Observation of Catalytic Variability of Single Enzyme Zeolitic Imidazole Framework‐8 Nanoparticles
Bibliographic record
Abstract
Abstract Metal–organic frameworks (MOFs), such as zeolitic imidazolate framework‐8 (ZIF‐8), are an emerging class of advanced materials for enzyme encapsulation due to their protective properties, stability, and biocompatibility. As a range of enzyme@ZIF‐8 nanoparticles (NPs) with specific functions for practical applications has been demonstrated, scientists aim to strategically design enzyme@ZIF‐8 NPs with optimized catalytic performance. Beyond demonstrating enzyme and ZIF‐8 combinations, a mechanistic understanding of catalytic performance is necessary for the strategic design of novel enzyme@ZIF‐8 NPs. Conventional methods for monitoring the catalytic performance of enzyme@ZIF‐8 NPs rely on ensemble‐averaged measurements, where variation in catalytic activity between individual particles remains unresolved and limits the ability to study catalytic performance. Here, the catalytic variability of single enzyme@ZIF‐8 NPs is investigated using a microwell‐based fluorescence method. Bovine serum albumin (BSA) is utilized to load horseradish peroxidase (HRP) as our model enzyme into ZIF‐8 to synthesize colloidal HRP+BSA@cZIF‐8 NPs. Single‐particle analysis shows distributions of activity in single HRP+BSA@cZIF‐8 NPs, and experiments provide insights into the subpopulations of NPs that produce catalytic variability. This work highlights the need for single‐particle methodologies to understand the mechanisms of catalytic activity of enzyme@MOF NPs, enabling the design of optimized, tailorable enzyme@MOF NPs and enhancing their potential in practical applications.
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".