Environmental-Friendly Adsorbent Composite Based on Hydroxyapatite/Hydroxypropyl Methyl-Cellulose for Removal of Cationic Dyes from an Aqueous Solution
Bibliographic record
Abstract
The aim of this study is to develop a new, efficient, and inexpensive natural-based adsorbent with high efficacy for the cationic dye methylene blue (MB). A natural-based nanocomposite based on hydroxyapatite (HAp) and hydroxypropyl methylcellulose (HPMC) was selected for this purpose. It was synthesized by the dissolution/reprecipitation method. A film with a homogeneous and smooth surface composed of nanoparticles was prepared from the nanocomposite. HPMC and HAp biopolymers were selected due to their compatibility, biodegradability, and non-toxicity. Total reflectance infrared spectroscopy (ATR-FTIR), scanning electron microscopy (SEM), and calorimetric/thermal gravimetric (DSC/TGA) analysis results revealed the existence of strong physical interaction between the composite components. Scanning electron microscopy (SEM) observations show a composite sheet with a homogenous and smooth surface, indicating excellent compatibility between HPMC and HAp in the composite. The nanocomposite was evaluated as an adsorbent for organic dyes in an aqueous solution. The effects of solution pH, initial MB concentration, composite concentration, and adsorption time on the adsorption efficiency were evaluated. The highest adsorption rate was seen as 52.0 mg of MB/g composite. The adsorption rate reached equilibrium in about 20 min. Fitting of the adsorption data to the Langmuir and Freundlich adsorption models was investigated. Results showed that the adsorption process follows the Langmuir isotherm model. The kinetic study results revealed that the adsorption process was pseudo-second-order. The herein composite is an excellent alternative for use as contemporary industrial-scale adsorbents.
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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".