Trivalent Chromium Ion Adsorption on Various Types of Wastewater Sludge
Bibliographic record
Abstract
This research aimed at evaluating the adsorption of trivalent chromium present in high concentrations [about 100–170mgCr(III)L−1] in effluents on various types of wastewater sludges, namely, primary sludge (PWS), secondary sludge (SWS), mixed sludge (primary+secondary) (MWS), physicochemical treatment sludge (CWS), and agro-processing industry sludge (AWS). Adsorption tests of chromium were carried out at 25±1°C in 500mL Erlenmeyer flasks at various adsorbent concentrations (2, 5, 10, 15, 20, and 30gL1) for all types of sludges studied (PS, SWS, MWS, CWS, and AWS). A synthetic chromium nitrate [Cr(NO3)3⋅9H2O] solution adjusted to pHi=3.2 at 112mgL−1 was used. The results revealed that CWS had the best adsorption capacity for chromium, followed by SWS, and adsorption capacity of the different wastewater sludges was in the following order: CWS>SWS⩾MWS>PWS>AWS. Kinetic adsorption studies also showed that almost complete removal of chromium in solution was reached in the first 2h of reaction with all types of sludges. Finally, the adsorption tests of chromium on tannery effluent ([Cr]i=143mgL−1) confirmed the effectiveness of SWS for the removal of chromium. A removal yield of 40.8% of chromium was observed following 2.0h of adsorption on 5.0gL−1 of SWS at a pHi=3.94. The quantity of chromium adsorbed on sludge during these tests corresponded to a load of 11.6mgCrg−1 dry weight.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".