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↗
Degradation of Anion Exchange Membranes by Cation Elimination: Impact on Water Uptake, Nanostructure, and Ionic Mobility
Anion exchange membranes (AEMs) are an attractive platform for fuel cell and electrolysis technologies since they enable the use of cheaper, nonplatinum group metal electrodes and components. However, the widespread adoption of AEM-based devices is limited by chemical degradation of the AEM in the highly alkaline medium of operation. Experimental studies report three major pathways of degradation, including substitution (S N 2), elimination (E2), and backbone cleavage. The decline in membrane performance is likely due to a cumulative effect of several pathways, which has proven to be difficult to disentangle through experiments. Here we use coarse-grained molecular simulations to isolate the impact of E2 degradation, where cationic sites are replaced by an alkene group, on AEM water uptake, nanoscale morphology, structure of the ion-conducting channels, water mobility, and ionic conductivity. Our studies focus on the well-studied model AEM polyphenylene oxide with tetraalkylammonium cationic sites (PPO-TMA). We find that about half of the hydrophobic groups resulting from the degradation retract into the hydrophobic domains of the membrane. The reduction in ion exchange capacity (IEC) and the hydrophobicity of the alkene groups decrease both the equilibrium water uptake (%WU) and the number of water molecules per cation (λ) of the membrane. Interestingly, the evolution of λ with the IEC seems to follow that of undegraded PPO-TMA membranes. Additionally, we ascribe this to the similarity between the structure of the degraded monomer and the neutral one in the polymer. We conclude that most of the performance losses originate in the lower hydration of the degraded membrane.
36 MATERIALS SCIENCE↗
Ultraviolet-Activated Solid-State Nitrene Cross-Linking: A Scalable Pathway to Prolonged Lifespan in Anion Exchange Membranes
Anion exchange membranes (AEMs) offer a cost-effective alternative to proton exchange membranes as alkaline fuel cells and electrolyzers permit the use of non-platinum group electrodes and components. Despite continued progress, the operational lifetime and stability of these membranes limit the widespread adoption of AEM-based electrochemical technologies. This study presents a flexible and easily implemented ultraviolet (UV)-initiated nitrene-based cross-linking method which uses a small, facile organic azide precursor. As a proof of concept, we demonstrate this approach on the well-studied poly(2,6-dimethyl-1,4-phenylene oxide) quaternary ammonium AEM (QPPO) polyelectrolyte. Further, a survey of cross-linker density (2.5–10 mol %) found that the addition of 10 mol % results in a 59% reduction in water uptake, a 58.8% decrease in the swell ratio, and a 31% increase in tensile strength vs the un-cross-linked material. Nitrene cross-linking also enhanced the membrane’s durability, enabling 1000 h of stable performance under electrochemical load. This UV-initiated cross-linking method may be easily integrated into production processes, allowing chemical cross-linking at any stage, including posthydration.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Elucidating the formation mechanisms of zeolites using data-driven modelling and in-situ characterization
Zeolites are the main solid catalysts used by the chemical industry. The use of zeolites in separations and as shape selective catalysts requires control of the width and connectivity of their pores. 235 distinct zeolite frameworks have been synthesized to date, of over 2 million that have been proposed. Recent work indicates that the limitation is in large part kinetic: new synthetic pathways are required to access new zeolites. Organic cations are used to direct the synthesis towards specific zeolites. However, the molecular mechanisms by which cations direct the nucleation towards specific zeolites is not known. Elucidating these mechanisms is key to realize new zeolites for catalysis and separations, and is the focus of this project. This project developed and implemented a synergistic, data-driven computational and experimental approach to resolve the molecular pathways of nucleation, growth, and polymorph selection of zeolites and the role of organic cations in directing their formation. The project developed computationally efficient and accurate models for the study of the nucleation and growth of pure silica zeolites in molecular simulations, using machine learning with data from experiments. Simulations with these models were integrated with scanning tunneling electron microscopy, computer vision, and deep learning to unveil the molecular pathways of formation of a zeolite. Of particular interest in this project was to elucidate the role of amorphous precursors in the nucleation of the zeolite. Previous experiments indicate that zeolites are born within non-crystalline aggregates in which the silicates and organic cations have local and medium range order similar to that of the zeolite. The organic cations that direct the formation of zeolites and those that direct the formation of ordered mesoporous silicas are similar. We hypothesized that the frustrated attraction that for large organic cations leads to the formation of stable mesophases that direct the synthesis of mesoporous silicas, could promote the formation of metastable mesophases that can assist in the nucleation and polymorph selection of zeolites. The simulations resolved how structure directing agents build crystalline order and showed that mesoscopic pre-ordering occurs is not required to facilitate the nucleation of zeolites, because the synthesis occurs at high driving forces, where the barriers for nucleation are negligible. This project unveiled that polymorph selection in zeolite synthesis occurs after nucleation, opening a distinct area of control through the kinetics of growth and not through nucleation barriers.
