A study on the formaldehyde emission parameters of porous building materials based on adsorption potential theory

吸附 热扩散率 材料科学 扩散 多孔性 热力学 甲醛 多孔介质 分配系数 化学 物理化学 复合材料 物理 色谱法 有机化学
作者
Xiaojun Zhou,Yanfeng Liu,Cong Song,Jiaping Liu
出处
期刊:Building and Environment [Elsevier]
卷期号:106: 254-264 被引量:23
标识
DOI:10.1016/j.buildenv.2016.07.003
摘要

The emission of formaldehyde from building materials is characterized by three key parameters: the initial emittable concentration, the partition coefficient, and the diffusion coefficient. Scholars have conducted considerable research to determine these three parameters. However, the experimental methods are mostly time consuming, and the experimental data cannot provide a mechanism to explain the influence of the main control factors on the emission parameters. Theoretical prediction models have been built to predict a certain parameter based on different theories and with different applicable scopes. Thus, it is necessary to establish a theoretical system that can analyze the formaldehyde emission process based on a fundamental theory and can simultaneously predict the emission parameters. Based on adsorption potential theory, this paper disassembled the pore structure of porous building materials and calculated the adsorption potential for a given pore diameter. According to the relationship between the molecular kinetic energy and the adsorption potential, the emittable ratios for each independent potential field were calculated. The initial emittable concentration of the whole material was finally obtained by reconstruction. Our previous study proposed a prediction model for the partition coefficient based on adsorption potential theory. Combining this proposed model with the effective diffusivity model, a new correlation between the diffusion coefficient and temperature was derived. Three types of medium density fiberboards and one type of particle board were used in the mercury intrusion porosimetry tests and the environmental chamber experiments of formaldehyde emission. The emission parameters calculated by the prediction models agreed well with the experimental data.

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