On the engineering of a laboratory <scp>LED</scp>‐based photocatalytic reactor for radiative and kinetic studies
Bibliographic record
Abstract
Abstract This contribution aims at developing engineering approaches to characterize radiative transfer and kinetics of a catalyst to be activated by electromagnetic radiation, one of the main bottlenecks when evaluating materials in heterogeneous photocatalytic processes. A specialized LED‐based photoreactor is engineered to obtain reliable information on the aforementioned mechanisms. The radiative transfer approach serves as a criterion to elucidate the capability of a catalyst to be activated or not by visible light or UV‐A light. When the catalyst favours the absorption of photons rather than their scattering or transmission, the experimental methodology allows the determination of intrinsic optical information being independent of fluid dynamics, and catalyst concentration. Radiative characterization, along with a mass transfer analysis, guides the proposal of the operational design of the photoreactor to carry out intrinsic kinetic studies. Thus, the operation of the photoreactor under pseudo‐isoactinic and differential reaction conditions leads to the determination of intrinsic initial reaction rates, enabling the understanding of the complex interaction among the optical properties of the catalyst, the reaction rate for the production of hydroxyl radicals (•OH), and the oxidation of a recalcitrant organic pollutant. The procedures assessed are worthy of being used for evaluating other materials, activated at different wavelength ranges or designed for different photocatalytic applications, paving the way for the scale‐up of the reactor, another of the main challenges in photocatalysis.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".