Dataset Information

Electricity generation from digitally printed cyanobacteria.

ABSTRACT: Microbial biophotovoltaic cells exploit the ability of cyanobacteria and microalgae to convert light energy into electrical current using water as the source of electrons. Such bioelectrochemical systems have a clear advantage over more conventional microbial fuel cells which require the input of organic carbon for microbial growth. However, innovative approaches are needed to address scale-up issues associated with the fabrication of the inorganic (electrodes) and biological (microbe) parts of the biophotovoltaic device. Here we demonstrate the feasibility of using a simple commercial inkjet printer to fabricate a thin-film paper-based biophotovoltaic cell consisting of a layer of cyanobacterial cells on top of a carbon nanotube conducting surface. We show that these printed cyanobacteria are capable of generating a sustained electrical current both in the dark (as a 'solar bio-battery') and in response to light (as a 'bio-solar-panel') with potential applications in low-power devices.

SUBMITTER: Sawa M

PROVIDER: S-EPMC5673893 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

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Electricity generation from digitally printed cyanobacteria.

Sawa Marin M Fantuzzi Andrea A Bombelli Paolo P Howe Christopher J CJ Hellgardt Klaus K Nixon Peter J PJ

Nature communications 20171106 1

Microbial biophotovoltaic cells exploit the ability of cyanobacteria and microalgae to convert light energy into electrical current using water as the source of electrons. Such bioelectrochemical systems have a clear advantage over more conventional microbial fuel cells which require the input of organic carbon for microbial growth. However, innovative approaches are needed to address scale-up issues associated with the fabrication of the inorganic (electrodes) and biological (microbe) parts of ...[more]

PMID: 29109396

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Project description:PurposeElectricity production is one of the largest sources of environmental emissions-especially greenhouse gases (GHGs)-in the USA. Emission factors (EFs) vary from region to region, which requires the use of spatially relevant EF data for electricity production while performing life cycle assessments (LCAs). Uncertainty information, which is sought by LCA practitioners, is rarely supplied with available life cycle inventories (LCIs).MethodsTo address these challenges, we present a method for collecting data from different sources for electricity generation and environmental emissions; discuss the challenges involved in agglomerating such data; provide relevant suggestions and solutions to merge the information; and calculate EFs for electricity generation processes from various fuel sources for different spatial regions and spatial resolutions. The EFs from the US 2016 Electricity Life Cycle Inventory (eLCI) are analyzed and explored in this study. We also explore the method of uncertainty information derivation for the EFs.Results and discussionWe explore the EFs from different technologies across Emissions & Generation Resource Integrated Database (eGRID) regions in the USA. We find that for certain eGRID regions, the same electricity production technology may have worse emissions. This may be a result of the age of the plants in the region, the quality of fuel used, or other underlying factors. Region-wise life cycle impact assessment (LCIA) ISO 14040 impacts for total generation mix activities provide an overview of the total sustainability profile of electricity production in a particular region, rather than only global warming potential (GWP). We also find that, for different LCIA impacts, several eGRID regions are consistently worse than the US average LCIA impact for every unit of electricity generated.ConclusionThis work describes the development of an electricity production LCI at different spatial resolutions by combining and harmonizing information from several databases. The inventory consists of emissions, fuel inputs, and electricity and steam outputs from different electricity production technologies located across various regions of the USA. This LCI for electricity production in the USA will prove to be an enormous resource for all LCA researchers-considering the detailed sources of the information and the breadth of emissions covered by it.

Project description:BACKGROUND: G. sulfurreducens is one of the commonest microbes used in microbial fuel cells (MFCs) for organic-to-electricity biotransformation. In MFCs based on this microorganism, electrons can be conveyed to the anode via three ways: 1) direct electron transfer (DET) mode, in which electrons of reduced c-type cytochromes in the microbial outer membrane are directly oxidized by the anode; 2) mediated electron transfer (MET) mode, in which the reducing potential available from cell metabolism in the form of NADH is targeted as an electron source for electricity generation with the aid of exogenous mediators; and 3) a putative mixed operation mode involving both electron transfer mechanisms described above (DET and MET). However, the potential of G. sulfurreducens for current output in these three operation modes and the metabolic mechanisms underlying the extraction of the reducing equivalents are still unknown. RESULTS: In this study, we performed flux balance analysis (FBA) of the genome-scale metabolic network to compute the fundamental metabolic potential of G. sulfurreducens for current output that is compatible with reaction stoichiometry, given a realistic nutrient uptake rate. We also developed a method, flux variability analysis with target flux minimization (FATMIN) to eliminate futile NADH cycles. Our study elucidates the possible metabolic strategies to sustain the NADH for current production under the MET and Mixed modes. The results showed that G. sulfurreducens had a potential to output current at up to 3.710 A/gDW for DET mode, 2.711 A/gDW for MET mode and 3.272 A/gDW for a putative mixed MET and DET mode. Compared with DET, which relies on only one contributing reaction, MET and Mixed mode were more resilient with ten and four reactions respectively for high current production. CONCLUSIONS: The DET mode can achieve a higher maximum limit of the current output than the MET mode, but the MET has an advantage of higher power output and more flexible metabolic choices to sustain the electric current. The MET and DET modes compete with each other for the metabolic resource for the electricity generation.

Dataset Information

Electricity generation from digitally printed cyanobacteria.

Publications

Electricity generation from digitally printed cyanobacteria.

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