Rapid Adsorption of Methylene Blue from Aqueous Solutions by Goethite Nanoadsorbents
Why this work is in the frame
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Bibliographic record
Abstract
Iron-oxide nanoadsorbents are attractive for wastewater treatment for two important reasons. First, nanoadsorbents can remove contaminants from wastewater rapidly. Second, iron oxide nanoadsorbents can be employed as catalysts for the decomposition of contaminants and thus eliminate sludge formation. This article investigates the use of iron-oxide nanoadsorbents to remove contaminants from wastewater. A later paper considers their use as catalysts for decomposition. In this study, goethite (a type of iron oxide) nanoparticles was employed for the removal of methylene blue from an aqueous solution, using a batch-adsorption technique. Effects of contact time, initial concentration of methylene blue, temperature, and solution pH on the adsorbed amount of methylene blue were investigated. Adsorption was rapid, as equilibrium was achieved within 20 minutes. An external mass transfer model fit adsorption kinetic results well and provided reasonable overall volumetric mass transfer coefficients. Increases in initial concentration, temperature, and pH favored the adsorption of methylene blue. Adsorption data fit both the Langmuir and Freundlich isotherm models well, with the better fit to the Langmuir model. Thermodynamic studies confirmed that the adsorption reaction was spontaneous and endothermic in nature.
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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) | 0.001 | 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 it