Removal of diclofenac from contaminated water using polypyrrole: A comprehensive adsorption study
Bibliographic record
Abstract
Abstract Polypyrrole (PPy) was synthesized via chemical oxidative polymerization using FeCl₃ as the oxidizing agent and evaluated as an adsorbent for removing diclofenac (DCF) from aqueous solutions. Unlike most studies focused on PPy composites, this study highlights the performance of pure, unmodified PPy, filling an important gap in the literature. The material exhibited a nodular, macroporous structure with a low specific surface area (9.017 m 2 g −1 ), and highly energetic adsorption sites. Adsorption studies showed that optimal DCF removal (97%) was achieved with low adsorbent dosages and adsorption capacity was influenced by pH, reaching a maximum at neutral conditions. Kinetic analysis revealed rapid adsorption, occurring primarily on the material's surface, while equilibrium data indicated an exothermic process with a maximum adsorption capacity of 305 mg g −1 . Desorption tests showed reversible adsorption, with ethanol being the most effective eluent. Physical interactions, such as hydrogen bonding, hydrophobic, and π–π interactions mainly drove adsorption mechanisms. Despite its low specific area, PPy demonstrated excellent DCF removal performance under mild operating conditions, making it a promising material for large‐scale water treatment applications, instilling hope for a more sustainable future.
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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) | 0.001 | 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 | 0.000 | 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".