Magnetic Nanoparticles Interaction with Humic Acid: In the Presence of Surfactants
Bibliographic record
Abstract
Adsorbed humic acid (HA) on surfaces of nanoparticles (NPs) will affect their transport, transfer, and fate in the aquatic environment, especially in the presence of surfactants, and thereby potentially alter exposures and bioavailable fractions of NPs and surfactants. This study investigated adsorption of HA on Fe3O4 NPs in the presence or absence of surfactant. Surfactant established a bridge connecting HA and Fe3O4 NPs, and significantly changed adsorption behavior of HA on NPs. Adsorption of HA in the absence of surfactant was 120.3 mg/g, but 350.0 mg/g and 146.5 mg/g in the present of CTAB (hexadecyl trimethylammonium bromide) and SDS (sodium dodecyl sulfate), respectively. Surfactants can form different stages (hemimicelles, mixed hemimicelles and admicelles) on Fe3O4 NPs by electrostatic and hydrophobic interactions, adsorption of HA was different for each of those stages. Adsorption of HA on surface of Fe3O4 NPs/CTAB was codetermined by hydrophobic, electrostatic interactions and ligand exchange. The presence of CTAB or SDS changed mechanisms for adsorption and effects of functional groups. Results of Fourier transform infrared (FTIR) and X-ray photoelectron spectroscopy (XPS) indicated that carbohydrate carbon was important in adsorption of HA on Fe3O4 NPs in the presence of surfactants.
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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".