Functionalization of cellulose pentamine as a promising nano‐amorphous sorbent for Zr( <scp>IV</scp> ) and Hf( <scp>IV</scp> ) ions recovery
Bibliographic record
Abstract
Abstract Due to the rising worldwide need for commercial zirconium and hafnium metals, various research studies have been conducted to investigate their extraction from ores and recovery from other waste products. By chorinating cellulose and then aminating it with tetraethylene pentamine, a cellulose‐tetraethylene pentamine (Cell‐TEPA) nanosorbent was synthesized, which resulted in active groups responsible for binding processes with the appropriate metal ions using a straightforward approach. The composition, chemical characteristics, and physical attributes of the Cell‐TEPA nanosorbent were comprehensively examined using a range of equipment, such as X‐ray diffraction (XRD), scanning electron microscope–energy dispersive X‐ray analysis (SEM–EDX), Fourier‐transform infrared spectroscopy (FT‐IR), Brunauer–Emmett–Teller (BET), and thermal gravimetric analysis (TGA). When bound to the Cell‐TEPA nanosorbent, Zr(IV) and Hf(IV) exhibited the highest absorption capacities of 70.4 and 38.2 mg/g, respectively. The most favourable sorption conditions were achieved with a feed solution pH of 1.5, a stirring period of 45 min, a metal ion concentration of 100 mg/L, and room temperature (25 ± 2°C). The adsorption data were consistent with both the Langmuir isothermal model and the pseudo‐2nd‐order reaction model. The Cell‐TEPA nanosorbent effectively extracted zirconium and hafnium ions from leach liquors derived from Wadi Rahba ore sample and Abu Khashaba concentrate sample, demonstrating their potential for future 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) | 0.000 | 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".