Synthesis and evaluation of green magnetic mesoporous molecularly imprinted polymers for adsorption removal of parabens from cosmetic samples
Bibliographic record
Abstract
Parabens are chemicals that are frequently used as preservatives in numerous cosmetic products. In recent years, the safety concern over these compounds has grown due to their endocrine-disrupting activity. In this research, a novel green magnetic molecularly imprinted polymer (GMMIP) was synthesised using propylparaben as a template and then applied as an adsorbent to selectively recognise and remove parabens from cosmetic samples. The green strategies were introduced by using Persicaria odorata or Kesum leaf extract as a reducing agent to synthesise green magnetic nanoparticles (MNP) as a magnetic core, and deep eutectic solvent (DES) has been designed as an environmentally friendly functional monomer that was used in the preparation of GMMIP. The GMMIP was characterised using Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), and Brunauer-Emmett-Teller (BET). The results of FESEM and BET indicated that the GMMIP exhibited an irregular spherical shape and mesoporous characteristics with a pore size of 17.74 nm. The adsorption pH, kinetics, isotherms, and thermodynamics parameters were performed to investigate the interactions that take place between GMMIP and propylparaben. The adsorption processes appeared to best fit the pseudo-second-order kinetic and Freundlich isotherm models at an optimum pH of 12. Findings from a thermodynamics study revealed the adsorption process was exothermic, spontaneous, and more favourable at 298 K. The optimised GMMIP was applied as an adsorbent to remove the parabens from cosmetic samples. When compared to methylparaben and ethylparaben, the GMMIP had the highest selectivity and effectively removed propylparaben, with recoveries ranging from 75.6% to 113.3%. It was found that the limits of detection (LOD) and quantification (LOQ) were between 0.03 and 0.05 mg/L and 0.11 and 0.16 mg/L, respectively. The synthesised GMMIP proved to be a convenient and effective adsorbent to remove parabens from cosmetic products.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 teacher head, 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".