Cu( <scp>II</scp> ) ions removal from wastewater using starch nanoparticles ( <scp>SNPs</scp> ): An eco‐sustainable approach
Bibliographic record
Abstract
Abstract The complex structured starch particles were reduced to the nanoscale size range through hydrolysis utilizing low concentration acid assisted by ultrasound irradiation. The synthesized starch nanoparticles (SNPs) were characterized by transmission electron microscopy (TEM), Fourier‐transform infrared (FTIR), and X‐ray diffraction (XRD) techniques. The synthesized SNPs possessed surface activated entities, as many cationic functional groups were confirmed through the FTIR spectrum. Also, these SNPs were effectively utilized to separate heavy Cu metal ions from the synthetic ion solution. The SNPs were characterized using field emission scanning electron microscope (FESEM), X‐ray photoelectron spectroscopy (XPS), and Brunauer–Emmett–Teller (BET) analysis for the surface modification after the adsorption process. The weak electrostatic interaction between the SNP surface and Cu ion was confirmed by the XPS spectrum and energy‐dispersive X‐ray spectroscopy. The maximum efficiency of Cu ions removal was about 93% at an optimal pH 5 and 25 mg/ml dosage. The adsorption equilibrium was obtained in 60 min. The nitrogen isotherm BET analysis of SNPs after adsorption shows a higher specific surface area of 18.552 m 2 /g, attributed to the interaction and presence of Cu ions on the SNP surface. The process feasibility was validated by the Langmuir isotherm model. The process exhibits pseudo‐second‐order adsorption kinetics and follows the Langmuir isotherm. The R L predicted by the Langmuir isotherm mechanism is 0.017, implying favourable adsorption. The process is reproducible and allows for the separation of heavy metal ions from the wastewater through biosorption effectively.
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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 | 0.001 | 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".