Dynamics of Anthracene Excimer Formation within a Water-Soluble Nanocavity at Room Temperature.

Excited anthracene is well-known to photodimerize and not to exhibit excimer emission in isotropic organic solvents. Anthracene (AN) forms two types of supramolecular host-guest complexes (2:1 and 2:2, H:G) with the synthetic host octa acid in aqueous medium. Excitation of the 2:2 complex results in intense excimer emission, as reported previously, while the 2:1 complex, as expected, yields only monomer emission. This study includes confirming of host-guest complexation by NMR, probing the host-guest structure by molecular dynamics simulation, following the dynamics AN molecules in the excited state by ultrafast time-resolved experiments, and mapping of the excited surface through quantum chemical calculations (QM/MM-TDDFT method). Importantly, time-resolved emission experiments revealed the excimer emission maximum to be time dependent. This observation is unique and is not in line with the textbook examples of time-independent monomer-excimer emission maxima of aromatics in solution. The presence of at least one intermediate between the monomer and the excimer is inferred from time-resolved area normalized emission spectra. Potential energy curves calculated for the ground and excited states of two adjacent anthracene molecules via the QM/MM-TDDFT method support the model proposed on the basis of time-resolved experiments. The results presented here on the excited-state behavior of a well-investigated aromatic molecule, namely the parent anthracene, establish that the behavior of a molecule drastically changes under confinement. The results presented here have implications on the behavior of molecules in biological systems.

Transferable Deep Learning Potential Reveals Intermediate-Range Ordering Effects in LiF-NaF-ZrF4 Molten Salt.

LiF-NaF-ZrF4 multicomponent molten salts are promising candidate coolants for advanced clean energy systems owing to their desirable thermophysical and transport properties. However, the complex structures enabling these properties, and their dependence on composition, is scarcely quantified due to limitations in simulating and interpreting experimental spectra of highly disordered, intermediate-ranged structures. Specifically, size-limited ab initio simulations and accuracy-limited classical models used in the past are unable to capture a wide range of fluctuating motifs found in the extended heterogeneous structures of liquid salt. This greatly inhibits our ability to design tailored compositions and materials. Here, accurate, efficient, and transferable machine learning potentials are used to predict structures far beyond the first coordination shell in LiF-NaF-ZrF4. Neural networks trained at only eutectic compositions with 29% and 37% ZrF4 are shown to accurately simulate a wide range of compositions (11-40% ZrF4) with dramatically different coordination chemistries, while showing a remarkable agreement with theoretical and experimental Raman spectra. The theoretical Raman calculations further uncovered the previously unseen shift and flattening of bending band at ∼250 cm-1 which validated the simulated extended-range structures as observed in compositions with higher than 29% ZrF4 content. In such cases, machine learning-based simulations capable of accessing larger time and length scales (beyond 17 Å) were critical for accurately predicting both structure and ionic diffusivities.