Preparation and application of novel chitosan-cellulose composite materials to adsorb Pb(Ⅱ) and Cr(Ⅵ) ions from water
Bibliographic record
Abstract
This paper reported the preparation and application of novel chitosan-cellulose composite absorbents for the adsorption of Pb(Ⅱ) and Cr(VI) ions in water. First, oxycellulose or dialdehyde cellulose (DAC) was prepared by sodium periodate oxidation of microcrystalline cellulose (MCC). Second, based on the mechanism of the Mannich reaction, a chitosan/cellulose-based adsorbent (TSFCD) was produced through a cross-linking reaction of thiosemicarbazide (TS) with DAC and chitosan (CS), which was designed specifically for the adsorption of Cr6+ ions from water. Similarly, another chitosan/cellulose-based adsorbent (DBFCM) was also prepared with 2,5-dithiobiurea (DB) as the cross-linking agent for the adsorption of Pb2+ ions in water. The adsorption performance of TSFCD and DBFCM for Cr6+ and Pb2+ ions, respectively, was investigated under various process conditions. Variables included adsorption temperature, time, initial metal ion concentration, pH, and adsorbent dosage. The adsorption kinetics of TSFCD and DBFCM were studied, and isothermal models were developed. Results showed that the adsorption amount increased with the increase of the reaction time, and reached a maximum at about 300 min for the TSFCD/Cr6+ system, and at about 240 min for DBFCM/Pb2+ system. The adsorption performance of TSFCD for Cr6+ and DBFCM for Pb2+ improved at higher temperature, and leveled off at 40℃ and 50℃, respectively. In addition, the removal rate of Cr6+ increased from 49.96% to 70.22% when the TSFCD dosage increased from 0.5 g/L to 3.5 g/L. Similarly, the removal rate of Pb2+ increased from 22.23% to 99.45% with the increase of DBFCM dosage from 0.5g/L to 5.0g/L. The adsorption processes of Pb2+ and Cr6+ were in line with the pseudo-second-order kinetic and the Langmuir isothermal model.
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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".