In this paper we show a very simple route for the incorporation of catalytically active niobium species on the surface of carbon materials, such as graphene oxide, carbon nanotubes and activated carbon. Some existing methods of incorporating a transition metal on a support have involved co-precipitation or wet impregnation, to obtain the corresponding oxides. These methods, however, cause reduction in the specific area of the support and can also form large metal oxide particles with loss of metal exposure. Therefore, here we present a novel way to add catalytically active species on the surfaces of different types of carbon through the formation of interaction complexes between the metal precursor and the functional groups of the carbon matrix. Because of the excellent catalytic properties exhibited by the niobium species we choose the NH4[NbO(C2O4)2(H2O)2]·2H2O salt as the model precursor. The characterization by XPS reveals the presence of the niobium species indicated by the displacement of the peaks between 206-212 eV related to the oxalate species according to the spectrum from pure niobium oxalate. Images obtained by TEM and SEM show the typical morphologies of carbonaceous materials without the niobium oxide formation signal, which indicates the presence of niobium complexes as isolated sites on the carbon surfaces. This new class of materials exhibited excellent properties as catalysts for pollutant oxidation. The presence of Nb promotes the catalytic activation of H2O2 generating hydroxyl radicals in situ, which allows their use in the organic compound oxidation processes. Tests for DBT oxidation indicate that Nb significantly improves the removal of such pollutants in biphasic reactions with removal around 90% under the tested conditions. Theoretical calculations showed that the most favorable adsorption model is an ionic complex presenting a ΔG = -108.7 kcal mol(-1) for the whole adsorption process.

In this article, the one-effective mode Marcus-Jortner-Levich (MJL) theory and the classical Marcus theory for electron transfer were applied to estimate the internal conversion rate constant, k IC , of organic molecules and a Rubased complex, all belonging to the Marcus inverted region. For this, the reorganization energy was calculated using the minimum energy conical intersection point to account for more vibrational levels, correcting the density of states. The results showed good agreement with experimental and theoretically determined k IC , with a small overestimation by the Marcus theory. Also, molecules less dependent on the solvent effects, like benzophenone, presented better results than molecules with an expressive dependence, like 1-aminonaphthalene. Moreover, the results suggest that each molecule possesses unique normal modes leading to the excited state deactivation that does not necessarily match the X-H bond stretching, as previously suggested.

Graphic abstract Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models—decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure–activity relationship model (SAR). Supplementary Information The online version contains supplementary material available at 10.1007/s11030-021-10261-z.