Nanocores Magnetic Humic Acid on Montmorillonite Nanoneedles for Adsorption Dye Via RSM: Adsorption Isotherm, Kinetic Modelling and Thermodynamic Studies
Bibliographic record
Abstract
Abstract The current inquiry aimed at using core-shell Fe3O4@Humic acid/montmorillonite nanocomposites for removing methyl orange dye from an aqueous solution. The samples were characterized by FT-IR, TEM, SEM, XRD, BJH, and BET analytical methods. The efficiency removal has been utilized for 300 min reaction time using the response surface methodology by a design of five-factor-three-level central composite. The adsorption kinetics followed the pseudo-second-order rate kinetic model, showing an acceptable correlation (R2 > 0.99). Langmuir, Freundlich, Temkin, Dubinin–RadushKevich, and Harkins–Jura isotherms were utilized for the analysis of the equilibrium data. Also, we have estimated standard Gibbs free energy (∆G°), standard enthalpy (∆H°), standard entropy (∆S°), and the activation energy (Ea). The findings of this investigation suggest that the absorption of methyl orange on the adsorbent pursues the Frondelich isothermal formulation. The ∆G° values obtained showed physical absorption in this adsorption process. ∆H° showed that the dye adsorption mechanism was endothermic, and ∆S° indicated an increase in irregularity and the probability of contact between methyl orange molecules and the adsorbent surface in the dye adsorption process. Positive Ea values pointed out the nature of the endothermic absorption process. S* value was minimal and close to zero, which established the process of physical absorption.
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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".