Investigation of Emulsified Oil Adsorption onto Functionalized Magnetic Nanoparticles—Kinetic and Isotherm Models
Bibliographic record
Abstract
Recently, considerable attention has been given to using magnetic nanoparticles (MNPs) for capturing oil from oil-in-water (O/W) emulsions, despite MNPs’ inherent instability and agglomeration. Their stabilization through changing surface chemistry is required to increase dispersivity. In this research, we use cetyltrimethylammonium bromide (CTAB) as a cationic surfactant to increase the positive surface charge of the particles, resulting in a better stability in the aqueous solution because of increased repulsive forces. The functionalized MNPs are characterized using transmission electron microscopy (TEM), zeta potential, and contact angle (CA) measurements. The aim of this study is to investigate the oil separation efficiency (SE) and equilibrium oil adsorption capacity of the synthesized particles, which are determined using gas chromatography analysis. We also study the adsorption behavior using isotherm and kinetic models. The SE values indicate the superior performance of MNP@CTAB for oil adsorption from dodecane-in-water nanoemulsion (SE = 99.80%) compared to the bare MNPs with SE of approximately 57.46%. These findings are attributed to the stronger electrostatic attraction between the MNP@CTAB having high positive charge and negatively charged oil droplets. The adsorption isotherm results using both linear and non-linear regression methods show that the Freundlich isotherm is the best fit to the experimental equilibrium data (with calculated R2 > 0.97), verifying a multilayer heterogeneous adsorption. Moreover, the pseudo-first-order kinetic model describes the experimental equilibrium data in a greater congruence (R2 = 0.99), suggesting physical adsorption of oil onto MNPs through van der Waals and physical bonding, which is also confirmed through zeta potential measurements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".