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.