Arraying of microphotosynthetic power cells for enhanced power output
Bibliographic record
Abstract
Abstract Microphotosynthetic power cells (µPSCs) generate power through the exploitation of living photosynthetic microorganisms by harvesting sunlight. The thermodynamic limitations of this process restrict the power output of a single µPSC. Herein, we demonstrate µPSCs in four different array configurations to enhance power output from these power cells. To this effect, six µPSCs were arrayed in series, parallel, and combinations of series and parallel configurations. Each µPSC was injected with a 2 mL liquid culture of photosynthetic microorganisms (Chlamydomonas reinhardtii) in the anode and 2 mL of 25% (w/v) electron acceptor potassium ferricyanide (K3Fe(CN)6) in the cathode. The combinations of µPSCs connected in series and parallel generated higher power than the individual series and parallel configurations. The combinations of six µPSCs connected in series and in parallel produced a high power density of 1914 mWm−2 in the presence of white fluorescent light illumination at 20 µEm−2s−1. Furthermore, to realize the array strategy for real-time applications, a 1.7 V/2 mA rating light-emitting diode (LED) was powered by combinations of series and parallel array configurations. The results indicate the reliability of µPSCs to produce electricity from photosynthetic microorganisms for low-power applications. In addition, the results suggest that a combination of microlevel photosynthetic cells in array format represents a powerful optimal design strategy to enhance the power output from µPSCs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".