The application of Py-GC/MS for the catalytic upgrading of oil separated from summer food waste leachate

The catalytic cracking of oil fractions separated from summer food waste leachate was investigated over BEA zeolite and Al-SBA-15 catalysts. In this study, a mixture of food waste oil fractions and catalyst was directly introduced to pyrolysis gas chromatography/mass spectrometry (Py-GC/MS), with the resulting vapor phase products being simultaneously analyzed. Various acid compounds, including oleic acid, produced by the non-catalytic pyrolysis of food waste leachate were reformed into valuable compounds, such as oxygenates, hydrocarbons, and aromatics. The BEA zeolite catalyst showed higher selectivity for hydrocarbon compounds, especially aromatics, within the gasoline range due to its superior cracking ability originating from its highly acidic sites. Conversely, the cracking performance of the Al-SBA-15 catalyst, possessing mild acidic sites, was lower than that of the BEA zeolite. Increasing the amount of Al-SBA-15 catalyst enhanced the cracking activity and resulted in higher selectivity for hydrocarbons.     

Development of a practical decontamination procedure for uranium-contaminated concrete waste

Practical decontamination procedure and equipments have been developed to decontaminate uranium-contaminated concrete waste generated from a uranium conversion plant. The direct burning of mortar block coated with epoxy and then the mechanical removal of the burned surface reduces the amount of sludge by preventing the dissolution of whole cement paste in concrete block. And the removal of calcium from the concrete washing solution for the use of electrokinetic equipment decreases the volume of waste by a recycling of the solution. This improved decontamination procedure makes more than 70 % of concrete waste be self-disposed and remarkably reduces secondary radioactive waste.     

Decontamination of uranium-contaminated concrete

This study examined the decontamination method by washing with nitric acid for uranium contaminated concrete pieces produced from the decommissioning of a uranium conversion plant and evaluated the amount of waste sludge generated from the treatment. In addition, carbonate solution, nitric acid and sulfuric acid were used as the extraction reagents for the uranium, and their efficiencies were also compared. For concrete pieces coated with and without epoxy, the separation method of cement paste from aggregates was preferable to the leaching of uranium from concrete because of its simpler decontamination process. When concrete pieces with epoxy were heated for 2 h at 400 °C, pulverized and sequentially washed with 0.1, 2.0 and 1.0 M of nitric acid, their radioactivity reached below the limit value of uranium for self-disposal. Unlike previous results, uranium dissolved in 1 and 2 M sulfuric acid solutions was not removed by strong anion exchange resins such as IRA 910 and Ag1×8.     

Catalytic pyrolysis of waste wood chip over mesoporous materials using Py-GC/MS

Research on Chemical Intermediates - Pyrolysis Gas Chromatography/Mass Spectroscopy (Py-GC/MS) was used to investigate the catalytic pyrolysis of waste wood chip. Two different mesoporous...     

Modeling refraction characteristics of silicon nitride film deposited in a SiH/sub 4/-NH/sub 3/-N/sub 2/ plasma using neural network

Silicon nitride has been deposited using plasma-enhanced chemical deposition (PECVD) equipment. The PECVD process was characterized by conducting a 2/sup 6-1/ fractional factorial experiment on six experimental factors, including substrate temperature, pressure, radio frequency (RF) power, ammonia NH/sub 3/, silane SiH/sub 4/, and nitrogen N/sub 2/ flow rates. Refractive characteristics of the deposited film were examined by modeling the refractive index as a function of experimental factors. A helium-neon laser with a wavelength 6328 /spl Aring/ was used to measure the refractive index. To evaluate the appropriateness of the model, the network trained with 32 experiments was then tested with 12 experiments not pertaining to the training data. Several learning factors involved in training neural networks were optimized and an accurate prediction model with the root mean-squared error of 0.018 was achieved. Compared to statistical regression model, the neural network model demonstrated an improvement of more than 65%. Using various three-dimensional plots, underlying deposition mechanisms were qualitatively estimated. For the limited experimental ranges, the index increased with increasing SiH/sub 4/ flow rate. With an increase in either NH/sub 3/ or N/sub 2/, meanwhile, the index decreased consistently. The index also increased with increasing substrate temperature or pressure. The effects of the temperature were very complex as it interacted with other factors.     

Non-bioaccumulative, environmentally preferable stabilizer architectures for the dispersion polymerization of MMA in supercritical CO2

To overcome the environmental concerns associated with long-chain perfluorinated compounds, in this report, non-bioaccumulative, environmentally friendly stabilizer architectures based on short-chain fluorinated polymers have been designed for the dispersion polymerization of methyl methacrylate (MMA) in supercritical CO₂. Random copolymers composed of 2-(diisopropylamino)ethyl methacrylate (DPAEMA) and 2,2,3,3,4,4,4-heptafluorobutyl methacrylate (FBMA) or 2,2,3,3,4,4,5,5-octafluoropentyl methacrylate were prepared with various comonomer ratios and utilized as stabilizers. It was found that the copolymers effectively stabilized PMMA latexes in CO₂, leading to the formation of free-flowing, spherical PMMA particles. With increase in the concentration of the stabilizer poly(FBMA-co-DPAEMA) from 2% to 6% (w/w with respected to MMA), the particles diameter decreased from 3.02 to 1.0 μm.