Ultrasonic flow reactors of modest size have become increasingly favored by researchers due to their utility as a valuable medium for investigating and regulating the operating mechanism of ultrasound technology. As a result, these reactors are employed for both research and a variety of applications in chemical, biological, and pharmaceutical processes, predominantly on laboratory scales and occasionally on pilot scales. Herein, an ultrasonic continuous-flow cell assembly (UCF) is utilized for the formulation of oil-in-water nanoemulsions (o/w NEs). The setup provides efficient energy input, inducing strong cavitation for effective droplet fragmentation and ensuring continuous o/w NEs production. The emulsification process is optimized by adjusting parameters (amplitude, pulse control mode, processing time, oil-to-surfactant ratio (OSR), and stability) supported by machine learning-based data analysis. At unit OSR, D_z-avg measures 124 nm, compared to 832 nm at OSR 12. At the optimal OSR, NE remains stable for 50 days; at higher OSR, coalescence and Ostwald ripening instabilities are observed. Optimized OSR is found to be 4 at optimal ultrasonic conditions. Higher surfactant concentration reduced D_z-avg, while increased oil concentration raised D_z-avg. A significant decrease in D_z-avg is observed up to 459 J/mL energy density, thereafter D_z-avg declined slowly. The D_z-avg prediction, modeled with energy density, exhibited a strong power law fit and is applicable for both scaling up and adjusting D_z-avg. This study proposes a standard process for formulating o/w NEs through an efficient continuous emulsification method. It offers valuable insights into optimizing emulsion formulation via ultrasonic cavitation techniques, contributing to scalable production in diverse industrial sectors.

中文翻译：

---

通过连续超声处理和集成机器学习算法增强纳米乳剂工程


中等尺寸的超声波流动反应器越来越受到研究人员的青睐，因为它们是研究和调节超声技术运行机制的宝贵介质。因此，这些反应器主要用于化学、生物和制药工艺的研究和各种应用，主要是实验室规模，偶尔也用于中试规模。在此，超声连续流通池组件 （UCF） 用于配制水包油纳米乳液 （o/w NEs）。该装置提供高效的能量输入，诱导强空化以实现有效的液滴碎裂，并确保连续的 o/w NEs 生产。通过调整基于机器学习的数据分析支持的参数（振幅、脉冲控制模式、加工时间、油与表面活性剂的比例 （OSR） 和稳定性）来优化乳化过程。在单位 OSR 下，D_z-avg 的测量值为 124 nm，而在 OSR 12 处为 832 nm。在最佳 OSR 下，NE 保持稳定 50 天;在较高的 OSR 下，观察到聚结和 Ostwald 成熟不稳定性。在最佳超声条件下，优化的 OSR 为 4。较高的表面活性剂浓度降低了 D_z-avg，而增加的油浓度提高了 D_z-avg。观察到 D_z-avg 显著降低，能量密度高达 459 J/mL，此后 D_z-avg 缓慢下降。以能量密度建模的 D_z-avg 预测表现出很强的幂律拟合，适用于放大和调整 D_z-avg。本研究提出了一种通过高效连续乳化法配制 o/w NEs 的标准工艺。 它为通过超声空化技术优化乳液配方提供了有价值的见解，有助于在不同工业领域实现规模化生产。