Role of the synthesis route on the properties of hybrid LDH-graphene as basic catalysts

Layered double hydroxides (LDH or HT) or their derived mixed oxides present marked acid-base properties useful in catalysis, but they lead to agglomerate inducing a weak accessibility to the active sites. In this study we report the preparation and characterization of HT/Graphene (HT/rGO) nanocomposites as active and selective basic catalysts for the acetone condensation reaction. The graphene high specific surface area and structural compatibility with the HT allowed increasing the number and accessibility of the active sites and activity of this later. Two series of HT/rGO nanocomposites with 0.5 = HT/rGO = 10 mass ratio were prepared by: i) direct HT coprecipitation in the presence of GO; ii) self-assembly of preformed HT with GO. The prepared HT/rGO nanocomposites were dried either in air at 80 °C or freeze-dried. A series of characterizations showed the great influence of the preparation method and HT/rGO mass ratio on both the nanocomposite structure and catalytic activity. An optimum activity was observed for a HT/rGO = 10 catalyst. Particularly, the highest catalytic activity was found in those nanocomposites obtained by coprecipitation and freeze dried (3 times more active than bulk HT) which can be connected to their structure with a better accessibility to the basic sites.Postprint (author's final draft

Interfacial Morphology Addresses Performance of Perovskite Solar Cells Based on Composite Hole Transporting Materials of Functionalized Reduced Graphene Oxide and P3HT

The development of novel hole transporting materials (HTMs) for perovskite solar cells (PSCs) that can enhance device's reproducibility is a largely pursued goal, even to the detriment of a very high efficiency, since it paves the way to an effective industrialization of this technology. In this work, we study the covalent functionalization of reduced graphene oxide (RGO) flakes with different organic functional groups with the aim of increasing the stability and homogeneity of their dispersion within a poly(3-hexylthiophene) (P3HT) HTM. The selected functional groups are indeed those recalling the two characteristic moieties present in P3HT, i.e., the thienyl and alkyl residues. After preparation and characterization of a number of functionalized RGO@P3HT blends, we test the two containing the highest percentage of dispersed RGO as HTMs in PSCs and compare their performance with that of pristine P3HT and of the standard Spiro-OMeTAD HTM. Results reveal the big influence of the morphology adopted by the single RGO flakes contained in the composite HTM in driving the final device performance and allow to distinguish one of these blends as a promising material for the fabrication of highly reproducible PSCs

The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks

The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One important aspect here is the management of robot errors, including fast and accurate online robot-error detection and correction. Analysis of brain signals from a human interacting with a robot may help identifying robot errors, but accuracies of such analyses have still substantial space for improvement. In this paper we evaluate whether a novel framework based on deep convolutional neural networks (deep ConvNets) could improve the accuracy of decoding robot errors from the EEG of a human observer, both during an object grasping and a pouring task. We show that deep ConvNets reached significantly higher accuracies than both regularized Linear Discriminant Analysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined with rLDA, both widely used EEG classifiers. Deep ConvNets reached mean accuracies of 75% +/- 9 %, rLDA 65% +/- 10% and FB-CSP + rLDA 63% +/- 6% for decoding of erroneous vs. correct trials. Visualization of the time-domain EEG features learned by the ConvNets to decode errors revealed spatiotemporal patterns that reflected differences between the two experimental paradigms. Across subjects, ConvNet decoding accuracies were significantly correlated with those obtained with rLDA, but not CSP, indicating that in the present context ConvNets behaved more 'rLDA-like' (but consistently better), while in a previous decoding study with another task but the same ConvNet architecture, it was found to behave more 'CSP-like'. Our findings thus provide further support for the assumption that deep ConvNets are a versatile addition to the existing toolbox of EEG decoding techniques, and we discuss steps how ConvNet EEG decoding performance could be further optimized

