Multifunctional cellulose fibers for the simultaneous removal of metal cations and anions: Adsorption study
Bibliographic record
Abstract
The efficient removal of heavy metals from wastewater is one of the most pressing environmental problems. This study examines the adsorption capacity of phosphorylated cellulose fibers functionalized with (3-aminopropyl)triethoxysilane for Cu²⁺ and C r O 4 2 − ions in aqueous media. The goal is to determine their adsorption capacities, model adsorption isotherms, elucidate adsorption mechanisms and study competitive adsorption. Adsorption kinetics show very rapid adsorption: more than 80 % of contaminants are eliminated within three minutes and equilibrium is reached after 20 min. Adsorption isotherms for Cu²⁺ and C r O 4 2 − were type IV. Two-stage Langmuir and Dubinin-Astakov models were tested, with the latter providing the best fit. The isotherms combine type I and type V behaviors, explained by the presence of two types of active sites on the adsorbent surface, resulting in different adsorbent-adsorbate interactions and sequential or multilayer adsorption. The adsorption capacities of Cu 2 + and C r O 4 2 − by functionalized fibers are around 117.0 mg/g and 97.63 mg/g, respectively. The adsorption of anions occurs via electrostatic interactions between amine groups and chromate, while copper adsorption involves complexation and ion exchange with amine and phosphate groups. Multi-ion adsorption tests confirmed the simultaneous removal of anions and cations. • Dual functionalized lignocellulosic fiber adsorbent. • Phosphorylated and silylated cellulose. • Simultaneous adsorption of metal cations and anions. • Competitive adsorption. • Two-stage isotherms.
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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".