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通过人工神经网络模型优化凝甲基烯基凝的特性

Ismail Can Karaoglu¹, Aybaran Olca Kebabci¹, Seda Kizilel¹

¹Chemical and Biological Engineering, Koc University, 34450 Sariyer, Istanbul, Turkey.

ACS applied materials & interfaces

|September 13, 2023

概括

这项研究优化了使用人工神经网络 (ANN) 模型的凝甲基烯基 (GelMA) 水凝合成. 该模型准确地预测了水凝的刚性和凝时间,使组织工程的标准化生产速度更快.

科学领域:

生物材料科学生物材料科学
组织工程是组织工程.
聚合物化学聚合物化学

背景情况:

凝甲基烯基 (GelMA) 水凝为组织工程提供生物相容性和可调性特性.
目前的GelMA水凝制剂耗时,阻碍了临床翻译.
关于GelMA水凝特性对关键参数 (Eosin Y,三甲胺,N-vinyl-2-pyrrolidone) 影响的综合性研究有限.

研究的目的:

系统地调查欧Y (EY),三甲胺 (TEA) 和N-维尼尔-2-罗利 (NVP) 度对GelMA水凝硬度和凝时间的影响.
开发和验证一个人工神经网络 (ANN) 模型,用于预测GelMA水凝的机械性能.
为生物医学应用确定EY,TEA和NVP的安全,无毒的度范围.

主要方法:

系统的实验设计改变了EY (0.0050.5毫米),TEA (101000毫米) 和NVP (101000毫米) 的度.
实时监测水凝刚度 (Young的模量) 和凝时间.
开发和验证一个人工神经网络 (ANN) 模型,将输入度与输出属性相关联.

主要成果:

根据EY,TEA和NVP度,ANN模型准确地预测了GelMA水凝的刚度和凝时间.
证明了为特定的生物医学应用而合成具有所需刚度的GelMA水凝的能力.
确定了EY,TEA和NVP的无毒度范围,确保更安全的水凝使用.

结论:

开发的ANN模型为优化GelMA水凝合成提供了强大的工具,降低了成本和精力.
这种方法使GelMA水凝生产用于组织工程和体内应用的标准化成为可能.
该研究为GelMA水凝提供了一个全面的表征库和预测模型,促进生物模拟材料设计.

关键词:

人工神经网络 (ANN) 是一个人工神经网络.凝甲基烯基 (GelMA) 是一种凝时间凝时间水凝是一种水凝.机器学习机器学习照片交叉链接的交叉链接硬性这是一种硬性.

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