Controlling enzyme function through immobilisation on graphene, graphene derivatives and other two dimensional nanomaterials
Bibliographic record
Abstract
Robust enzyme immobilisation methods that preserve enzyme activity while enabling enzymes to be recovered and reused multiple times have important applications in biocatalysis. However, immobilisation can change the functionality of enzymes, both in terms of their level of activity and their selectivity. These changes in activity are unpredictable and at present cannot be controlled, but if fully understood at a fundamental level could offer the opportunity to create highly targetted enzyme systems for specific applications. In this review, we will highlight the use of two dimensional nanomaterials (2D NMs), particularly graphene and its derivatives, as immobilisation materials to modify and control the selectivity and activity of various enzymes. The fundamental information obtained from immobilising enzymes on 2D NMs allows for the implementation of improved immobilisation strategies and assists in the design of next generation nano- and macro-materials for enzyme immobilisation. We hope that this review will highlight the potential for tailoring enzyme activity and selectivity through immobilisation.
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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 | 0.001 | 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".