Thermo‐responsive macroporous p( <scp>NIPAM</scp> ) cryogel affords enhanced thermal stability and activity for ɑ‐glucosidase enzyme by entrapping in situ
Bibliographic record
Abstract
Abstract The concept of using a macroporous thermo‐responsive poly(N‐isopropylacrylamide) (p(NIPAM)) polymer matrix for enzyme immobilization having lower critical solution temperature (LCST) is rationalized by the availability of the many compartments (pores) to entrap enzymes to operate within pores of three‐dimensional matrix providing special environmental conditions. Therefore, the ɑ‐glucosidase (ɑ‐G) immobilization within p(NIPAM) cryogel (ɑ‐G@p(NIPAM)) was carried out under the storage conditions of enzymes, generally ~ −20°C to afford the unnecessary loss of enzyme functionality in comparison to the other enzyme entrapment methods. The LCST value for the prepared p(NIPAM)‐based cryogels was determined as 34.8 ± 1.4°C. The immobilization yield, immobilization efficiency, and activity recovery% values were calculated as 89.4 ± 3.1, 66.2 ± 3.3, and 74.0% ± 3.3%, respectively, at pH 6.8 and 37°C for ɑ‐G@p(NIPAM) cryogel system. Interestingly, the optimum working conditions were attained as 25°C and pH 6.8 with higher activity, 98.4% ± 0.2% for the prepared ɑ‐G@p(NIPAM) cryogel system. The reuse and storage stability studies revealed that the prepared ɑ‐G@p(NIPAM) cryogel system is more effective than the native ɑ‐G enzyme; for example, it showed at least 80% activity after the fifth usage and provided higher activity up to a 10‐day room temperature storage time. Moreover, the kinetic parameters such as K m and V max of native ɑ‐G enzyme and ɑ‐G@p(NIPAM) cryogel system were calculated by non‐linear Lineweaver–Burk plot equations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".