Modeling of Bio-Electrochemical and Mechanical Interactions in a Photosynthetic Cell
Bibliographic record
Abstract
Micro photosynthetic cell (µPSC) is an electrochemical device, which generates electricity, by harnessing the electrons from photosynthesis and respiration processes of the photoautotrophs. Till date, focus has been mostly on experimental aspects of and very little work has been pursued on the development of mathematical models for µPSC. Modeling a system like µPSC is complex due to the fact that the device operation depends on interactions of microorganisms with several operational parameters such as light intensity, quantum yield and so on. Further, the electrode structure and the electrochemical interactions at the surface of the electrodes affect the device performance. Modeling of µPSC could help in understanding the performance limiting step(s) in the series of processes that occur during the operation of device. The performance of the device can be improved by focusing on the rate of limiting steps. Modeling could also help in determining optimal design and operational parameters which can maximize the device performance. A simple mathematical model based on first principles is proposed to predict the performance of the µPSC. Sensitivity analysis is performed to obtain the most sensitive rate parameters of the model. The optimal values of sensitive rate constants are obtained from the experimental data through optimization. The developed model is validated by comparing the predictions of the model with the experimental data obtained from the response of the system to step changes in load. Figures 1 and 2 show the model performance in terms of i-V characteristics and comparison of experimental and estimated voltage profiles for step changes in external loads. Keywords:, First principles model, Parameter estimation, and Optimization and Sensitivity analysis. Figure 1
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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 | 0.001 |
| 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) | 0.002 | 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".