Experimental study of the degradation of 2,4-D induced by vacuum-UV radiation
Bibliographic record
Abstract
Vacuum-UV (VUV) photoinduced degradation of the herbicide 2,4-D was studied. A flow-through VUV photoreactor was used (i) in batch mode to study the kinetics of degradation and (ii) in continuous mode under steady state to analyze the potential utilization of this process in commercial applications. In both cases, the reactants were recycled to minimize diffusive resistances. Experimental results from the batch studies showed that the initial degradation rate of 2,4-D in ultrapure water was independent of the initial concentration of the herbicide. However, a reduction in the reaction rate was obtained over the course of the treatment, largely due to the formation of 2,4-D partial oxidation by-products which compete with 2,4-D molecules for HO (scavenging effect). Increases in water alkalinity reduced 2,4-D degradation rate as a consequence of the scavenging of HO by carbonates and bicarbonates. The degradation of 2,4-D in raw surface waters was also investigated. A noticeable reduction in the degradation rate was observed because of the presence of NOM and alkalinity, both being known HO scavengers. Additionally, the presence of inorganic species/ions that absorb VUV may also have contributed to the reduction of the overall degradation rate. High conversions were obtained in the continuous system. At a residence time of 25 seconds, conversions of 97% and 65% were achieved for inlet herbicide concentrations of 1 and 10 mg L(-1), respectively. Under these conditions, the received dose of 185 nm radiation was 44.8 mJ cm(-2).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".