Ternary Metal-Alginate-Chitosan Composites for Controlled Uptake of Methyl Orange
Bibliographic record
Abstract
Three ternary metal composites (TMCs) with iron nitrate, aluminum nitrate, and copper nitrate (Fe-TMC-N, Al-TMC-N, Cu-TMC-N) were synthesized and their physicochemical properties were investigated. Characterization of the TMCs was achieved by elemental analysis (XPS), infrared (IR) spectroscopy and thermogravimetric analysis (TGA). The surface charge of the TMCs was estimated from the point-of-zero-charge (PZC), which depended on the type of metal nitrate precursor. The adsorption properties of the TMCs showed the vital role of the metal center, where methylene blue (MB) is a cationic dye probe that confirmed the effects of surface charge for effective methyl orange (MO) anion dye uptake. MB uptake was negligible for Al-TMC-N and Cu-TMC-N, whereas moderate MB uptake occurs for Fe-TMC-N (26 mg/g) at equilibrium. The adsorption capacity of MO adopted the Langmuir isotherm model, as follows: Al-TMC-N (422 mg/g), Cu-TMC-N (467 mg/g) and Fe-TMC-N (42 mg/g). The kinetic adsorption profiles followed the pseudo-second order model. Generally, iron incorporation within the TMC structure is less suitable for MO anion removal, whereas Cu- or Al-based materials show greater (10-fold) MO uptake over Fe-based TMCs. The dye uptake results herein provide new insight on adsorbent design for controlled adsorption of oxyanion species from water.
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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".