Review on Microphotosynthetic Power Cells—A Low‐Power Energy‐Harvesting Bioelectrochemical Cell: From Fundamentals to Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.005 | 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 it