Studies of kinetic models and adsorption isotherms: application on the interaction of insulin with synthetic hydroxyapatite
Bibliographic record
Abstract
The non-stoichiometric, calcium-deficient hydroxyapatite was prepared through a low-temperature from aqueous solutions method and characterized using Physico-chemical methods. The potential of this hydroxyapatite to adsorb and release insulin from aqueous solutions was evaluated under physiological conditions. The effect of contact time and initial concentration were studied in batch experiments. The adsorption rate reached up to 81±5% in the first half-hour of contact, while the release rate of insulin incubation was about 41 ± 5% after 1 hour. The pseudo-first-order, pseudo-second-order, Elovich equation, Weber and Morris intraparticle diffusion model and Bangham’s pore diffusion model were applied to study the kinetics of the adsorption process. The pseudo-second-order kinetic model provided the best correlation R2(0.998) of the used experimental data compared to the other models. The adsorption of insulin onto hydroxyapatite was correlated well R2(0.998) with the Langmuir model as compared to Freundlich, Temkin and Dubinin–Kaganer–Radushkevich (D-K-R) models, with a maximum adsorption capacity of 24.46 mg/g. The isotherms parameters values of ΔG0, b_t and E show that the adsorption process is favorable, spontaneous, exothermic, and controlled by physisorption. The point of zero charge (pHZPC) of hydroxyapatite and the isoelectric point (pI) of insulin indicate that the interaction of insulin molecules with prepared apatite can be well described as an ions exchange reaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".