Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to create wearable devices for detecting hazardous gases surrounding us. However, chemiresistive gas sensors typically suffer from poor selectivity and zero-cross selectivity toward similar types of gases. Herein, a flexible, RT operable chemiresistive gas sensors array is designed, featuring reduced graphene oxide (rGO) and rGO decorated with zinc oxide (ZnO), titanium dioxide (TiO₂), and tin dioxide (SnO₂) nanoparticles (NPs) on a flexible polyimide (PI) substrate. The sensor array consists of four different sensing layers capable of the selective identification of various hazardous gases such as NO₂, NO, and SO₂ using machine learning (ML). The gas sensor array exhibits a stable response even when mechanically deformed or exposed to high humidity (up to 60%). Each gas sensor, due to the different metal oxide NPs, shows unique responses in terms of sensitivity, responsiveness, response time, and recovery time to different gases. Consequently, the sensor array generates distinct response patterns that effectively differentiate between the target gases. By leveraging these distinctive recovery patterns and employing a data fusion approach in ML, specific concentrations of target gases can be distinguished. Using ML with fused array sensing data, the training and test accuracies achieved were 98.20 and 97.70%, respectively. This innovative combination of sensor arrays and ML offers significant potential for selective gas detection in environmental monitoring and personal safety applications.

中文翻译：

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通过机器学习，使用基于还原氧化石墨烯和金属氧化物纳米颗粒的灵活、室温可操作传感器阵列选择性识别有害气体


为确保人身安全，非常需要选择性检测和监测具有相似特性的有害气体。柔性和室温 （RT） 可操作的化学电阻气体传感器的开发为创建用于检测我们周围有害气体的可穿戴设备提供了绝佳的机会。然而，化学电阻式气体传感器通常对类似类型气体的选择性和零交叉选择性较差。在此，设计了一种柔性的、RT 可操作的化学电阻气体传感器阵列，其特点是在柔性聚酰亚胺 （PI） 衬底上用氧化锌 （ZnO）、二氧化钛 （TiO₂） 和二氧化锡 （SnO₂） 纳米颗粒 （NPs） 装饰的还原氧化石墨烯 （rGO） 和 rGO。传感器阵列由四个不同的传感层组成，能够使用机器学习 （ML） 选择性识别各种有害气体，例如 NO₂、NO 和 SO₂。气体传感器阵列即使在机械变形或暴露于高湿度（高达 60%）时也能表现出稳定的响应。由于金属氧化物 NP 不同，每个气体传感器在灵敏度、响应性、响应时间和对不同气体的恢复时间方面表现出独特的响应。因此，传感器阵列产生不同的响应模式，有效地区分目标气体。通过利用这些独特的回收模式并在 ML 中采用数据融合方法，可以区分目标气体的特定浓度。使用带有融合阵列传感数据的 ML 时，训练和测试准确率分别为 98.20% 和 97.70%。 传感器阵列和 ML 的这种创新组合为环境监测和个人安全应用中的选择性气体检测提供了巨大的潜力。