Cross-linked CoMoO4/rGO nanosheets as oxygen reduction catalyst

Development of inexpensive and robust electrocatalysts towards oxygen reduction reaction (ORR) is crucial for the cost-affordable manufacturing of metal-air batteries and fuel cells. Here we show that cross-linked CoMoO4 nanosheets and reduced graphene oxide (CoMoO4/rGO) can be integrated in a hybrid material under one-pot hydrothermal conditions, yielding a composite material with promising catalytic activity for oxygen reduction reaction (ORR). Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) were used to investigate the efficiency of the fabricated CoMoO4/rGO catalyst towards ORR in alkaline conditions. The CoMoO4/rGO composite revealed the main reduction peak and onset potential centered at 0.78 and 0.89 V (vs. RHE), respectively. This study shows that the CoMoO4/rGO composite is a highly promising catalyst for the ORR under alkaline conditions, and potential noble metal replacement cathode in fuel cells and metal-air batteries

Reduction of a Single Layer Graphene Oxide Film on Pt(111)

Graphene oxide (GO) is one of chemically modified graphenes and has been extensively studied worldwide. A monolayer sheet of GO which is chemically produced in solution can be deposited on various substrates. We have proved that use of graphite powder with a large grain size enables preparation of a single layer GO film that is larger than 100 micrometers easily and reproducibly. If it is possible to reduce the GO film completely, one can obtain graphene without mechanical exfoliation. However, the reduction methods employed so far have been insufficient to remove oxygen or to restore the long range order in graphene lattice. In the present work we annealed the GO sheet which was placed on Pt(111) in ultrahigh vacuum. The STM observation of the annealed specimen reveals that a honeycomb lattice appears together with moire structures of long range ordering. The XPS result indicated complete removal of oxygen from the GO sheet. These results would open a new way to synthesize a high quality graphene film, which replaces the conventional CVD method.Comment: 11 pages, 4 figure

Space charge limited conduction with exponential trap distribution in reduced graphene oxide sheets

We elucidate on the low mobility and charge traps of the chemically reduced graphene oxide (RGO) sheets by measuring and analyzing temperature dependent current-voltage characteristics. The RGO sheets were assembled between source and drain electrodes via dielectrophoresis. At low bias voltage the conduction is Ohmic while at high bias voltage and low temperatures the conduction becomes space charge limited with an exponential distribution of traps. We estimate an average trap density of 1.75x10^16 cm^-3. Quantitative information about charge traps will help develop optimization strategies of passivating defects in order to fabricate high quality solution processed graphene devices.Comment: 6 pages, 3 figures, 1 tabl

\u3cem\u3eIn vitro\u3c/em\u3e Effect of Graphene Structures as an Osteoinductive Factor in Bone Tissue Engineering: A Systematic Review

Graphene and its derivatives have been well‐known as influential factors in differentiating stem/progenitor cells toward the osteoblastic lineage. However, there have been many controversies in the literature regarding the parameters effect on bone regeneration, including graphene concentration, size, type, dimension, hydrophilicity, functionalization, and composition. This study attempts to produce a comprehensive review regarding the given parameters and their effects on stimulating cell behaviors such as proliferation, viability, attachment and osteogenic differentiation. In this study, a systematic search of MEDLINE database was conducted for in vitro studies on the use of graphene and its derivatives for bone tissue engineering from January 2000 to February 2018, organized according to the PRISMA statement. According to reviewed articles, different graphene derivative, including graphene, graphene oxide (GO) and reduced graphene oxide (RGO) with mass ratio ≤1.5 wt % for all and concentration up to 50 μg/mL for graphene and GO, and 60 μg/mL for RGO, are considered to be safe for most cell types. However, these concentrations highly depend on the types of cells. It was discovered that graphene with lateral size less than 5 µm, along with GO and RGO with lateral dimension less than 1 µm decrease cell viability. In addition, the three‐dimensional structure of graphene can promote cell‐cell interaction, migration and proliferation. When graphene and its derivatives are incorporated with metals, polymers, and minerals, they frequently show promoted mechanical properties and bioactivity. Last, graphene and its derivatives have been found to increase the surface roughness and porosity, which can highly enhance cell adhesion and differentiation