Application of Chattim tree (devil tree, <i>Alstonia scholaris</i>) saw dust as a biosorbent for removal of hexavalent chromium from contaminated water
Bibliographic record
Abstract
Abstract “Devil tree saw dust”; a novel biosorbent has been utilised successfully for the removal of hexavalent chromium from contaminated water. Batch adsorption procedure is utilised to test the ability of saw dust as an adsorbent for hexavalent chromium (reduction coupled adsorption). The contribution of various parameters on sorption, such as contact time, sorbate concentration, pH of the medium and temperature were estimated and maximum uptake of hexavalent chromium from contaminated water was 333.33 mg g −1 at pH 2.0 and temperature of 35°C. Hexavalent chromium uptake from contaminated water followed the pseudo‐first‐order rate expression. The standard free energy change (Δ G 0 ), standard enthalpy change (Δ H 0 ) and standard entropy change (Δ S 0 ) have also been evaluated and it has been concluded that the sorption was feasible, spontaneous and endothermic in nature. The process follows well Langmuir isotherm. Fourier Transform Infra‐Red (FTIR) spectroscopy and scanning electron microscopy (SEM) of hexavalent chromium loaded and unloaded saw dust were performed, SEM clearly indicates chromium adsorption. FTIR spectroscopy revealed the involvement of carbonyl, hydroxyl and amide groups on the cell surfaces in chromium binding. Very good adsorption capacity and low cost or cost free of devil tree saw dust makes this biosorbent as one of the best adsorbents for removal of hexavalent chromium from contaminated water. © 2012 Canadian Society for Chemical Engineering
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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 | 0.000 | 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 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".