Development and treatment procedure of arsenic-contaminated water using a new and green chitosan sorbent: kinetic, isotherm, thermodynamic and dynamic studies
Bibliographic record
Abstract
Abstract Arsenic is classified as one of the most toxic elements for humans by the World Health Organization (WHO). With the tightening drinking water regulation to 10 μg L−1 by the WHO, it is necessary to find efficient sorbent materials for arsenic. In this work, the removal of arsenic(V) from water is achieved with an insoluble chitosan sorbent in the protonated form obtained by a simple heating process. Kinetic studies show a very fast sorption (less than 10 min). The Langmuir isotherm model is best describing experimental data with a capacity of 42 mg g−1 at pH 8. The sorption process is based on anion exchange (chemisorption) determined from the Dubinin-Radushkevich model. The sorption efficiency of the chitosan sorbent is 97% at low concentrations (e.g. 100 μg L−1). Thermodynamic analysis reveals that the sorption process is exothermic and is controlled by enthalpic factors. Breakthrough curves (BTC) were acquired in real-time by instrumental chromatography and was better described by the Thomas model. BTC from column sorption and desorption with a salt solution suggest that this sorbent is relevant for large scale applications. With this new renewable product, it will be possible to treat arsenic contaminated water at low cost and with little waste (concentration factor of 1500).
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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".