Neptunyl Peroxide Chemistry: Synthesis and Spectroscopic Characterization of a Neptunyl Triperoxide Compound, Ca2[NpO2(O2)3]·9H2O.

Little is known about the crystal chemistry of neptunyl peroxide compounds compared to uranyl peroxide compounds, for which dozens of structures have been described. Uranyl peroxides are formed over a broad range of pH and solution conditions, but neptunyl peroxide chemistry is complicated by the ability of H2O2 to act as an oxidizing or reducing agent for Np, depending on the conditions present. The combination of Np(V) in 1 M HCl, H2O2, and CaCl2 under alkaline conditions leads to the immediate crystallization of a neptunyl triperoxide monomer, Ca2[NpO2(O2)3]·9H2O, which is the first Np(VI)-based peroxide compound to be characterized in the solid state and is isostructural to Ca2[UO2(O2)3]·9H2O. The crystal structure reveals bond distances of 1.842(7) Å that are the longest reported to date for nonbridging Np(VI)-Oyl bonds. Computational studies probe the oxidation state and bond distances of the monomer unit and differences in Raman spectra of the neptunyl and uranyl triperoxide compounds.

C2 -Symmetric Bicyclic Bisborane Catalysts: Kinetic or Thermodynamic Products of a Reversible Hydroboration of Dienes.

We prepared a new class of chiral C2 -symmetric bicyclic bisborane catalysts by addition reactions of internal dienes with the Piers borane, HB(C6 F5 )2 , and an analogue, HB(p-C6 F4 H)2 . The dependence of the addition pattern on the reaction temperature allowed us to selectively prepare two diastereomeric catalysts from a single diene precursor. The bisboranes prepared in situ exhibited excellent activity (turnover numbers up to 200 at -40 °C) and enantioselectivity (up to 95 % ee) in imine hydrogenation reactions.

A Pseudo-Surfactant Chemical Permeation Enhancer to Treat Otitis Media via Sustained Transtympanic Delivery of Antibiotics.

Chemical permeation enhancers (CPEs) represent a prevalent and safe strategy to enable noninvasive drug delivery across skin-like biological barriers such as the tympanic membrane (TM). While most existing CPEs interact strongly with the lipid bilayers in the stratum corneum to create defects as diffusion paths, their interactions with the delivery system, such as polymers forming a hydrogel, can compromise gelation, formulation stability, and drug diffusion. To overcome this challenge, differing interactions between CPEs and the hydrogel system are explored, especially those with sodium dodecyl sulfate (SDS), an ionic surfactant and a common CPE, and those with methyl laurate (ML), a nonionic counterpart with a similar length alkyl chain. Notably, the use of ML effectively decouples permeation enhancement from gelation, enabling sustained delivery across TMs to treat acute otitis media (AOM), which is not possible with the use of SDS. Ciprofloxacin and ML are shown to form a pseudo-surfactant that significantly boosts transtympanic permeation. The middle ear ciprofloxacin concentration is increased by 70-fold in vivo in a chinchilla AOM model, yielding superior efficacy and biocompatibility than the previous highest-performing formulation. Beyond improved efficacy and biocompatibility, this single-CPE formulation significantly accelerates its progression toward clinical deployment.

Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach.

Molecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the application of ab initio potential energy surface, the nuclear quantum effect (NQE) is becoming increasingly important for the robustness of the simulation. However, the state-of-the-art path-integral molecular dynamics simulation, which incorporates NQE in MM, is still too expensive to conduct for most biological and material systems. In this work, we analyze the locality of NQE, using both analytical and numerical approaches, and conclude that NQE is an extremely localized phenomenon in nonreactive molecular systems. Therefore, we can use localized machine learning (ML) models to predict quantum force corrections both accurately and efficiently. Using liquid water as example, we show that the ML facilitated centroid MD can reproduce the NQEs in both the thermodynamical and the dynamical properties, with a minimal increase in computational time compared to classical molecular dynamics. This simple approach thus largely decreases the computational cost of quantum simulations, making it really accessible to the studies of large-scale molecular systems.

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation.

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn-Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a "black-box" mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.