journal article·2023-02

Preparation of hierarchical porous microspheres composite phase change material for thermal energy storage concrete in buildings

Daokui Li, Yili Tang, Xiaochao Zuo, Xiaoguang Zhao, Xinyi Zhang, Huaming Yang

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Abstract

Phase change materials with high latent heat significantly reduce building energy consumption. However, the serious leakage issue, low thermal conductivity, and the poor photothermal response utterly hinder their wide application in architecture. We reported an effective strategy for constructing hierarchical porous composite microspheres (PCN) through spray drying, calcination, and acid activation, using palygorskite (Pal) as the raw material. PCN presented a spherical hierarchical porous structure constructed by crosslinking Pal nanofibers and cellulose nanocrystals in a particular proportion. Paraffin-PCN (P-PCN) composite phase change materials (PCMs) with high shape stability, excellent photothermal conversion ability and latent heat storage capacity were synthesized. The heat preservation time of the P-PCN natural cooling from 35 °C to 30 °C is approximately twice that of the P-Pal. The P-PCN indicates obvious advantages in building thermal management, with the melting enthalpy promoted to 130.2 J/g. Further, the P-PCN-based building materials (P-PCN-B) with the P-PCN and the building aggregate maintain superior light-thermal energy conversion and thermal properties after multiple cycles. The P-PCN-B indicates outstanding mechanical properties (compression strength reaching 14.2 MPa) and flame-retarded properties. This work provides an innovative design strategy for developing multifunctional intelligent energy storage concrete and paves the way for the sustainable utilization of energy storage materials.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Phase Change Materials Research · Adsorption and Cooling Systems · Solar Thermal and Photovoltaic Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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