Versatile waste wood-chitosan composites for 2,4-D and paraquat adsorption: Isotherm modelling and thermodynamic evaluation
Bibliographic record
Abstract
2,4-dichlorophenoxy acetic acid (2,4-D) and 1,1-dimethyl-4,4-bipiridinium chloride (paraquat) are among the most widely used herbicides and are known to be toxic. Fabrication of green adsorbents which are capable of removing both herbicides remains a challenge. Here, we fabricate a novel adsorbent from tropical waste wood and use a facile, chitosan-mediated N-heteroatom functionalization technique to augment surface nitrogen and improve specific surface area. The addition of 20 wt% chitosan to the waste wood feedstock prior to activation, increased specific surface area by 300 m 2 /g (∼25%) and nitrogen content by 7-fold. This functionalized material removed 69% of 2,4-D and 82% of paraquat at initial concentrations of 4 ppm and 40 ppm from model solutions at pH 7. It also removed 39% 2,4-D and 93% paraquat from binary mixtures demonstrating its versatility. 2,4-D adsorption increased with chitosan addition suggesting synergistic effects between protonated amine functions and the anionic herbicide form. Paraquat adsorption was negatively correlated with chitosan addition, implying antagonistic interaction between protonated amine functions and quaternary nitrogen atoms on herbicide molecules. Adsorption of both herbicides was spontaneous, entropically favored and exothermic with ΔG ° : 19.2 kJ/mol and −28.8 kJ/mol; ΔS ° : 7.42 and 28.6 J/Kmol and ΔH ° : 17.0 kJ/mol and −20.1 kJ/mol for 2,4-D and paraquat respectively. Chitosan addition therefore provides a facile and green alternative for N-heteroatom functionalization, and these nitrogen-doped materials are promising candidates for the removal of multiple herbicides from aqueous systems. • Optimized chitosan-greenheart AC was obtained by addition of 20 wt% chitosan. • Chitosan doping increased nitrogen content by 7-fold and surface area by 300 m 2 /g. • 2,4-D and paraquat removal efficiencies were 69 % and 82%. • Physisorption enthalpies were −17.0 kJ/mol (2,4-D) and −20.1 kJ/mol (paraquat).
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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".