Preparation and Characterization of Activated Carbon Based on Wood (<i>Acacia auriculeaformis</i>, Côte d’Ivoire)
Bibliographic record
Abstract
The objective of this work is to prepare one of the best activated carbon (CA) based on wood (Acacia auriculeaformis). The chemical activation method was used for varying the chemical agent namely phosphoric acid H3PO4 (CAA), sodium hydroxide NaOH (CAB), and sodium chloride NaCl (CAS). The physico-chemical analysis of the three activated carbons indicated that, under the conditions of preparation, the activated carbons possess activation efficiencies lower than 50% (41.81% for CAA, 26.25% for CAB and 48.87% for CAS), low ash content (CAA: 5.00%, CAB: 14.90 and CAS: 6.60%) and iodine values ranging from 190.35 mg/g to 380.71 mg/g, suggesting that the good quality of the prepared activated carbon. The surface functional groups using Boehm test and the zero point charge (pHZPC) methods confirmed the acidic, basic and neutral character for CAA, CAB and CAS respectively (CAA: pHZPC = 4.8, CAB: pHZPC = 8.2, CAS: pHZPC = 6.8). The surface specific areas were determined through the liquid phase adsorption of acetic acid and methylene blue using the Langmuir method and BET analysis. Also, the porosity was determined. The BET surface areas of CAA, CAB and CAS were respectively 561.60 m2/g, 265.00 m2/g and 395.40 m2/g. The influence of chemical activation agent on pores formation was confirmed by scanning electron microscopic (SEM) analysis. CAA was selected as the best activated carbon because of its good surface area and good pore volume compared to those found in the literature. Therefore, its application as an adsorbent for effluents treatment could be explored. In addition, the best activating agent for coal from Acacia auriculeaformis was found to be phosphoric acid.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".