Photo‐catalytic degradation of formaldehyde using nitrogen‐doped TiO<sub>2</sub> nano‐photocatalyst: Statistical design with response surface methodology (RSM)
Bibliographic record
Abstract
Abstract Formaldehyde is considered as a common pollutant in industrial wastewaters requiring removal techniques designed to reduce its harmful effects due to distribution in the environment. In this study, TiO2 nanoparticles are modified through nitrogen doping by sol‐gel technique and their photo‐catalytic performance for formaldehyde degradation is studied under UV and solar irradiation. Various characterization techniques such as: PSA, FTIR, XRD, and SEM are performed on the synthesized nano‐photocatalyst with a ∼20 nm average size. The significance of parameters such as initial formaldehyde concentration, catalyst molar ratio N/Ti, pH, and removal time under UV and solar radiation are assessed using response surface methodology based on central composite design. Formaldehyde removal was measured as a function of irradiation time using UV‐Visible spectrophotometry. The results show that the maximum formaldehyde removal at optimal conditions (pH = 5 for 400 ppm initial formaldehyde concentration) by nitrogen‐doped TiO2 is 64.02 % under solar and 60.15 % under UV radiation suggesting it as an effective photo‐catalyst for formaldehyde removal.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".