Kinetic, isotherm and thermodynamic studies of the adsorption of phenol and tyrosine onto apatitic tricalcium phosphate
Bibliographic record
Abstract
The present study was conducted to evaluate the feasibility of apatitic tricalcium phosphate with a Ca/P ratio of 1.50 for the adsorption of phenol and tyrosine from aqueous solutions. The adsorbent was synthesized at room temperature using an aqueous double decomposition method and characterized through physicochemical methods. Batch adsorption studies were conducted as a function of contact time, initial adsorbate concentration, temperature, and pH. The adsorption kinetics of phenol and tyrosine were well fitted to the pseudo-second-order model. The maximum adsorption capacity was found to be 5.56 mg/g for phenol and 9.65 for tyrosine mg/g at 298 K. The adsorption of phenol and tyrosine was well explained using the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevick models. The Langmuir model is the most suitable, with a maximum monolayer adsorption capacity of 7.32 mg/g for phenol and 11.43 mg/g for tyrosine at 298 K. The thermodynamic parameters indicate that the adsorption process is favorable, spontaneous, exothermic, and controlled by physisorption with electrostatic interactions between compounds containing the phenolic group and apatite. The results of this study have demonstrated the potential utility of apatitic tricalcium phosphate, which could be developed into a viable technology for the adsorption of compounds containing the phenolic group from aqueous solutions.
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 | 0.001 |
| 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) | 0.001 | 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".