Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures

Nanoscale water behavior and its impact on adsorption: A case study with CNTs and diclofenac

Water is a fundamental component of life, playing a critical role in regulating metabolic processes and facilitating the dissolution and transport of essential molecules. However, emerging contaminants, such as pharmaceuticals, pose significant challenges to water quality and safety. Nanomaterial-based technologies emerge as a promising solution for removing those contaminants from water. Nevertheless, interfacial water plays a major role in the adsorption of chemical compounds in nanomaterials-as it plays in biological processes such as protein folding, enzyme activity, and drug delivery. To understand this role, in this study, we employ molecular dynamics simulations to explore the adsorption dynamics of potassium diclofenac on single-walled carbon nanotubes (SWCNTs) and double-walled carbon nanotubes (DWCNTs), considering both dry and wet conditions. Our findings reveal that the structuring of water molecules around CNTs creates hydration layers that significantly influence the accessibility of active sites and the interaction strength between contaminants and adsorbents. Our analysis indicates higher energy barriers for adsorption in DWCNTs compared to SWCNTs, which is attributed to stronger water-surface interactions. This research highlights the importance of understanding nanoscale water behavior for optimizing the design and functionality of nanomaterials for water purification. These findings can guide the development of more efficient and selective nanomaterials, enhancing contaminant removal and ensuring safer water resources while contributing to a deeper understanding of fundamental biological interactions.

Microscopic mechanisms of pressure-induced amorphous-amorphous transitions and crystallisation in silicon

Some low-coordination materials, including water, silica, and silicon, exhibit polyamorphism, having multiple amorphous forms. However, the microscopic mechanism and kinetic pathway of amorphous-amorphous transition (AAT) remain largely unknown. Here, we use a state-of-the-art machine-learning potential and local structural analysis to investigate the microscopic kinetics of AAT in silicon after a rapid pressure change. We find that the transition from low-density-amorphous (LDA) to high-density-amorphous (HDA) occurs through nucleation and growth, resulting in non-spherical interfaces that underscore the mechanical nature of AAT. In contrast, the reverse transition occurs through spinodal decomposition. Further pressurisation transforms LDA into very-high-density amorphous (VHDA), with HDA serving as an intermediate state. Notably, the final amorphous states are inherently unstable, transitioning into crystals. Our findings demonstrate that AAT and crystallisation are driven by joint thermodynamic and mechanical instabilities, assisted by preordering, occurring without diffusion. This unique mechanical and diffusion-less nature distinguishes AAT from liquid-liquid transitions.

Correlations between defect propensity and dynamical heterogeneities in supercooled water

A salient feature of supercooled liquids consists in the dramatic dynamical slowdown they undergo as temperature decreases while no significant structural change is evident. These systems also present dynamical heterogeneities (DH): certain molecules, spatially arranged in clusters, relax various orders of magnitude faster than the others. However, again, no static quantity (such as structural or energetic measures) shows strong direct correlations with such fast-moving molecules. In turn, the dynamic propensity approach, an indirect measure that quantifies the tendency of the molecules to move in a given structural configuration, has revealed that dynamical constraints, indeed, originate from the initial structure. Nevertheless, this approach is not able to elicit which structural quantity is, in fact, responsible for such a behavior. In an effort to remove dynamics from its definition in favor of a static quantity, an energy-based propensity has also been developed for supercooled water, but it could only find positive correlations between the lowest-energy and the least-mobile molecules, while no correlations could be found for those more relevant mobile molecules involved in the DH clusters responsible for the system's structural relaxation. Thus, in this work, we shall define a defect propensity measure based on a recently introduced structural index that accurately characterizes water structural defects. We shall show that this defect propensity measure provides positive correlations with dynamic propensity, being also able to account for the fast-moving molecules responsible for the structural relaxation. Moreover, time dependent correlations will show that defect propensity represents an appropriate early-time predictor of the long-time dynamical heterogeneity.