Superassembled Biocatalytic Porous Framework Micromotors with Reversible and Sensitive pH‐Speed Regulation at Ultralow Physiological H<sub>2</sub>O<sub>2</sub> Concentration
Bibliographic record
Abstract
Abstract Synthetic nano/micromotors are a burgeoning class of materials with vast promise for applications ranging from environmental remediation to nanomedicine. The motility of these motors is generally controlled by the concentration of accessible fuel, and therefore, engineering speed‐regulation mechanisms, particularly using biological triggers, remains a continuing challenge. Here, control over the movement of superassembled porous framework micromotors via a reversible, biological‐relevant pH‐responsive regulatory mechanism is demonstrated. Succinylated β‐lactoglobulin and catalase are superassembled in porous framework particles, where the β‐lactoglobulin is permeable at neutral pH. This permeability allows the fuel (H2O2) to access catalase, leading to autonomous movement of the micromotors. However, at mild acidic pH, succinylated β‐lactoglobulin undergoes a reversible gelation process, preventing the access of fuel into the micromotors where the catalase resides. To one's knowledge, this study represents the first example of chemically driven motors with rapid, reversible pH‐responsive motility. Furthermore, the porous framework significantly enhances the biocatalytic activity of catalase, allowing ultralow H2O2 concentrations to be exploited at physiological conditions. It is envisioned that the simultaneous exploitation of pH and chemical potential of such nanosystems could have potential applications as stimulus‐responsive drug delivery vehicles that benefit from the complex biological environment.
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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 | 0.000 | 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".