Facile Synthesis of Gold Nanoparticles by Amino Acid Asparagine: Selective Sensing of Arsenic.

The amino acid asparagine (ASP) was used as a benign reducing and stabilizing agent for the production of monodisperse gold nanoparticles (AuNPs) using green chemistry principles. With an increasing concentration of ASP (0.5 to 10 mM), the absorbance intensity at 525 nm increased; however, no effects on the color, size, or shape of the AuNPs were observed.

Dynamic modelling of high biomass density cultivation and biohydrogen production in different scales of flat plate photobioreactors.

This paper investigates the scaling-up of cyanobacterial biomass cultivation and biohydrogen production from laboratory to industrial scale. Two main aspects are investigated and presented, which to the best of our knowledge have never been addressed, namely the construction of an accurate dynamic model to simulate cyanobacterial photo-heterotrophic growth and biohydrogen production and the prediction of the maximum biomass and hydrogen production in different scales of photobioreactors. To achieve the current goals, experimental data obtained from a laboratory experimental setup are fitted by a dynamic model.

Analysis of green algal growth via dynamic model simulation and process optimization.

Chlamydomonas reinhardtii is a green microalga with the potential to generate sustainable biofuels for the future. Process simulation models are required to predict the impact of laboratory-scale growth experiments on future scaled-up system operation. Two dynamic models were constructed to simulate C.

Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach.

Background: Metabolism is a vital cellular process, and its malfunction can be a major contributor to many human diseases. Metabolites can serve as a metabolic disease biomarker. An detection of such biomarkers plays a significant role in the study of biochemical reaction and signaling networks.

Individual-based and stochastic modeling of cell population dynamics considering substrate dependency.

In this article, we propose an individual-based and stochastic modeling approach that is capable of describing the bacterial cell population dynamics during a batch culture. All stochastic nature inherent in intracellular molecular level reactions and cell division processes were considered in a single model framework by embedding a sub-model describing individual cell's growth kinetics in a discrete event simulation algorithm. The resultant unique feature of the model is that the effects of the stochasticities on the cell population dynamics can be investigated for different substrate-dependent cell growth kinetics.