Thermodynamic and kinetic studies for the adsorption of malachite green on diatomite
Bibliographic record
Abstract
Pollution by dyes is one of the main pollutions of industrial effluents. This research examined Moroccan diatomite as a low-cost adsorbent to remove one of the dangerous dyes, malachite green (MG). Brunauer–Emmett–Teller analysis, scanning electron microscopy, pHpzc and X-ray diffraction were used to characterise diatomite. The studies were carried out at different levels of pH, diatomite masses and MG concentrations at a temperature of 25°C. Equilibrium, kinetics and thermodynamic characteristics were also studied. The results show that the diatomite consisted of an integrated and nearly circular sieve plate with many small ordered pores, giving it a high specific surface area of 17.36 m2/g. The ideal pH was found to be 7, with a saturation period of 90 min. The results indicate that MG removal increased accordingly as the pH increased and also as the adsorbent mass increased. The results of the isotherms showed that the Langmuir model is the most adequate, while the adsorption of MG is better characterised as monolayer adsorption. The maximum adsorption capacity obtained from the Langmuir model is about 22.17 mg/g at 25°C. Kinetic studies indicated the pseudo-second-order model as the most adequate model. It was also revealed that intraparticle diffusion is not the only rate-controlling step. From the thermodynamic data, it was concluded that the adsorption is endothermic – that is, the adsorption is more favourable at high temperatures. The research confirmed the applicability of diatomite as an effective and inexpensive adsorbent for removing hazardous materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".