Microenvironmental modulation breaks intrinsic pH limitations of nanozymes to boost their activities
Bibliographic record
Abstract
Functional nanomaterials with enzyme-mimicking activities, termed as nanozymes, have found wide applications in various fields. However, the deviation between the working and optimal pHs of nanozymes has been limiting their practical applications. Here we develop a strategy to modulate the microenvironmental pHs of metal-organic framework (MOF) nanozymes by confining polyacids or polybases (serving as Brønsted acids or bases). The confinement of poly(acrylic acid) (PAA) into the channels of peroxidase-mimicking PCN-222-Fe (PCN = porous coordination network) nanozyme lowers its microenvironmental pH, enabling it to perform its best activity at pH 7.4 and to solve pH mismatch in cascade systems coupled with acid-denatured oxidases. Experimental investigations and molecular dynamics simulations reveal that PAA not only donates protons but also holds protons through the salt bridges between hydroniums and deprotonated carboxyl groups in neutral pH condition. Therefore, the confinement of poly(ethylene imine) increases the microenvironmental pH, leading to the enhanced hydrolase-mimicking activity of MOF nanozymes. This strategy is expected to pave a promising way for designing high-performance nanozymes and nanocatalysts for 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".