Trapping It Softly: Ultrasoft Zirconium Metallogels for Macromolecule Entrapment and Reconfiguration
Bibliographic record
Abstract
Trapping nanosized drugs in ultrasoft, shear-thinning hydrogels with large pores is of particular interest, yet a persistent challenge in nanomedicine due to the lack of hydrodynamic confinement. Engineering molecular interactions between a macromolecule and a supramolecular gel may address this shortcoming, providing a key route to develop advanced drug carriers without compromising matrix elasticity. Here, we show that ultrasoft zirconium-based metallogels are able to trap and reconfigure model nanodrugs (e.g., dextran) through complexation and hydrogen bonding. The diffusion coefficients of dextran molecules ( M w ∼ 10–2000 kDa, a ∼ 2–20 nm) in zirconium carbonate (ZC) metallogels ( G ′ < 30 Pa) were measured by pulsed field gradient nuclear magnetic resonance (PFGNMR), which revealed the coexistence of hindered and enhanced collective diffusion regimes for the first time. This work may pave the way toward designing next generation ultrasoft drug carriers and functional templates to control biomacromolecular processes, such as protein folding.
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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".