Flexible fluid-based encapsulation platform for water-sensitive materials.

The next-generation semiconductors and devices, such as halide perovskites and flexible electronics, are extremely sensitive to water, thus demanding highly effective protection that not only seals out water in all forms (vapor, droplet, and ice), but simultaneously provides mechanical flexibility, durability, transparency, and self-cleaning. Although various solid-state encapsulation methods have been developed, no strategy is available that can fully meet all the above requirements. Here, we report a bioinspired liquid-based encapsulation strategy that offers protection from water without sacrificing the operational properties of the encapsulated materials. Using halide perovskite as a model system, we show that damage to the perovskite from exposure to water is drastically reduced when it is coated by a polymer matrix with infused hydrophobic oil. With a combination of experimental and simulation studies, we elucidated the fundamental transport mechanisms of ultralow water transmission rate that stem from the ability of the infused liquid to fill-in and reduce defects in the coating layer, thus eliminating the low-energy diffusion pathways, and to cause water molecules to diffuse as clusters, which act together as an excellent water permeation barrier. Importantly, the presence of the liquid, as the central component in this encapsulation method provides a unique possibility of reversing the water transport direction; therefore, the lifetime of enclosed water-sensitive materials could be significantly extended via replenishing the hydrophobic oils regularly. We show that the liquid encapsulation platform presented here has high potential in providing not only water protection of the functional device but also flexibility, optical transparency, and self-healing of the coating layer, which are critical for a variety of applications, such as in perovskite solar cells and bioelectronics.

Spectral Denoising for Accelerated Analysis of Correlated Ionic Transport.

Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive, as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displacement covariance to learn a set of diffusion eigenmodes that encode the correlation structure and form a basis for analyzing the ionic trajectories. This allows systematic reduction of the uncertainty and accelerate computations of ionic conductivity in systems with a steady-state correlation structure. We provide mathematical and numerical proofs of the method's robustness and demonstrate it on realistic electrolyte materials.

Fundamental Limits to the Electrochemical Impedance Stability of Dielectric Elastomers in Bioelectronics.

Incorporation of elastomers into bioelectronics that reduces the mechanical mismatch between electronics and biological systems could potentially improve the long-term electronics-tissue interface. However, the chronic stability of elastomers in physiological conditions has not been systematically studied. Here, using electrochemical impedance spectrum we find that the electrochemical impedance of dielectric elastomers degrades over time in physiological environments. Both experimental and computational results reveal that this phenomenon is due to the diffusion of ions from the physiological solution into elastomers over time. Their conductivity increases by 6 orders of magnitude up to 10-8 S/m. When the passivated conductors are also composed of intrinsically stretchable materials, higher leakage currents can be detected. Scaling analyses suggest fundamental limitations to the electrical performances of interconnects made of stretchable materials.

Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics.

The restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different compositions and morphologies at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of long-time scale restructuring of Pd deposited on Ag using microscopy, spectroscopy, and novel simulation methods. By developing and performing accelerated machine-learning molecular dynamics followed by an automated analysis method, we discover and characterize previously unidentified surface restructuring mechanisms in an unbiased fashion, including Pd-Ag place exchange and Ag pop-out as well as step ascent and descent. Remarkably, layer-by-layer dissolution of Pd into Ag is always preceded by an encapsulation of Pd islands by Ag, resulting in a significant migration of Ag out of the surface and a formation of extensive vacancy pits within a period of microseconds. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. Our approach is broadly applicable to complex multimetallic systems and enables the previously intractable mechanistic investigation of restructuring dynamics at atomic resolution.

Nanoparticle Organization Controls Their Potency as Universal Glues for Gels.

Nanoparticles have been recently shown to act as universal glues for both synthetic and biological gels, providing a tunable, cheap, and general solution to the centuries-old problem of sticking soft materials together. The design of new adhesive solutions based on this platform, however, requires an understanding of how nanoparticles' design parameters concur to determine the final adhesion strength. Here, we use coarse-grained modeling and molecular dynamics simulations to investigate such links. Our main aim is to show that, at experimentally relevant concentrations, adhesion is strongly influenced by the way nanoparticles organize at the interface, resulting in non-monotonous reinforcement behavior. Our findings represent an important step toward rationalizing this new class of nanoparticle-based adhesives.

