Customizable metal-phenolic supraparticles based on rationally designed building blocks
Bibliographic record
Abstract
Metal-phenolic networks (MPNs) as a versatile platform for particle engineering have been well developed due to their integrated benefits of both metal ions and phenolic molecules. However, the approaches to broaden their applications are limited due to the single-driving force from the coordination of these two components. Herein, we developed a universal approach to introducing programmable assembles into MPNs to form metal-phenolic supraparticles based on the rationally designed phenolic building blocks. These as-prepared building blocks can first assemble into primary nanoparticles driven by various controllable intermolecular interactions (i.e., metal-organic coordination, host-guest interaction, and hydrophobic interaction), followed by particle assembly with metal ions to coat on different templates. The introduction of multiple assembly modalities into phenolic building blocks enriches the functionalities of these metal-phenolic supraparticles, such as dual-pH responsibility, light-controllable permeability, and rapid fluorescence labeling of living cells. Our work provides a conceptual and practical paradigm for customizing MPNs with hierarchical structures by importing various assembly strategies via rationally designed phenolic building blocks.
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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) | 0.001 | 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".