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Design, Preparation, and Solvent Identification Application of
Antheraea pernyi Silk Paper-based Electrochemical Sensor- Acta Polymerica Sinica Vol. 55, Issue 8, Pages: 954-965(2024)
- 作者机构：
1.上海科技大学物质科学与技术学院 上海 201210
2.安徽农业大学材料与化学学院 合肥 230023
3.(3.先进医用材料与医疗器械全国重点实验室(上海科技大学)
4.上海临床医学中心 上海 201210
- 作者简介：
Ke Zheng, E-mail: zhengke@ahau.edu.cn
Wen-li Gao, E-mail: gaowl@shanghaitech.edu.cn
Sheng-jie Ling, E-mail: lingshj@shanghaitech.edu.cn
- 基金信息：
DOI：10.11777/j.issn1000-3304.2024.24050
CLC：Published：20 August 2024，
Published Online：14 May 2024，
Received：20 February 2024，
Accepted：01 April 2024
- 稿件说明：
移动端阅览
孙奕, 吴伟, 文飘, 任婧, 郑可, 高文丽, 凌盛杰. 基于蒸腾驱动的柞蚕超纤纸电化学传感器的设计、制备及其溶剂在线鉴别应用研究. 高分子学报, 2024, 55(8), 954-965
Sun, Y.; Wu, W.; Wen, P.; Ren, J.; Zheng, K.; Gao, W. L.; Ling, S. J. Design, preparation, and solvent identification application of antheraea pernyi silk paper-based electrochemical sensor. Acta Polymerica Sinica, 2024, 55(8), 954-965
孙奕, 吴伟, 文飘, 任婧, 郑可, 高文丽, 凌盛杰. 基于蒸腾驱动的柞蚕超纤纸电化学传感器的设计、制备及其溶剂在线鉴别应用研究. 高分子学报, 2024, 55(8), 954-965 DOI： 10.11777/j.issn1000-3304.2024.24050.
Sun, Y.; Wu, W.; Wen, P.; Ren, J.; Zheng, K.; Gao, W. L.; Ling, S. J. Design, preparation, and solvent identification application of antheraea pernyi silk paper-based electrochemical sensor. Acta Polymerica Sinica, 2024, 55(8), 954-965 DOI： 10.11777/j.issn1000-3304.2024.24050.
摘要
以具有优异耐溶剂性能的柞蚕超纤纸为基材，结合二硫化钼(MoS
2
)功能单元，成功开发出一种可持续、高灵敏的柞蚕超纤纸电化学传感器. 该传感器的电化学传感原理源于蒸腾驱动的发电机制. 由于不同溶剂与传感器产生不同的相互作用，从而产生具有差异性的蒸腾驱动电信号. 虽然这些电信号具有谱带形状的复杂性，缺乏尖锐峰或特异性的突变点，但对特定溶剂体系展现出高度可重复的变化趋势. 因此，进一步采用前馈神经网络构建了基于柞蚕超纤纸电化学传感器的溶剂识别模型，以处理和分析这些复杂但重复性高的电信号. 所构建的AI驱动的柞蚕超纤纸电化学传感器不仅能准确鉴别化学结构和性质高度相似的溶剂如甲醇、乙醇、异丙醇和去离子水等，还能识别化学性质相似的混合溶剂体系，如不同比例的乙醇和水的混合溶剂体系. 在100次的识别测试中，准确率为100%. 本研究所开发的AI驱动的柞蚕超纤纸电化学传感器为实现快速、准确的溶剂检测提供了一种便捷、经济有效的方法，在酒精检测、环境监测和化学分析等领域具有重要意义.
Abstract
This study focuses on the development of a sustainable and highly sensitive
Antheraea pernyi (A. pernyi)
silk paper-based electrochemical sensor
utilizing the excellent solvent resistance of silk paper as the substrate and incorporating molybdenum disulfide (MoS
2
) functional units. The electrochemical sensing principle of this sensor is based on a transpiration-driven electricity generation mechanism. Different solvents interact distinctly with the sensor
resulting in transpiration-driven electrical signals with varying characteristics. Although these electrical signals display complex band-shaped patterns lacking sharp peaks or specific mutation points
they exhibit highly reproducible trends for specific solvent systems. Consequently
this study further employs a feedforward neural network to construct a solvent identification model based on the oak silk fibroin ultrafine paper electrochemical sensor. This model processes and analyzes these complex yet highly repetitive electrical signals. The AI-driven
A. pernyi
silk paper-based electrochemical sensor not only accurately identifies solvents with highly similar chemical structures and properties
such as methanol
ethanol
isopropanol
and deionized water
but also recognizes chemically similar mixed solvent systems
like varying proportions of ethanol-water mixtures. In 100 identification tests
the accuracy was 100%. The development of this AI-driven
A. pernyi
silk paper-based electrochemical sensor offers a convenient
cost-effective method for rapid and accurate solvent detection
holding significant implications in fields such as alcohol testing
environmental monitoring
and chemical analysis.
关键词
柞蚕超纤纸共熔溶剂分子解聚电化学传感器深度学习
Keywords
Silk paperEutectic solventDepolymerizationElectrochemical sensorDeep learning
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