Effect of pore structure and moisture content on gas diffusion and permeability in porous building stones

被引:0
|
作者
D. Benavente
C. Pla
机构
[1] Universidad de Alicante,Departamento de Ciencias de la Tierra y el Medio Ambiente
[2] Universidad de Alicante,Departamento de Ingeniería Civil
来源
Materials and Structures | 2018年 / 51卷
关键词
CO; Built heritage; Air quality; Porous materials; Indoor gas concentration;
D O I
暂无
中图分类号
学科分类号
摘要
Hazardous gases in buildings are a concern for public health and security. These gases can be released from the building materials to indoor air and their concentration may become critical where ventilation is hindered, as such in hypogean or more energetically efficient airtight constructions. Furthermore, the gas ventilation and the indoor gas concentration can considerably increase by the vapour condensation on the ceiling and walls of buildings. In this paper, we characterise the CO2 gas diffusion for a representative range of building porous stones with the aim of establishing the effect of the water content in the gaseous diffusion coefficient. We propose a new methodology to measure gas diffusion with a laboratory device that works under different hygrometric conditions. Results reveal water pore condensation reduces both connected porosity and pore size and therefore, the CO2 diffusion coefficient. This variation occurs in all the studied porous building stones although it is especially important in stones with small pores. Thus, the reduction of CO2 diffusion coefficient for the stone with thinnest pores is by 50% when relative humidity varies from 20 to 90%. Permeability and gas diffusion coefficients present similar trends. Porous stones with larger pores and higher porosity values present the highest CO2 diffusion, water and gas permeability coefficients. Pore size is the conclusive parameter within the transport coefficients. It greatly affects both the tortuosity factor of the CO2 gaseous diffusion and the slip parameter of the Klinkenberg’s model for gas permeability coefficient. Finally, for studied samples, we establish a power regression, which correlates thoroughly both coefficients.
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