REMOVAL OF AQUEOUS Cu(II) WITH NATURAL KAOLIN: KINETICS AND EQUILIBRIUM STUDIES
Bibliographic record
Abstract
Copper pollution is common across the world and has caused serious public health problems recently.Among the conventional methods, adsorption has proved effective, economic, versatile and simple for the removal of aqueous Cu(II) contaminants.Deposits of natural Kaolin are abundant around the world, and are regarded as valueless due to its limited usage in industry.In this study, the adsorption behavior of natural Kaolin towards Cu(II) was evaluated.According to the results, the isothermal adsorption data are well fitted with Sips model, and the adsorption capacity of natural Kaolin to Cu(II) is determined to be 76 mg/g.Several factors can affect the adsorption performance of Cu(II), including dosage of adsorbent, initial Cu(II) concentration, solution pH, temperature and contact time.The kinetics data are also well predicted by the pseudo-first order kinetics and the pseudo-second order kinetics; the equation of the intraparticle diffusion model could be considered as a supplement.The thermodynamic behavior reveals the endothermic and spontaneous nature of the adsorption.The mechanism for the adsorption behavior was studied based on XRD spectra, and ion-exchange reaction and surface complexation were regarded as predominant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".