•A pilot microbial electrolysis cell (MEC) was tested for H2O2 production.•Passive air diffusion to a carbon electrode successfully produced H2O2.•The highest H2O2 conversion was only 7.2%.•Catholyte pH over 11 can mitigate H2O2 loss in MECs.

Bio-capacitive coulombs were tested for determination of the 5-day biochemical oxygen demand (BOD5) using a dual-chamber microbial electrochemical cell (MxC) operated at charging (open circuit) and discharging (close circuit) conditions. For acetate medium, the cumulative coulombs charged in a capacitive biofilm anode (open circuit) were well correlated with BOD concentrations (R2 ~ 0.9). The maximum detectable BOD5 concentration with the bio-capacitance MxC was close to 250 mg/L, and the cumulative coulombs were saturated for above the maximum BOD5 concentration (Monod pattern). The bio-capacitance MxC sensor consistently showed high linearity between the cumulative coulombs and BOD5 concentrations for domestic wastewater influent (R2 = 0.93–0.99), despite of 1 min charging. High correlation between the coulombs and BOD5 concentration was also obtained for wastewater effluent at 1 min charging, which indicates that the bio-capacitance MxC sensor can semi-continuously measure BOD5 concentration in wastewater at every 2 min (1 min charging and 1 min discharging).

This study assessed the conductivity of a Geobacter-enriched biofilm anode in a microbial electrochemical cell (MxC) equipped with two gold anodes (25 mM acetate medium), as different proton gradients were built throughout the biofilm. There was no pH gradient across the biofilm anode at 100 mM phosphate buffer (current density 2.38 A/m2) and biofilm conductivity (Kbio) was as high as 0.87 mS/cm. In comparison, an inner biofilm became acidic at 2.5 mM phosphate buffer in which dead cells were accumulated at ∼80 μm of the inner biofilm anode. At this low phosphate buffer, Kbio significantly decreased by 0.27 mS/cm, together with declined current density of 0.64 A/m2. This work demonstrates that biofilm conductivity depends on the composition of live and dead cells in the conductive biofilm anode.

In this work, the mechanisms of energy loss in parallel connection of microbial fuel cells (MFCs) is explored using two MFC units producing different open circuit voltage (OCV) and current. In open circuit mode, non-Faradaic current flows in low OCV unit, implying energy loss caused by different OCVs in parallelly stacked MFCs. In a stacked MFC in parallel under close circuit mode, it is confirmed that energy loss occurs until the working voltage in high OCV unit becomes identical to the other unit having low OCV. This result indicates that different voltage between individual MFC units can cause energy loss due to both non-Faradic and Faradaic current that flow from high voltage unit to low voltage unit even in parallelly stacked MFCs.

Voltage reversal is a critical issue for serially stacking microbial fuel cells (MFCs). It occurs when current density in stacked MFCs increases over critical current density (j(critical)). In this study, we clearly show that no voltage reversal occurs in stacked MFCs if current density is maintained below j(critical) where the anode and the cathode potential in an inferior unit become identical, with an external resistance placed between individual MFCs. We define threshold resistance (R-threshold) that enables current density below j(critical) in stacked MFCs, and demonstrate the validity of R-threshold theoretically and experimentally. Voltage reversal is controlled in the stacked MFC equipped with R-threshold by which the current density in the stacked MFC is kept below j(critical). In comparison, a stacked MFC without R-threshold faces voltage reversal over j(critical). Energy loss in R-threshold is comparable to energy loss with other voltage control methods, such as passive or active methods. However, the R-threshold approach is a simple, inexpensive way of controlling voltage reversal, especially for small MFCs (<50 mL). (C) 2015 Elsevier B.V. All rights reserved.

Cathode potential and O2 supply methods were investigated to improve H2O2 synthesis in an electrochemical cell, and optimal cathode conditions were applied for microbial electrochemical cells (MECs). Using aqueous O2 for the cathode significantly improved current density, but H2O2 conversion efficiency was negligible at 0.3-12%. Current density decreased for passive O2 diffusion to the cathode, but H2O2 conversion efficiency increased by 65%. An MEC equipped with a gas diffusion cathode was operated with acetate medium and domestic wastewater, which presented relatively high H2O2 conversion efficiency from 36% to 47%, although cathode overpotential was fluctuated. Due to different current densities, the maximum H2O2 production rate was 141 mg H2O2/L-h in the MEC fed with acetate medium, but it became low at 6 mg H2O2/L-h in the MEC fed with the wastewater. Our study clearly indicates that improving anodic current density and mitigating membrane fouling would be key parameters for large-scale H2O2-MECs.

One approach for utilizing geoscience models for management or policy analysis is via a simulation-based optimization framework—where an underlying model is linked with an optimization search algorithm. In this regard, MATLAB and Python are high-level programming languages that implement numerous optimization routines, including gradient-based, heuristic, and direct-search optimizers. The ever-expanding number of available algorithms makes it challenging for practitioners to identify optimizers that deliver good performance when applied to problems of interest. Thus, the primary contribution of this paper is to present a series of numerical experiments that investigated the performance of various MATLAB and Python optimizers. The experiments considered two simulation-based optimization case studies involving groundwater flow and contaminant transport. One case study examined the design of a pump-and-treat system for groundwater remediation, while the other considered least-squares calibration of a model of strontium (Sr) transport. Using these case studies, the performance of 12 different MATLAB and Python optimizers was compared. Overall, the Hooke–Jeeves direct search algorithm yielded the best performance in terms of identifying least-cost and best-fit solutions to the design and calibration problems, respectively. The IFFCO (implicit filtering for constrained optimization) direct search algorithm and the dynamically dimensioned search (DDS) heuristic algorithm also consistently yielded good performance and were up to 80% more efficient than Hooke–Jeeves when applied to the pump-and-treat problem. These results provide empirical evidence that, relative to gradient- and population-based alternatives, direct search algorithms and heuristic variants, such as DDS, are good choices for application to simulation-based optimization problems involving groundwater management.