Light-harvesting properties of photocatalyst supports—no photon left behind
Bibliographic record
Abstract
Abstract In this work, we set out to elucidate the light-harvesting properties of various random and ordered photocatalyst supports (PSs) with different macropore sizes. To accomplish this, we propose two studies of increasing relevance, enabled by computed tomography (CT) reconstructions and ray-tracing COMSOL Multiphysics simulations: (a) a 360-degree light release study approximating a PS situated within a compound parabolic concentrator (CPC) or cylindrical LED reactor with open ends; and (b) the same system as before but with closed ends. The ordered geometry is of interest, as it can be 3D printed at scale with a tailored morphology and porosity, and it can potentially be refined using machine learning models to optimize its light-harvesting properties. As will be shown, the local volumetric light absorption (LVLA) data suggests that an ordered PS with a more open pore interior and a smaller pore exterior would begin to approach the more isophotonic light-harvesting properties of random PSs.
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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) | 0.001 | 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".