Phenalenyl and a wide variety of its derivatives form stable radicals, which often associate in various aggregates with interesting properties that include magnetism and high electrical conductivity. The two main modes of aggregation involve π-stacking pancake multicenter bond formation and σ-bond formation. We explore the energetics of the various σ- and π-dimers for six phenalenyl derivatives with both computational and experimental methods. A modern density functional theory (M05-2X) is used to survey the potential energy surface revealing the mechanism of the aggregation. In order to enrich experimental data, the triphenyl and trimethyl derivatives are newly prepared and their aggregation behaviors are investigated by various analytical methods including ESR, (1)H NMR, UV-vis, and single-crystal X-ray diffraction. The agreement between computations and experiments are very good forming the basis of describing trends in this series. We find that π-dimer formation can proceed via an asynchronous concerted path from the monomers or in a stepwise process via σ-dimers. The strength of the π-stacking pancake interaction depends strongly on substituents and covers a wide range both in terms of binding energies and contact distances. The spin densities in the π-stacking dimers reflect these trends and display a wide range of diradicaloid characters. Many σ-dimer configurations compete some of which are separated by small barriers leading to fluxional structures between σ-bonded configurations or σ- and π-bonded configurations.

3D Fe3O4-graphene nanocomposites were conveniently prepared via a direct hydrothermal grafting method. On the basis of the unique properties of both single-crystalline Fe3O4 and 3D chemically reduced graphene oxide, with characteristics such as ultralow density and high surface area, the as-prepared graphene-Fe3O4 nanocomposites showed high-performance microwave absorption ability and have the potential for application as advanced microwave absorbers.

Direct evidence for σ-bond fluxionality in a phenalenyl σ-dimer was successfully obtained by a detailed investigation of the solution-state dynamics of 2,5,8-trimethylphenalenyl (TMPLY) using both experimental and theoretical approaches. TMPLY formed three diamagnetic dimers, namely, the σ-dimer (RR/SS), σ-dimer (RS), and π-dimer, which were fully characterized by (1)H NMR spectroscopy and electronic absorption measurements. The experimental findings gave the first quantitative insights into the essential preference of these competitive and unusual dimerization modes. The spectroscopic analyses suggested that the σ-dimer (RR/SS) is the most stable in terms of energy, whereas the others are metastable; the energy differences between these three isomers are less than 1 kcal mol(-1). Furthermore, the intriguing dynamics of the TMPLY dimers in the solution state were fully revealed by means of (1)H-(1)H exchange spectroscopy (EXSY) measurements and variable-temperature (1)H NMR studies. Surprisingly, the σ-dimer (RR/SS) demonstrated a sixfold σ-bond shift between the six sets of α-carbon pairs. This unusual σ-bond fluxionality is ascribed to the presence of a direct interconversion pathway between the σ-dimer (RR/SS) and the π-dimer, which was unambiguously corroborated by the EXSY measurements. The proposed mechanism of the sixfold σ-bond shift based on the experimental findings was well-supported by theoretical calculations.

Conjugated radicals are capable of forming π-stacking "pancake-bonded" dimers. Members of the family of triangulene hydrocarbons, non-Kekulé neutral multiradicals, can utilize more than one singly occupied molecular orbital (SOMO) to form multiple pancake-bonded dimers with formal bond orders of up to five. The resulting dimer binding energies can be quite high and the intermolecular contacts rather small compared to the respective van der Waals values. The preferred configurations are driven by the large stabilization energy of overlapping SOMOs.

Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequence‐ and structure‐based annotation approaches are often accurate for predicting broad categories of substrate specificity, they generally cannot predict which specific molecules will be accepted as substrates for a given enzyme, particularly within a class of closely related molecules. Combining targeted experimental activity data with structural modeling, ligand docking, and physicochemical properties of proteins and ligands with various machine learning models provides complementary information that can lead to accurate predictions of substrate scope for related enzymes. Here we describe such an approach that can predict the substrate scope of bacterial nitrilases, which catalyze the hydrolysis of nitrile compounds to the corresponding carboxylic acids and ammonia. Each of the four machine learning models (logistic regression, random forest, gradient‐boosted decision trees, and support vector machines) performed similarly (average ROC = 0.9, average accuracy = ~82%) for predicting substrate scope for this dataset, although random forest offers some advantages. This approach is intended to be highly modular with respect to physicochemical property calculations and software used for structural modeling and docking.