Reduced resting skeletal muscle protein synthesis is rescued by resistance exercise and protein ingestion following short-term energy deficit

The myofibrillar protein synthesis (MPS) response to resistance exercise (REX) and protein ingestion during energy deficit (ED) is unknown. We determined, in young men (n=8) and women (n=7), protein signaling, resting post-absorptive MPS during energy balance [EB: 45 kcal∙(kg FFM∙d)-1] and after 5d of ED [30 kcal∙(kg FFM∙d)-1] as well as MPS while in ED after acute REX in the fasted state and with the ingestion of whey protein (15 and 30 g). Post-absorptive rates of MPS were 27% lower in ED than EB (P<0.001), but REX stimulated MPS to rates equal to EB. Ingestion of 15 and 30 g of protein after REX in ED increased MPS ~16 and ~34% above resting EB, (P<0.02). p70 S6Kthr389 phosphorylation increased above EB only with combined exercise and protein intake (~2-7 fold; P<0.05). In conclusion, short-term ED reduces post-absorptive MPS, however, a bout of REX in ED restores MPS to values observed at rest in EB. The ingestion of protein after REX further increases MPS above resting EB in a dose-dependent manner. We conclude that combining REX with increased protein availability after exercise enhances rates of skeletal muscle protein synthesis during short term ED and could, in the long term, preserve muscle mass.

Numerical investigation of graphene and graphene-polymer nanocomposites

This thesis is a comprehensive and deep investigation on graphene and graphene-polymer nanocomposites. It explores the strong structure-property relationships in both graphene and graphene-based polymeric nanocomposites. A number of significant conclusions, including failure mechanism in graphene, interfacial load transfer and thermal transport mechanisms in graphene-polymer nanocomposites, have been drawn through both atomistic simulations and theoretical analysis. These results can provide direct guidelines for development of new graphene-based materials and devices.

Effects of HNO3 molecular doping in graphene/Si Schottky barrier solar cells

Schottky barrier solar cells based on graphene/n-silicon heterojunction have been fabricated and characterized and the effect of graphene molecular doping by HNO3 on the solar cells performances have been analyzed. Different doping conditions and thermal annealing processes have been tested to asses and optimize the stability of the devices. The PCE of the cells increases after the treatment by HNO3 and reaches 5% in devices treated at 200 °C immediately before the exposition to the oxidant. Up to now our devices retain about 80% of efficiency over a period of two weeks, which represents a good stability result for similar devices.

Cyclododecane as support material for clean and facile transfer of large-area few-layer graphene

The transfer of chemical vapor deposited graphene is a crucial process, which can affect the quality of the transferred films and compromise their application in devices. Finding a robust and intrinsically clean material capable of easing the transfer of graphene without interfering with its properties remains a challenge. We here propose the use of an organic compound, cyclododecane, as a transfer material. This material can be easily spin coated on graphene and assist the transfer, leaving no residues and requiring no further removal processes. The effectiveness of this transfer method for few-layer graphene on a large area was evaluated and confirmed by microscopy, Raman spectroscopy, x-ray photoemission spectroscopy, and four-point probe measurements. Schottky-barrier solar cells with few-layer graphene were fabricated on silicon wafers by using the cyclododecane transfer method and outperformed reference cells made by standard methods.

Rapid and highly efficient growth of graphene on copper by chemical vapor deposition of ethanol

The growth of graphene by chemical vapor deposition on metal foils is a promising technique to deliver large-area films with high electron mobility. Nowadays, the chemical vapor deposition of hydrocarbons on copper is the most investigated synthesis method, although many other carbon precursors and metal substrates are used too. Among these, ethanol is a safe and inexpensive precursor that seems to offer favorable synthesis kinetics. We explored the growth of graphene on copper from ethanol, focusing on processes of short duration (up to one min). We investigated the produced films by electron microscopy, Raman and X-ray photoemission spectroscopy. A graphene film with high crystalline quality was found to cover the entire copper catalyst substrate in just 20 s, making ethanol appear as a more efficient carbon feedstock than methane and other commonly used precursors.

