Adsorption of Cadmium (II) Ions Using Cassava Peel Activated Carbon: Study of Adsorption Kinetics, Isotherms and Thermodynamics
Bibliographic record
Abstract
Heavy metals Cd2+ is a dangerous pollutant of water and ecological systems if it is above the quality standard threshold. Cassava peel has a fairly high carbon element content, namely 59,31%, so it can be used as an activated carbon to break down Cd2+ metals ions in water. This research aims to determine kinetic modeling and adsorption isotherms of Cd2+ ions using CPAC activated by H3PO4, determine the optimum pH and temperature conditions for Cd2+ ion adsorption and determine the adsorption capacity of Cd2+ ions using CPAC activated by H3PO4. CPAC is made through a carbonization process at a temperature of 500 °C for 1 hour. Next, it was impregnated with H3PO4 at a concentration of 30 % with a ratio of 1:5 (w/w) for 24 hours and physically activated at a temperature of 600 °C for 1 hour. Based on research, kinetic modeling and adsorption isotherms of Cd2+ ions using CPAC activated by H3PO4 following Pseudo-Second-Order (PSO) kinetic modeling and Freundlich isotherm, the optimum pH and temperature conditions for Cd2+ ion adsorption were obtained at pH 5 and temperature 25 °C. Thermodynamic parameters such as Gibbs energy (∆G°), enthalpy (∆H°), and entropy (∆S°), have been evaluated and indicate physical, spontaneou, and exothermic adsorption processes. The adsorption capacity of Cd2+ ions using CPAC activated by H3PO4 is 5.2219 mg/g.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".