Search for: All records

Variable temperature electron paramagnetic resonance (VT-EPR) was used to investigate the role of the environment and oxidation states of several coordinated Eu compounds. We find that while Eu(III) chelating complexes are diamagnetic, simple chemical reduction results in the formation of paramagnetic species. In agreement with the distorted D3h symmetry of Eu molecular complexes investigated in this study, the EPR spectrum of reduced complexes showed axially symmetric signals (g⊥ = 2.001 and g∥ = 1.994) that were successfully simulated with two Eu isotopes with nuclear spin 5/2 (151Eu and 153Eu with 48% and 52% natural abundance, respectively) and nuclear g-factors 151Eu/153Eu = 2.27. Illumination of water-soluble complex Eu(dipic)3 at 4 K led to the ligand-to-metal charge transfer (LMCT) that resulted in the formation of Eu(II) in a rhombic environment (gx = 2.006, gy = 1.995, gz = 1.988). The existence of LMCT affects the luminescence of Eu(dipic)3, and pre-reduction of the complex to Eu(II)(dipic)3 reversibly reduces red luminescence with the appearance of a weak CT blue luminescence. Furthermore, encapsulation of a large portion of the dipic ligand with Cucurbit[7]uril, a pumpkin-shaped macrocycle, inhibited ligand-to-metal charge transfer, preventing the formation of Eu(II) upon illumination. 

A lithium-air battery based on lithium oxide (Li₂O) formation can theoretically deliver an energy density that is comparable to that of gasoline. Lithium oxide formation involves a four-electron reaction that is more difficult to achieve than the one- and two-electron reaction processes that result in lithium superoxide (LiO₂) and lithium peroxide (Li₂O₂), respectively. By using a composite polymer electrolyte based on Li₁₀GeP₂S₁₂nanoparticles embedded in a modified polyethylene oxide polymer matrix, we found that Li₂O is the main product in a room temperature solid-state lithium-air battery. The battery is rechargeable for 1000 cycles with a low polarization gap and can operate at high rates. The four-electron reaction is enabled by a mixed ion–electron-conducting discharge product and its interface with air. 

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.