Fundamental approach to the fluence-based kinetic and electrical energy efficiency parameters in photochemical degradation reactions: polychromatic light
Bibliographic record
Abstract
When the logarithm of the concentration of a photolyzed component is plotted against the fluence, one usually obtains a first-order plot, and from the slope one can obtain a "fluence-based rate constant". In this study, theoretical expressions are derived for fluence-based rate constants for both monochromatic and polychromatic radiation, and are shown to be fundamental and not dependent on experimental parameters. Therefore, such rate constants can be reproduced from one laboratory to another, as long as the polychromatic light source exhibits similar spectral characteristics. An important quantity that can be obtained from the fluence-based rate constant is the quantum yield. As an example, the quantum yield for the photolysis of atrazine is determined to be 0.033 using the monochromatic fluence-based rate constant. The analysis is extended to the polychromatic light sources, and the expression of polychromatic fluence-based rate constant is derived and validated experimentally for atrazine and NDMA. The new concepts are developed further to analyze the figure-of-merit electrical energy per order (EEO), and it is shown that the EEO depends on the same fundamental photochemical parameters. Key words: fluence-based rate constant, quantum yield, UV radiation, fluence rate, kinetics, photochemical parameters.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".