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A novel approach for assessing fog collection efficiency using hydrophobic surfaces

Author

Listed:
  • Azeem, Musaddaq
  • Amor, Nesrine
  • Petru, Michal
  • Wiener, Jakub
  • Noman, Muhammad Tayyab

Abstract

Fog harvesting is an emerging technology for harnessing atmospheric moisture, particularly in arid regions facing water scarcity. This study presents the development and validation of a novel measurement system designed to accurately quantify fog water collection efficiency using textile-based fog collection elements (FCEs). A specialized evaluation setup was designed to improve the precision and reproducibility of water capture measurements under controlled fog simulation conditions. The system enables real-time measurement of water accumulation on various fibrous collectors, including hydrophobic polyester monofilaments, multifilaments, and spun yarns, allowing for comparative performance assessments. The proposed measurement approach addresses limitations in existing techniques by minimizing droplet loss, ensuring consistent air-fog flow conditions, and enhancing data acquisition reliability. Results demonstrated that hydrophobic surfaces facilitated improved water collection efficiency compared to untreated polyester. Additionally, spun yarns with surface hairiness exhibited enhanced droplet capture capacity compared to smooth monofilaments and multifilaments, owing to increased active collection sites. The study highlights the significance of accurate measurement methods in fog harvesting research and provides a validated system adaptable for evaluating diverse materials and configurations. This measurement approach can serve as a standard framework for optimizing fog harvesting technologies and supporting the development of water collection solutions in resource-scarce regions.

Suggested Citation

  • Azeem, Musaddaq & Amor, Nesrine & Petru, Michal & Wiener, Jakub & Noman, Muhammad Tayyab, 2025. "A novel approach for assessing fog collection efficiency using hydrophobic surfaces," Chaos, Solitons & Fractals, Elsevier, vol. 198(C).
  • Handle: RePEc:eee:chsofr:v:198:y:2025:i:c:s0960077925006381
    DOI: 10.1016/j.chaos.2025.116625
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