Optical Interactions in Bio-Electricity Generation from Photosynthesis in Microfluidic Micro-Photosynthetic Power Cells
Bibliographic record
Abstract
Within the realm of renewable energy sources, biological-based power systems have emerged as pivotal players particularly suited for low- and ultra-low-power applications. Unlike microbial fuel cells (MFCs), which invariably rely on external carbon feedstock, micro-photosynthetic cells (µPSCs) exhibit a unique feature by operating independently of organic fuel. They harness the principles of photosynthesis and respiration to generate electricity in both illuminated and dark settings through water-splitting reactions. Here, we present a viable, easy, and cost-effective method to fabricate µPSCs. We meticulously examined the performance of a fabricated µPSC under varying illuminations and even in the absence of light. With an electrode surface area spanning 4.84 cm2, the µPSC achieved its peak power output of 200.6 µW when exposed to an illumination of 2 µmolm−2s−1 (equivalent to 147 lux). Of the three light intensities studied, 2 µmolm−2s−1, 8 µmolm−2s−1 (595 lux), and 20 µmolm−2s−1 (1500 lux), the µPSC exhibited its optimal performance at a light intensity of 2 µmolm−2s−1, establishing this as the ideal operational illumination. Furthermore, intermittent toggling of the illumination had no discernible impact on the µPSC’s performance. However, subjecting it to a dark environment for 30 min resulted in a reduction in the maximum power to 81 µW, marking a significant 119% decrease when compared to the peak power output achieved under 2 µmolm−2s−1 illumination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".