The mechanism of catalase loading into porous vaterite CaCO<sub>3</sub> crystals by co-synthesis
Bibliographic record
Abstract
Porous vaterite CaCO3 crystals are nowadays extensively used as high-capacity bio-friendly sacrificial templates for the fabrication of such protein-containing nano- and micro-particles as capsules and beads. The first step in the protein encapsulation is performed through loading of the protein molecules into the crystals. Co-synthesis is one of the most useful and simple methods proven to effectively load crystals with proteins; however, the loading mechanism is still unknown. To understand the mechanism, in this study, we focus on the loading of a model protein catalase into the crystals by means of adsorption into pre-formed crystals (ADS) and co-synthesis (COS). Analysis of the physico-chemical characteristics of the protein in solution and during the loading and simulation of the protein packing into the crystals are performed. COS provides more effective loading than ADS giving protein contents in the crystals of 20.3 and 3.5 w/w%, respectively. Extremely high loading for COS providing a local protein concentration of about 550 mg mL-1 is explained by intermolecular protein interactions, i.e. formation of protein aggregates induced by CaCl2 during the co-synthesis. This is supported by a lower equilibrium constant obtained for COS (5 × 105 M-1) than for ADS (23 × 105 M-1), indicating a higher affinity of single protein molecules rather than aggregates to the crystal surface. Fitting the adsorption isotherms by classical adsorption models has shown that the Langmuir and BET models describe the adsorption phenomenon better than the Freundlich model, proving the aggregation in solution followed by adsorption of the aggregates into the crystals. We believe that this study will be useful for protein encapsulation through CaCO3 crystals using the COS method.
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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) | 0.001 | 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".