Controlled Self‐Assembly of Natural Polyphenols Driven by Multiple Molecular Interactions
Bibliographic record
Abstract
Nature has exhibited a high degree of control over the structures and functions. Supramolecules have been utilized to mimic the subtle assembly in nature. However, sophisticated synthesis of molecular skeletons or programmable design of the driving forces raises great challenges in fabricating high-level superstructures in a controlled manner. Natural polyphenols show great promises as building blocks for a diverse of assemblies with controlled structures and functionalities. The intrinsically embedded phenolic groups (i. e., catechol and galloyl groups) are readily forming multiple molecular interactions, including coordination, hydrogen bonding, and π-π interactions with various materials of inorganic particles, organic compounds, synthetic polymers, and biomacromolecules, providing the self-assembled structures or nanocoating on surfaces. Subsequent assembly occurred by further bonding of polyphenols to construct supraparticles. To gain control over the self-assembly, the key lies in the interplay among the molecular interactions with one or two being dominant. In this Perspective, we introduce the representative polyphenol-based assemblies and their derived supraparticles to exhibit the effective harness of the controlled self-assembly by polyphenols.
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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".