Molecularly imprinted nanozymes with faster catalytic activity and better specificity
Bibliographic record
Abstract
Nanozymes are nanomaterials mimicking the activity of natural enzymes, while most nanozymes lack substrate specificity. Molecular imprinting on nanozymes provides a simple solution to this problem, and the catalytic activity is also enhanced. To understand enhanced activity, a surface science approach is taken by dissecting the nanozyme reaction into adsorption of substrates, reaction, and product release. Each step is individually studied using reaction kinetics measurement, dynamic light scattering, UV-vis spectrometry. Enrichment of local substrate concentration due to imprinting is around 8-fold, and increased substrate concentration could contribute to increased activity. Diffusion of the substrate across the imprinted gel layer is studied by a pre-incubation experiment, also highlighting the difference between imprinted and non-imprinted gel layers. The activation energy is measured and a substrate-imprinted sample had the lowest activation energy of 13.8 kJ mol-1. Product release is also improved after imprinting as indicated by isothermal titration calorimetry using samples respectively imprinted with the substrate and the product. This study has rationalized improved activity and specificity of molecularly imprinted nanozymes and may guide further rational design of such materials.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".