The Effects of Several Paper Characteristics and Application Methods on the Sublimation Rate of Cyclododecane

Cyclododecane (CDD) is a waxy, solid cyclic hydrocarbon (C12H24) that sublimes at room temperature and possesses strong hydrophobicity. In paper conservation CDD is used principally as a temporary fixative of water-soluble media during aqueous treatments. Hydrophobicity, ease of reversibility, low toxicity, and absence of residues are reasons often cited for its use over alternative materials although the latter two claims continue to be debated in the literature. The sublimation rate has important implications for treatment planning as well as health and safety considerations given the dearth of reliable information on its toxicity and exposure limits. This study examined how the rate of sublimation is affected by fiber type, sizing, and surface finish as well as delivery in the molten phase and as a saturated solution in low boiling petroleum ether. The effect of warming the paper prior to application was also evaluated. Sublimation was monitored using gravimetric analysis after which samples were tested for residues with gas chromatography-flame ionization detection (GC-FID) to confirm complete sublimation. Water absorbency tests were conducted to determine whether this property is fully reestablished. Results suggested that the sublimation rate of CDD is affected minimally by all of the paper characteristics and application methods examined in this study. The main factors influencing the rate appear to be the thickness and mass of the CDD over a given surface area as well as temperature and ventilation. The GC-FID results showed that most of the CDD sublimed within several days of its disappearance from the paper surface regardless of the application method. Minimal changes occurred in the water absorbency of the samples following complete sublimation.

Estimation of Sample Size and Power For Quantile Regression

Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.