Synthesis and evaluation of recoverable activated carbon/Fe3O4 composites for removal of polycyclic aromatic hydrocarbons from aqueous solution
Bibliographic record
Abstract
Three different synthesis methods were employed to prepare magnetic powder and granular activated carbons (MPAC and MGAC) as recoverable adsorbents to remove polycyclic aromatic hydrocarbons (PAHs) from aqueous solutions. The MPAC and MGAC composites were characterized by XRD, and the XRD patterns confirmed the presence of Fe3O4 particles with cubic crystal structure on the adsorbents surface. FE-SEM images showed that the magnetic composites had spherical morphologies, with clusters of iron oxide nanoparticles formed in the ACs pores and distributed evenly on their surface. FTIR spectra of the PAH-loaded adsorbents revealed that the analytes were attached to the surfaces of MPACs and MGACs by π-π and H-π interactions formed between the PAHs and functional groups of the adsorbents. All the synthesized magnetic ACs were very effective in removing PAH compounds from aqueous solution with removal percentages between 87.2% to 99.3%. The precipitation method of magnetization resulted in the highest PAHs removal efficiency (99.3%) using PAC as the base AC, while the co-precipitation method of magnetization provided the highest PAHs removal efficiency (98.3%) using GAC as the base AC. The PAHs desorption tests indicated that low molecular weight PAHs were more easily desorbed from the magnetic ACs ranging from 38.1 to 60.1%, compared with high molecular PAHs ranging from 23.4 to 57.2%. This shows that the increase in the number of PAH rings would lead to the formation of more covalent bonds between the adsorbate and the adsorbent.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".