Graphene with patterned fluorination: Morphology modulation and implications

Recent years have witnessed a large volume of works on the modification of graphene; however, an understanding of the associated morphology or mechanical properties changes is still lacking, which is vital for its engineering implementation. By taking the C4F fluorination as an example, we find that the morphology of both graphene sheet (GS) and graphene nanoribbon (GNR) can be effectively tailored by fluorination patterning via molecular dynamics simulations. The fluorine atom produces out-of-plane forces which trigger several intriguing morphology changes to monolayer graphene, including zigzag, folded, ruffle, nanoscroll, and chain structures. Notably, for multilayer GNR, the delamination and climbing phenomena of the surface layer are observed. Further studies show that the fluorination pattern can also be utilized to modulate the mechanical properties of graphene, e.g., about 40% increase of the effective yield strain is observed for the examined GNR with fluorination patterns. This study not only demonstrates the significant impacts on the morphology of graphene from fluorination but also suggests an effective avenue to tailor the morphology and thus mechanical properties of GS and GNR.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Improved photovoltaic performance of dye-sensitized solar cells with modified self-assembling highly ordered mesoporous TiO 2 photoanodes

Strategies for improving the photovoltaic performance of dye-sensitized solar cells (DSSCs) are proposed by modifying highly transparent and highly ordered multilayer mesoporous TiO 2 photoanodes through nitrogen-doping and top-coating with a light-scattering layer. The mesoporous TiO 2 photoanodes were fabricated by an evaporation-induced self-assembly method. In regard to the modification methods, the light-scattering layer as a top-coating was proved to be superior to nitrogen-doping in enhancing not only the power conversion efficiency but also the fill factor of DSSCs. The optimized bifunctional photoanode consisted of a 30-layer mesoporous TiO 2 thin film (4.15 μm) and a Degussa P25 light-scattering top-layer (4 μm), which gives rise to a ∼200% higher cell efficiency than for unmodified cells and a fill factor of 0.72. These advantages are attributed to its higher dye adsorption, better light scattering, and faster photon-electron transport. Such a photoanode configuration provides an efficient way to enhance the energy conversion efficiency of DSSCs.