title: Detecting recalcitrant organic chemicals in water with microbial fuel cells and artificial neural networks.
abstract: This study integrates artificial neural network (ANN) processing with microbial fuel cell (MFC)-based biosensing in the detection of three organic pollutants: aldicarb, dimethyl-methylphosphonate (DMMP), and bisphenol-A (BPA). Overall, the use of the ANN proved to be more reliable than direct correlations for the determination of both chemical concentration and type. The ANN output matched the appropriate chemical concentration and type for three different concentrations and throughout a wide range of stepwise tests. Additionally, chemicals dissolved in the acetate-based feed medium (FM) were accurately identified by the ANN even though the acetate masked the pollutants' effects on electrical current. The ANN also accurately revealed the identity of chemical mixtures. This study is the first to incorporate ANN modeling with MFC-based biosensing for the detection and quantification of organic pollutants that are not readily biodegradable. Furthermore, this work provides insight into the flexibility of MFC-based biosensing as it pertains to limits of detection and its applicability to scenarios where mixtures of pollutants and unique solvents are involved. This research effort is expected to serve as a guide for future MFC-based biosensing efforts.
authors.affiliation: Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base, OH 45433, USA.
authors.forename: Scott T
authors.initials: ST
authors.lastname: King
authors.affiliation: Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base, OH 45433, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Sylvander
authors.affiliation: Spelman College, Department of Physics, 350 Spelman Lane, Atlanta, GA 30314, USA.
authors.forename: Mekhakhem
authors.initials: M
authors.lastname: Kheperu
authors.affiliation: Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base, OH 45433, USA.
authors.forename: LeeAnn
authors.initials: L
authors.lastname: Racz
authors.affiliation: Department of Systems Engineering and Management, Air Force Institute of Technology, 2950 Hobson Way, Wright Patterson Air Force Base, OH 45433, USA. Electronic address: willie.harper@afit.edu.
authors.forename: Willie F
authors.initials: WF
authors.lastname: Harper
mesh_terms: D001674:Bioelectric Energy Sources; D004784:Environmental Monitoring / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D009930:Organic Chemicals; D014874:Water Pollutants, Chemical
keywords: Artificial neural networks; Biosensing; Microbial fuel cells; Organic pollutants; Water quality