Improving the ZnO-photocatalytic degradation of humic acid using powdered residuals from water purification plant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Alum residuals were collected from a water treatment plant and used for improving the photocatalytic degradation of humic acid (HA) by combinations of zinc oxide (ZnO) and powdered residuals from a water purification plant (PRWPP). The influence of operating conditions such as initial humic acid concentration, pH, irradiation time, PRWPP to ZnO ratio, catalyst dose, and light illuminance have been investigated. The optimum PRWPP to ZnO ratio was 10:90. Using the prepared composites instead of bare ZnO raised the HA removal efficiency from 85.5% to 97.8%, and from 38% to 48.1% at catalyst doses of 1.2 g/l and 0.4 g/l, respectively. Moreover, it reduced energy consumption from 210.4 to 166.2 Wh per mg of HA. An artificial neural network model (ANN) was developed to predict the removal efficiency under different operating conditions. The optimum ANN structure yielded a coefficient of determination (R2 = 0.993). A modified Langmuir-Hinshelwood pseudo-first-order model was used for describing the degradation kinetics at different initial concentrations of HA.
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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 | 0.001 |
| 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 it