Microbial cellulose membranes have attracted a great deal of attention as novel wound-dressing materials, especially for the healing of skin burns and chronic wounds, because of their high water holding capacity and biocompatibility. However, the high humidity around the wound sometimes allows the growth of bacteria, as well as the regeneration of the tissue. In this study, silver nanoparticles were incorporated into the cellulose membranes via a chemical reduction method using a silver salt, silver nitrate (AgNO(3)) and a reducing agent, sodium borohydride (NaBH(4)). The silver nanoparticles were evenly adsorbed on the overall surface of the cellulose nanofibrils without any local aggregation and had a spherical shape with uniform size (8+/-2 nm) which allowed them to show antimicrobial properties. The interaction between the oxygen in cellulose and silver nanoparticles resulted in the stable adsorption of the silver nanoparticles on cellulose nanofibrils. The cellulose membrane with silver nanoparticles exhibited an antimicrobial activity of more than 99.99% against Escherichia coli and Staphylococcus aureus, so that it could be used as an antimicrobial wound-dressing material for chronic wounds and burns.

We describe a novel class of electrically conductive transparent materials based on multiwalled carbon nanotubes (MWCNTs). Transparent nanocomposites were fabricated by incorporating an aqueous silk fibroin solution into bacterial cellulose membranes. The transparent nanocomposites had a high transmittance in the visible and infrared regions, regardless of the bacterial cellulose fiber content, due to the nanosize effect of the bacterial cellulose nanofibrils. This phenomenon allowed the preparation of a novel electrically conductive transparent paper. The high dispersity of the MWCNTs was realized by utilizing a bacterial cellulose membrane as a template to deposit them uniformly, thereby achieving electrically conductive transparent papers with outstanding optical transparency. The light transmittance and electrical conductivity varied according to the concentration of the MWCNT dispersion. Good optimal transparency and electrical properties were obtained with a light transmittance of 70.3% at 550 nm and electrical conductivity of 2.1 × 10−3 S/cm when the electrically conductive transparent paper was fabricated from a 0.02 wt % aqueous MWCNT dispersion. In addition, the electrically conductive transparent papers showed remarkable flexibility without any loss of their initial properties. © 2008 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 46: 1235–1242, 2008

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Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.