Medium-density amorphous ice unveils shear rate as a new dimension in water's phase diagram.

Recent experiments revealed a new amorphous ice phase, medium-density amorphous ice (MDA), formed by ball-milling ice at 77 K [Rosu-Finsen , Science , 474-478 (2023)]. MDA has density between that of low-density amorphous (LDA) and high-density amorphous (HDA) ices, adding to the complexity of water's phase diagram, known for its glass polyamorphism and two-state thermodynamics. The nature of MDA and its relation to other amorphous ices and liquid water remain unsolved.

Interzeolite Transformation through Cross-Nucleation: A Molecular Mechanism for Seed-Assisted Synthesis.

Polymorph selection and efficient crystallization are central goals in zeolite synthesis. Crystalline seeds are used for both purposes. While it has been proposed that zeolite seeds induce interzeolite transformation by dissolving into structural units that promote nucleation of the daughter crystal, the seed's structural elements do not always match those of the target zeolite.

Hierarchical assembly and environmental enhancement of bacterial ice nucleators.

Bacterial ice nucleating proteins (INPs) are exceptionally effective in promoting the kinetically hindered transition of water to ice. Their efficiency relies on the assembly of INPs into large functional aggregates, with the size of ice nucleation sites determining activity. Experimental freezing spectra have revealed two distinct, defined aggregate sizes, typically classified as class A and C ice nucleators (INs).

The smallest electrochemical bubbles.

Many of the relevant electrochemical processes in the context of catalysis or energy conversion and storage, entail the production of gases. This often implicates the nucleation of bubbles at the interface, with the concomitant blockage of the electroactive area leading to overpotentials and Ohmic drop. Nanoelectrodes have been envisioned as assets to revert this effect, by inhibiting bubble formation.

Machine Learning a Simple Interpretable Short-Range Potential for Silica.

A wide array of models, spanning from computationally expensive ab initio methods to a spectrum of force-field approaches, have been developed and employed to probe silica polymorphs and understand growth processes and atomic-level dynamical transitions in silica. However, the quest for a model capable of making accurate predictions with high computational efficiency for various silica polymorphs is still ongoing. Recent developments in short-range machine-learned models, such as GAP and NNPScan, have shown promise in providing reasonable descriptions of silica, but their computational cost remains high compared to force fields such as BKS which are based on simple interpretable functional forms.