在复杂介质中利用小分子分析剂检测:结合分子打印聚合物,电解晶体管和机器学习
Gabrielle Coelho Lelis1, Wilson Tiago Fonseca1, Alessandro Henrique de Lima1
1Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-100, Brazil.
ACS applied materials & interfaces
|December 22, 2023
概括
这项研究引入了一种使用分子打印聚合物和2D材料晶体管检测希普酸的新型护理设备. 机器学习分析显著提高了早期疾病检测的灵敏度.
科学领域:
- 生物医学工程 生物医学工程
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
背景情况:
- 小分子检测对于健康监测和早期疾病诊断至关重要.
- 当前的护理点 (PoC) 技术在复杂的生物样本中检测特定标记物方面面临着挑战.
研究的目的:
- 开发一种新型的PoC设备来检测酸 (HA),这是各种健康状况的关键生物标志物.
- 整合分子印记聚合物 (MIP),基于二维材料的电解质门晶体管 (EGT) 和机器学习 (ML) 进行增强的分析物检测.
主要方法:
- 使用减少的石墨烯氧化物 (rGO) 作为感觉材料和MIPs作为EGT中的超分子受体.
- 采用监督的ML技术,包括符号回归和压力传感,用于EGT转移曲线的多变量分析.
- 开发了一种PoC设备,用于在人工尿中检测酸.
主要成果:
- 该设备显示低工作电压 (<0.5V) 和快速响应时间 (≤10秒).
- 实现了广泛的检测范围 (0.05200 nmol L-1),其低检测极限 (LoD) 为39 pmol L-1.
- 与人类数据分析相比,基于ML的多变量分析将设备的灵敏度提高了2.5倍.
结论:
- 开发的方法提供了一种准确而敏感的方法,用于使用PoC技术在复杂介质中检测小分子.
- MIP,2D EGT和ML的组合代表了医疗点诊断的重大进步.
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