Review on Microphotosynthetic Power Cells—A Low‐Power Energy‐Harvesting Bioelectrochemical Cell: From Fundamentals to Applications
Bibliographic record
Abstract
Biophotoelectrochemical cells are gaining prominence in recent years due to the necessity of sustainable power generation at both micro‐ and macroscale. Toward this direction, microphotosynthetic power cells (μ‐PSC) play a vital role in generating clean energy. The μ‐PSC generates sustainable power under light and in the dark through the photosynthesis and respiration of photosynthetic microorganisms or cells, such as cyanobacteria and green algae. Herein, particulars on μ‐PSCs from fundamentals to real‐time applications are provided. The state of the art of μ‐PSCs, in terms of the principle of operation, design, and materials is presented. μ‐PSCs reported to date are classified based on design, operating parameters, and photosynthetic organisms. In addition, details on the metrics and factors influencing the performance of μ‐PSCs are also discussed. The need for the development of mathematical and electrical equivalent models of μ‐PSCs and the progress in these areas are briefed. Current challenges for μ‐PSCs’ commercialization are identified as high cost and low power densities, and the factors that are leading to low power density and high cost are explored and are also discussed. In addition, the potential solutions to overcome these challenges are investigated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".