Removal of methylene blue (aq) using untreated and acid‐treated eucalyptus leaves and GA‐ANN modelling
Bibliographic record
Abstract
In the present batch study, eucalyptus leaves (EUL), H2SO4‐treated eucalyptus leaves (SEUL), and H3PO4‐treated eucalyptus leaves (PEUL) are used as bio‐adsorbents for the removal of methylene blue (MB). The bio‐adsorption is executed to inspect the results of the variation between different experimental variables such as pH (2–10), adsorbent dose (1–10 g/L), contact time (5–360 min), and temperature (298–318 K) on the bio‐adsorption of MB. The Langmuir isotherm (R2 = 0.99) fitted adequately to the bio‐adsorption data for the initial MB concentrations of 10–300 mg/L. It is also necessary to mention that the MB bio‐adsorption occurred in the order of a monolayer on the EUL, SEUL, and PEUL. The bio‐adsorption kinetics have been fitted by the pseudo‐second‐order model (R2 ≥ 0.99) for various MB concentrations. The maximum bio‐adsorption capacity was 194.34 mg/g and was achieved for the H3PO4‐treated eucalyptus leaves (PEUL). These results showed that EUL, SEUL, and PEUL may be utilized as a favourable low‐cost bio‐adsorbent to eliminate MB from aqueous solutions. With safe disposal methods in mind, this investigation has revealed the eco‐friendliness of the bio‐adsorbents. A prediction of the removal percentage of methylene blue using a genetic algorithm (GA) from the data collected from the experiment has also been tested. The results related to the prediction using the GA‐ANN are accurate.
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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 | 0.001 |
| 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, 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".