Chitosan-Hydroxyapatite Bio-Based Composite in Film Form: Synthesis and Application in Wastewater
Bibliographic record
Abstract
Water purification from toxic metals was the main objective of this work. A composite in film form was prepared from the biomaterials hydroxyapatite, chitosan and glycerol using the dissolution/recrystallization method. A nanoparticle-based film with a homogenous and smooth surface was produced. The results of total reflectance infrared spectroscopy (ATR-FTIR) and thermal gravimetric analysis (TGA/DTA) demonstrated the presence of a substantial physical force between composite components. The composite was tested for its ability to absorb Cd2+ and Zn2+ ions from aqueous solutions. Cd2+ and Zn2+ adsorption mechanisms are fit using the Langmuir model and the pseudo-second-order model. Thermodynamic parameters indicated that Cd2+ and Zn2+ ion adsorption onto the composite surface is spontaneous and preferred at neutral pH and temperatures somewhat higher than room temperature. The adsorption studies showed that the maximum adsorption capacity of the HAp/CTs bio-composite membrane for Cd2+ and Zn2+ ions was in the order of cadmium (120 mg/g) > Zinc (90 mg/g) at an equilibrium time of 20 min and a temperature of 25 °C. The results obtained on the physico-chemical properties of nanocomposite membranes and their sorption capacities offer promising potential for industrial and biological activities.
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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".