Grupo de Tecnología de Sensores Avanzados (SENSAVAN), Instituto de Tecnologías Físicas y de la Información (ITEFI), CSIC, 28006 Madrid, Spain.
Department of Electronics, University of Alcala, 28871 Alcala de Henares, Madrid, Spain.
Sensors (Basel). 2022 Feb 7;22(3):1261. doi: 10.3390/s22031261.
In this research, a compact electronic nose (e-nose) based on a shear horizontal surface acoustic wave (SH-SAW) sensor array is proposed for the NO detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH), benzene (CH) and acetone (CHO). Carbon-based nanostructured materials (CBNm), such as mesoporous carbon (MC), reduced graphene oxide (rGO), graphene oxide (GO) and polydopamine/reduced graphene oxide (PDA/rGO) are deposited as a sensitive layer with controlled spray and Langmuir-Blodgett techniques. We show the potential of the mass loading and elastic effects of the CBNm to enhance the detection, the classification and the discrimination of NO among different gases by using Machine Learning (ML) techniques (e.g., PCA, LDA and KNN). The small dimensions and low cost make this analytical system a promising candidate for the on-site discrimination of sub-ppm NO.
在这项研究中，提出了一种基于剪切水平表面声波（SH-SAW）传感器阵列的紧凑型电子鼻（e-nose），用于检测、分类和区分一些最相关的周围有毒化学物质，如一氧化碳（CO）、氨（NH）、苯（CH）和丙酮（CHO）。碳基纳米结构材料（CBNm），如介孔碳（MC）、还原氧化石墨烯（rGO）、氧化石墨烯（GO）和聚多巴胺/还原氧化石墨烯（PDA/rGO），通过喷雾和 Langmuir-Blodgett 技术沉积作为敏感层。我们展示了 CBNm 的质量负载和弹性效应的潜力，通过使用机器学习（ML）技术（例如，PCA、LDA 和 KNN）来增强对不同气体中的 NO 的检测、分类和区分。该分析系统具有尺寸小、成本低的特点，有望成为现场亚 ppm 级 NO 区分的候选方案。
