Bioconjugation of Alkaline Phosphatase to Mechanically Processed, Aqueous Suspendible Electrospun Polymer Nanofibers for Use in Chemiluminescent Detection Assays
Bibliographic record
Abstract
Aqueous suspendible polymer nanostructures were prepared by simple microtome processing of electrospun nylon 6 nanofibers and were used to immobilize calf intestinal alkaline phosphatase (ALP) by either covalent or noncovalent bioconjugation chemistries. It was found that noncovalent immobilization of ALP to the mechanically cut nanofibers (mean length approximately 4 microm; mean diameter approximately 80 nm) using a multi-stacked, layer-by-layer (LBL) approach with the cationic polymer Sapphire II resulted in the highest enzyme loading (48.1 +/- 0.4 microg . mg(-1) nanofiber) when compared to other covalent immobilization methods based on glutaraldehyde crosslinking. The biofunctionalized nanofibers were also characterized for their chemiluminescent activity with the dioxetane substrate, CSPD. The results indicate that the kinetic parameters, K(m) and V(max), for the catalytic activity of the nanostructure-bound ALP enzyme were influenced by the particular types of immobilization methods employed. In terms of the overall catalytic performance of the various immobilized ALP systems, a single-stacked LBL assembly approach resulted in the highest level of enzymatic activity per unit mass of nanofiber support. To the best of our knowledge, this study represents the first report examining the preparation of mechanically shortened, aqueous dispersed electrospun polymer nanofibers for potential application as enzyme scaffolds in chemiluminescent-based assay systems.
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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".