Encapsulation of Hydrogen Molecules in C50 Fullerene: An ab Initio Study of Structural, Energetic, and Electronic Properties of H2@C50 and 2H2@C50 Complexes.

Various DFT functionals, including those containing long-range interactions and dispersion, together with HF and MP2 theoretical methods, were used to identify the number of H2 molecules that can be encapsulated inside a C50 cage. It is demonstrated that the 2H2@C50 complex is thermodynamically unstable based on its positive complexation energy. Some discrepancies, however, were found with respect to the stability of the H2@C50 complex. Indeed, SVWN5, PBEPBE, MP2, B2PLYP, and B2PLYPD calculations confirmed that the H2@C50 complex is thermodynamically stable, while HF, BP86, B3LYP, BHandHLYP, LC-wPBE, CAM-B3LYP, and wB97XD showed that this complex is thermodynamically unstable. Nevertheless, examination of strain and dispersion energies further supported the fact that one H2 molecule can indeed be encapsulated inside the C50 cage. Other factors, such as the host-guest interactions and bond dissociation energy, were analyzed and discussed.

Escherichia coli bacteria detection by using graphene-based biosensor.

Graphene is an allotrope of carbon with two-dimensional (2D) monolayer honeycombs. A larger detection area and higher sensitivity can be provided by graphene-based nanosenor because of its 2D structure. In addition, owing to its special characteristics, including electrical, optical and physical properties, graphene is known as a more suitable candidate compared to other materials used in the sensor application. A novel model employing a field-effect transistor structure using graphene is proposed and the current-voltage (I-V) characteristics of graphene are employed to model the sensing mechanism. This biosensor can detect Escherichia coli (E. coli) bacteria, providing high levels of sensitivity. It is observed that the graphene device experiences a drastic increase in conductance when exposed to E. coli bacteria at 0-10(5) cfu/ml concentration. The simple, fast response and high sensitivity of this nanoelectronic biosensor make it a suitable device in screening and functional studies of antibacterial drugs and an ideal high-throughput platform which can detect any pathogenic bacteria. Artificial neural network and support vector regression algorithms have also been used to provide other models for the I-V characteristic. A satisfactory agreement has been presented by comparison between the proposed models with the experimental data.