Magnetic phosphorylated lignocellulosic fibers: A hybrid material for water purification – Part II adsorption capacity for metallic anions and cations
Bibliographic record
Abstract
Heavy metal contamination in water poses a significant threat to human health and the environment. In this study, the adsorption performance of a composite (PKF@Fe₃O₄,) based on phosphorylated kraft fibers (PKF) and magnetite nanoparticles (Fe₃O₄) was investigated. Batch adsorption tests were conducted to evaluate its effectiveness under various conditions, including pH, contact time, and temperature. Kinetic modeling and adsorption isotherms were also studied. Particular attention was given to the use of both linear and nonlinear regression models in describing the adsorption process. Additionally, several statistical parameters were employed to thoroughly validate the data. The results indicate that metal adsorption on the composite material was strongly dependent on pH. For example, the optimal adsorption pH is 2.0 for Cr (VI) and 5.0 for Cu (II) ions. Kinetic studies revealed that Cr (VI) adsorption follows the Elovich model, whereas Cu (II) adsorption is best described by the pseudo-second-order kinetic model. Isotherm analysis highlighted distinct behaviors: Cr (VI) adsorption aligns with the Langmuir model, suggesting monolayer adsorption, whereas Cu (II) adsorption exhibits a two-step isotherm, reflecting the heterogeneity of the composite and the presence of multiple adsorption sites. The increase in temperature enhances the adsorption capacity. The maximum adsorption capacities for Cu (II) and Cr (VI) ions are 140 and 16 mg/g, indicating that the composite is a better adsorbent for copper than for chromates. The thermodynamic study shows spontaneous adsorption for the cations and anions studied, with an endothermic character. This study demonstrates the potential of the PKF@Fe₃O₄ composite as an environmentally friendly, versatile solution for water decontamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".