ABSTRACT
This paper reports a method for determining the oil absorption value of inorganic powder based on tracer-assisted headspace gas chromatographic (HS-GC) technique. The method was carried out by adding 25 µL droplet of toluene-Dioctyl Phthalate solution onto the surface of 1.0 g inorganic powder, then sealing the headspace vial and shaking it to make the powder spherical. The amount of toluene that not been adsorbed by inorganic powder was quantified using HS-GC with the optimal equilibrium temperature and time conditions of 100 °C and 7 min, respectively. A new mathematical model shows that the oil absorption value can be determined from the signal of toluene. The results show that the employed method has good precision (the relative standard deviation < 3.6 %) and accuracy (R2 = 0.993). This method is simple and accurate, and can be an reliable tool for testing the oil absorption value of inorganic powder sample.
Subject(s)
Toluene , Powders , Chromatography, Gas/methods , Temperature , Toluene/analysisABSTRACT
This paper presents a multiple headspace extraction (MHE) analysis technique to determine the water vapor transmission rate of cellulose-based papers. The water vapor passing through the sample in a closed headspace vial is determined by MHE-gas chromatography. The results show that the employed method offers good precision (the relative standard deviation < 3.49 %) and good accuracy. The method is rapid and accurate, and is promising for the determination of the water vapor transmission rate of cellulose-based papers in future studies.
ABSTRACT
High quality and high quantity few-layer graphene was successfully prepared using a new impinging jet method. Natural graphite flakes were first agitated in N-methyl pyrrolidone (NMP) with the assistance of supercritical CO2, then the half-exfoliated graphite was further stripped using the shear stress derived from the impinging jets. After the energy conversion and stress analysis of the graphite particles during the whole exfoliation process, it was revealed that the size of the target mesh, the distance between the nozzle and the target, the decompression rate, and the size of the raw materials had a significant influence on the exfoliation process. Additionally, a microscopic view of the exfoliation and dispersion mechanism of graphene in the CO2-NMP system was investigated using molecular dynamics simulation, and CO2 was found to be beneficial for the penetration of NMP into the graphite sheets. Finally, the concentration and quality characteristics of the prepared graphene were characterized using ultraviolet-visible spectroscopy, transmission electron microscopy, Raman spectroscopy, and atomic force microscopy. The maximum concentration was as high as 0.689 mg ml-1, the thickness of 68% of the product was less than 2.5 nm, and the lateral dimension was from 0.5 to 3.0 µm. These results indicate that this impinging jet method is promising for large-scale industrial production.
ABSTRACT
Multilevel item response theory (MLIRT) models are used widely in educational and psychological research. This type of modeling has two or more levels, including an item response theory model as the measurement part and a linear-regression model as the structural part, the aim being to investigate the relation between explanatory variables and latent variables. However, the linear-regression structural model focuses on the relation between explanatory variables and latent variables, which is only from the perspective of the average tendency. When we need to explore the relationship between variables at various locations along the response distribution, quantile regression is more appropriate. To this end, a quantile-regression-type structural model named as the quantile MLIRT (Q-MLIRT) model is introduced under the MLIRT framework. The parameters of the proposed model are estimated using the Gibbs sampling algorithm, and comparison with the original (i.e., linear-regression-type) MLIRT model is conducted via a simulation study. The results show that the parameters of the Q-MLIRT model could be recovered well under different quantiles. Finally, a subset of data from PISA 2018 is analyzed to illustrate the application of the proposed model.
