Co-immobilization of multiple enzymes by metal coordinated nucleotide hydrogel nanofibers: improved stability and an enzyme cascade for glucose detection
Bibliographic record
Abstract
Preserving enzyme activity and promoting synergistic activity via co-localization of multiple enzymes are key topics in bionanotechnology, materials science, and analytical chemistry. This study reports a facile method for co-immobilizing multiple enzymes in metal coordinated hydrogel nanofibers. Specifically, four types of protein enzymes, including glucose oxidase, Candida rugosa lipase, α-amylase, and horseradish peroxidase, were respectively encapsulated in a gel nanofiber made of Zn(2+) and adenosine monophosphate (AMP) with a simple mixing step. Most enzymes achieved quantitative loading and retained full activity. At the same time, the entrapped enzymes were more stable against temperature variation (by 7.5 °C), protease attack, extreme pH (by 2-fold), and organic solvents. After storing for 15 days, the entrapped enzyme still retained 70% activity while the free enzyme nearly completely lost its activity. Compared to nanoparticles formed with AMP and lanthanide ions, the nanofiber gels allowed much higher enzyme activity. Finally, a highly sensitive and selective biosensor for glucose was prepared using the gel nanofiber to co-immobilize glucose oxidase and horseradish peroxidase for an enzyme cascade system. A detection limit of 0.3 μM glucose with excellent selectivity was achieved. This work indicates that metal coordinated materials using nucleotides are highly useful for interfacing with biomolecules.
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) | 0.000 | 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".