Graphene-based supercapacitor with carbon nanotube film as highly efficient current collector

Flexible graphene-based thin film supercapacitors were made using carbon nanotube (CNT) films as current collectors and graphene films as electrodes. The graphene sheets were produced by simple electrochemical exfoliation, while the graphene films with controlled thickness were prepared by vacuum filtration. The solid-state supercapacitor was made by using two graphene/CNT films on plastic substrates to sandwich a thin layer of gelled electrolyte. We found that the thin graphene film with thickness <1 μm can greatly increase the capacitance. Using only CNT films as electrodes, the device exhibited a capacitance as low as ~0.4 mF cm−2, whereas by adding a 360 nm thick graphene film to the CNT electrodes led to a ~4.3 mF cm−2 capacitance. We experimentally demonstrated that the conductive CNT film is equivalent to gold as a current collector while it provides a stronger binding force to the graphene film. Combining the high capacitance of the thin graphene film and the high conductivity of the CNT film, our devices exhibited high energy density (8–14 Wh kg−1) and power density (250–450 kW kg−1).

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Predicting reduced driver alertness on monotonous highways : a driving simulator study

Impaired driver alertness increases the likelihood of drivers’ making mistakes and reacting too late to unexpected events while driving. This is particularly a concern on monotonous roads, where a driver’s attention can decrease rapidly. While effective countermeasures do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behavior in real-time. The aim of this study is to predict drivers’ level of alertness through surrogate measures collected from in-vehicle sensors. Electroencephalographic activity is used as a reference to evaluate alertness. Based on a sample of 25 drivers, data was collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device. Various classification models were tested from linear regressions to Bayesians and data mining techniques. Results indicated that Neural Networks were the most efficient model in detecting lapses in alertness. Findings also show that reduced alertness can be predicted up to 5 minutes in advance with 90% accuracy, using surrogate measures such as time to line crossing, blink frequency and skin conductance level. Such a method could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring, in real-time, drivers' behavior on highways.

Formation of carbon nanoscrolls from graphene nanoribbons : a molecular dynamics study

Carbon nanoscrolls (CNSs) are one of the carbon-based nanomaterials similar to carbon nanotubes (CNTs) but are not widely studied in spite of their great potential applications. Their practical applications are hindered by the challenging fabrication of the CNSs. A physical approach has been proposed recently to fabricate the CNS by rolling up a monolayer graphene nanoribbon (GNR) around a CNT driven by the interaction energy between them. In this study, we perform extensive molecular dynamics (MD) simulations to investigate the various factors that impact the formation of the CNS from GNR. Our simulation results show that the formation of the CNS is sensitive to the length of the CNT and temperature. When the GNR is functionalized with hydrogen, the formation of the CNS is determined by the density and distribution of the hydrogen atoms. Graphyne, the allotrope of graphene, is inferior to graphene in the formation of the CNS due to the weaker bonds and the associated smaller atom density. The mechanism behind the rolling of GNR into CNS lies in the balance between the GNR–CNT van der Waals (vdW) interactions and the strain energy of GNR. The present work reveals new important insights and provides useful guidelines for the fabrication of the CNS.

Graphene-type sheets of Nb1-xWxS2 : synthesis and in situ functionalization

Enlightened by the discovery of graphenes, a variety of inorganic analogues have been synthesized and characterized in recent years. Solvated Nb1-xWxS2 analogues of graphene-type sheets were prepared by lithiation and exfoliation of multistacked Nb1-xWxS2 coin roll nanowires (CRNWs), followed by in situ functionalization with gold nanoparticles to synthesize gold-loaded Nb1-xWxS2/Au nanocomposites. The Nb1-xWxS2 nanosheets and the corresponding Nb1-xWxS2/Au nanocomposites were characterized by high resolution electron microscopy (HRTEM), energy-dispersive X-ray spectroscopy (EDX), scanning transmission electron microscopy (STEM), dynamic light scattering (DLS) and scanning force microscopy (AFM). The graphene-type sheets are stable in water and other solvents and can be functionalized similarly as chalcogen-terminated surfaces (e.g. with Au nanoparticles).