Development of Poly(diallyldimethylammonium) Chloride-Modified Activated Carbon for Efficient Adsorption of Methyl Red in Aqueous Systems
Bibliographic record
Abstract
A modified activated carbon (AC) was developed by modifying with poly(diallyldimethylammonium) chloride (PDADMAC) to enhance its adsorption performance for water treatment applications. Different PDADMAC concentrations were explored and evaluated using methyl red as a model contaminant, with 8 w/v% PDADMAC yielding the best adsorption performance. The kinetics data were well described by the pseudo-first-order equation and homogeneous surface diffusion model. The Freundlich isotherm fit the equilibrium data well, indicating multilayer adsorption and diverse interaction types. The removal efficiency remained similar across a pH range of 5–9 and in the presence of background inorganic (NaCl)/organic compounds (sodium acetate) at different concentrations. Rapid small-scale column tests were performed to simulate continuous flow conditions, and the PDADMAC-modified AC effectively delayed the breakthrough of the contaminant compared to raw AC. Regeneration experiments showed that 0.1 M NaOH with 70% methanol effectively restored the adsorption capacity, retaining 80% of the initial efficiency after five cycles. Quantum chemical analysis revealed that non-covalent interactions, including electrostatic and Van der Waals forces, governed the adsorption mechanism. Overall, the results of this study prove that PDADMAC-AC shows great potential for enhanced organic contaminant removal in water treatment systems.
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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 | 0.001 | 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".