Efficient adsorption of Cd(ІІ) ions from aqueous media onto a semi‐interpenetrating bio‐composite
Bibliographic record
Abstract
Abstract New bio‐composite, semi‐interpenetrating biopolymers (obtained from hydrolyzed carboxymethyl cellulose grafted polyacrylonitrile [h‐CMC‐g‐PAN] and sodium alginate [Na‐Alg] using CaCl2 as a cross linker; semi‐interpenetrating biopolymers [s‐IPNs]) have been prepared and fully characterized using spectroscopic (FTIR, scanning electron microscopy, EDS), elemental analysis, and thermal analysis measurements measurements. The morphology and structure of these s‐IPNs are different from those obtained with solely h‐CMC‐g‐PAN and Na‐Alg indicating successive functionalization. The availability of the functional‐rich bio‐composites has afforded an excellent opportunity to test them as sorbents for the uptake of toxic Cd(II) ions from aqueous media. Subsequently, the uptake of Cd(II) ions was shown to be dependent on the pH, shaking time, temperature, amount of sorbent, and the initial concentration of the Cd(II) ions. The maximum Cd(II) ion uptake was 99.5% at pH 6, using 50 mg of sorbent with 120 min shaking time at 25°C. The adsorption isotherm fitted well with Langmuir model, with calculated maximum adsorption capacity 49.75 mg g−1. The kinetic studies were modeled using a pseudo second‐order reaction. The thermodynamic parameters (ΔHo, ΔGo, and ΔSo) of the uptake of Cd(II) ions onto s‐IPNs were found to be −13,176.07 Jmol−1, −4,572.7 Jmol−1, and 28.87 J K−1 mol−1, respectively, verifying spontaneous exothermic process. Successive desorption and reusability of s‐IPNs for the uptake of Cd(II) indicated, its high efficiency over three cycles.
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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) | 0.000 | 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".