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials.

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transferable; i.e., the MLIP can be applied to mixtures of ions not directly trained on, while only being trained on a few mixtures of the same ions. We also investigated the accuracy of MLIPs for a novel IL, which we experimentally synthesize and characterize. Our MLIP trained on ∼200 DFT frames is in reasonable agreement with our experiments and DFT.

3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers.

Electronic devices for recording neural activity in the nervous system need to be scalable across large spatial and temporal scales while also providing millisecond and single-cell spatiotemporal resolution. However, existing high-resolution neural recording devices cannot achieve simultaneous scalability on both spatial and temporal levels due to a trade-off between sensor density and mechanical flexibility. Here we introduce a three-dimensional (3D) stacking implantable electronic platform, based on perfluorinated dielectric elastomers and tissue-level soft multilayer electrodes, that enables spatiotemporally scalable single-cell neural electrophysiology in the nervous system. Our elastomers exhibit stable dielectric performance for over a year in physiological solutions and are 10,000 times softer than conventional plastic dielectrics. By leveraging these unique characteristics we develop the packaging of lithographed nanometre-thick electrode arrays in a 3D configuration with a cross-sectional density of 7.6 electrodes per 100 µm2. The resulting 3D integrated multilayer soft electrode array retains tissue-level flexibility, reducing chronic immune responses in mouse neural tissues, and demonstrates the ability to reliably track electrical activity in the mouse brain or spinal cord over months without disrupting animal behaviour.

Understanding Relationships between Free Volume and Oxygen Absorption in Ionic Liquids.

Understanding the factors that govern gas absorption in ionic liquids is critical to the development of high-capacity solvents for catalysis, electrochemistry, and gas separations. Here, we report experimental probes of liquid structure that provide insights into how free volume impacts the O2 absorption properties of ionic liquids. Specifically, we establish that isothermal compressibility─measured rapidly and accurately through small-angle X-ray scattering─reports on the size distribution of transient voids within a representative series of ionic liquids and is correlated with O2 absorption capacity. Additionally, O2 absorption capacities are correlated with thermal expansion coefficients, reflecting the beneficial effect of weak intermolecular interactions in ionic liquids on free volume and gas absorption capacity. Molecular dynamics simulations show that the void size distribution─in particular, the probability of forming larger voids within an ionic liquid─has a greater impact on O2 absorption than the total free volume. These results establish relationships between the ionic liquid structure and gas absorption properties that offer design strategies for ionic liquids with high gas solubilities.

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

Unraveling the Role of Architecture in Polymer-Based Glues for Hydrogels.

We investigate polymers of different architectures as potential candidates for the development of glues for hydrogels. Using a combination of coarse-grained modeling and molecular dynamics simulations, we systematically characterize the link between experimentally tunable parameters and adhesion energy. We find that, for a broad set of parameters, adhesion is controlled almost exclusively by the total amount of glue at the interface and by the glue-hydrogel affinity. Instead, it is largely independent of changes in polymer architecture and size, a conclusion that shines new light on previously observed experimental trends. Additionally, we show that the scaling behavior of the properties we measure can be explained by modeling the glue as an ensemble of ideal, noninteracting, and linear polymer segments. We expect that the fundamental insights herein provided will aid the design of new polymer-based adhesives for hydrogels.

Salt-in-Ionic-Liquid Electrolytes: Ion Network Formation and Negative Effective Charges of Alkali Metal Cations.

