Degradation of Phenolic Compounds Through UV and Visible- Light-Driven Photocatalysis: Technical and Economic Aspects
Bibliographic record
Abstract
Phenolic compounds are found in surface and groundwater as well as wastewater from several industries. It is necessary to eliminate phenols and phenolic compounds from contaminated water before releasing into water bodies due to their toxicity to human beings. Photocatalytic degradation seems to be a promising technology for the degradation of several phenolic compounds. Complete mineralization of phenol and phenolic compound has been achieved with TiO2-based photocatalysts under both UV and visible-light irradiation. This chapter will evaluate the conventional processes and advanced oxidation processes for the degradation of phenol and phenolic compounds. The process economics and efficiencies of different advanced oxidation processes will also be discussed. The main focus of the chapter is photocatalytic degradation processes under UV and visible light along with a detailed review of several factors affecting degradation of phenol and phenolic compounds. Photocatalytic degradation process is governed by reactions with hydroxyl radical or superoxide ion. The extent of degradation depends on light sources (UV, visible, and solar), the type of photocatalyst, and experimental conditions (pH, photocatalyst dosage, initial concentration of phenolic compounds, light intensity, electron donor concentration, etc.).Visible-light-active photocatalysts are applied by several researchers to exploit sunlight and to make the photocatalysis process sustainable. In the future, using sunlight in place of UV could make photocatalysis economically more efficient.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".