36 MATERIALS SCIENCE↗
Multistage Condensation Pathway Minimizes Hysteresis in Water Harvesting with Large-Pore Metal–Organic Frameworks
Metal-organic frameworks (MOFs) have emerged as promising materials for atmospheric water harvesting (AWH). Large-pore MOFs provide high water capacity, but their significant hysteresis between sorption and desorption makes them unsuitable for AWH. Co2Cl2(BTDD) is a noteworthy exception. This MOF has large, 2.2 nm diameter one-dimensional pores, and combines both record-high water capacity and minimal hysteresis, making it an excellent material for water capture in arid areas. Sorption reversibility in Co2Cl2(BTDD) has been attributed to continuous water uptake. However, the sharp adsorption/desorption in the isotherms supports a discontinuous first-order transition. Here we use molecular simulations to compute the water adsorption and desorption pathways and isotherms in a Co2Cl2(BTDD) model, to elucidate how does this MOF achieve reversibility despite its large pore size. The simulations reveal a multi-stage mechanism of discontinuous water uptake facilitated by spatial segregation of rows of hydrophilic metal sites bridged by ~1 nm hydrophobic ligands. The multi-stage mechanism breaks the barrier of capillary condensation into smaller, easier to surmount ones, resulting in a facile process despite the sharp density discontinuity between confined liquid and vapor. Our results explain why exchanging Co2+ for Ni2+ or Clfor F- in the MOF have minimal impact on the condensation and desorption pressures. On the other hand, we predict that a decrease in hydrophilicity of the MOF vertices would strongly increase the hysteresis. We expect that the relationships between spatial distribution of hydrophilic sites and hysteresis unraveled in this study assist the design of water harvesting materials with maximal capacity and reversibility.
Zaragoza, Alberto↗
Multi-reward reinforcement learning based development of inter-atomic potential models for silica
Abstract Silica is an abundant and technologically attractive material. Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics, the development of accurate models to predict the structure, energetics and properties of silica polymorphs remain challenging. Current models for silica range from computationally efficient Buckingham formalisms (BKS, CHIK, Soules) to reactive (ReaxFF) and more recent machine-learned potentials that are flexible but computationally costly. Here, we introduce an improved formalism and parameterization of BKS model via a multireward reinforcement learning (RL) using an experimental training dataset. Our model concurrently captures the structure, energetics, density, equation of state, and elastic constants of quartz (equilibrium) as well as 20 other metastable silica polymorphs. We also assess its ability in capturing amorphous properties and highlight the limitations of the BKS-type functional forms in simultaneously capturing crystal and amorphous properties. We demonstrate ways to improve model flexibility and introduce a flexible formalism, machine-learned ML-BKS, that outperforms existing empirical models and is on-par with the recently developed 50 to 100 times more expensive Gaussian approximation potential (GAP) in capturing the experimental structure and properties of silica polymorphs and amorphous silica.
