Adsorption of Cationic Dyes on a Magnetic 3D Spongin Scaffold with Nano-Sized Fe3O4 Cores
Bibliographic record
Abstract
The renewable, proteinaceous, marine biopolymer spongin is yet the focus of modern research. The preparation of a magnetic three-dimensional (3D) spongin scaffold with nano-sized Fe3O4 cores is reported here for the first time. The formation of this magnetic spongin–Fe3O4 composite was characterized by X-ray diffraction (XRD), thermogravimetric analysis (TGA), differential thermal analysis (DTA) (TGA-DTA), vibrating sample magnetometer (VSM), Fourier-transform infrared spectroscopy (FTIR), and zeta potential analyses. Field emission scanning electron microscopy (FE-SEM) confirmed the formation of well-dispersed spherical nanoparticles tightly bound to the spongin scaffold. The magnetic spongin–Fe3O4 composite showed significant removal efficiency for two cationic dyes (i.e., crystal violet (CV) and methylene blue (MB)). Adsorption experiments revealed that the prepared material is a fast, high-capacity (77 mg/g), yet selective adsorbent for MB. This behavior was attributed to the creation of strong electrostatic interactions between the spongin–Fe3O4 and MB or CV, which was reflected by adsorption mechanism evaluations. The adsorption of MB and CV was found to be a function of pH, with maximum removal performance being observed over a wide pH range (pH = 5.5–11). In this work, we combined Fe3O4 nanoparticles and spongin scaffold properties into one unique composite, named magnetic spongin scaffold, in our attempt to create a sustainable absorbent for organic wastewater treatment. The appropriative mechanism of adsorption of the cationic dyes on a magnetic 3D spongin scaffold is proposed. Removal of organic dyes and other contaminants is essential to ensure healthy water and prevent various diseases. On the other hand, in many cases, dyes are used as models to demonstrate the adsorption properties of nanostructures. Due to the good absorption properties of magnetic spongin, it can be proposed as a green and uncomplicated adsorbent for the removal of different organic contaminants and, furthermore, as a carrier in drug delivery applications.
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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) | 0.001 | 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".