机器学习驱动的电化学感应传感器,用于在可变pH环境中同时监测二甲和双甲
Kyungyeon Lee1, Seong Min Ha2, N G Gurudatt2
1Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea; Department of Medical Engineering, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea.
Journal of hazardous materials
|October 21, 2023
概括
一个新的机器学习感应传感器在河流中检测到塑料污染物二二甲基酸 (DEHP) 和双A (BPA). 这种创新型传感器确保了尽管pH值不同,但环境监测准确.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 生物技术是生物技术.
背景情况:
- 塑料废物释放有害污染物,如DEHP和BPA进入生态系统.
- 准确检测这些污染物对于保护环境和人类健康至关重要.
- 现有的检测方法可能会受到环境因素的影响,例如pH值的变化.
研究的目的:
- 开发一种机器学习 (ML) 驱动的电化学适应传感器,用于同时检测DEHP和BPA.
- 为了最大限度地减少因河水pH值波动造成的电化学信号误差.
- 提供可靠和准确的方法来评估各种水生环境中的塑料污染.
主要方法:
- 使用金纳米花来进行表面修饰的电化学适应传感器的制造.
- 整合最小平方提升 (LSBoost) ML算法以纠正pH值变化.
- 同时检测DEHP和BPA,检测极限较低 (0.58和0.59ppg/mL).
主要成果:
- 用ML驱动的食欲传感器在检测DEHP和BPA方面表现出高的特异性和稳定性.
- 通过LSBoost算法,可以在各种pH值范围内准确地监测目标.
- 在12个不同pH条件的河流地区成功应用了食感器,证实了其可靠性.
结论:
- 本研究介绍了首个基于ML的平台,用于同时评估DEHP和BPA.
- 开发的适应传感器为环境塑料污染物监测提供了强大而准确的解决方案.
- 这一突破通过减轻外部干扰,促进了对塑料污染影响的全面研究.
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