Preparation and Characterization of Biopolymer/Calcium Phosphate Composite and Their Application for Dye-Contaminated Wastewater Treatment
Bibliographic record
Abstract
The current chapter book describes of the chemical modification of calcium phosphate surface by lambda carrageenan biopolymer and its using for the removal of methylene blue (MB) from aqueous solution. The prepared adsorbents (CaP-Carr) adsorbents were characterized by X-ray diffraction (XRD), infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) analysis. The X-ray powder analysis results showed that the crystallinity was unaffected by the presence of biopolymer. In order to investigate the impact of various parameters, including temperature, pH level, contact time, and initial MB concentration, batch adsorption experiments were carried. The adsorption of MB onto the studied adsorbents may have been controlled by chemisorption process that suggested a pseudo-second order. Langmuir and Freundlich isotherms provided a detailed description of the adsorption mechanisms on the surface of modified and unmodified calcium phosphate, respectively. Overall, the experimental results suggest that calcium phosphate-carrageenan composite has promising potential as an adsorbent for the treatment of MB dye-contaminated wastewater treatment. Additionally, they might offer a fresh avenue for research into the creation of functionalized calcium phosphate that could find value in other contexts.
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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 | 0.001 |
| 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) | 0.004 | 0.002 |
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".