The antimicrobial effects of silver (Ag) ion or salts are well known, but the effects of Ag nanoparticles on microorganisms and antimicrobial mechanism have not been revealed clearly. Stable Ag nanoparticles were prepared and their shape and size distribution characterized by particle characterizer and transmission electron microscopic study. The antimicrobial activity of Ag nanoparticles was investigated against yeast, Escherichia coli, and Staphylococcus aureus. In these tests, Muller Hinton agar plates were used and Ag nanoparticles of various concentrations were supplemented in liquid systems. As results, yeast and E. coli were inhibited at the low concentration of Ag nanoparticles, whereas the growth-inhibitory effects on S. aureus were mild. The free-radical generation effect of Ag nanoparticles on microbial growth inhibition was investigated by electron spin resonance spectroscopy. These results suggest that Ag nanoparticles can be used as effective growth inhibitors in various microorganisms, making them applicable to diverse medical devices and antimicrobial control systems.

We report the selective detection of single nitric oxide (NO) molecules using a specific DNA sequence of d(AT)(15) oligonucleotides, adsorbed to an array of near-infrared fluorescent semiconducting single-walled carbon nanotubes (AT(15)-SWNT). While SWNT suspended with eight other variant DNA sequences show fluorescence quenching or enhancement from analytes such as dopamine, NADH, L-ascorbic acid, and riboflavin, d(AT)(15) imparts SWNT with a distinct selectivity toward NO. In contrast, the electrostatically neutral polyvinyl alcohol enables no response to nitric oxide, but exhibits fluorescent enhancement to other molecules in the tested library. For AT(15)-SWNT, a stepwise fluorescence decrease is observed when the nanotubes are exposed to NO, reporting the dynamics of single-molecule NO adsorption via SWNT exciton quenching. We describe these quenching traces using a birth-and-death Markov model, and the maximum likelihood estimator of adsorption and desorption rates of NO is derived. Applying the method to simulated traces indicates that the resulting error in the estimated rate constants is less than 5% under our experimental conditions, allowing for calibration using a series of NO concentrations. As expected, the adsorption rate is found to be linearly proportional to NO concentration, and the intrinsic single-site NO adsorption rate constant is 0.001 s(-1) μM NO(-1). The ability to detect nitric oxide quantitatively at the single-molecule level may find applications in new cellular assays for the study of nitric oxide carcinogenesis and chemical signaling, as well as medical diagnostics for inflammation.

46Process-based hydrological models have a long history dating back to the 1960s. 47Criticized by some as over-parameterized, overly complex, and difficult to use, a more 48 nuanced view is that these tools are necessary in many situations and, in a certain class of 49 problems, they are the most appropriate type of hydrological model. This is especially the 50 case in situations where knowledge of flow paths or distributed state variables and/or 51 preservation of physical constraints is important. Examples of this include: spatiotemporal 52 variability of soil moisture, groundwater flow and runoff generation, sediment and 53 contaminant transport, or when feedbacks among various Earth's system processes or 54 understanding the impacts of climate non-stationarity are of primary concern. These are 55 situations where process-based models excel and other models are unverifiable. This article 56 presents this pragmatic view in the context of existing literature to justify the approach where 57 applicable and necessary. We review how improvements in data availability, computational 58 resources and algorithms have made detailed hydrological simulations a reality. Avenues for 59 the future of process-based hydrological models are presented suggesting their use as virtual 60 laboratories, for design purposes, and with a powerful treatment of uncertainty. 61

There are a growing number of large-scale, complex hydrologic models that are capable of simulating integrated surface and subsurface flow. Many are coupled to land-surface energy balance models, biogeochemical and ecological process models, and atmospheric models. Although they are being increasingly applied for hydrologic prediction and environmental understanding, very little formal verification and/or benchmarking of these models has been performed. Here we present the results of an intercomparison study of seven coupled surface-subsurface models based on a series of benchmark problems. All the models simultaneously solve adapted forms of the Richards and shallow water equations, based on fully 3-D or mixed (1-D vadose zone and 2-D groundwater) formulations for subsurface flow and 1-D (rill flow) or 2-D (sheet flow) conceptualizations for surface routing. A range of approaches is used for the solution of the coupled equations, including global implicit, sequential iterative, and asynchronous linking, and various strategies are used to enforce flux and pressure continuity at the surface-subsurface interface. The simulation results show good agreement for the simpler test cases, while the more complicated test cases bring out some of the differences in physical process representations and numerical solution approaches between the models. Benchmarks with more traditional runoff generating mechanisms, such as excess infiltration and saturation, demonstrate more agreement between models, while benchmarks with heterogeneity and complex water table dynamics highlight differences in model formulation. In general, all the models demonstrate the same qualitative behavior, thus building confidence in their use for hydrologic applications.