Micro photosynthetic cell for power generation from algae: Bio-electrochemical modeling and verification
Bibliographic record
Abstract
A simple first-principles mathematical model is developed to predict the performance of a micro photosynthetic power cell ([Formula: see text]PSC), an electrochemical device which generates electricity by harnessing electrons from photosynthesis in the presence of light. A lumped parameter approach is used to develop a model in which the electrochemical kinetic rate constants and diffusion effects are lumped into a single characteristic rate constant [Formula: see text]. A non-parametric estimation of [Formula: see text] for the [Formula: see text]PSC is performed by minimizing the sum square errors (SSE) between the experimental and model predicted current and voltages. The developed model is validated by comparing the model predicted [Formula: see text] characteristics with experimental data not used in the parameter estimation. Sensitivity analysis of the design parameters and the operational parameters reveal interesting insights for performance enhancement. Analysis of the model also suggests that there are two different operating regimes that are observed in this [Formula: see text]PSC. This modeling approach can be used in other designs of [Formula: see text]PSCs for performance enhancement studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".