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<p>Colloidal gas aphrons (CGAs) are promising fluids for environmental remediation due to their stability, high viscosity, and effective gas transport. However, previous studies have been limited by narrow concentration ranges, leaving key mechanisms behind bubble stability insufficiently understood. This study systematically explores bubble stability across a wide range of xanthan gum (XG) and sodium dodecyl sulfate (SDS) concentrations (1–10× CMC and 1000–10,000 ppm) using advanced AI-assisted image analysis and molecular dynamics (MD) simulations. AI-enhanced microscopy enabled precise quantification of bubble size and morphology, while MD provided molecular-level insights into surfactant-polymer interactions at bubble interfaces. Results showed optimal CGA stability at specific surfactant-polymer ratios, significantly reducing gas diffusion and bubble coalescence. These integrated experimental and computational methods offer valuable guidelines for designing robust CGA-based remediation fluids.</p>

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Stability of colloidal gas aphrons based on polymer-surfactant formulations and molecular dynamics insights

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Ayaulym Amankeldiyeva,
  • Samal Kaumbekova,
  • Aigerim Khalidulliyeva,
  • Zhanat Salimova,
  • Aizhan Ibrayeva,
  • Maxime Cochennec,
  • Stéfan Colombano,
  • Yerlan Amanbek,
  • Yanwei Wang,
  • Sagyn Omirbekov

摘要

Colloidal gas aphrons (CGAs) are promising fluids for environmental remediation due to their stability, high viscosity, and effective gas transport. However, previous studies have been limited by narrow concentration ranges, leaving key mechanisms behind bubble stability insufficiently understood. This study systematically explores bubble stability across a wide range of xanthan gum (XG) and sodium dodecyl sulfate (SDS) concentrations (1–10× CMC and 1000–10,000 ppm) using advanced AI-assisted image analysis and molecular dynamics (MD) simulations. AI-enhanced microscopy enabled precise quantification of bubble size and morphology, while MD provided molecular-level insights into surfactant-polymer interactions at bubble interfaces. Results showed optimal CGA stability at specific surfactant-polymer ratios, significantly reducing gas diffusion and bubble coalescence. These integrated experimental and computational methods offer valuable guidelines for designing robust CGA-based remediation fluids.