Customizable Supraparticles Constructed from Catechol‐Terminated Molecular Building Blocks with Controllable Intermolecular Interactions**
Bibliographic record
Abstract
Colloidal supraparticles integrated with multicomponent primary particles come with emerging or synergetic functionalities. However, achieving the functional customization of supraparticles remains a great challenge because of the limited options of building blocks with tailorability and functional extensibility. Herein, we developed a universal approach to construct customizable supraparticles with desired properties from molecular building blocks obtained by the covalent conjugation of catechol groups with a series of orthogonal functional groups. These catechol-terminated molecular building blocks can assemble into primary particles driven by various intermolecular interactions (i.e. metal-organic coordination, host-guest, and hydrophobic interactions), and then further assemble into supraparticles governed by catechol-mediated interfacial interactions. Our strategy enables the formation of supraparticles with diverse functionalities, such as dual-pH responsiveness, light-controllable permeability, and non-invasive fluorescence labeling of living cells. The ease with which these supraparticles can be fabricated, and the ability to tailor their chemical and physical properties through the choice of metals and orthogonal functional groups used, should enable a variety of applications.
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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 | 0.001 |
| 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".