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Impact of sediment parameters in the prediction of benthic microbial fuel cell performance
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 e...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9055322/ https://www.ncbi.nlm.nih.gov/pubmed/35519731 http://dx.doi.org/10.1039/d0ra03459b |
Sumario: | 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. |
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