Jeffrey C. Grossman

Water is a salient component in catalytic systems and acts as a reactant, product and/or spectator species in the reaction. Confined water in distinct local environments can display significantly different behaviors from that of bulk water. Therefore, the wide-ranging chemistry of confined water can provide tremendous opportunities to tune the reaction kinetics. In this review, we focus on drawing the connection between confined water properties and reaction kinetics for heterogeneous (electro)catalysis. First, the properties of confined water are presented, where the enthalpy, entropy, and dielectric properties of water can be regulated by tuning the geometry and hydrophobicity of the cavities. Second, experimental and computational studies that investigate the interactions between water and inorganic materials, such as carbon nanotubes (1D confinement), charged metal or metal oxide surfaces (2D), zeolites and metal-organic frameworks (3D) and ions/solvent molecules (0D), are reviewed to demonstrate the opportunity to create confined water structures with unique H-bonding network properties. Third, the role of H-bonding structure and dynamics in governing the activation free energy, reorganization energy and pre-exponential factor for (electro)catalysis are discussed. We highlight emerging opportunities to enhance proton-coupled electron transfer by optimizing interfacial H-bond networks to regulate reaction kinetics for the decarbonization of chemicals and fuels.

Moisture-capturing materials can enable potentially game-changing energy-water technologies such as atmospheric water production, heat storage, and passive cooling. Hydrogel composites recently emerged as outstanding moisture-capturing materials due to their low cost, high affinity for humidity, and design versatility. Despite extensive efforts to experimentally explore the large design space of hydrogels for high-performance moisture capture, there is a critical knowledge gap on our understanding behind the moisture-capture properties of these materials. This missing understanding hinders the fast development of novel hydrogels, material performance enhancements, and device-level optimization. In this work, we combine synthesis and characterization of hydrogel-salt composites to develop and validate a theoretical description that bridges this knowledge gap. Starting from a thermodynamic description of hydrogel-salt composites, we develop models that accurately capture experimentally measured moisture uptakes and sorption enthalpies. We also develop mass transport models that precisely reproduce the dynamic absorption and desorption of moisture into hydrogel-salt composites. Altogether, these results demonstrate the main variables that dominate moisture-capturing properties, showing a negligible role of the polymer in the material performance under all considered cases. Our insights guide the synthesis of next-generation humidity-capturing hydrogels and enable their system-level optimization in ways previously unattainable for critical water-energy applications.

Moisture-capturing materials can enable potentially game-changing energy-water technologies such as atmospheric water production, heat storage, and passive cooling. Hydrogel composites recently emerged as outstanding moisture-capturing materials due to their low cost, high affinity for humidity, and design versatility. Despite extensive efforts to experimentally explore the large design space of hydrogels for high-performance moisture capture, there is a critical knowledge gap on our understanding behind the moisture-capture properties of these materials. This missing understanding hinders the fast development of novel hydrogels, material performance enhancements, and device-level optimization. In this work, we combine synthesis and characterization of hydrogel-salt composites to develop and validate a theoretical description that bridges this knowledge gap. Starting from a thermodynamic description of hydrogel-salt composites, we develop models that accurately capture experimentally measured moisture uptakes and sorption enthalpies. We also develop mass transport models that precisely reproduce the dynamic absorption and desorption of moisture into hydrogel-salt composites. Altogether, these results demonstrate the main variables that dominate moisture-capturing properties, showing a negligible role of the polymer in the material performance. Our insights guide the synthesis of next-generation humidity-capturing hydrogels and enable their system-level optimization in ways previously unattainable for critical water-energy applications.

Enhancing the power of lithium-ion batteries necessitates understanding the molecular processes governing Li+ ion transfer across the electrode-electrolyte interface. Here, employing enhanced sampling molecular dynamics simulations, we investigated the driving force and the thermodynamic barrier of Li+ ion adsorption onto Li0.5CoO2 (104) in LiClO4-, LiPF6-, and LiTFSI-based EC/EMC (3:7) electrolytes. The weaker cation-anion pairing in LiTFSI compared to LiClO4 was found to enhance the driving force for adsorption from -0.48 eV in LiClO4 to -1.26 eV in LiTFSI for an electrode-electrolyte interface with zero cation coverage, which was accompanied by an increased thermodynamic barrier from 0.33 eV in LiClO4 to 0.43 eV in LiTFSI at equilibrium surface coverage of Li+ ions. The hindered diffusivity of the solvent molecules in the electric double layer (EDL) at the electrode-electrolyte interface was the main contributor to the thermodynamic barrier for ion transport. The entropic component of the thermodynamic barrier was found to be more than one order of magnitude smaller for ClO4 compared to the TFSI-, which can be attributed to the presence of more ClO4 than TFSI- in EDL, causing more structural changes in EDL. The strong dependence of the entropic component of the thermodynamic barrier on the EDL structure enables its decoupling from the enthalpic components (e.g., ion-pairing that can be tuned independently) and thus can be used to control the kinetics of the interfacial transport. This work provides a fundamental understanding of the thermodynamic and kinetic parameters involved in Li+ ion adsorption, which is a crucial step in the performance of Li+ ion batteries.

As demands on Li-ion battery performance increase, the need for electrolytes with high ionic conductivity and a high Li⁺ transference number (t_Li) becomes crucial to boost power density. Unfortunately, t_Li in liquid electrolytes is typically + migrating via a vehicular mechanism, whereby Li⁺ diffuses along with its solvation shell, making its diffusivity slower than the counteranion. Designing liquid electrolytes where the Li⁺ ion diffuses independently of its solvation shell is of significant interest to enhance the transference number. In this work, we elucidate how the properties of the solvent influence the Li⁺ transport mechanism. Using classical molecular dynamics simulations, we find that a vehicular mechanism can be increasingly preferred with a decreasing solvent viscosity and increasing interaction energy between the solvent and Li⁺. Thus, a weaker interaction energy can enhance t_Li through a solvent-exchange mechanism, ultimately improving Li-ion battery performance. Finally, metadynamics simulations show that in electrolytes where a solvent-exchange mechanism is preferable, the energy barrier to changing the coordination environment of Li⁺ is much lower than in electrolytes where a vehicular mechanism dominates.

Open material databases storing thousands of material structures and their properties have become the cornerstone of modern computational materials science. Yet, the raw simulation outputs are generally not shared due to their huge size. In this work, we describe a cloud-based platform to enable fast post-processing of the trajectories and to facilitate sharing of the raw data. As an initial demonstration, our database includes 6286 molecular dynamics trajectories for amorphous polymer electrolytes (5.7 terabytes of data). We create a public analysis library at https://github.com/TRI-AMDD/htp_md to extract ion transport properties from the raw data using expert-designed functions and machine learning models. The analysis is run automatically on the cloud, and the results are uploaded onto an open database. Our platform encourages users to contribute both new trajectory data and analysis functions via public interfaces. Finally, we create a front-end user interface at https://www.htpmd.matr.io/ for browsing and visualization of our data. We envision the platform to be a new way of sharing raw data and new insights for the materials science community.