Al/Cu-PILC as a Photo-Fenton Catalyst: Paracetamol Mineralization
Bibliographic record
Abstract
Abstract Pillared clays have shown to effectively catalyze the photo-Fenton process without the necessity of acidic conditions, which is a very attractive feature from the perspective of environmentally friendly processes, especially when high natural abundance of chemical elements are incorporated. In this work, the catalytic activity of Al/Cu interlayered pillared clays for the degradation and mineralization of paracetamol through a photo-Fenton-like process was investigated. Al/Cu-pillared clays were prepared by adding ane Al/Cu pillaring solution to a bentonite suspension. X-ray diffraction (XRD) confirmed the enlargement of the interlayer space of the clay provoked by the pillaring process and Al and Cu species in the prepared samples were verified by atomic absorption spectroscopy (AAS). The specific surface area of pure bentonite was 2-fold increased after the Al/Cu pillaring process. A synthetic paracetamol solution with an initial concentration of 100 ppm was prepared for the assessment of the activity of the prepared materials. Different catalyst concentrations were tested (0.2, 0.5, 0.75, and 1 g L–1) and the complete removal of paracetamol was achieved in all cases, but the highest mineralization rate (69.8 mg total organic carbon (TOC) gcat–1 h–1) corresponds to the catalyst loading of 0.5 g L–1. An ultraviolet-C (UVC) light source was employed, and no adjustment of the pH to acidic conditions was needed to achieve these results. Liquid chromatography coupled to mass spectroscopy (LC-MS) was employed to identify the reaction intermediates of paracetamol degradation. A proposed pathway for the oxidation of paracetamol molecule is presented. The effect of Cu content in the pillared clay and the stability and reusability of the catalyst were also assessed. The kinetic constants of paracetamol removal were 0.2318 and 0.0698 min–1, under photo-Fenton and UV + H2O2 processes, respectively.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".