Rare earth oxides as carbon oxidation catalysts

The behavior of a number of rare earth oxides as catalysts for the oxidation of graphite in air has been investigated by the methods of thermal analysis. Of the oxides studied, only CeO₂ showed significant activity in accelerating the gasification of graphite by oxygen between 500 and 1000°C. Cerium salts, which decompose to a finely dispersed oxide phase at low temperatures, e.g. Ce (III) nitrate and ammonium Ce (IV) nitrate, were found to be very active catalysts. The catalytic effect may be due to a redox process involving the cyclic conversion of the oxide from the Ce (IV) to the Ce (III) oxidation state, or the oxide particles may provide sites for the dissociative chemisorption of oxygen.     

Reaction scheme and kinetic modelling for the MTO process over a SAPO-18 catalyst

A kinetic model for simulation of the MTO process over SAPO-18 catalyst in a wide range of operating conditions has been proposed. The kinetic model predicts the experimental evolution of reaction products with time on stream, which follows three consecutive periods: initiation (where olefin production increases), a period of maximum olefin production and a period in which this production decreases. The kinetic scheme takes into account these three steps that evolve with time on stream: formation of active intermediate compounds, an step where olefins are formed by reaction of oxygenates (methanol/DME) with these intermediates and deactivation of intermediates by degradation to coke. The presence of water in the reaction medium attenuates the reaction rate of these steps. Discrimination of kinetic equations and calculation of the parameters of best fit have been carried out by solving the mass conservation equations of the individual components of the kinetic scheme together with the kinetic equation for deactivation and taking into account the effect of water on the kinetics of each step.     

Retrospective mapping of burnt areas in Central Siberia using a modification of the normalised difference water index

The boreal forest contains almost half the total carbon pool of world forest ecosystems, and so has a very significant role in global biogeochemical cycles. The flux of greenhouse gases in and out of these forests is influenced strongly by disturbances such as diseases, logging and predominantly fire. It is important to quantify these disturbances to enable the modelling of major greenhouse gases. However, because of the remoteness and vastness of the boreal forest, little data is available on the type, extent, frequency and severity of these disturbances in Siberia. For burnt areas, two of the more responsive wavelengths are the short wave infra-red (SWIR) and the near infra-red (NIR). These produce a vegetation index, the normalised difference SWIR (NDSWIR) capable of detecting retrospective disturbances. Here we combine the NDSWIR from MODIS imagery acquired in the summer of 2003 with thermal anomaly data from 1992 to 2003 to detect and date areas which burnt at some point between 1992 and 2003. The semi-automated method is called SWIR and Thermal ANomalies for Detecting Disturbances (STANDD) and is complemented by an Normalised Difference Vegetation Index (NDVI) differencing method using MODIS 2002 and 2003 imagery to ensure reliable detection of area burnt in the year of image acquisition (i.e. 2003). The area of this study covers approximately 3 million km² stretching from Lake Baikal in the south to the Laptev Sea in the north, above the Arctic Circle. Landsat ETM+ images were used to validate the shape and areal extent of the burnt areas resulting in an 81% overall accuracy with a kappa coefficient of agreement of 0.63.     

Carbon nanofibers as hydrogen adsorbing materials for power sources

Porous carbon nanofibers are synthesized by CVD method from acetylene with use of iron-containing catalysts. Activation of the nanofibers in melted potassium hydroxide results in increasing surface area from initial 300–400 m² g⁻¹ to 1700 m² g⁻¹. As follows from XRD data, activated nanofibers do not contain regular packages of graphene layers, but retain high electric conductivity. Deposition of copper improves electrochemical hydrogen storing characteristics of carbon nanofibers. Carbon nanomaterials obtained can be used as hydrogen storing materials in batteries instead of hydride forming metals.     

Photochemical reduction of molecular weight and number of double bonds in natural rubber film

Natural rubber (NR) can be degraded depending on various factors such as heat, mechanical force, chemical reaction, and light. Light is a very interesting factor because it can cause the NR to degrade under low temperature and pressure. The photo-degradation of NR films was carried out to investigate the effects of the light and the temperature on the reduction of the weight-average molecular weight (Mw) and the double bonds in the NR films. The NR films, with and without catalysts, titanium dioxide (TiO₂), and potassium persulfate (K₂S₂O₈), were exposed to light from a mercury light bulb at 7,000 and 36,000 lux, and at the temperature of 25 °C and 80 °C for 192 hrs. After exposure, the Mw of the NR films was analyzed by gel permeation chromatography (GPC). Changes in the Mw were used to construct a kinetic model for the process, (1/Mw)=(1/Mw₀)+(kt/2M₀) where k is the rate constant, and M₀ is the Mw of the monomer unit. The linear relationship between 1/Mw and time suggested pseudo first-order processes with random scission. The Mw distribution information from the GPC was used to calculate the number of double bonds in the NR films. The trend of the double bonds reduction curves was quite similar to the result obtained from the calculation from the FTIR spectra. This indicated that this calculation method might possibly be another alternative way to obtain the number of double bonds in the NR.     

Sea-air flux of CO2 in the Caribbean Sea estimated using in situ and remote sensing data

Empirical relationships between sea surface carbon dioxide fugacity (fCO₂^sw) and sea surface temperature (SST) were applied to datasets of remotely sensed SST to create fCO₂^sw fields in the Caribbean Sea. SST datasets from different sensors were used, as well as the SST fields created by optimum interpolation of bias corrected AVHRR data. Empirical relationships were derived using shipboard fCO₂^sw data, in situ SST data, and SST data from the remote sensing platforms. The results show that the application of a relationship based on shipboard SST data, on fields of remotely sensed SST yields biased fCO₂^sw values. This bias is reduced if the fCO₂^sw-SST relationships are derived using the same SST data that are used to create the SST fields. The fCO₂^sw fields found to best reproduce observed fCO₂^sw are used in combination with wind speed data from QuikSCAT to create weekly maps of the sea-air CO₂ flux in the Caribbean Sea in 2002. The region to the SW of Cuba was a source of CO₂ to the atmosphere throughout 2002, and the region to the NE was a sink during winter and spring and a source during summer and fall. The net uptake of CO₂ in the region was doubled when potential skin layer effects on fCO₂^sw were taken into account.     

Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests

The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and advanced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semi-deciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes.The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors.When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed.     

Sandwich panels with Kagome lattice cores reinforced by carbon fibers

Stretching dominated Kagome lattices reinforced by carbon fibers were designed and manufactured. The sandwich panels were assembled with bonded laminate skins. The mechanical behaviors of the sandwich panels were tested by out-of-plane compression, in-plane compression and three-point bending. Different failure modes of the sandwich structures were revealed. The experimental results showed that the carbon fiber reinforced lattice grids are much stiffer and stronger than foams and honeycombs. It was found that buckling and debonding dominate the mechanical behavior of the sandwich structures, and that more complaint skin sheets might further improve the overall mechanical performance of the sandwich panels.