High biosorption of cationic dye onto a novel material based on paper mill sludge
Bibliographic record
Abstract
Abstract The valorization of paper mill sludge (PMS) is the main goal of this study. The emissions of PMS continue to increase at global scale, especially from packaging paper and board sectors. The raw sludge was used to prepare an adsorbent to remove toxic pollutants from wastewater, the methylene blue (MB), an organic dye. Firstly, the physico-chemical characterization of PMS was done determining the crystalline phases of PMS fibers, the content of main elements, and the pH zero point charge, which was determined at around pH 7. The adsorption of MB on PMS powder was studied at 18 °C with an agitation of 200 rpm, being the best operating conditions 30 min of contact time, 250 mg L−1 of initial MB concentration and 0.05 g in 25 mL of adsorbent dose. Experimental data of MB adsorption was fitted to Langmuir and Freundlich isotherm equations. The Langmuir model was more accurate for the equilibrium data of MB adsorption at pH 5.1. The PFOM and PSOM were adjusted to experimental adsorption kinetics data, being PSOM, which describes better the MB adsorption by PMS powder. This was confirmed by calculating the maximum adsorption capacity with PSOM, which was 42.7 mg g−1, being nearly similar of the experimental value of 43.5 mg g−1. The analysis of adsorption thermodynamics showed that the MB was adsorbed exothermically with a ΔH0 = − 20.78 kJ mol−1, and spontaneously with ΔG0 from − 0.99 to − 6.38 kJ mol−1 in the range of temperature from 291 to 363 K, respectively. These results confirm that the sludge from paper industry can be used as biosorbent with remarkable adsorption capacity and low cost for the treatment of wastewater. PMS can be applied in the future for the depollution of the effluents from the textile industry, which are highly charged with dyes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".