Adsorption of Methyl Red on Poly(diallyldimethylammonium) Chloride-Modified Clay
Bibliographic record
Abstract
A novel, eco-friendly and cost-effective adsorbent, poly(diallyldimethylammonium) chloride (PDADMAC)-modified clay was developed to enhance its efficacy in removing Methyl Red (MR) from water. Different concentrations of PDADMAC solutions were evaluated during the synthesis and the effects of different operating conditions were investigated. The kinetic data closely followed the pseudo-first-order model, while equilibrium data were well described by Freundlich isotherm. MR removal efficiency decreased as solution pH or NaCl concentration increased, suggesting that electrostatic interaction plays a key role in the adsorption process. Regeneration studies using NaCl solutions revealed that a 1% NaCl solution effectively restored the adsorbent's capacity. The findings indicate that PDADMAC clay is a promising and sustainable adsorbent for MR removal. Additionally, a three-layer backpropagation artificial neural network (ANN) was developed to predict the MR removal efficiency based on the initial MR concentration, pH, NaCl concentration, and adsorption time. Among these variables, pH was identified as the most influential factor. This approach provides valuable insight into the outcome prediction of a given adsorption process.
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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) | 0.000 | 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 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".