Olive stone as an eco-friendly bio-adsorbent for elimination of methylene blue dye from industrial wastewater
Bibliographic record
Abstract
Abstract Adsorbents synthesized by activation and nanoparticle surface modifications are expensive and might pose health and ecological risks. Therefore, the interest in raw waste biomass materials as adsorbents is growing. In batch studies, an inexpensive and effective adsorbent is developed from raw olive stone (OS) to remove methylene blue (MB) from an aqueous solution. The OS adsorbent is characterized using scanning electron microscopy (SEM), Fourier Transform Infra-Red (FTIR), and Brunauer–Emmett–Teller (BET) surface area. Four isotherms are used to fit equilibrium adsorption data, and four kinetic models are used to simulate kinetic adsorption behavior. The obtained BET surface area is 0.9 m2 g−1, and the SEM analysis reveals significant pores in the OS sample that might facilitate the uptake of heavy compounds. The Langmuir and Temkin isotherm models best represent the adsorbtion of MB on the OS, with a maximum monolayer adsorption capacity of 44.5 mg g−1. The best dye color removal efficiency by the OS is 93.65% from an aqueous solution of 20 ppm at the OS doses of 0.2 g for 90 min contact time. The OS adsorbent serves in five successive adsorption cycles after a simple filtration-washing-drying process, maintaining MB removal efficiency of 91, 85, 80, and 78% in cycles 2, 3, 4, and 5, respectively. The pseudo second-order model is the best model to represent the adsorption process dynamics. Indeed, the pseudo second-order and the Elovich models are the most appropriate kinetic models, according to the correlation coefficient (R2) values (1.0 and 0.935, respectively) derived from the four kinetic models. The parameters of the surface adsorption are also predicted based on the mass transfer models of intra-particle diffusion and Bangham and Burt. According to the thermodynamic analysis, dye adsorption by the OS is endothermic and spontaneous. As a result, the OS material offers an efficient adsorbent for MB removal from wastewater that is less expensive, more ecologically friendly, and economically viable.
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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) | 0.000 | 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) | 0.001 | 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".