The undesirable acetylation of cellulose by the acetate ion of 1-ethyl-3-methylimidazolium acetate

The ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate ([C2mim]OAc) is considered to be an inert solvent of cellulose and lignocellulosic biomass. Acetylation (1.7 % mol, or DS 0.017) of cellulose after dissolution in [C2mim]OAc (150 °C for 20 min), is demonstrated by compositional analysis, FTIR analysis and 13C NMR spectroscopy (in [C2min]OAc with 13C enriched acetate). This acetylation, in the absence of added acylating agents, has not been reported before and may limit [C2mim]OAc utility in industrial scale biomass processing, even at this low extent. For example, cellulose acetylation may contribute to IL loss in processes where the IL is recovered and reused and inhibit enzyme saccharification of cellulose in lignocellulosic biofuel production processes based on saccharification and fermentation.

Tuning the reduction power of supported gold nanoparticle photocatalysts for selective reductions by manipulating wavelength of visible light irradiation

Gold nanoparticles supported on CeO2 were found to be efficient photocatalysts for three selective reductions of organic compounds at ambient temperatures, under irradiation of visible light; their reduction ability can be tuned by manipulating the irradiation wavelength.

Controlled growth of carbon nanotubes for electronic and photovoltaic applications

Carbon nanotubes (CNTs), experimentally observed for the first time twenty years ago, have triggered an unprecedented research effort, on the account of their astonishing structural, mechanical and electronic properties. Unfortunately, the current inability in predicting the CNTs’ properties and the difficulty in controlling their position on a substrate are often limiting factors for the application of this material in actual devices. This research aims at the creation of specific methodologies for controlled synthesis of CNTs, leading to effectively employ them in various fields of electronics, e.g. photovoltaics. Focused Ion Beam (FIB) patterning of Si surfaces is here proposed as a means for ordering the assembly of vertical-aligned CNTs. With this technique, substrates with specific nano-structured morphologies have been prepared, enabling a high degree of control over CNTs’ position and size. On these nano-structured substrates, the growth of CNTs has been realized by chemical vapor deposition (CVD), i.e. thermal decomposition of hydrocarbon gases over a heated catalyst. The most common materials used as catalysts in CVD are transition metals like Fe and Ni; however, their presence in the CNT products often results in shortcomings for electronic applications, especially for those based on silicon, being the metallic impurities incompatible with very-large-scale integration (VLSI) technology. In the present work the role of Ge dots as an alternative catalysts for CNTs synthesis on Si substrates has been thoroughly assessed, finding a close connection between the catalytic activity of such material and the CVD conditions, which can affect both size and morphology of the dots. Successful CNT growths from Ge dots have been obtained by CVD at temperatures ranging from 750 to 1000°C, with mixtures of acetylene and hydrogen in an argon carrier gas. The morphology of the Si surface is observed to play a crucial role for the outcome of the CNT synthesis: natural (i.e. chemical etching) and artificial (i.e. FIB patterning, nanoindentation) means of altering this morphology in a controlled way have been then explored to optimize the CNTs yield. All the knowledge acquired in this study has been finally applied to synthesize CNTs on transparent conductive electrodes (indium-tin oxide, ITO, coated glasses), for the creation of a new class of anodes for organic photovoltaics. An accurate procedure has been established which guarantees a controlled inclusion of CNTs on ITO films, preserving their optical and electrical properties. By using this set of conditions, a CNTenhanced electrode has been built, contributing to improve the power conversion efficiency of polymeric solar cells.

Zeolite supported gold nanoparticles for selective photooxidation of aromatic alcohols under visible light irradiation

With new photocatalysts of gold nanoparticles supported on zeolite supports (Au/zeolite), oxidation of benzyl alcohol and its derivatives into the corresponding aldehydes can proceed well with a high selectivity (99%) under visible light irradiation at ambient temperature. Au/zeolite photocatalysts were characterized by UV/Vis, XPS, TEM, XRD, EDS, BET, IR, and Raman techniques. The Surface Plasmon Resonance (SPR) effect of gold nanoparticles, the adsorption capability of zeolite supports, and the molecular polarities of aromatic alcohols were demonstrated to have an essential correlation with the photocatalytic performances. In addition, the effects of light intensity, wavelength range, and the role of molecular oxygen were investigated in detail. The kinetic study indicated that the visible light irradiation required much less apparent activation energy for photooxidation compared with thermal reaction. Based on the characterization data and the photocatalytic performances, we proposed a possible photooxidation mechanism.

Modeling ion channel dynamics through reflected stochastic differential equations

Ion channels are membrane proteins that open and close at random and play a vital role in the electrical dynamics of excitable cells. The stochastic nature of the conformational changes these proteins undergo can be significant, however current stochastic modeling methodologies limit the ability to study such systems. Discrete-state Markov chain models are seen as the "gold standard," but are computationally intensive, restricting investigation of stochastic effects to the single-cell level. Continuous stochastic methods that use stochastic differential equations (SDEs) to model the system are more efficient but can lead to simulations that have no biological meaning. In this paper we show that modeling the behavior of ion channel dynamics by a reflected SDE ensures biologically realistic simulations, and we argue that this model follows from the continuous approximation of the discrete-state Markov chain model. Open channel and action potential statistics from simulations of ion channel dynamics using the reflected SDE are compared with those of a discrete-state Markov chain method. Results show that the reflected SDE simulations are in good agreement with the discrete-state approach. The reflected SDE model therefore provides a computationally efficient method to simulate ion channel dynamics while preserving the distributional properties of the discrete-state Markov chain model and also ensuring biologically realistic solutions. This framework could easily be extended to other biochemical reaction networks. © 2012 American Physical Society.