Partial ozonation pre-treatment for sludge solubilization and simultaneous degradation of bisphenol A: quantification studies
Bibliographic record
Abstract
Ozonation pre-treatment was investigated for the enhancement of sludge solids and organic matter solubilization and simultaneous degradation of bisphenol A (BPA), an endocrine disruptor compound from wastewater sludge (WWS). The ultrafast method (15 s per sample) used for the analysis of BPA in WWS is based on Laser Diode Thermal Desorption/Atmospheric Pressure Chemical Ionization coupled to tandem Mass Spectrometry. The statistical methods used for optimization studies comprised the response surface method with fractional factorial designs and central composite designs. The ozonation pre-treatment process was carried out with four independent variables, namely WWS solids concentration (15-35 g l(-1)), pH (5-7), ozone dose (5-25 mg g(-1) SS) and ozonation time (10-30 min). It was observed that among all the variables studied, ozone dose had more significantly (probability (p) < 0.001) affected the efficiency of the ozonation pre-treatment by increasing sludge solids (suspended solids (SS) and volatile solids) solubilization and organic matter (soluble chemical oxygen demand and soluble organic carbon) increment and BPA degradation from WWS. During the optimization process, it was found that higher BPA degradation (100%) could be obtained with 24 g l(-1) SS, 6.23 pH with an ozone dose of 26.14 mg g(-1) SS for 16.47 min ozonation time. The higher ozone dose used in this study was observed to be cost effective on the basis of solids and organic matter solubilization and degradation of BPA.
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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) | 0.000 | 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".