Fast deployable real-time bioelectric dissolved oxygen sensor based on a multi-source data fusion approach

氧传感器 传感器融合 生物系统 环境科学 微生物燃料电池 工艺工程 计算机科学 微型计算机 阳极 氧气 分析化学(期刊) 材料科学 化学 环境化学 电极 工程类 人工智能 电信 炸薯条 有机化学 物理化学 生物
作者
Yongyun Li,Yahui Chen,Yi Chen,Renwei Qing,Xinyu Cao,Peng Chen,Wei Liu,Yao Wang,Guangwu Zhou,Heng Xu,Likai Hao,Can Wang,Shun Li,Yong‐Guan Zhu,Stefan B. Haderlein,Fei Xu
出处
期刊:Chemical Engineering Journal [Elsevier BV]
卷期号:475: 146064-146064 被引量:4
标识
DOI:10.1016/j.cej.2023.146064
摘要

Commercially available dissolved oxygen (DO) sensors are hardly suitable for stereoscopic and precise DO monitoring due to their design, high cost and susceptibility to matrix effects. Here, we have developed a DO biosensor based on an integrated chamber-free microbial fuel cell (DOMFC) as the core and a Raspberry Pi microcomputer as the data acquisition system. This biosensor is low cost, readily available and compact in configuration. To this end, stable microbial biofilms with oxygen gradients were established on bioaffinity aluminum foam as anode. The DOMFC sensor has a low internal resistance (9.62 Ω) that can respond to DO changes in less than one minute and produce a reliable voltage signal to record DO (0.15–9.5 mg·L-1) under challenging conditions. After training the GA-BPNN model with multidimensional data by automatically applying a data fusion strategy from multiple sources, accurate DO predictions (R2 = 0.997, RMSE = 0.0447, MAE = 0.0401) were obtained. The DOMFC sensor and the prediction model showed excellent agreement (R2 = 0.954) in complex natural applications (different pH values, conductivities, water temperatures, etc.), covering a wide range of applications. Since the sensor mini-monitoring system is inexpensive and easy to make and use on a large scale, it is a promising alternative for oxygen measurements in both natural and artificial waters.
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