Enabling Ultrastable Microbubbles With Graphene Aerogel Enrichment and Machine Learning for Highly Efficient Carbon Storage

Abstract

Microbubbles (MBs) have garnered significant attention across various scientific disciplines, including medical imaging, drug delivery, materials science, and environmental engineering due to their unique properties and versatile utility. However, their inherent limitations regarding stability and pressure resilience have impeded their potential application in demanding conditions. Here, an innovative paradigm is presented for next-generation CO₂-filled ultrastable microbubbles (UMBs) by incorporating hydrophobic graphene aerogel microparticles (HAG-MPs) into the aphron MB shells, resulting in MBs with exceptional resilience and longevity. The findings demonstrate that the reinforced UMBs, enhanced with 0.16 wt% HAG-MPs, display a significantly improved elastic response and mechanical stiffness so that these UMBs exhibit remarkable bubble survival rates of approximately 71% and exhibit an amazing 490% increase in cyclic pressure stability (about 6 times) under a high-pressurizing cycle up to 400 bar. This research serves as a catalyst for the creation of advanced UMB systems capable of revolutionizing diverse applications in carbon capture, storage, and utilization. Furthermore, a multi-output machine learning (ML) framework based on multi-target regressor stacking (MTRS) is developed to predict key UMB performance parameters, achieving prediction errors as low as 3% for half-life time, approximately 4% for shell thickness-to-diameter ratio, and 3% for cyclic pressure stability, representing up to an 82% reduction in prediction error compared to classical single-output ML methods.

Keywords

aerographene microparticle / colloidal aphron microbubble / high pressure cycle / machine learning / multi-output regression / robust shell / ultrastability

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