Powdered activated carbon adsorbent for eosin Y removal: modeling of adsorption isotherm data, thermodynamic and kinetic studies
Bibliographic record
Abstract
Abstract An investigation was conducted to examine the adsorption of eosin Y (EY) from aqueous solution using Powdered Activated Charcoal (PAC) obtained from Biochem Chemopharma (Quebec, Canada) with a surface area of 270 mg/g using the methylene blue method. The adsorption experiments showed that a contact time of 60 min resulted in a high removal efficiency of 98.25 % for EY at a concentration of 10 ppm. The study also offered insights into the effectiveness of different treatment processes and described the main physicochemical processes involved. Various parameters such as adsorbent dosage, contact time, substrate concentration, and pH were evaluated, and the data were analyzed using Freundlich, Langmuir, and Temkin isotherms. The study found that the pseudo-second-order kinetic model provided a better fit to the experimental data compared to the pseudo-first-order model. To optimize the process parameters and enhance overall efficiency, contour plots were employed in the experimental design, considering variables such as adsorbent dosage, contact time, and pH levels. These plots visually represented the relationship between the variables and the removal efficiency of EY, enabling the identification of optimal operating conditions. The investigation’s findings contribute valuable insights into the adsorption of EY using PAC and offer practical implications for improving the efficiency of EY removal in various applications. The use of contour plots in experimental design was highlighted as a crucial tool for refining adsorption process parameters.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".