Pit-lake remediation by chemically activated Chlorocardium rodiei: Simultaneous metal ion removal from acidic waters
Bibliographic record
Abstract
The current study investigates the valorization of waste wood from a tropical hardwood found in Guyana (Greenheart – Chlorocardium rodiei) to high surface area activated carbons (AC). We subsequently deployed these adsorbents to sequester Mn 2+, Fe3+, and Al3+ from the acidic waters of a recreational bauxite pit-lake. We studied the impact of activation parameters such as temperature, impregnation ratio and acid concentration on the texture and surface chemistry of ACs and demonstrate that optimized low pH at point-of-zero charge (pHpzc) ACs are efficient adsorbents for the target ions. A mesoporous AC with specific surface area of 2208 m2/g, 11 % surface oxygen and pHpzc of 1.98 was produced under optimized conditions. ACs removed 93–100 % of target ions from pit-lake waters at a native pH of 3.1. Al3+ exerted an antagonistic effect on Mn2+ adsorption in synthetic binary ion systems reducing adsorption by as much as 56 %. The Sips Model fitted the adsorption data best predicting maximum adsorption capacities for Mn2+, Fe3+ and Al3+ of 17.8 mg/g, 23.7 mg/g, and 6.12 mg/g for these low pHpzc optimized materials. These materials show great promise in removing heavy metals from acidic waters.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".