At the nanoscale when the system shrinks down, interfacial interactions govern fluid behavior, often defying bulk expectations. Using classical molecular simulations, we directly calculate the surface tension of water confined in carbon nanotubes (CNTs). Unlike planar interfaces, where confinement enhances surface tension monotonically, cylindrical confinement reveals a striking non-monotonic trend. In narrow CNTs, strong water–carbon (WC) repulsion yields giant surface tensions exceeding twice the bulk value. At a critical pore radius, water organizes into ordered hexagonal and pentagonal networks, driving an unexpected collapse—and even negative values—of the surface tension. These behaviors were also recovered for confined methane and for a different water model, further supporting the confinement effect on surface tension. Comparison with thermodynamic models, including Gibbs and Tolman representations as well as the recently introduced concepts of differential and integral surface tension, highlights their limitations in capturing the pore-radius dependence and the anomalous values observed. We establish water–carbon repulsion, curvature, and molecular structuring as the key determinants of confined-water surface tension, with broad implications for nanofluidics and interfacial thermodynamics.

Development of conjugate heat transfer coupling model for supercritical water flowing in 2 × 2 ballooning ATF rod bundles

Assessing the accuracy of bioelectrical impedance analysis for fat-free mass estimation in growing pigs using dual-energy X-ray absorptiometry as a reference method.

Daily estimation of Water–Salt–Carbon coordination in high-standard cotton fields using uav and PlanetScope satellite image fusion

How well do U.S. National Water Model short-range forecasts predict flood event timing and magnitude?

The HSiR3/KO t Bu catalytic system affords a notable regioselective C-H silylation reaction using alkaline earth metals, yet its mechanistic origins and the temperature-dependent selectivity inversion remain unresolved. Here, we combine active learning neural network potentials with long-timescale ab initio quality molecular dynamics and enhanced sampling to simulate the HSiEt3/KO t Bu catalyzed deprotonation reaction of N-methylindole in a complete operando solution environment. We found that KO t Bu can rapidly aggregate in organic solution to form catalyst clusters, which are able to activate and stabilize a hydride derived from silane, and this operando generated hydride is the catalytically active species. The hydride is able to deprotonate C2 and C3 protons on N-methylindole, and this reaction site selectivity is affected by the stability of the cluster controlled by solvent and temperature coupling, which gives rise to nonlinear activation entropy of the C3 pathway in the neat silane environment. Unlike tetrahydrofuran solutions, the weak polarity of neat silanes makes it difficult to support deprotonation reactions at the less acidic C3 position at high temperature, thus affecting temperature-dependent selectivity. These results provide a microscopic predictive framework for tuning the selectivity in base-promoted hydrosilane chemistry and illustrate how explicit operational solvation and cluster formation can reshape mechanistic conclusions drawn from static models.

Aiming at the problems of a fragile ecological environment, water shortage and system uncertainty in inland arid irrigation districts in Xinjiang, this study takes sustainable development as the guide, selects the Tailan River Irrigation District in Xinjiang as an example, and constructs a multi-objective optimal allocation model of agricultural water and soil resources in irrigation districts driven by water–carbon–economy synergy. The model aims to minimise irrigation water shortage, maximise crop carbon absorption and maximise economic benefits. By comparing six multi-objective algorithms such as APSEA, CMEGL, DCNSGA-III, DRLOS-EMCMO, MOEA/D-CMT and θ-DEA-CPBI, the optimal is selected based on the hypervolume (HV) index. The surface water, groundwater and crop-planting structure of five decision-making units in the irrigation district from 2021 to 2024 were optimised. Further, combined with the entropy weight–TOPSIS coupling-coordination comprehensive-evaluation model, the scheme evaluation system is constructed to screen the optimal configuration scheme of each year and unit. The results show that the MOEA/D-CMT algorithm has the highest HV value in each unit model over the years, which is the best solution algorithm for the model in this paper. The comprehensive evaluation value and coupling coordination degree of the optimal scheme of each unit fluctuate between years, and the difference between units is significant. Compared with the original planting and water source allocation scheme of the irrigation district from 2021 to 2024, the overall planting area of the optimised irrigation district is moderately reduced, forming an optimised pattern of ‘cotton pressure, grain expansion, economic increase and strong forest’; after optimization, the overall water shortage in the irrigation district is reduced by 1.4~11 million m3; the total amount of crop carbon absorption increased by 90.3~128.8 million kg; the net economic benefits increased by CNY 21.5~68.2 million. The research can provide decision support for the optimisation of the water and soil resource system in arid irrigation districts and has a scientific reference value for promoting the sustainable development and modernisation of agriculture in the inland irrigation districts of Northwest China.

