reinterpretation of classical and relativistic dynamics.

Gas transport in nanoscale ceramic membranes is fundamentally governed by the Knudsen number (Kn), which differentiates between molecular dominated and continuum dominated flow regimes. This study experimentally evaluates the sensitivity of Kn to transmembrane pressure across multilayer alumina membranes comprising a 15 nm selective layer, a 200 nm intermediate layer, and a 6000 nm macroporous support. Hydrogen (H₂), carbon dioxide (CO₂), and air were investigated at 100 °C over a pressure range of 20–300 kPa. The results consistently show an inverse relationship between Kn and pressure for all gases and pore sizes, as predicted by kinetic gas theory due to the pressure dependent decrease in molecular mean free path. Among the gases studied, hydrogen exhibits the strongest pressure sensitivity in the 15 nm layer, followed by CO₂ and air, reflecting differences in molecular size and diffusivity. Linear regression applied to the experimental Kn–ΔP trends yields coefficients of determination of R² ≈ 0.8875 across all gases and pore diameters, confirming high linearity and strong internal consistency in the measurements. Although the R² values remain constant, each gas exhibits a distinct regression slope and intercept, as shown in Figures 4–6, indicating differing Kn pressure response characteristics. Complementary SEM and dynamic wettability measurements further support the mechanistic interpretation. The 15 nm top layer shows the highest hydrophilicity (equilibrium contact angle ≈ 60°), promoting enhanced gas–wall interactions. The 200 nm layer exhibits intermediate wettability (≈ 70°), while the 6000 nm support is weakly hydrophobic (≈ 93°). These structural and surface properties help explain the observed trends: smaller and more hydrophilic pores intensify molecule–wall collisions, amplifying Knudsen-dominated transport. Overall, the findings provide validated experimental benchmarks for modelling rarefied gas transport in composite ceramic membranes, with implications for hydrogen purification, CO₂ separation, and catalytic membrane reactor design.

We report the first magnesium-catalyzed regioselective reduction of quinolines to 1,2-dihydroquinolines, utilizing H3N·BH3. This protocol operates under mild conditions and tolerates a broad range of functional groups. Comprehensive mechanistic investigations, including kinetic analysis, deuterium labeling, and density functional theory calculations, elucidate a concerted hydride transfer pathway. This work serves as a blueprint for employing s-block metals with high-precision reactivity, which are traditionally reserved for transition metals.

Electrochemical CO2 reduction represents a sustainable negative carbon technology, yet its widespread application is hindered by sluggish reaction kinetics, particularly in the initial adsorption and activation of CO2─the rate-determining step. Herein, we report an inverted Ag-based catalyst architecture in which CeO2 nanoparticles are supported on metallic Ag, creating abundant CeO2/Ag interfaces. These interfaces facilitate the formation of oxygen vacancies at CeO2/Ag interfaces under reductive conditions while maintaining high electrical conductivity through the metallic Ag matrix. Kinetic analyses and density functional theory reveal that these interfacial oxygen vacancies significantly enhance the CO2 activation and lower the activation barrier, thereby accelerating the overall CO2RR kinetics. In situ Raman and attenuated total reflectance surface-enhanced infrared absorption spectroscopy directly confirm the generation and dynamic behavior of oxygen vacancies during electrolysis. Notably, alternating atmosphere experiments demonstrate reversible consumption and regeneration of oxygen vacancies upon exposure to CO2 and inert gas, providing compelling evidence that oxygen vacancies actively participate in CO2 activation. This work highlights the critical role of engineered metal-oxide interfaces and defect engineering in enhancing electrocatalytic CO2 conversion.

We report the numerical observation of a far-from-equilibrium equation of state (EOS) in the Gross-Pitaevskii (GP) model. We first show that the momentum distribution of the turbulent cascade is well described by wave-turbulent kinetic theory in the appropriate limits. Calculating the energy and particle fluxes Π ϵ ( k ) and Π N ( k ) , we show that the turbulent state possesses the hallmarks of a direct energy cascade. Building on this, we show that the GP model encodes a universal EOS in the form of a relationship between the turbulent cascade’s momentum distribution amplitude n 0 and the energy flux ε in the steady state. We find that in our regime of “mixed” turbulence—where both vortices and waves play a significant role— n 0 ∝ ε 0.67 ( 2 ) , a result that is not captured by any existing theory of turbulence but that agrees with a recent experimental measurement for large energy fluxes. Finally, we find that the concept of quasi-static thermodynamic processes between equilibrium states extends to far-from-equilibrium steady states.

Kinetic theory offers a promising alternative to conventional turbulence modelling by providing a mesoscopic perspective that naturally captures non-equilibrium physics such as non-Newtonian effects. In this work, we present an extension and theoretical analysis of the kinetic model for incompressible turbulent flows developed by Chen et al. ( Atmosphere , 2023, vol. 14(7), p. 1109), constructed for unbounded flows. The first extension is to reselect a relaxation time such that the turbulent transport coefficients are obtained consistently and better align with well-established turbulence theory. The Chapman–Enskog (CE) analysis of the kinetic model reproduces the linear eddy-viscosity and gradient diffusion models for Reynolds stress and turbulent kinetic energy flux at the first order, and yields nonlinear eddy-viscosity and closure models at the second order. In particular, a previously unreported CE solution for turbulent kinetic energy flux is obtained. The second extension is to enable the model for wall-bounded turbulent flows with preserved near-wall asymptotic behaviours. This involves developing a low-Reynolds-number model incorporating wall damping effects and viscous diffusion, with boundary conditions enabling both viscous sublayer resolution and wall function application. Comprehensive validation against experimental and direct numerical simulation data for turbulent Couette flow demonstrates excellent agreement in predicting mean velocity profiles, skin friction coefficients and Reynolds shear-stress distributions, although the near-wall-normal stress anisotropy is underestimated. The results show that averaged turbulent flow behaves similarly to rarefied-gas flow at finite Knudsen number, capturing non-Newtonian effects beyond linear eddy-viscosity models. This kinetic model provides a physics-based foundation for turbulence modelling with reduced empirical dependence.

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.

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.

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.

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.