Dissociative adsorption of H2 molecules on steric graphene surface: Ab initio MD study based on DFT

Improving Vibrational Mode Interpretation Using Bayesian Regression

To streamline the interpretation of vibrational spectra, this work introduces the use of Bayesian linear regression with automatic relevance determination as a viable approach to decompose the atomic motions along any vibrational mode as a weighted combination of displacements along chemically meaningful internal coordinates. This novel approach denominated vibrational mode automatic relevance determination (VMARD) is presented and compared with the well-established potential energy decomposition (PED) scheme. Good agreement is generally attained between the two methods. VMARD returns a decomposition of the atomic displacement using only a small number of internal coordinates, thus aiding the interpretation of the vibrational spectra. Moreover, the results show that the VMARD descriptions are resilient toward the addition of additional internal coordinates, achieving a concise description of the vibrational modes despite the use of redundant internal coordinates. Potential applications of VMARD involving the gathering of physical insights on the atomic motions along the reaction coordinate at transition state structures, as well as the improvement of theoretically predicted vibrational frequencies, are also presented under a proof-of-concept perspective.

Exciting H2 Molecules for Graphene Functionalization

Hydrogen functionalization of graphene by exposure to vibrationally excited H₂ molecules is investigated by combined scanning tunneling microscopy, high-resolution electron energy loss spectroscopy, X-ray photoelectron spectroscopy measurements, and density functional theory calculations. The measurements reveal that vibrationally excited H₂ molecules dissociatively adsorb on graphene on Ir(111) resulting in nanopatterned hydrogen functionalization structures. Calculations demonstrate that the presence of the Ir surface below the graphene lowers the H₂ dissociative adsorption barrier and allows for the adsorption reaction at energies well below the dissociation threshold of the H-H bond. The first reacting H₂ molecule must contain considerable vibrational energy to overcome the dissociative adsorption barrier. However, this initial adsorption further activates the surface resulting in reduced barriers for dissociative adsorption of subsequent H₂ molecules. This enables functionalization by H₂ molecules with lower vibrational energy, yielding an avalanche effect for the hydrogenation reaction. These results provide an example of a catalytically active graphene-coated surface and additionally set the stage for a re-interpretation of previous experimental work involving elevated H₂ background gas pressures in the presence of hot filaments.

Reaction-diffusion wave model for self-assembled network formation of poly(dA)·poly(dT) DNA on mica and HOPG surfaces

Deoxyribonucleic acid (DNA) is a vital molecule for life since it contains genetic information. However, DNA has recently been reported to have unique properties that make it suitable for bionanoelectronic applications, such as the possibility of electrical conductivity and self-organisation. Self-assembled DNA network structures have been observed on several substrates, but the detailed self-assembly mechanism has yet to be determined. The present study investigates self-assembled structures of DNA both theoretically and experimentally. We developed a reaction-diffusion model and used it to investigate pattern formations observed by atomic force microscopy. The computational results qualitatively replicate the network patterns of DNA molecules based on a quantitative agreement with the surface size and timescale. The model can account for the effect of the DNA concentration on pattern formation. Furthermore, peculiar geometric patterns are simulated for mica and highly oriented pyrolytic graphite surfaces.