Biophotovoltaics (BPVs) have increasingly gained interest due to their potential to generate low-carbon electricity and chemicals from just sunlight and water using photosynthetic microorganisms. A key hurdle in developing commercial...

Background: Understanding the extracellular electron transport pathways in cyanobacteria is a major factor towards developing biophotovoltaics. Stressing cyanobacteria cells environmentally and then probing changes in physiology or metabolism following a significant change in electron transfer rates is a common approach for investigating the electron path from cell to electrode. However, such studies have not explored how the cells' concurrent morphological adaptations to the applied stresses affect electron transfer rates. In this paper, we establish a ratio to quantify this effect in mediated systems and apply it to Synechococcus elongatus sp. PCC7942 cells grown under different nutritional regimes. Results: The results provide evidence that wider and longer cells with larger surface areas have faster mediated electron transfer rates. For rod-shaped cells, increase in cell area as a result of cell elongation more than compensates for the associated decline in mass transfer coefficients, resulting in faster electron transfer. In addition, the results demonstrate that the extent to which morphological adaptations account for the changes in electron transfer rates changes over the bacterial growth cycle, such that investigations probing physiological and metabolic changes are meaningful only at certain time periods. Conclusion: A simple ratio for quantitatively evaluating the effects of cell morphology adaptations on electron transfer rates has been defined. Furthermore, the study points to engineering cell shape, either via environmental conditioning or genetic engineering, as a potential strategy for improving the performance of biophotovoltaic devices.

Graphical Highlight2 Highlights (<85 characters per highlight; 3-5 highlights needed)  Nitrogen-containing hydrogen gas mix supplied to PEFC dead-ended anode. Voltage decay seen as both nitrogen content in fuel and fuel cell load increase. Design of Experiments methodology used to assess stack efficiency. Optimised purge strategy identified for nitrogen-containing hydrogen fuel. AbstractThe effect of nitrogen content within the hydrogen fuel supplied to a polymer electrolyte fuel cell (PEFC) operating in dead-ended anode mode is examined, with a view to using an ammonia decomposition product gas mix (containing 75H2:25N2) as the hydrogen-containing fuel. The impact of this impure hydrogen stream, supplied to the anode, was evaluated in terms of mean cell voltage and in relation to actual operating conditions (purge interval, dead-ended interval and fuel cell load). Design of Experiments (DoE) methodology, using multi-linear models, assessed hydrogen utilisation in terms of stack efficiency and identified an effective and viable dead-ended anode purge strategy for this nitrogen-containing hydrogen fuel.

Electrons from cyanobacteria photosynthetic and respiratory systems are implicated in current generated in biophotovoltaic (BPV) devices. However, the pathway that electrons follow to electrodes remains largely unknown, limiting progress of applied research. Here we use Hilbert-Huang transforms to decompose Synechococcus elongatus sp. PCC7942 BPV current density profiles into physically meaningful oscillatory components, and compute their instantaneous frequencies. We develop hypotheses for the genesis of the oscillations via repeat experiments with iron-depleted and 20% CO2 enriched biofilms. The oscillations exhibit rhythms that are consistent with the state of the art cyanobacteria circadian model, and putative exoelectrogenic pathways. In particular, we observe oscillations consistent with: rhythmic D1:1 (photosystem II core) expression; circadian-controlled glycogen accumulation; circadian phase shifts under modified intracellular %ATP; and circadian period shortening in the absence of the iron-sulphur protein LdpA. We suggest that the extracted oscillations may be used to reverse-identify proteins and/or metabolites responsible for cyanobacteria exoelectrogenesis.

Background Biophotovoltaics (BPVs) promise a low-cost, sustainable option for electricity and solar fuels production from just light as an energy source, water as an electron source, and air as an inorganic carbon source. This is achieved by harnessing the processes of photosynthesis and respiration by exoelectrogenic photosynthetic microorganisms in electrochemical cells1,2.BPVs are advantageous over microbial fuel cells in which the microbes require an external organic energy source and an oxygen-free enclosure. BPVs are also advantageous over synthetic solar PV cells in that 24-hour power production is possible courtesy of respiration in the dark, and the living organisms can self-repair damaged photoactive components. This makes them particularly suited for operation in off-grid and remote locations. Research Challenge BPV power outputs remain too low for commercially feasibility. Work on BPVs has hitherto largely been an experimental exercise, with only a handful of computational studies in the literature3. Developing computational models that can aid in interpreting experimental results, or that can be used for rapid sensitivity analyses and device optimisation, has proved challenging. This is largely due to the gaps in our knowledge of the full path electrons take from within the microorganisms, to the non-living electrodes. Approach To overcome these gaps, “deep learning” was applied to predict BPV current density and photo-response, since this approach does not require upfront definition of all the interacting electrochemical, biological and physical phenomena occurring within the system. In particular, Long Short-Term Memory (LSTM) networks were used4. Current density profiles from BPV devices operating in galvanic mode with a 33 MΩ load, under a 12h:12h on:off light cycle, were decomposed with Seasonal and Trend Decomposition using locally estimated scatter plot smoothing or LOESS (STL), into their trend, seasonal, and remainder components. The seasonal current density, which captures the photoresponse induced by the periodic light, was then used to train a LSTM network to predict the one-step-ahead seasonal current density. Results It is shown that the trained LSTM network is able to predict the seasonal current density and photoresponse in the BPVs to a high accuracy, using only lagged values of the current density and light status (on/off) as the predictive inputs. Mean absolute errors of 0.007, 0.014 and 0.013 μA m-2 were achieved on the training, validation and test data sets using a network of 35 neurones and a window size of 144. Errors were largest when light was switched on and, to a lessor extent, off. It is hypothesised that biofilm fluorescence yield may be an additional input used to improve predictions during light changes. This is an important first step to developing useful predictive models and optimisation algorithms for BPVs. McCormick, A. J. et al. Biophotovoltaics: Oxygenic photosynthetic organisms in the world of bioelectrochemical systems. Energy Environ. Sci. 8, 1092–1109 (2015...