Advanced Fabrication, Modeling, and Testing of a Microphotosynthetic Electrochemical Cell for Energy Harvesting Applications
Bibliographic record
Abstract
Unconventional renewable energy sources are scarce and have not been explored thoroughly or exploited. The photosynthetic power cell (PSC) is one among them. Though there are few prototypes fabricated earlier, there has not been a comprehensive electrical equivalent model developed. This paper proposes an electrical equivalent model for a microphotosynthetic power cell (μPSC), which is tested and authenticated with experimental verification on a fabricated prototype. The developed model is further used for testing emulation behavior, to efficiently and accurately design an energy harvesting power electronic converter. The principle of the operation of the device is based on “photosynthesis.” Photosynthesis and respiration both involve an electron transfer chain. The electrons are extracted with the help of electrodes and a redox agent, and a power electronic converter is designed to harvest the energy. The fabricated cell is capable of producing an open-circuit voltage of 0.9 V and about 200 μW of peak power. The μPSC has an active area of 4.84 cm2, which approximately translates to a power density of 400 mW/m2. This makes it as one of the best-performing μPSC. The other top-performing μPSC devices report power densities of between 100 and 250 mW/m2. The PSC produces energy under both dark and light conditions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".