Abstract

The benthic microbial fuel cell (BMFC) is a promising technology for harvesting renewable energy from marine littoral environments. The scientific community has researched BMFC technology for well over a decade, but the in situ performance remains challenging. To address this challenge, BMFC power experiments were performed on sediment collected from San Diego Bay (CA, USA), La Spezia (Italy) and Honolulu (HI, USA) in the ever-changing littoral environment. Analysis of BMFC laboratory data found the power density varied substantially across 11 sites in San Diego Bay. In addition, data from experiments repeated at four locations in San Diego Bay showed significant differences between experiments performed in 2014, 2016 and 2019. Multivariable linear analysis showed BMFC 90 day cumulative power density was positively correlated with the total organic carbon (p < 0.05) and negatively correlated with the black carbon in the sediment (p < 0.05). Regression coefficients trained on the San Diego Bay data from 2014 facilitated accurate predictions of BMFC performance in 2016 and 2019. The modeling paradigm accurately explained variations in BMFC power performance in La Spezia and showed sediment parameters can impact BMFC performance differently across geographic regions. The results demonstrate a great potential to use sediment parameters and statistical modeling to predict BMFC power performance prior to deployment in oceanographic environments, thereby reducing cost, work force and resources.

Highlights

  • Oceanographic devices and sensors are powered via battery or similar sources, that become depleted and need constant replacement, maintenance, or are discarded in place

  • During these benthic microbial fuel cell (BMFC) experiments, the power density curves for each of the 11 sites tended to begin with a lag-phase lasting anywhere from days to weeks, with a slight peak followed by either a steady or slowly decaying state

  • These traits are typical of power density curves observed in BMFCs—during the startup phase, there is a sufficient supply of nutrient to the bio lm, but as nutrients are consumed over time, power production becomes dependent on a fresh supply of nutrient presumably through pore water

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Summary

Introduction

Oceanographic devices and sensors are powered via battery or similar sources, that become depleted and need constant replacement, maintenance, or are discarded in place. Reimers et al (2006) showed that a cold seep has the potential to provide more power than neighboring ocean sediments in Monterey Canyon (CA, USA).[11] Donovan et al (2008) deployed a SMFC in the Palouse River (WA, USA) to power a wireless temperature sensor[2] and Tender et al (2008) demonstrated MFCs can power sonobuoys located in the Potomac River, Washington D.C and a salt marsh near Tuckerton.[3] In more recent and relevant studies, Zhao et al (2016) collected sediment from Horseshoe Lake (North China) to investigate how variation in organic matter loading affected electricity generation in SMFCs15 and Kubota et al (2019) operated a set of ve SMFC's at a single site on the sea oor in Tokyo Bay, to evaluate their electrochemical characteristics and effects of sediment on the anode.[16] Such geographically con ned case studies may vastly misrepresent MFC performance across large regions as microbial communities may shi or adapt due to varying substrate distributions or other environmental pressures This is crucial, as locations with different sediment quality may yield varying results in sediment based MFC power leading to either the success or failure of their desired application. The predictors relative dispersion and a measure of their contribution to the predicted BMFC power performance response provided insight into possible physical mechanisms behind long-term BMFC performance in different sediment conditions

Station sediment sampling and preparation for analyses
BMFC power production experiments
Statistical analysis and modeling
San Diego Bay 2014 experiments and modeling
Predicting BMFC performance in San Diego Bay
Extending the modeling paradigm to other regions
Conclusions
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