Activated Carbon Based on Acacia Wood (<i>Auriculeaformis</i>, C&ocircte d’Ivoire) and Application to the Environment through the Elimination of Pb<sup>2+</sup> Ions in Industrial Effluents
Bibliographic record
Abstract
The objective of this study is to develop carbon, that of Acacia auriculeaformis through its activation in order to eliminate lead in an aqueous medium. A series of activated carbon has been prepared by chemical activation with phosphoric acid, sodium hydroxide and sodium chloride. The determination of the physico-chemical properties of the prepared carbon guided the choice of phosphoric acid activated carbon as the best adsorbent for the elimination of lead(II) in an aqueous solution. Pb2+ adsorption tests in batch mode have shown that the adsorption capacity is influenced by various parameters such as mass, pH, concentration of Pb2+ ions and contact time linked to the medium and the adsorbent. Adsorption isotherms, kinetic models and thermodynamics have been used to describe the adsorption process. The equilibrium data for activated charcoal correspond well to the model of Freundlich, Langmuir, Temkin and Kiselev. The kinetic adsorption data proved to be better described by the pseudo-second order model with external and intraparticle diffusion which are two decisive steps in the adsorption process of Pb2+ ions. Thermodynamics and adsorption isotherms predict a spontaneous exothermic surface reaction, of the chemisorption type, with ion retention in orderly monolayers on the heterogeneous surface of the adsorbent. Tests carried out with this adsorbent material have contributed to the elimination of the Pb2+ ions contained in an industrial effluent with a reduction rate reaching 42.52% against 85.90% in a synthetic solution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".