The complex formation between peptides and nucleosides underlies the molecular recognition and regulation processes in biological systems. The present study focuses on the optimized structures of complexes formed between the glycyl-L-glutamate and four nucleosides-adenosine, guanosine, cytidine, and uridine. The most stable structures are found for complexes in which the preferred binding sites and the relative strength of hydrogen bonds are established. The terminal charged NH3+ and γ-COO- groups, as well as the amide fragment, can act as H-bonding sites in a peptide. The functional groups NH2, NH, CO, and N in nucleosides are potential H-bonding sites for the peptide. Unlike nucleobases, the hydroxyl groups of the ribose moiety in nucleoside molecules are additional H-bonding sites. The involvement of each of these groups depends on the complementarity of the peptide and nucleoside structures. The peptide affinity for nucleosides increases in the series: cytidine > guanosine > adenosine > uridine. The preference of each nucleoside depends on the balance of contribution from the structure rearrangement and intermolecular interaction, including electrostatic, orbital, dispersion interactions, and Pauli repulsion. The study combines DFT/B97-D/6-311++G(3d,3p) calculations with topological (QTAIM) and energy decomposition (EDA) analyses to investigate ion-molecular complexes between the tripolar anion of glycyl-glutamic acid and the neutral nucleosides. Solvation effects were taken into account within the PCM (water) model. Several initial structures with different coordination modes were generated according to the molecular electrostatic potential (MEP) distribution. Hydrogen bond parameters were obtained from the properties of bond critical points. The interaction energies and complex formation energies were determined to estimate the stability of the systems. Energy decomposition analysis (EDA) performed using the ORCA software allowed the contributions of electrostatic, polarization, dispersion, and Pauli repulsion components to the total interaction energy to be separated.

Effective water resource management, particularly in assessing the impacts of climate change on water-abundant regions, is crucial for ensuring long-term sustainability. This study investigates the hydrological response of a cold humid basin with strong groundwater-surface water interactions located in the Great Lakes Basin. We employ a coupled groundwater-surface water model (GSFLOW) to simulate the complex hydrological processes and evaluate the potential buffering capacity of groundwater on surface water systems. Results reveal a strong linear relationship between changes in mean annual precipitation and groundwater recharge (R2 = 0.96), with annual precipitation increases over 165 mm required to generate positive changes in recharge. The groundwater system demonstrates significant buffering capacity, particularly in areas with shallow water tables and during hydrological recession periods. Key buffering mechanisms include lagged responses to projected changes in precipitation and recharge relative to the reference period, shifts toward winter recharge, and stable baseflow during dry periods. The aquifer shows greater resilience under the more extreme SSP5 8.5 scenario, suggesting more dynamic surface-groundwater interactions under intense climate conditions. Spatial analysis indicates higher sensitivity to precipitation changes in areas with deeper water tables. The shifts in recharge timing suggest a transition from snow-dominated to rain-dominated winter hydrology, potentially enhancing the system's buffering capacity. These findings provide crucial insights for water resource management in cold humid regions, emphasizing the importance of considering a range of climate scenarios in long-term planning and highlighting the critical role of groundwater in maintaining hydrological resilience under changing climate conditions.

