Regularities and mechanism of heavy metal cations sorption and (or) proton desorption by chitosan from aqueous solutions
Bibliographic record
Abstract
The sorption process of heavy metal ions from aqueous solutions using chitosan flakes and chitosan beads was studied. Chitosan gel beads were prepared using covalent crosslinking of chitosan chains by epichlorohydrin with ionic gelation by sodium tripolyphosphate. The capability of chitosan beads to absorb the heavy metal ions from aqueous solutions was studied, and the physicochemical characteristics of the sorbent were evaluated using SEM and FTIR on the model solution treatment. It was found that competitive proton sorption takes place in acidic media, which results in a transformation of amino groups into inactive ammonium-salt form and decreases in heavy metal sorption onto chitosan from aqueous acidic media. Batch adsorption experiments were performed to examine the influence of various factors such as the initial concentration of metal salts, pH, and agitation duration on the process. It was established that metal ion sorption is pH dependent and has an optimum effect at a pH of 6.0. Following the Langmuir equation, the maximum sorption of Cu2+ ions is estimated to be 1,6 mol/kg of modified chitosan. The kinetic study revealed that the adsorption kinetics are well-fitted to the kinetic equation of pseudo second order. Thus, sorption of heavy metal ions by chitosan is considered to be a competitive process that occurs on amino groups of the sorbent with equivalent coordinated participation of metal cations, protons, and anions.
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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".