Confocal microscopy study of polymer microcapsules for enzyme immobilisation in paper substrates
Bibliographic record
Abstract
Abstract The goal of this research is to develop the technology platform required for the production of bioactive paper based on enzymes as bioactive agents. The immobilization platform described here is based on microencapsulation, which consists in the entrapment of biomolecules in the core of hollow spheres made by a semipermeable membrane. The capsules containing the enzymes can be either deposited on paper or mixed with paper pulp to prepare a bioactive paper. The activity of encapsulated laccase was compared with that of free enzyme using its reaction with the o‐phenylenediamine (OPD) substrate. Confocal Laser Scanning Microscopy (CLSM) is used to study the location of protein in microcapsules and provides explanations for differences in activity of encapsulated laccase. The location of protein in microcapsules was determined using BSA modified with the fluorescent tag sulforhodamine. Polyethyleneimine microcapsules were modified with fluorescein isothiocyanate allowing the simultaneous identification of capsule walls and of encapsulated proteins. From CLSM analysis, proteins were found to favor the wall of the capsules because of strong ionic attraction with the charged polymer. BSA was found to some extent in the core of the capsules and encapsulation of higher loadings increased the proportion of core proteins. We will also present our results on the incorporation of microcapsules in a paper substrate. CLSM was used in this section to determine the distribution and density of tagged microcapsules in the paper substrate. The response of immobilized laccase to a common substrate will also be described. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".