Experimental study of hygro-thermal characteristics of novel cement-cork mortars
Source abstract
Abstract
Abstract Since cork has low density, low thermal conductivity, and water resistance, mixing cork with mortar can effectively improve the thermal and moisture insulation of mortar materials. In this study, cement-cork mortars with different cork volume admixtures was prepared. The fresh state consistency and compressive strength of cement-cork mortars were studied. The porosity and pore distribution characteristics of the prepared samples were obtained using microparticle mercury porosimeter. The thermo-physical parameters (thermal conductivity, specific heat, heat storage coefficient, and thermal diffusivity) and hygric properties (isothermal adsorption curve, liquid water diffusion coefficient, and water vapor permeability) of cement-cork mortars were studied experimentally. Meanwhile, The variation of thermo-physical parameters of the prepared samples with temperature (within the range of 20–70 °C) and moisture content (with the relative humidity range of 0–100%) were obtained. The results show that the hygro-thermal parameters of cement-cork mortars decreased with the increase of cork content (the largest decrease in thermal conductivity was 52.6%, whereas the drop in liquid water diffusion coefficient was 87.9%). Furthermore, temperature and moisture content had great influence on the thermo-physical parameters of the samples (the maximum growth rates of thermal conductivity and specific heat within the studied temperature range were 7.5% and 27.3%, respectively, whereas the corresponding values within the studied range of moisture content were 60.7% and 26.7%, respectively).
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Hygrothermal properties of building materials · Building materials and conservation · Recycling and utilization of industrial and municipal waste in materials production
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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