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Updated: Jun 11, 2025

Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment
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双核银涂等离子体传感器模拟与机器学习.

Chanchal Saha1, Farzana Haque1, Nazrul Islam2,3

  • 1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh.

Heliyon
|October 10, 2024
PubMed
概括
此摘要是机器生成的。

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这项研究介绍了一种新型的双核等离子体传感器,具有增强的灵敏度和分辨率,用于精确检测分析物. 集成机器学习算法进一步优化其性能,用于先进的生物传感应用.

科学领域:

  • 塑制剂是一种塑制剂.
  • 纳米技术纳米技术
  • 传感器技术 传感器技术

背景情况:

  • 表面等离子体共振 (SPR) 传感器对于敏感的,无标签的检测至关重要.
  • 现有的传感器在复杂环境中面临着灵敏度和分辨率的限制.

研究的目的:

  • 开发一种具有卓越灵敏度和分辨率的新型双核银涂等离子体传感器.
  • 整合机器学习 (ML) 算法,以增强数据分析和预测能力.

主要方法:

  • 制造一个双核银涂等离子体传感器.
  • 在1.34至1.41的折射率 (RI) 范围内的传感器性能表征.
  • 实施ML算法 (MLR,SVR,RFR) 用于数据分析和预测.

主要成果:

  • 实现了最高报告的波长灵敏度为30,000nm/RIU.
  • 证明了高分辨率 (RIU) 对于精确的分析物检测.
  • ML算法成功预测了各种分析物的限制损失和波长.

结论:

  • 双核等离子体传感器在灵敏度和分辨率方面取得了显著的进步.
关键词:
双核银涂层的双核心机器学习 机器学习这是一个PCF-SPR传感器.塑传感器的感应器表面等离子体共振是什么?

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  • 机器学习集成增强了传感器适应性和数据驱动的洞察力.
  • 这种传感器是先进生物传感应用的一个有前途的工具.