Synergistic Degradation of 4-Nitrophenol Using Hydrodynamic Cavitation in Combination with Hydrogen Peroxide
Bibliographic record
Abstract
p-Nitrophenol (PNP), a widely utilized intermediate, is a persistent pollutant present in industrial effluent streams. The inherent toxicity of PNP necessitates its treatment before releasing it in the environment. The conventional approach pertaining to degradation of PNP is based on chemical and biological methods for decomposition. Alternatively, Hydrodynamic Cavitation (HC) is emerging as a promising technology for waste water treatment. This study investigates HC as an alternative technology to degrade PNP and subsequently enhance efficiency by varying involved parameters. The HC-H2O2 system is reported to exhibit synergism for pollutant oxidation, the applicability of which is also investigated for degrading PNP. A PNP solution of fixed concentration was subjected to HC using a circular Venturi. Degradation was studied by varying time, pressure, pH and H2O2 concentration. Decompostion of p- Nitrophenol was quantified by UV-Visible Spectroscopy at 405nm. Degradation of PNP was observed to be directly proportional to time at constant pressure and an initial increase in pressure led to higher degradation. However, on achieving a peak decomposition level, the extent of decomposition declined with further increase in pressure. Experiments done at acidic pH resulted in over two times the decomposition than those done at basic pH. The PNP- H2O2 system exhibited 91% more degradation than the sum of degradations affected by PNP and H2O2 individually. Moreover, subjecting PNP:H2O2 in a molar ratio of 1:5 to HC resulted in near-complete (>95%) degradation. This study proposes variations of parameters for optimum decomposition of PNP using HC and explores the HC-H2O2 system as a promising alternative for the degradation PNP.
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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 | 0.001 |
| 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".