Engineering Science and Mechanics, Penn State University, University Park, PA, USA.
Electrical Engineering, Penn State University, University Park, PA, USA.
Nature. 2024 Oct;634(8034):572-578. doi: 10.1038/s41586-024-08003-w. Epub 2024 Oct 9.
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.
离子敏感场效应晶体管（ISFET）已经成为化学传感应用中不可或缺的工具。ISFET 通过将化学溶液成分变化转换为电信号来工作，使其成为环境监测、医疗诊断和工业过程控制的理想选择。最近，ISFET 技术的进步，包括功能化的多路复用阵列和先进的数据分析，提高了它们的性能。在这里，我们通过使用 ISFET 传感器生成的大量数据集来构建分类和定量任务的预测模型，说明了将机器学习算法集成到其中的优势。这种集成还揭示了 ISFET 除了可以从人类专业知识中得出的以外的工作原理。此外，它还减轻了与循环到循环、传感器到传感器和芯片到芯片变化相关的实际挑战，为 ISFET 在商业应用中的更广泛采用铺平了道路。具体来说，我们使用非功能化的基于石墨烯的 ISFET 阵列生成的数据来训练人工神经网络，这些神经网络具有出色的辨别食品欺诈、食品变质和食品安全问题的能力。我们预计，紧凑、节能且可重复使用的基于石墨烯的 ISFET 技术与强大的机器学习算法的融合有可能彻底改变对微妙化学和环境变化的检测，提供适用于广泛应用的快速、数据驱动的见解。
