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相关概念视频

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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生物油墨配方的数据驱动优化用于基于挤出的生物打印:一种预测建模方法.

Rokeya Sarah1, Riley Rohauer2, Kory Schimmelpfennig3

  • 1Department of Sustainable Product Design and Architecture, Keene State College, 229 Main Street, Keene, NH 03435.

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概括

这项研究开发了用于组织工程的ALGEC生物墨水的预测模型. 这些模型优化生物墨水组成,以提高可打印性和再生医学应用中的结构完整性.

关键词:
在3D生物打印中使用3D生物打印在CAD/CAM/CAE中使用.添加剂制造 添加剂制造 添加剂制造先进的材料和加工和加工.生物医学制造业 生物医学制造业这是一种水凝.机器学习是机器学习.多重回归的多重回归方法多项式适合的多项式打印参数的参数快速原型和固体自由形式制造.

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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.
  • 类风病学 类风病学 类风病学

背景情况:

  • 基于挤出的生物打印对于制造复杂的组织结构至关重要.
  • 生物油墨的整形学,特别是粘度,决定了可打印性和结构完整性.
  • 了解生物墨水成分-粘度关系对于成功的生物打印至关重要.

研究的目的:

  • 为了研究新型ALGEC生物油墨 (Alginate,Gelatin,TEMPO氧化纳米纤维化纤维素) 的质行为.
  • 根据成分和剪切率,开发生物墨水粘度的预测模型.
  • 优化ALGEC配方,以提高生物打印性能.

主要方法:

  • 准备的ALGEC生物墨水含有不同度的酸盐,凝和TO-NFC.
  • 在一系列剪速 (0.1100s−1) 的范围内测量粘度.
  • 开发并验证了多项式适合和多重回归模型来预测粘度.

主要成果:

  • 最好的预测模型实现了0.98的R2和0.12的MAE.
  • 优化的ALGEC配方显示出更好的打印性和结构稳定性.
  • 基于模型的优化成功引导了针对目标粘度的生物墨水配方.

结论:

  • 预测性风湿学模型对于优化组织工程中的生物墨水配方是有效的.
  • 优化的ALGEC生物墨水提高了生物打印结构的打印能力和稳定性.
  • 这种方法通过改进生物制造过程,促进了再生医学的发展.