Floating ZnO QDs-Modified TiO2/LLDPE Hybrid Polymer Film for the Effective Photodegradation of Tetracycline under Fluorescent Light Irradiation: Synthesis and Characterisation
Bibliographic record
Abstract
In this work, mesoporous TiO2-modified ZnO quantum dots (QDs) were immobilised on a linear low-density polyethylene (LLDPE) polymer using a solution casting method for the photodegradation of tetracycline (TC) antibiotics under fluorescent light irradiation. Various spectroscopic and microscopic techniques were used to investigate the physicochemical properties of the floating hybrid polymer film catalyst (8%-ZT@LLDPE). The highest removal (89.5%) of TC (40 mg/L) was achieved within 90 min at pH 9 due to enhanced water uptake by the LDDPE film and the surface roughness of the hybrid film. The formation of heterojunctions increased the separation of photogenerated electron-hole pairs. The QDs size-dependent quantum confinement effect leads to the displacement of the conduction band potential of ZnO QDs to more negative energy values than TiO2. The displacement generates more reactive species with higher oxidation ability. The highly stable film photocatalyst can be separated easily and can be repeatedly used up to 8 cycles without significant loss in the photocatalytic ability. The scavenging test indicates that the main species responsible for the photodegradation was O2●−. The proposed photodegradation mechanism of TC was demonstrated in further detail based on the intermediates detected by LC-time-of-flight/mass spectrometry (LC/TOF-MS).
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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) | 0.001 | 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".