Simultaneous removal of anionic and cationic dyes from wastewater with biosorbents from banana peels
Bibliographic record
Abstract
Abstract Untreated banana peel (BP) is able to adsorb efficiently cationic dyes like methylene blue (MB) but weakly anionic dyes like orange G (OG). One way to enhance the sorption capacity for both dyes is to convert BP into activated carbon (BPAC) after thermochemical treatment. Among the various BPACs tested, the highest sorption capacity for both dyes was achieved when BP was modified with NaOH and pyrolyzed at 700°C (BPAC‐NaOH‐700). The pore structure of adsorbents was analyzed with scanning‐electron‐microscopy (SEM), nitrogen sorption isotherms, and mercury intrusion porosimetry (MIP), and the meso‐ and micro‐pore size distribution of BPAC‐NaOH‐700 was the narrowest one with the smallest mean value and the highest specific surface area. Equilibrium tests in batch mode were fitted with the extended Langmuir and Freundlich isotherms and used to estimate thermodynamic properties. The MB and OG sorption dynamics onto BPAC‐NaOH‐700 was approximated with the multi‐compartment model, and the external and internal mass‐transfer coefficients were estimated. The maximum sorption capacity of BPAC‐NaOH‐700 was found to be equal to 323 mg/g for MB and 76 mg/g for OG, both higher than the corresponding values for BP, and fully consistent with the high values of its surface area ( = 530 m 2 /g) and total pore volume ( = 1.81 cm 3 /g). The synergistic interaction for the sorption of both dyes onto BPAC‐NaOH‐700 was associated with a push‐pull mechanism, while the selective sorption of MB onto BPAC‐NaOH‐700 was attributed to the very slow rates of OG pore and surface diffusion in meso‐ and micro‐porosity, respectively. The adsorption was exothermic for BP, and endothermic for OG.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".