Polyaniline chitosan beads for enhanced methyl red adsorption: Experimental and molecular simulation insights
Bibliographic record
Abstract
This study proposes a novel adsorbent - polyaniline (PANI) chitosan (CS) beads - for the efficient removal of methyl red (MR) dye from aqueous solutions. Multiple synthesis strategies were explored to optimize adsorption performance. The synthesized PANI CS beads exhibited enhanced MR adsorption kinetics and capacity than traditional adsorbents such as activated carbon. The MR adsorption kinetics followed pseudo first-order model and homogeneous surface diffusion model, and the Langmuir and Freundlich isotherm models fit the equilibrium data well. The synthesized beads maintained effective performance across a range of pH conditions and in the presence of competitive organic and inorganic species, demonstrating robustness under realistic water treatment scenarios. Different regenerants were tested to evaluate the reusability of PANI CS beads, and 0.1 M HCl solution showed the best regeneration performance, maintaining their adsorption performance effectively over 6 cycles. Molecular simulation was conducted and confirmed strong MR adsorption on PANI, driven primarily by electrostatic interaction and supported by van der Waals force. The negative adsorption energy and total energy validated the adsorption spontaneity and system stability. These findings demonstrate that PANI CS beads provide a distinct advancement over the conventional adsorbent, offering a high-performance, regenerable material for practical application in water treatment. • Novel chitosan beads synthesized for effective methyl red dye removal from water. • Modified chitosan beads demonstrated high adsorption rate and capacity. • The synthesized beads exhibited excellent reusability with 0.1 M HCl as regenerant. • Molecular simulation aligned well with experimental adsorption behavior. • Adsorption of methyl red was driven mainly by electrostatic forces.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".