Rigid three-point water models are widely used in molecular simulations, yet they cannot simultaneously reproduce thermodynamic, dielectric, and dynamical properties. We show that these failures do not stem from incomplete parameter optimization, but from physical constraints that define the topology of the model parameter space. Treating the density anomaly as a master thermodynamic constraint, we find that viable geometries and electrostatics collapse onto a low-dimensional physical manifold governed by scaling relations. Within this framework, we identify two topological constraints, an empirical no-go principle, intrinsic to rigid three-point models with standard Lennard-Jones interactions. First, in the small-angle regime (θ ≲ 108°), matching the experimental dielectric constant requires molecular elongation that destabilizes the hydrogen-bond network and shifts the temperature of maximum density. Second, enforcing the density anomaly increases network rigidity, suppressing molecular mobility, and preventing agreement with the experimental self-diffusion coefficient. Together, these results define the fundamental limits of rigid three-point water models and recast their development as a constrained design problem rather than an empirical optimization task.

Water's anomalous thermodynamic behavior arises from the presence of intricate hydrogen-bond networks that are highly sensitive to many-body interactions, challenging molecular modeling for decades. The ongoing machine learning revolution has opened the possibility of performing quantum-accurate liquid-structure calculations at affordable computational cost. Beyond reproducing water's thermodynamic properties with high fidelity, such simulations provide a stringent benchmark for theoretical models and a route to deeper physical understanding. We use the recently developed machine-learned Deep Potential Many-Body Polarizable water model to show that the free energy of supercooled water can be accurately modeled with the potential energy landscape formalism. The resulting equation of state predicts the presence of a liquid-liquid critical point in excellent agreement with recent estimates. Together with previous studies based on empirical classical water potentials, it confirms that the potential energy landscape of water is Gaussian, providing a unifying framework for extracting thermodynamic behavior across model complexity, from empirical force fields to quantum-trained neural network models.

Over the past few decades, the field of electrochemistry has witnessed rapid advances in computational methods. This review highlights recent methodological progress in computational electrocatalysis, with a specific focus on the accurate prediction of electrochemical reaction kinetics. Particular emphasis is placed on our group's contributions using single-atom catalysts as model systems to quantitatively simulate the kinetics of energy-relevant small-molecule electrocatalytic reactions. By simultaneously capturing atomic-scale interfacial phenomena in the electric double layer, such as cation effects, explicit solvation structures, proton transfer dynamics, and potential distribution, our approach bridges the gap between idealized models and realistic electrochemical environments and predicts experimental observables, such as current density-potential curves and coverages. The current framework has also revealed previously inaccessible kinetic insights, including hydrogen-bond-mediated intermediate reorganization and its impact on transition states, and potential-driven solvent reorganization that dictates proton transfer kinetics. These advances provide both fundamental kinetic insights into electrocatalytic mechanisms and practical design principles for energy conversion catalysts.

ABSTRACT Flood mapping is critical for flood forecasting, preparedness, and risk management. In the United States, multiple federal agencies and organizations generate or archive flood inundation maps (FIM) using diverse models, data sources, and standards. The National Water Center (NWC) currently provides the only national‐scale operational FIM through its Height above nearest drainage (HAND) ‐based workflow; however, most other authoritative flood datasets remain isolated and underutilized due to inherent discrepancies in models, formats, standards, and limited accessibility. In this study, we present an integrated relational database accompanied by Python‐based tools and workflows that together enable the acquisition of flood maps from multiple agency inventories or models, the generation of baseline HAND maps for corresponding areas, and optimization of FIM datasets for efficient storage, visualization, and cross‐model comparison. This framework complements the NWC's operational system by allowing multiple‐source FIM integration and evaluation within a unified environment. The functionality is equally applicable at the local level for training, comparison, and teaching practical differences in action between flood models. Leveraging different sources of flood datasets under a common framework enhances confidence in the results and promotes informed action, including improved early warning and response.