Salt-in-ionic liquid electrolytes have attracted significant attention as potential electrolytes for next generation batteries largely due to their safety enhancements over typical organic electrolytes. However, recent experimental and computational studies have shown that under certain conditions alkali cations can migrate in electric fields as if they carried a net negative effective charge. In particular, alkali cations were observed to have negative transference numbers at small mole fractions of alkali-metal salt that revert to the expected net positive transference numbers at large mole fractions. Simulations have provided some insights into these observations, where the formation of asymmetric ionic clusters, as well as a percolating ion network, could largely explain the anomalous transport of alkali cations. However, a thermodynamic theory that captures such phenomena has not been developed, as ionic associations were typically treated via the formation of ion pairs. The theory presented herein, based on the classical polymer theories, describes thermoreversible associations between alkali cations and anions, where the formation of large, asymmetric ionic clusters and a percolating ionic network are a natural result of the theory. Furthermore, we present several general methods to calculate the effective charge of alkali cations in ionic liquids. We note that the negative effective charge is a robust prediction with respect to the parameters of the theory and that the formation of a percolating ionic network leads to the restoration of net positive charges of the cations at large mole fractions of alkali metal salt. Overall, we find excellent qualitative agreement between our theory and molecular simulations in terms of ionic cluster statistics and the effective charges of the alkali cations.

Chelation-Induced Reversal of Negative Cation Transference Number in Ionic Liquid Electrolytes.

Strong anion-cation interaction in lithium-salt/ionic liquid electrolytes leads to ionic association that decreases the Li transference number, even causing it to be negative. We show that these interactions can be greatly reduced by adding cyclic ethylene oxide molecules, and we quantitatively examine the effect using rigorous multispecies concentrated solution theory coupled with molecular dynamics simulations. The added molecules, primarily lithium ionophore V also known as 12-crown-4, have high affinity to lithium, therefore disrupting the lithium cation-anion coupling, resulting in a significantly improved transference number. First, we investigate the lithium-anion spatial correlation by studying their clusters and show that the 12-crown-4 ether allows the formation of previously nonexisting positively charged lithium-containing complexes. We then prove that the chelators actively compete with the anion to coordinate lithium ions by showing that the persistence-over-time of a given anion coordination cage decreases when ionophore molecules are added to the system. Last, we report an increase in the lithium transference number for a variety of chemistries as a function of added 12-crown-4 (and another ionophore, 18-crown-6) molecules, and even positive values can be reached. Our results provide a foundation for new design and optimization strategies to reverse the sign of and increase the transference number in highly correlated concentrated electrolytes.

General Trend of a Negative Li Effective Charge in Ionic Liquid Electrolytes.

We show that strong cation-anion interactions in a wide range of lithium-salt/ionic liquid mixtures result in a negative lithium transference number, using molecular dynamics simulations and rigorous concentrated solution theory. This behavior fundamentally deviates from that obtained using self-diffusion coefficient analysis and explains well recent experimental electrophoretic nuclear magnetic resonance measurements, which account for ion correlations. We extend these findings to several ionic liquid compositions. We investigate the degree of spatial ionic coordination employing single-linkage cluster analysis, unveiling asymmetrical anion-cation clusters. We formulate a way to compute the effective lithium charge and show that lithium-containing clusters carry a negative charge over a remarkably wide range of compositions and concentrations. This finding has significant implications for the overall performance of battery cells based on ionic liquid electrolytes. It also provides a rigorous prediction recipe and design protocol for optimizing transport properties in next-generation highly correlated electrolytes.

Role of solvent-anion charge transfer in oxidative degradation of battery electrolytes.

Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry to investigate the oxidative decomposition that govern voltage stability of multi-component organic electrolytes, we find that electrolyte decomposition is a process involving the solvent and the salt anion and requires explicit treatment of their coupling. We find that the ionization potential of the solvent-anion system is often lower than that of the isolated solvent or the anion. This mutual weakening effect is explained by the formation of the anion-solvent charge-transfer complex, which we study for 16 anion-solvent combinations. This understanding of the oxidation mechanism allows the formulation of a simple predictive model that explains experimentally observed trends in the onset voltages of degradation of electrolytes near the cathode. This model opens opportunities for rapid rational design of stable electrolytes for high-energy batteries.