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The inner space of carbon nanotubes has already been proven to provide a type of confinement, which can dramatically alter the energetics of chemical reactions when compared to the gas phase. Moreover, BN doping can be used to fine-tune electronic properties, which might influence the enthalpy and activation energy of chemical reactions that take place inside their inner space. The energy profile of the prototype Menshutkin SN2 reaction between ammonia and chloromethane has been analyzed in a variety of carbon-based materials at the DFT (density functional theory) level. Pristine zigzag (9,0) and (12,0) single-walled carbon nanotubes and graphene sheets have been doped with boron and nitrogen at different stoichiometries, namely, BN, BNC, BNC2, and BNC4, that resulted in remarkable variations of their catalytic effects. Graphene has revealed to be the support material, which depends less on doping in terms of enthalpy and energy barrier of the reaction. However, when graphene is rolled up into tubular forms, the influence of doping becomes increasingly stronger as the nanotube radius decreases. In the case of BNC4doping of (9,0) nanotubes, the activation energy drops 10 kcal/mol when compared to the pristine case, and the reaction became even exothermic by more than 15 kcal/mol.

The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3Dstructure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely α-helices and β-sheets, which are intermediate levels of the local structure.

The interaction of nickel(II) and cobalt(II) with a set of 3‐hydroxy‐4‐pyridinone ligands (Hmpp, Hmepp, Hetpp, Hempp, Hdepp and Hdmpp) was investigated in aqueous solution. The family of chelators under study is pharmaceutically relevant in the treatment of iron‐overloaded patients and it is foreseen to be of use in occupational health situations associated with accumulation of nickel and cobalt such as nasal/pulmonary and skin hazards and allergies. Ligand dissociation constants and stability constants for the cobalt(II) and nickel(II) complexes have been determined by potentiometric methods allowing the establishment of the corresponding speciation plots as a function of pH. The values of the stability constants are consistent with the Irving–Williams series and indicate the formation of stable ML2complexes which were isolated and characterized. Following the trend observed for 3‐hydroxy‐4‐pyridinone ligands the solids were obtained as hydrates whose composition was characterized as [ML2(H2O)2]·xH2O complexes. DFT calculations allowed geometry optimization and provided information about bond lengths and angles.

Graphene oxide (GO), a nanomaterial with promising applications that range from water purification to enzyme immobilization, is actively present in scientific research since its discovery. GO studies with computational methodologies such as molecular dynamics are frequently reported in the literature; however, the models used often rely on approximations, such as randomly placing functional groups and the use of generalized force fields. Therefore, it is important to develop new MD models that provide a more accurate description of GO structures and their interaction with an aqueous solvent and other adsorbate molecules. In this paper, we derived new force field non-bonded parameters from linear-scaling density functional theory calculations of nanoscale GO sheets with more than 10,000 atoms through an atoms-in-molecules (AIM) partitioning scheme. The resulting GAFF2-AIM force field, derived from the bonded terms of GAFF2 parameterization, reproduces the solvent structure reported in ab initio MD simulations better than the force field nowadays widely used in the literature. Additionally, we analyzed the effect of the ionic strength of the medium and of the C/O ratio on the distribution of charges surrounding the GO sheets. Finally, we simulated the adsorption of natural amino acid molecules to a GO sheet and estimated their free energy of binding, which compared very favorably to their respective experimental values, validating the force field presented in this work.