Detection of 4-nitrophenol, a model toxic compound, using multi-stage microbial fuel cells
Detection of 4-nitrophenol, a model toxic compound, using multi-stage microbial fuel cells
The upstream protection of the biomass present in biological treatment processes is a vital challenge as the consequences of failure could include exposure of water users to hazardous chemicals in addition to loss of treatment performance. Online detection of toxic compounds in wastewater could enable processes to be monitored in real-time and promote pro-active responses to pollution incidents. Recently, Microbial Fuel Cells (MFCs) which generate electricity from organic matter oxidation have shown potential as sensors for online detection of toxicity. In this study, the detection of a model toxicant (4-nitrophenol) was investigated using a multi-stage MFC-based toxicity sensor. MFCs were operated with synthetic wastewater to maintain realistic conditions while enabling organic carbon levels to be controlled. A positive correlation was observed between the 4-NP concentrations and the current drop area showing that the response was proportional to the toxicity level. In addition, the sensor anodic biofilm exhibited resilience to acute toxic events with recovery of 75% of the initial current following a toxic event comprising 500 mg/L 4-NP after 4 h. However, repetitive toxicity events could lead to the selection of resistant bacteria able to degrade the toxic compounds. In this study, a maximal 4-NP degradation rate of 36 mg/h was observed. This limitation could be overcome by re-calibration after a determined number of toxic events. An additional feature of the multi-stage configuration of the sensor is that a drop in output caused by the presence of a toxic compound could be distinguished from a drop in output caused by a decrease in BOD. The microbial community on the sensor anode was characterized by 16S rRNA gene sequencing and shown to comprise an anaerobic community of fermentative bacteria capable of producing volatile fatty acids and hydrogen that were consumed by electrogenic Geobacter spp (2.76 to 21.39% of the anode community) that generated the electrical signal in the sensor. The multi-stage MFC biosensor could provide an early warning system capable of alerting process operators to the presence and level of toxicity in influent wastewater.
aminophenol, bioelectrochemistry, biosensors, microbial fuel cells (MFCs), multi-stage, nitrophenol, toxicity, wastewater
Godain, Alexiane
d503861b-a87d-4a66-8e7f-3d4d2b529adb
Spurr, Martin W.A.
78e1284f-f5fe-4abb-a3fe-292a4b6a728f
Boghani, Hitesh C.
b1f8f7d2-fcc2-4559-8210-372b17e651fc
Premier, Giuliano C.
318775e0-df2e-4ee1-bbab-7100c0da3804
Yu, Eileen H.
28e47863-4b50-4821-b80b-71fb5a2edef2
Head, Ian M.
45e5ea84-bd86-4ffd-a6e3-64b23dc711d2
31 January 2020
Godain, Alexiane
d503861b-a87d-4a66-8e7f-3d4d2b529adb
Spurr, Martin W.A.
78e1284f-f5fe-4abb-a3fe-292a4b6a728f
Boghani, Hitesh C.
b1f8f7d2-fcc2-4559-8210-372b17e651fc
Premier, Giuliano C.
318775e0-df2e-4ee1-bbab-7100c0da3804
Yu, Eileen H.
28e47863-4b50-4821-b80b-71fb5a2edef2
Head, Ian M.
45e5ea84-bd86-4ffd-a6e3-64b23dc711d2
Godain, Alexiane, Spurr, Martin W.A., Boghani, Hitesh C., Premier, Giuliano C., Yu, Eileen H. and Head, Ian M.
(2020)
Detection of 4-nitrophenol, a model toxic compound, using multi-stage microbial fuel cells.
Frontiers in Environmental Science, 8, [5].
(doi:10.3389/fenvs.2020.00005).
Abstract
The upstream protection of the biomass present in biological treatment processes is a vital challenge as the consequences of failure could include exposure of water users to hazardous chemicals in addition to loss of treatment performance. Online detection of toxic compounds in wastewater could enable processes to be monitored in real-time and promote pro-active responses to pollution incidents. Recently, Microbial Fuel Cells (MFCs) which generate electricity from organic matter oxidation have shown potential as sensors for online detection of toxicity. In this study, the detection of a model toxicant (4-nitrophenol) was investigated using a multi-stage MFC-based toxicity sensor. MFCs were operated with synthetic wastewater to maintain realistic conditions while enabling organic carbon levels to be controlled. A positive correlation was observed between the 4-NP concentrations and the current drop area showing that the response was proportional to the toxicity level. In addition, the sensor anodic biofilm exhibited resilience to acute toxic events with recovery of 75% of the initial current following a toxic event comprising 500 mg/L 4-NP after 4 h. However, repetitive toxicity events could lead to the selection of resistant bacteria able to degrade the toxic compounds. In this study, a maximal 4-NP degradation rate of 36 mg/h was observed. This limitation could be overcome by re-calibration after a determined number of toxic events. An additional feature of the multi-stage configuration of the sensor is that a drop in output caused by the presence of a toxic compound could be distinguished from a drop in output caused by a decrease in BOD. The microbial community on the sensor anode was characterized by 16S rRNA gene sequencing and shown to comprise an anaerobic community of fermentative bacteria capable of producing volatile fatty acids and hydrogen that were consumed by electrogenic Geobacter spp (2.76 to 21.39% of the anode community) that generated the electrical signal in the sensor. The multi-stage MFC biosensor could provide an early warning system capable of alerting process operators to the presence and level of toxicity in influent wastewater.
Text
fenvs-08-00005
- Version of Record
More information
Accepted/In Press date: 9 January 2020
Published date: 31 January 2020
Keywords:
aminophenol, bioelectrochemistry, biosensors, microbial fuel cells (MFCs), multi-stage, nitrophenol, toxicity, wastewater
Identifiers
Local EPrints ID: 498896
URI: http://eprints.soton.ac.uk/id/eprint/498896
ISSN: 2296-665X
PURE UUID: 068ea2d5-6e3d-4578-90c0-86ff80313de9
Catalogue record
Date deposited: 04 Mar 2025 17:51
Last modified: 22 Aug 2025 02:45
Export record
Altmetrics
Contributors
Author:
Alexiane Godain
Author:
Martin W.A. Spurr
Author:
Hitesh C. Boghani
Author:
Giuliano C. Premier
Author:
Eileen H. Yu
Author:
Ian M. Head
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics