Ordered Assembly of Natural Phenolic Building Blocks for Supramolecular Crystalline Materials
Bibliographic record
Abstract
Biomacromolecules such as DNA, proteins, and lipids in nature are constructed by 'bottom-up' assembly with diverse functions and structural ordered characteristics. Supramolecular assemblies have been employed to mimic the natural complexity by manipulating the subtle variations of functional groups. Nevertheless, the intricate design of the driving forces or sophisticated synthesis of molecular skeletons poses challenges in fabricating highly ordered assemblies. Natural phenolic molecules with anisotropic functional groups exhibit potential as versatile building blocks for a wide range of supramolecular crystalline materials with tailored assembly and controlled functionalities. The inherent and anisotropic phenolic groups engage in ordered assembly with various materials via directional covalent bonds (e. g., condensation and coordination) as well as multiple molecular interactions (e. g., hydrogen bonding and π-π interactions), leading to the formation of supramolecular crystalline materials with diverse functionalities. This Concept presents the assembly mechanisms of crystalline phenolic materials and their applications, showcasing the effective utilization of ordered assembly by natural 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".