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Plasmonic aerogel window with structural coloration for energy-efficient and sustainable building envelopes

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Listed:
  • Huang, Maoquan
  • Tang, G.H.
  • Si, Qiaoling
  • Pu, Jin Huan
  • Sun, Qie
  • Du, Mu

Abstract

Renowned for superior thermal insulation properties, silica aerogel holds substantial promise for eco-friendly building construction. This study presents a novel colored transparent aerogel (CTA) window design that simultaneously achieves good light transmission, thermal insulation, and color modulation, which has not been reported before. The radiative properties of the core–shell plasmonic particle-doped aerogel were theoretically predicted by the combination of Mie theory and Monte Carlo methods. The results showed that the CTA window has high visible light transmittance (∼ 45%) and low thermal conductivity (∼ 0.018 W/m K), all while displaying an array of vivid colors. An energy consumption simulation of CTA windows in various climates in China showed potential energy savings of up to 90% compared to the single tinted window in severe cold regions. Moreover, a cost assessment of CTA windows is conducted to evaluate their economic feasibility and environmental benefits. This study introduces an innovative avenue to meet the escalating demand for both visually pleasing and energy-efficient materials, contributing profound insights into the realm of sustainable architectural design.

Suggested Citation

  • Huang, Maoquan & Tang, G.H. & Si, Qiaoling & Pu, Jin Huan & Sun, Qie & Du, Mu, 2023. "Plasmonic aerogel window with structural coloration for energy-efficient and sustainable building envelopes," Renewable Energy, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s0960148123009187
    DOI: 10.1016/j.renene.2023.119006
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    References listed on IDEAS

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    1. Zhao, Xinpeng & Mofid, Sohrab Alex & Jelle, Bjørn Petter & Tan, Gang & Yin, Xiaobo & Yang, Ronggui, 2020. "Optically-switchable thermally-insulating VO2-aerogel hybrid film for window retrofits," Applied Energy, Elsevier, vol. 278(C).
    2. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
    3. Buratti, C. & Moretti, E., 2012. "Experimental performance evaluation of aerogel glazing systems," Applied Energy, Elsevier, vol. 97(C), pages 430-437.
    4. Huang, Yu & Niu, Jian-lei, 2015. "Application of super-insulating translucent silica aerogel glazing system on commercial building envelope of humid subtropical climates – Impact on space cooling load," Energy, Elsevier, vol. 83(C), pages 316-325.
    5. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
    6. Yu, Xiyu & Huang, Maoquan & Wang, Xinyu & Sun, Qie & Tang, G.H. & Du, Mu, 2022. "Toward optical selectivity aerogels by plasmonic nanoparticles doping," Renewable Energy, Elsevier, vol. 190(C), pages 741-751.
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