Optimisation and characterisation of bio-adsorbent based on barley straw and coconut shell
Bibliographic record
Abstract
Cost efficiency and environmental friendliness of biomass-derived adsorbents for wastewater treatment are explored. Preparation of bio-adsorbents that are economically and environmentally sustainable has achieved an enormous interest in water treatment. A single-step method for preparing a high-capacity adsorbent is shown in this study by refluxing barley straw (BS) and coconut shell (CS) in concentrated sulfuric acid. Using response surface methodology predicts the optimum parameters for production of the bio-adsorbent based on the yield and the adsorption capacity for methylene blue dye. Optimal conditions for the sulfuric acid refluxing stage were obtained at 94% sulfuric acid, 10 liquid/solid (L/S) ratio for 2 h and 98% sulfuric acid, 4 L/S ratio for 2.5 h for BS and CS, respectively. The produced bio-adsorbent was characterised by scanning electron microscopy coupled with energy dispersive X-ray scattering, X-ray diffraction, Fourier transform infrared spectroscopy, thermo gravimetric analysis, Raman spectroscopy, and Brunauer–Emmett–Teller specific surface area, analysis. The detailed analysis showed that the bio-adsorbent produced from BS and CS has good adsorptive properties – thermal stability and high specific surface area, which are 11.759 and 1.165 m 2 /g, respectively. The results indicate that BS and CS are critical and potential low-cost raw materials for the production of bio-adsorbents.
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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".