Ion adsorption or exclusion from surfaces plays a major role in many systems and processes. Unfortunately, thermodynamic information characterizing the relative surface adsorption of individual ions is not currently available from approaches based on the Gibbs adsorption isotherm for mixed electrolytes without approximation. Here, we address this issue for electrolyte solutions containing any number of components at any concentration in a single phase in the presence of any type of fixed charged or uncharged surface in the absence of chemisorption or other chemical reactions. This is achieved by reference to local and global electroneutrality requirements between integrals over the surface-ion distributions. The results indicate that the surface-ion distribution integrals can be decomposed into two independent contributions: one that leads to surface charge neutralization, and the other that explains the surface thermodynamics. The resulting surface-ion integral relationships obtained here indicate exactly how the presence of additional electrolytes affects the surface distribution of any target ion of interest. The validity of the resulting relationships is confirmed using classical all atom explicit solvent molecular dynamics simulations. Using these relationships, one can then obtain individual relative surface-ion adsorptions from experimental data. We illustrate how to use the approach to extract more detailed information from experimental data than was previously available for two experimental mixed electrolyte systems involving vacuum electrolyte solution interfaces. The approach is exact and does not require a particular model for the surface region or the use of single ion chemical potentials.

Compact analytical forms are derived for the interactions involving thin disks in two dimensions using an integration approach. These include interactions between a disk and a material point, between two disks, and between a disk and a wall. Each object is treated as a continuous medium of materials points interacting by the Lennard-Jones 12-6 potential. By integrating this potential in a pairwise manner, expressions for the potentials and resultant forces between extended objects are obtained. All the results are validated with numerical integrations. The analytical potentials are implemented in LAMMPS and used to simulate two-dimensional suspension of disks with an explicit solvent modeled as a Lennard-Jones liquid. In monodisperse disk suspensions, a disorder-to-order transition of disk packing is observed as the area fraction of disks is increased or as the solvent evaporates. In bidisperse disk suspensions being rapidly dried, stratification is found with the smaller disks enriched at the evaporation front. Such "small-on-top" stratification echoes the similar phenomenon occurring in three-dimensional polydisperse colloidal suspensions that undergo fast drying. These potentials can be applied to a wide range of two-dimensional systems involving disk-like objects.

Mercury dihalides (HgX2, X = Cl, Br, I) undergo photoreduction much more rapidly in aqueous environments than in the gas phase. Using ab initio molecular dynamics simulations and high-level electronic structure calculations, we investigate how solvation shapes the molecular structure, electronic distribution, and excited-state character of HgX2 complexes. We find that strong Hg-solvent interactions induce pronounced deviations from linear geometries and lead to partial negative charge accumulation on HgX2 in polar solution. Moreover, we identify that the second absorption band in the deep-UV region exhibits a strong solvent-to-solute charge-transfer (CT) character. Combining the accumulation of partial negative charge in the ground state with the enhanced solvent-to-solute CT character promotes efficient electron localization on the Hg center after photoexcitation, thereby accelerating photoreduction in solution. By providing atomistic insight into solvation-driven excited-state reactivity, this work establishes the molecular basis for the accelerated photochemistry of HgX2 in aqueous media and underscores the essential role of explicit solvation in modeling the solution-phase photochemistry of mercury species relevant to the global mercury cycle.

Perovskite oxynitride LaTiO2N holds promise for visible-light-driven photocatalytic water splitting, yet its surface dynamics at the catalyst-water interface remain elusive. This study employs state-of-the-art density functional theory molecular dynamics (DFT-MD) and electrochemical measurements to unravel the intricate interplay of water arrangement and adsorption on the LaTiO2N(100) surface. By considering explicit solvent effects, we reveal a pronounced hydrophilic character, with spontaneous water dissociation forming hydroxyl groups at undercoordinated Ti sites at the surface, stabilizing the latter and potentially enhancing the OER activity. Our simulations identify the thermodynamically stable termination and demonstrate its role in fostering robust hydrogen-bond networks that facilitate proton-coupled electron transfer. The surface Pourbaix diagram underscores hydroxylated configurations across diverse electrochemical conditions, corroborated by DFT-MD insights. These findings highlight the critical role of treating an explicit solvent environment and its dynamics at a given temperature in the modeling of LaTiO2N, offering a blueprint for designing high-performance, sustainable water-splitting catalysts.