36 MATERIALS SCIENCE↗
Post-Hydration Crosslinking of Ion Exchange Membranes to Control Water Content
The utilization of crosslinking as a technique to limit excess water uptake in ion exchange membranes is a well-established approach. However, traditional methods involve performing the crosslinking reaction before the membrane self-segregates into hydrophilic and hydrophobic domains upon exposure to water. In this study, we employ numerical and molecular simulations to explore the potential of post-hydration crosslinking of the polymer at subsaturated conditions. This process allows for the development of a nanosegregated morphology, which establishes well-connected but narrow water channels, while limiting additional sorption of water. The significant reduction in membrane swelling observed in our study indicates that post-hydration crosslinking could be a promising approach to regulate water uptake in ion exchange membranes. Here based on our analyses, we propose chemical crosslinking strategies that may yield high membrane gel fractions.
36 MATERIALS SCIENCE↗
CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases. CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D, clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal) noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture. Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approach is material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces to microscale grain boundaries.
36 MATERIALS SCIENCE↗
Exponential Water Uptake in Ionomer Membranes Results from Polymer Plasticization
Water content is the most influential factor in the performance of ion-exchange membranes (IEMs), controlling their mechanical rigidity, ionic conductivity, and degradation rates. Membranes absorb water exponentially at high water activity (a w ), making that region of the sorption isotherm the most influential on membrane properties. Plasticization of the polymer by water has been proposed to cause this exponential uptake at high a w . However, an integrated microscopic picture of the structure, thermodynamic, and mechanical properties of IEMs as a function of a w has remained elusive. In this work, we use large-scale molecular simulations validated with experimental measurements to compute the sorption isotherms, Young’s modulus, polymer dynamics, and structure of IEMs. The simulations unveil that the exponential increase in water uptake (WU) coincides in all cases with the glass to rubber transition of the membrane, as measured through its Young’s modulus and segmental polymer dynamics. Functionalization of the polymer with alkyl groups further contributes to its plasticization, increasing the WU at a given a w and ion-exchange capacity (IEC). The alkyl chains act synergistically with water to plasticize the polymer matrix and allow water penetration in the membrane. The simulations reveal that the width of the water channels depends on the ratio λ of water to ions in the polymer but is independent of its IEC. We conclude that differences in the polymer matrix–not the water channels–are responsible for the distinct uptake response of IEMs to the thermodynamic driving force of water activity.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
What Is the Smallest Zeolite That Could Be Synthesized?**
Abstract Zeolites with a few unit cells are promising as catalyst and adsorbents. The quest to synthesize the smallest zeolites has recently resulted in 4 to 8 nm nanozeolites, about 2 to 4 unit cells. These findings pose the question of what is the smallest zeolite that could be obtained by hydrothermal synthesis. Here we address this question using molecular simulations and thermodynamic analysis. The simulations predict that amorphous precursors as small as 4 nm can crystallize zeolites, in agreement with the experiments. We find that interfacial forces dominate the structure of smaller particles, resulting in size‐dependent compact isomers that have ring and pore distributions different from open framework zeolites. The instability of zeolites smaller than 3±0.5 nm precludes a classical mechanism of nucleation from solution or through assembly of small nanoslabs.
Dhabal, Debdas↗
What Is the Smallest Zeolite That Could Be Synthesized?**
Zeolites with a few unit cells are promising as catalyst and adsorbents. The quest to synthesize the smallest zeolites has recently resulted in 4 to 8 nm nanozeolites, about 2 to 4 unit cells. These findings pose the question of what is the smallest zeolite that could be obtained by hydrothermal synthesis. We address this question using molecular simulations and thermodynamic analysis. The simulations predict that amorphous precursors as small as 4 nm can crystallize zeolites, in agreement with the experiments. We find that interfacial forces dominate the structure of smaller particles, resulting in size-dependent compact isomers that have ring and pore distributions different from open framework zeolites. The instability of zeolites smaller than 3±0.5 nm precludes a classical mechanism of nucleation from solution or through assembly of small nanoslabs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Unstable and Metastable Mesophases Can Assist in the Nucleation of Porous Crystals
Not provided.
Chemistry↗
2019 Chemistry and Physics of Liquids GRC/GRS
Final Conference Program and Registration List
Molinero, Valeria↗