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材料层面的对策,以确保微流体生物芯片的安全.

Navajit Singh Baban1, Sohini Saha2, Sofija Jancheska3

  • 1Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates. nsb359@nyu.edu.

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概括
此摘要是机器生成的。

保护微流体生物芯片免受假冒和知识产权盗窃至关重要. 本研究引入了一种使用光染料和机器学习的新型水印方案,以检测PDMS组件的材料级攻击.

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科学领域:

  • 生物技术和生物医学工程 生物技术和生物医学工程
  • 材料科学 材料科学 材料科学
  • 网络安全 网络安全

背景情况:

  • 基于流的微流体生物芯片 (FMB) 越来越多地用于诊断和计算.
  • 外包制造使FMB暴露在隐蔽的物质水平攻击中,包括PDMS通过溶剂或改变硬化比率的降解.
  • 这些攻击威胁到FMB的功能,使假冒成为可能,并促进知识产权的盗窃.

研究的目的:

  • 为基于聚甲基 (PDMS) 的FMB开发一个动态的材料级水印方案.
  • 创建一种可靠的方法来检测FMB中的材料降解和制造异常.
  • 保护FMB免受假冒和知识产权盗窃的侵害.

主要方法:

  • 在PDMS微门中实施了标有烯的光染料,在405nm激光激发下创建了一个独特的排挤剂强度峰值.
  • 通过测量气动启动期间光强度向下转移的量化机械应变.
  • 利用机器学习模型训练力位移数据从机械冲击测试检测固化比异常.

主要成果:

  • 使用光显微镜,证明了正常化的排外体强度变化和微膜菌株之间的高相关性 (R2 = 0.971).
  • 在用机器学习模型检测固化比异常时,获得了超过99%的准确性.
  • 验证了水标方案和ML模型在识别物质级攻击方面的有效性.

结论:

  • 拟议的动态水标方案有效地检测PDMS微流体生物芯片的材料级攻击.
  • 机器学习模型提供高度准确的制造异常检测,特别是治愈比率变化.
  • 这些对策为FMB提供了对假冒的积极保护,并确保在关键应用程序 (如护理点诊断) 中的设备完整性.