Microbial electrolysis using aqueous fractions derived from Tail-Gas Recycle Pyrolysis of willow and guayule

Satinover, Scott J.; Elkasabi, Yaseen; Núñez, Alberto; Rodríguez, Miguel; Borole, Abhijeet P.

doi:10.1016/j.biortech.2018.11.099

Cited by 13 publications

(6 citation statements)

References 45 publications

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“…HPLC was used to quantify compounds of interest using methods that have been previously described [66]. Briefly, two detectors and one column were used to detect compounds.…”

Section: Compound Detection In Mecsmentioning

confidence: 99%

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Performance and community structure dynamics of microbial electrolysis cells operated on multiple complex feedstocks

Satinover

Rodríguez

Campa

et al. 2020

Biotechnol Biofuels

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Background Microbial electrolysis is a promising technology for converting aqueous wastes into hydrogen. However, substrate adaptability is an important feature, seldom documented in microbial electrolysis cells (MECs). In addition, the correlation between substrate composition and community structure has not been well established. This study used an MEC capable of producing over 10 L/L-day of hydrogen from a switchgrass-derived bio-oil aqueous phase and investigated four additional substrates, tested in sequence on a mature biofilm. The additional substrates included a red oak-derived bio-oil aqueous phase, a corn stover fermentation product, a mixture of phenol and acetate, and acetate alone. Results The MECs fed with the corn stover fermentation product resulted in the highest performance among the complex feedstocks, producing an average current density of 7.3 ± 0.51 A/m2, although the acetate fed MECs outperformed complex substrates, producing 12.3 ± 0.01 A/m2. 16S rRNA gene sequencing showed that community structure and community diversity were not predictive of performance, and replicate community structures diverged despite identical inoculum and enrichment procedure. The trends in each replicate, however, were indicative of the influence of the substrates. Geobacter was the most dominant genus across most of the samples tested, but its abundance did not correlate strongly to current density. High-performance liquid chromatography (HPLC) showed that acetic acid accumulated during open circuit conditions when MECs were fed with complex feedstocks and was quickly degraded once closed circuit conditions were applied. The largest net acetic acid removal rate occurred when MECs were fed with red oak bio-oil aqueous phase, consuming 2.93 ± 0.00 g/L-day. Principal component analysis found that MEC performance metrics such as current density, hydrogen productivity, and chemical oxygen demand removal were closely correlated. Net acetic acid removal was also found to correlate with performance. However, no bacterial genus appeared to correlated to these performance metrics strongly, and the analysis suggested that less than 70% of the variance was accounted for by the two components. Conclusions This study demonstrates the robustness of microbial communities to adapt to a range of feedstocks and conditions without relying on specific species, delivering high hydrogen productivities despite differences in community structure. The results indicate that functional adaptation may play a larger role in performance than community composition. Further investigation of the roles each microbe plays in these communities will help MECs to become integral in the 21st-century bioeconomy to produce zero-emission fuels.

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“…HPLC was used to quantify compounds of interest using methods that have been previously described [66]. Briefly, two detectors and one column were used to detect compounds.…”

Section: Compound Detection In Mecsmentioning

confidence: 99%

“…Compounds of interested were influenced from prior work [22,66]. Compound classes included organic acids, phenolics, and furans.…”

Section: Compound Detection In Mecsmentioning

confidence: 99%

Performance and community structure dynamics of microbial electrolysis cells operated on multiple complex feedstocks

Satinover

Rodríguez

Campa

et al. 2020

Biotechnol Biofuels

Self Cite

View full text Add to dashboard Cite

show abstract

“…Electrochemical analysis was performed as previously described [22,64]. Brie y, the productivity metrics calculated included average current density, in units of Amps per meter of projected surface area squared (A/m 2 ), Hydrogen productivity in units of liters of H 2 per liter of anode volume per day (L/L-day).…”

Section: Mec Electrochemical Analysismentioning

confidence: 99%

“…HPLC was used to quantify compounds of interest using methods that have been previously described [64]. Brie y, two detectors and one column were used to detect compounds.…”

Section: Compound Detection In In Mecsmentioning

confidence: 99%

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Performance and Community Structure Dynamics of Microbial Electrolysis Cells Operated on Multiple Complex Feedstocks

Satinover

Rodríguez

Campa

et al. 2020

Preprint

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Background Microbial electrolysis is a promising technology for converting aqueous wastes into hydrogen. Substrate adaptability is an important feature, seldom documented in Microbial Electrolysis Cells (MECs). The correlation between substrate composition and community structure has not been well established. This study used a MEC capable of producing over 10 L/L-day of hydrogen from a switchgrass-derived bio-oil aqueous phase and investigated four additional substrates. The additional substrates included a red oak-derived bio-oil aqueous phase, a corn stover fermentation product, a mixture of phenol and acetate, and acetate alone. Results The MEC fed with the corn stover fermentation product resulted in the highest performance among the complex feedstocks, producing an average current density of 7.3 A/m2, although the acetate fed MEC outperformed complex substrates, producing 12 ± A/m2. 16S rRNA gene sequencing showed that community structure and community diversity were not predictive of performance, and replicate community structures diverged despite identical inoculum and enrichment procedure. The trends in each replicate, however, were indicative of the influence of the substrates. Geobacter was the most dominant genus across most of the samples tested, but its abundance did not correlate strongly to current density. High-performance liquid chromatography (HPLC) showed that acetate accumulated during open-circuit conditions when MECs were fed with complex feedstocks and was quickly degraded once closed-circuit conditions were applied. The largest net acetic acid removal rate occurred when MECs were fed with red oak bio-oil aqueous phase, consuming 2.93 ± 0.00 g/L-day. Principal component analysis found that MEC performance metrics such as current density, hydrogen productivity, and COD removal were closely correlated. Net acetic acid removal was also found to correlate with performance. However, no bacterial genus correlated to performance metrics, and the analysis suggested that less than 70% of the variance was accounted for by the two components. Conclusions This study demonstrates the robustness of microbial communities to adapt to a range of feedstocks and conditions without relying on specific species, delivering high hydrogen productivities, thus indicating functional adaptation vs. compositional requirement. MECs may,, play a central role in the 21st-century bioeconomy as factories producing a zero-emission fuel.

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Integrating Biomass Pyrolysis with Microbial Conversion Processes to Produce Biofuels and Biochemicals

Doddapaneni

Kikas

2020

Biofuels and Biorefineries

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Microbial electrolysis using aqueous fractions derived from Tail-Gas Recycle Pyrolysis of willow and guayule

Cited by 13 publications

References 45 publications

Performance and community structure dynamics of microbial electrolysis cells operated on multiple complex feedstocks

Performance and community structure dynamics of microbial electrolysis cells operated on multiple complex feedstocks

Performance and Community Structure Dynamics of Microbial Electrolysis Cells Operated on Multiple Complex Feedstocks

Integrating Biomass Pyrolysis with Microbial Conversion Processes to Produce Biofuels and Biochemicals

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