A novel machine learning model for innovative microencapsulation techniques and applications in advanced materials, textiles, and food industries

文献类型： 外文期刊

第一作者： Wang, Yingwen

作者： Wang, Yingwen;Yang, Jinguang;Brahmia, Ameni;Shahbaz, Amirhosein;Sahramaneshi, Hani;Alkhalifah, Tamim

作者机构：

关键词： Microencapsulation; Advanced materials; Smart textiles; Food ingredients; Controlled release; Machine Learning

期刊名称：RENEWABLE & SUSTAINABLE ENERGY REVIEWS （ 影响因子：16.3； 五年影响因子：17.5 ）

ISSN： 1364-0321

年卷期： 2025 年 224 卷

页码：

收录情况： SCI

摘要： This paper shows the latest advancements in microencapsulation techniques and their innovative applications in advanced materials, textiles, and food industries. The ability to encapsulate active agents, such as phase change materials, catalysts, and corrosion inhibitors, within microcapsules has led to the creation of novel materials with improved thermal management, improved catalytic activity, and superior corrosion resistance. This review delves into the diverse microencapsulation techniques, including spray drying, emulsion-based methods, and novel approaches such as microfluidics and aerosol-based technologies. The advantages and limitations of each technique are discussed, along with their suitability for different applications. Furthermore, the review shows the latest trends and future perspectives in microencapsulation, including the integration of smart materials, the use of green and sustainable encapsulation processes, and the growing emphasis on personalized and tailored microencapsulation solutions. This review shows a holistic understanding of the transformative potential of microencapsulation in driving innovation and addressing critical challenges across advanced materials, textiles, and food industries. This article utilized an artificial neural network (ANN) to show the interrelationships between parameters affecting biodegradation rate, compressive strength, and hardness based on porosity and bone growth. ANNs effectively handle noisy training data and are applied in diverse fields like speech and image recognition. While requiring more time for learning than decision trees, the ANN predictions demonstrated significant correlations among the parameters. Linear regression confirmed the predictions' accuracy, showing an acceptable error margin compared to experimental results.

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