From Bonds to Breakdown: Multiscale Insights into Chemical Degradation of Anion Exchange Membrane
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organic↗
Engineering topics
Molinero, Valeria
Publications and source records attributed to Molinero, Valeria.
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 I h at 77 K [Rosu-Finsen et al., Science 379, 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. Here, we use molecular simulations under controlled pressure and shear rate at 77 K to produce and investigate MDA. Here. we find that MDA formed at constant shear rate is a steady-state nonequilibrium shear-driven amorphous ice (SDA), that can be produced by shearing ice I h , LDA, or HDA. Our results suggest that MDA could be obtained by ball-milling water glasses without crystallization interference. Increasing the shear rate at ambient pressure produces SDAs with densities ranging from LDA to HDA, revealing shear rate as a new thermodynamic variable in the nonequilibrium phase diagram of water. Indeed, shearing provides access to amorphous states inaccessible by controlling pressure and temperature alone. SDAs produced with shearing rates as high as 10 6 s −1 sample the same region of the potential energy landscape than hyperquenched glasses with identical density, pressure, and temperature. Intriguingly, SDAs obtained by shearing at ~10 8 s −1 have density, enthalpy, and structure indistinguishable from those of water “instantaneously” quenched from room temperature to 77 K over 10 ps, making them good approximants for the “true glass” of ambient liquid water.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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. This discrepancy raises the question of how the seed promotes the daughter phase. Here, we present the first molecularly resolved investigation of seed-assisted zeolite synthesis. Using molecular simulations, we reproduce the experimental finding that a parent zeolite can promote the nucleation of a daughter zeolite even when it lacks common composite building units (CBUs) or crystal planes. Modeling the seed-assisted synthesis of an AFI-type zeolite using zeolite CHA, our simulations indicate that stand-alone CBUs from the parent seed do not facilitate daughter crystal formation. However, introducing the intact seed significantly reduces the synthesis time, supporting that seed integrity is key to increased efficiency. This reduction arises from the cross-nucleation of the AFI-type zeolite on the CHA (001) face. We find that parent and daughter zeolites are connected by an interfacial transition layer with an order distinct from that of both zeolites. Simulations reveal that cross-nucleation occurs over a broad range of synthesis conditions. We argue that cross-nucleation would be most favorable for zeolite pairs that share crystalline planes such as those forming intergrowths. In conclusion, our findings suggest that the prevalence of intergrowths with a common lattice plane in zeolite synthesis is likely a kinetic effect of accelerated cross-nucleation.
Crystallization↗
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. Here, in this study, we build on the recent success of our reinforcement learning (RL) workflow to derive a new set of optimal parameters for a promising short-range BKS-based model proposed by Soules. We use RL to navigate the eight-dimensional parameter space of the Soules potential using an experimental training data set that includes both local and global structural features from approximately 21 experimentally realized silica polymorphs, including high density phases and porous zeolites. We compare the performance of our machine-learned ML-Soules model with other high quality models including our recent machine-learned parametrization of BKS (ML-BKS), a machine-learned potential (GAP), as well as predictions of ab initio calculations with the highly fidelity SCAN functional. The ML-Soules accurately captures the relative energetic ordering of various polymorphs as well as their structural features at a significantly reduced computational expense. The ML-Soules model also reasonably captures the structure, density, and elastic constants of quartz, as well as metastable silica polymorphs. We further discuss the limitations of the Soules functional form and propose potential enhancements, including the incorporation of additional three-body terms and/or the utilization of different short-ranged functional forms to achieve greater accuracy for both global and local features in the modeling of silica while retaining low computational cost.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