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<p>This study advances the synthesis of crosslinked dextran microspheres (CDMs) by addressing a critical gap in understanding real-time droplet evolution during inverse suspension crosslinking. Using optical and SEM microscopy, we characterize droplet progression through four distinct stages—transition, quasi-steady-state, growth, and identification—revealing the role of viscosity-dependent breakage-coalescence dynamics in size distribution changes. We further develop the first population balance model (PBM) integrated with genetic algorithm (GA) optimization to predict transient particle behavior. By incorporating a reaction-conversion parameter, <i>X</i>(<i>t</i>), our model links rheology to crosslinking kinetics, achieving high predictive accuracy (MSE &lt; 5%). Experimental results demonstrate that increasing dextran concentration (12.5–50% w/v) elevates viscosity by &gt; 3000%, suppressing droplet breakage and producing larger particles (27 → 188&#xa0;µm) with broader distributions (Span 0.98 → 2.61). This work represents a significant improvement over previous statistical approaches, offering the first quantitative PBM-GA framework for connecting processing conditions to dynamic particle evolution. Our findings provide new insights into CDM formation kinetics and enable rational microsphere design for biomedical applications, bridging the gap between empirical observation and mechanistic control in dextran-based particle synthesis.</p>

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Optimizing Crosslinked Dextran Microspheres: Morphology Control and Particle Size Engineering via Population Balance Modeling and Genetic Algorithm

  • 期刊论文

  • 发表日期：

    2025年11月29日

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  • Zeinab Yousefpour,
  • Hamed Salimi-Kenari,
  • Mohammad Imani,
  • Azizollah Nodehi,
  • Iman Esmaili Paeen Afrakoti

摘要

This study advances the synthesis of crosslinked dextran microspheres (CDMs) by addressing a critical gap in understanding real-time droplet evolution during inverse suspension crosslinking. Using optical and SEM microscopy, we characterize droplet progression through four distinct stages—transition, quasi-steady-state, growth, and identification—revealing the role of viscosity-dependent breakage-coalescence dynamics in size distribution changes. We further develop the first population balance model (PBM) integrated with genetic algorithm (GA) optimization to predict transient particle behavior. By incorporating a reaction-conversion parameter, X(t), our model links rheology to crosslinking kinetics, achieving high predictive accuracy (MSE < 5%). Experimental results demonstrate that increasing dextran concentration (12.5–50% w/v) elevates viscosity by > 3000%, suppressing droplet breakage and producing larger particles (27 → 188 µm) with broader distributions (Span 0.98 → 2.61). This work represents a significant improvement over previous statistical approaches, offering the first quantitative PBM-GA framework for connecting processing conditions to dynamic particle evolution. Our findings provide new insights into CDM formation kinetics and enable rational microsphere design for biomedical applications, bridging the gap between empirical observation and mechanistic control in dextran-based particle synthesis.