Here, we explore the effect of strongly hydrophilic cylindrical confinement (R = 10-50 Å) on the solid-liquid coexistence and structural dynamics of water using molecular dynamics simulations in LAMMPS with the monatomic water (mW) model. Studying the melting behavior of water under nanoscale confinement is essential for elucidating biological processes and materials science at the atomic or molecular level, yet the underlying mechanisms remain poorly understood. This study extends our previous work on slit-pore confinement [Sinha, V. K.; Das, C. K. J. Mol. Liq. 2025, 435, 128163], while demonstrating that cylindrical nanopores exhibit distinct thermodynamic melting behavior and structural ordering owing to their surface curvature and larger interfacial area (circumference) exposed to water molecules. We analyzed the Gibbs free energy difference (ΔG) between the solid and liquid phases to rigorously determine the melting temperature (Tm) from the coexistence condition (ΔG = 0), which is also supported by structural analysis. Depression in Tm with smaller pore radii or higher surface hydrophilicity indicates greater stability of the liquid phase under confinement. Structural analysis shows that the fraction of 2D hexagonal motifs at the pore surface increases with stronger surface interactions, while cubic and hexagonal diamond structures dominate mainly the bulk region. The variation in ice polymorph distribution provides a structural rationale for the observed depression of Tm under confinement. Similar trends but significantly lower melting temperatures for the cylindrical nanopores were observed compared to our previous results on slit-pore confinement. This work signifies the importance of nanoscale confinement geometry and surface hydrophilicity when dealing with solid-liquid phase transformation of water.

Most biomolecular simulations depend on the quality of empirical force fields, and the use of hybrid restraint potentials has emerged as a promising approach. In this contribution, we extend the application of hybrid potentials to membrane proteins by developing optimized restraints derived from experimentally determined NMR data. NMR chemical shift, chemical shift anisotropy, dipolar coupling, and NOE distance information are combined with appropriately weighted empirical force fields to study two transmembrane systems, namely sarcolipin and phospholamban. To remedy the problems of rare events and broken ergodicity, the energy landscape framework, including basin-hopping global optimization and discrete path sampling, is employed for exploring the underlying energy landscapes. Much of the appeal of the hybrid potential approach is the ability to study membrane proteins in the absence of conventional explicit or implicit solvent and lipid molecules, thereby simplifying the sampling of complex biomolecular conformational spaces. Our results suggest that the hybridization of NMR constraints as penalty energies with empirical force fields improves global optimization and energy landscape analysis by excluding experimentally incompatible structures.

We present a software to calculate phase-resolved resonant vibrational sum-frequency generation (vSFG) susceptibility χ(2)(ω) of water and hydroxyls at planar interfaces, e.g., air/water or solid/liquid or (bio)membrane/liquid interfaces of aqueous solutions. The released code (i) reads several formats of molecular trajectories, both from ab initio (AIMD) and classical MD (CMD), (ii) calculates instantaneous surfaces to allow flexible interfaces, (iii) is written in FORTRAN, parallelized by OpenMP and optimized for memory usage, (iv) allows processing of systems up of tens of thousand atoms and for unlimited simulation time, and (v) includes many tunable processing parameters. The code and its documentation are available via GitHub. Flexible models of water and surface hydroxyl (if evaluated) (CMD or AIMD) must be used. The derivatives of the polarizability tensors and dipole moments with the change of O-H distance must be calculated externally by ab initio methods and provided as input data. We present the impact of various parameters of the MD simulations (simulation length, nonbonded interaction cutoff, size of the system, and thermostat relaxation time) as well as of the processing code (filter relaxation, cutoff of cross-terms) and provide representative results for air/water, charged quartz (101)/aqueous solution, and neutral α-alumina (0001)/aqueous solution interfaces. Further extensions are planned to distinguish signals from specific O-H or C-H bonds of interfacial molecules.