Optimization of ion exchange cryogel functionalization via the epoxy method for protein adsorption from ora‐pro‐nobis ( <scp> <i>Pereskia aculeata</i> </scp> )
Bibliographic record
Abstract
Abstract Ora‐pro‐nobis (OPN) is a plant with a high protein content. The development of new adsorbents that enable the separation of biomolecules while preserving their bioactivity has attracted the interest of researchers. This work reports the functionalization of cryogel via the epoxy method and its application in the adsorption of OPN proteins through ion exchange, using amino acids. Among the pH values and types of ion exchange ligands evaluated (cysteine, taurine, polyethyleneimine, and glutamic acid), the highest adsorption of OPN proteins (54.44 mg g −1 ) was observed at pH 5.5 for the cryogel functionalized with glutamic acid (cryogel‐GA). The optimal conditions for cryogel functionalization were obtained at 50°C for 28 h, with an adsorption capacity of 103.37 ± 4.60 mg g −1 . The effective bonding between glutamic acid's carboxylic/amine groups and the cryogel's epoxy groups increases adsorption site density. The cryogel‐GA retained high porosity (0.92%) and water retention capacity, crucial for protein purification from crude extracts. The Langmuir model was fitted to the equilibrium adsorption isotherms, with a maximum adsorption capacity of 76.34 mg g −1 at 8°C, 172.41 mg g −1 at 15°C, 252.56 mg g −1 at 25°C, and 400 mg g −1 at 35°C. OPN protein adsorption on cryogel‐GA increased with temperature. Thermodynamic analysis based on the Van't Hoff relationship indicated that the process was spontaneous ( = −18.03 kJ mol −1 at 35°C) and entropically driven, confirming its feasibility. These results demonstrate that cryogel‐GA is a promising matrix for ion exchange protein capture processes.
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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) | 0.001 | 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) | 0.000 | 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".