▲ 作者:Andrew Pannone, Aditya Raj, Harikrishnan Ravichandran, Sarbashis Das, Ziheng Chen, Collin A. Price, et al.
▲ 链接:
https://www.nature.com/articles/s41586-024-08003-w
▲ 摘要:
离子敏感场效应晶体管(ISFETs)已成为化学传感应用中不可或缺的工具。ISFETs通过将化学溶液组成的变化转换成电信号来运行,使其成为环境监测、医疗诊断和工业过程控制的理想选择。ISFET技术的最新进展(包括功能化多路复用阵列和先进数据分析)提高了其性能。
研究组阐明了结合机器学习算法来构建预测模型的优势,该模型使用ISFET传感器生成的广泛数据集来进行分类和量化任务。这种整合也为ISFET的工作提供了超越人类专业知识的新视角。
此外,该策略还克服了与循环间、传感器间和芯片间变化相关的实际难题,为ISFET在商业应用中的广泛采用铺平了道路。具体而言,研究组使用非功能化石墨烯基ISFET阵列生成的数据来训练人工神经网络,这些神经网络具有识别食品欺诈、食品腐败和食品安全问题的卓越能力。
研究组预计,紧凑、节能和可重复使用的石墨烯基ISFET技术与稳健机器学习算法的融合,有可能彻底改变精细化学和环境变化的检测,提供适用于广谱应用的快速、数据驱动的见解。
▲ Abstract:
Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications. ISFETs operate by converting changes in the composition of chemical solutions into electrical signals, making them ideal for environmental monitoring, healthcare diagnostics and industrial process control. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance. Here we illustrate the advantages of incorporating machine learning algorithms to construct predictive models using the extensive datasets generated by ISFET sensors for both classification and quantification tasks. This integration also sheds new light on the working of ISFETs beyond what can be derived solely from human expertise. Furthermore, it mitigates practical challenges associated with cycle-to-cycle, sensor-to-sensor and chip-to-chip variations, paving the way for the broader adoption of ISFETs in commercial applications. Specifically, we use data generated by non-functionalized graphene-based ISFET arrays to train artificial neural networks that possess a remarkable ability to discern instances of food fraud, food spoilage and food safety concerns. We anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.