Solar energy harvesting mechanisms of the frustules of Nitzschia filiformis diatoms
Bibliographic record
Abstract
Diatoms are major contributors to the global oxygen and carbon cycles. Their ability to thrive on photosynthesis, even in low and intermittent lighting conditions, is attributed to the optical response of the frustule, among other factors. However, how the frustule functions as a biophotonic feature is unknown. Using a toolkit consisting of numerical models and four microscopy techniques, we evaluated the optical response of frustules belonging to the species Nitzschia filiformis . Localized regions of the frustule exhibited functionalities including diffraction, lensing, waveguiding, circulation, filtering, resonances, and dispersion control. We show that these functionalities are complementary to each other in contributing to the solar energy harvesting mechanisms of capture, redistribution, and retention. In this context, frustule performance is evidently enhanced by perturbations to its sub-wavelength structure. We therefore modeled the frustule as a photonic circuit from which we estimated a contribution of approximately 9.83% to photosynthetic activity. To our knowledge, this represents the first model of the entire frustule, including its inherent disorder and the complementary behavior of localized optical functionalities. This provides quantitative support to the hypothesis that the frustule enhances photosynthesis in the cell. It supports the case for cultivating diatoms as sustainably mass-manufacturable devices with applications in solar energy, carbon sequestration, sensing, medicine, and metamaterials.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".