A packed-bed reactor was established to study the effect of temperature on the controlled air oxidation (CAO) performance of a mixture of polypropylene and sawdust at a fixed feed gas flow rate. The reactor temperature was varied from 400 to 800°C. Attention was focused on product distribution, compositions of liquid and gas products, and technical parameters. The chemical composition of the liquid products was analyzed by gas chromatography/mass spectrometry. The results indicated an obvious impact of the temperature on the described parameters. The increase in temperature led to the decrease in solid fraction and a convex shape curve for the gas yield as well as to a decrease of alkanes and alkenes, and favored the generation of oxygen-containing hydrocarbons. According to criteria of CAO conversion, the optimum temperature in the primary chamber was found to be 700°C.

Traditional photodetectors usually respond to photons larger than the bandgap of a photosensitive material. In contrast to traditional photodetectors for broad-spectrum detection, the currently reported PbS/PMMA/PbSe CQDs silicon-based photodetectors can...

No abstract

No abstract

This paper looks at both the prepayment risks of housing mortgage loan credit default swaps (LCDS) as well as the fuzziness and hesitation of investors as regards prepayments by borrowers. It further discusses the first default pricing of a basket of LCDS in a fuzzy environment by using stochastic analysis and triangular intuition-based fuzzy set theory. Through the ‘fuzzification’ of the sensitivity coefficient in the prepayment intensity, this paper describes the dynamic features of mortgage housing values using the One-factor copula function and concludes with a formula for ‘fuzzy’ pricing the first default of a basket of LCDS. Using analog simulation to analyze the sensitivity of hesitation, we derive a model that considers what the LCDS fair premium is in a fuzzy environment, including a pure random environment. In addition, the model also shows that a suitable pricing range will give investors more flexible choices and make the predictions of the model closer to real market values.