Degradation of Phenol With A Microwave-Uv Irradiation Treatment System Using NANO-TiO2
Bibliographic record
Abstract
The degradation of phenol from various industrial effluents becomes essential and studied in this work. The microwave (MW), ultra-violet (UV) and combination treatment systems were designed and TiO2 nanoparticles were used as photocatalyst for the degradation of 1500ppm phenol in a solution. It was observed that the degradation efficiency was less than 10% in both MW and MW-UV systems without a catalyst. However, the addition of TiO2 particles in MW-UV system has increased the phenol degradation efficiency significantly. The extent of increase in degradation efficiency is dependent on the structural and optical characteristics of TiO2, which is affected by the TiO2 preparation method. In this work, the TiO2 nanoparticles with anatase structure were synthesized by hydrothermal (HT) and sol-gel (SG) methods. The synthesized materials were characterized using X-ray diffraction, FT-IR, thermogravimetric analysis, SEM, high resolution TEM and BET method. The higher degradation efficiency of 24% shown by MW-UV-TiO2 (HT) system in 120 minutes as compared to 20% shown by MW-UV-TiO2 (SG) system could be due to higher surface area and better textural properties of TiO2 prepared by hydrothermal treatment. The effect of various initial concentration of phenol (500-1500ppm) on degradation efficiency of MW-UV-TiO2 (HT) system revealed that the increase in the initial phenol concentration decreased the phenol degradation efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".