Selective Solar Concentrators for Biofuel Production and Photovoltaic Applications
Bibliographic record
Abstract
Algae-based biofuels have become of increasing interest in recent years as a renewable energy source to replace energy derived from fossil fuels. Algae exhibits remarkable potential for producing large amounts of energy, for example as much as 60% of their Biomass can be converted to oil, with 30 to 50% more energy output per gallon than gasoline Algae also generates about 60% of the Earth's atmospheric oxygen and, in good cultivation conditions, algae produces protein and energy biomass 30 to 100 times faster than land plants Furthermore, algae does not require the entire incident solar spectrum to perform photosynthesis. That is, algae primarily utilizes the blue and red portions of the solar spectrum, referred to as Photosynthetic Active Radiation (PAR), while a large portion of the green and near-infrared light received from the sun is not used in the photosynthetic reaction In this context, incident solar radiation can be utilized for agrivoltaic applications The incident PAR and non-PAR solar irradiance is used to simultaneously drive biofuel production and photovoltaic cells, respectively. For this purpose, photonic micro/nano structures are integrated into solar spectrum splitters that transmit PAR to enable underlying algae cultivation, while concentrating non-PAR at the side-walls of the solar spectrum splitter to power photovoltaic cells. Moreover, in this study we also investigate the benefits of utilizing the aforementioned solar spectrum splitter in energy efficient agrivoltaic greenhouses that generate photovoltaic power while producing crops.
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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 | 0.001 |
| 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) | 0.005 | 0.002 |
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".