Performance of pseudo‐specific cryogel in lysozyme purification from chicken egg white
Bibliographic record
Abstract
Abstract The application of cryogels for biomolecule purification has expanded due to their adsorption efficiency and operational advantages. In this study, polyacrylamide cryogels functionalized with l ‐phenylalanine (cryogel‐Phe) via the glutaraldehyde method were designed for lysozyme adsorption. Cryogel functionalization was confirmed by Fourier‐transform infrared spectroscopy and Kjeldahl analysis, indicating the immobilization of 458.65 mg phenylalanine g cryogel −1 . Cryogel‐Phe showed high porosity (0.95) and a Young's modulus of 526.71 kPa. Thermogravimetric analysis indicated that thermal degradation occurred above 200°C. Differential scanning calorimetry and X‐ray diffraction confirmed that the cryogel material was amorphous. In addition, the column presented a hydraulic permeability of 4.15 × 10 −13 m 2 , axial dispersion ranging from 10 −7 to 10 −6 m 2 s −1 , and a height equivalent to a theoretical plate ranging from 0.10 to 0.21 cm. The highest adsorption of lysozyme (67.65 mg g −1 ) was obtained using sodium thiocyanate saline solution (0.025 mol L −1 , pH 5.0). The ability of the cryogel‐Phe column to capture and purify lysozyme was confirmed by high enzymatic activity (1294.17 U ml −1 ), purity (87.92%), purification factor (11.49), and sulphate‐polyacrylamide electrophoresis gel (SDS‐PAGE) electrophoresis gel.
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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) | 0.000 | 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".