Preparation of aldehydic electrospun PAN mats for ammonia removal from wastewater
Bibliographic record
Abstract
Novel electrospun polyacrylonitrile (PAN) nanofiber mats and PAN fabric were chemically modified by dissolved anhydrous stannous chloride diethyl ether saturated with hydrogen chloride to contain aldehyde groups on their surfaces, which are suitable for ammonium adsorption due to their high adsorption affinity for NH4+ ion. Scanning electron microscopy (SEM), and Fourier-transform infrared spectra (FT-IR) were employed to characterize the prepared adsorbents. FT-IR spectra of these adsorbents confirmed that aldehyde groups are successfully formed on the surface of these chemically modified adsorbents. The aldehydic electrospun PAN nanofiber mats and aldehydic PAN fabric were assessed for their chelating property with NH4+ ion from aqueous solution. The effects of contact time on the amounts of ammonium adsorbed into the prepared adsorbents were also studied. Results revealed that ammonium removal increased by increasing contact time which finally reached equilibrium at about 3.5 h and 4 h for aldehydic electrospun PAN nanofiber mats and aldehydic PAN fabric, respectively. The adsorption performance of these prepared adsorbents for ammonium adsorption with initial ammonium concentration of 300 ppm via isotherm studies was investigated. The maximum ammonium removal efficiency (% R) was 48.33 and 70 for aldehydic electrospun PAN nanofiber mats and PAN fabric, respectively. Results indicated that the adsorption of ammonium by both prepared adsorbents followed Langmuir isotherm.
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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".