In-plane staging in lithium-ion intercalation of bilayer graphene.

The ongoing efforts to optimize rechargeable Li-ion batteries led to the interest in intercalation of nanoscale layered compounds, including bilayer graphene. Its lithium intercalation has been demonstrated recently but the mechanisms underpinning the storage capacity remain poorly understood. Here, using magnetotransport measurements, we report in-operando intercalation dynamics of bilayer graphene. Unexpectedly, we find four distinct intercalation stages that correspond to well-defined Li-ion densities. Transitions between the stages occur rapidly (within 1 sec) over the entire device area. We refer to these stages as 'in-plane', with no in-plane analogues in bulk graphite. The fully intercalated bilayers represent a stoichiometric compound C14LiC14 with a Li density of ∼2.7·1014 cm-2, notably lower than fully intercalated graphite. Combining the experimental findings and DFT calculations, we show that the critical step in bilayer intercalation is a transition from AB to AA stacking which occurs at a density of ∼0.9·1014 cm-2. Our findings reveal the mechanism and limits for electrochemical intercalation of bilayer graphene and suggest possible avenues for increasing the Li storage capacity.

Electric field induced associations in the double layer of salt-in-ionic-liquid electrolytes.

Ionic liquids (ILs) are an extremely exciting class of electrolytes for energy storage applications. Upon dissolving alkali metal salts, such as Li or Na based salts, with the same anion as the IL, an intrinsically asymmetric electrolyte can be created for use in batteries, known as a salt-in-ionic liquid (SiIL). These SiILs have been well studied in the bulk, where negative transference numbers of the alkali metal cation have been observed from the formation of small, negatively charged clusters. The properties of these SiILs at electrified interfaces, however, have received little to no attention. Here, we develop a theory for the electrical double layer (EDL) of SiILs where we consistently account for the thermoreversible association of ions into Cayley tree aggregates. The theory predicts that the IL cations first populate the EDL at negative voltages, as they are not strongly bound to the anions. However, at large negative voltages, which are strong enough to break the alkali metal cation-anion associations, these IL cations are exchanged for the alkali metal cation because of their higher charge density. At positive voltages, we find that the SiIL actually becomes more aggregated while screening the electrode charge from the formation of large, negatively charged aggregates. Therefore, in contrast to conventional intuition of associations in the EDL, SiILs appear to become more associated in certain electric fields. We present these theoretical predictions to be verified by molecular dynamics simulations and experimental measurements.

Gate-Switchable Molecular Diffusion on a Graphene Field-Effect Transistor.

Controlling the surface diffusion of particles on 2D devices creates opportunities for advancing microscopic processes such as nanoassembly, thin-film growth, and catalysis. Here, we demonstrate the ability to control the diffusion of F4TCNQ molecules at the surface of clean graphene field-effect transistors (FETs) via electrostatic gating. Tuning the back-gate voltage (VG) of a graphene FET switches molecular adsorbates between negative and neutral charge states, leading to dramatic changes in their diffusion properties. Scanning tunneling microscopy measurements reveal that the diffusivity of neutral molecules decreases rapidly with a decreasing VG and involves rotational diffusion processes. The molecular diffusivity of negatively charged molecules, on the other hand, remains nearly constant over a wide range of applied VG values and is dominated by purely translational processes. First-principles density functional theory calculations confirm that the energy landscapes experienced by neutral vs charged molecules lead to diffusion behavior consistent with experiment. Gate-tunability of the diffusion barrier for F4TCNQ molecules on graphene enables graphene FETs to act as diffusion switches.

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.