Biodiesel has gained a significant amount of attention over the past decade as an environmentally friendly fuel that is capable of being utilized by a conventional diesel engine. However, the biodiesel production process generates glycerol-containing waste streams which have become a disposal issue for biodiesel plants and generated a surplus of glycerol. A value-added opportunity is needed in order to compensate for disposal-associated costs. Microbial conversions from glycerol to valuable chemicals performed by various bacteria, yeast, fungi, and microalgae are discussed in this review paper, as well as the possibility of extending these conversions to microbial electrochemical technologies.

Establishing a core microbiome is the first step in understanding and subsequently optimizing microbial interactions in anodic biofilms of microbial fuel cells (MFCs) for increased power, efficiency, and decreased start-up times. In the present study, we used 454 pyrosequencing to demonstrate that a core anodic community would consistently emerge over a period of 4 years given similar conditions. The development and variation across reactor designs of these communities was also explored. The core members present in all high-power generating biofilms were Geobacter, Aminiphilus, Sedimentibacter, Acetoanaerobium, and Spirochaeta, accounting for 72 ± 9 % of all genera. Aminiphilus spp., member of the Synergistetes phylum was present at higher abundances than previously reported in any other ecological studies. Results suggest a stable core microbiome in acetate-fed MFCs on both phylogenetic and functional levels.

The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including eight separate substrates and three different wastewaters. Artificial Neural Networks (ANNs) were used to establish mathematical relationships between wastewater/solution characteristics, biofilm communities, and reactor performance. ANN models that incorporated biotic interactions predicted reactor performance outcomes more accurately than those that did not. The average percent error of power density predictions was 16.01 ± 4.35%, while the average percent error of Coulombic efficiency and COD removal rate predictions were 1.77 ± 0.57% and 4.07 ± 1.06%, respectively. Predictions of power density improved to within 5.76 ± 3.16% percent error through classifying taxonomic data at the family versus class level. Results suggest that the microbial communities and performance of bioelectrochemical systems can be accurately predicted using data-mining, machine-learning techniques.

No abstract

No abstract

The discovery of direct extracellular electron transfer offers an alternative to the traditional understanding of diffusional electron exchange via small molecules. The establishment of electronic connections between electron donors and acceptors in microbial communities is critical to electron transfer via electrical currents. These connections are facilitated through conductivity associated with various microbial aggregates. However, examination of conductivity in microbial samples is still in its relative infancy and conceptual models in terms of conductive mechanisms are still being developed and debated. The present review summarizes the fundamental understanding of electrical conductivity in microbial aggregates (e.g. biofilms, granules, consortia, and multicellular filaments) highlighting recent findings and key discoveries. A greater understanding of electrical conductivity in microbial aggregates could facilitate the survey for additional microbial communities that rely on direct extracellular electron transfer for survival, inform rational design towards the aggregates-based production of bioenergy/bioproducts, and inspire the construction of new synthetic conductive polymers.

No abstract

Stability as evaluated by functional resistance and resilience is critical to the effective operation of environmental biotechnologies. To date, limited tools have been developed that allow operators of these technologies to predict functional responses to environmental and operational disturbances. In the present study, 17 Microbial Fuel Cells (MFCs) were exposed to a low pH perturbation. MFC power dropped 52.7 ± 35.8% during the low pH disturbance. Following the disturbance, 3 MFCs did not recover while 14 took 60.7 ± 58.3 h to recover to previous current output levels. Machine learning models based on genomic data inputs were developed and evaluated on their ability to predict resistance and resilience. Resistance and resilience levels corresponding to risk of deactivation could be classified with 70.47 ± 15.88% and 65.33 ± 19.71% accuracy, respectively. Models predicting resistance and resilience coefficient values projected postperturbation current drops within 6.7−15.8% and recovery times within 5.8−8.7% of observed values. Results suggest that abundances of specific genera are better predictors of resistance while overall microbial community structure more accurately predicts resilience. This approach can be used to assess operational risk and is a first step toward the further understanding and improvement of overall stability of environmental biotechnologies.