Tunable Affinity and Molecular Architecture Lead to Diverse Self-Assembled Supramolecular Structures in Thin Films
Bibliographic record
Abstract
The self-assembly behavior of specifically designed giant surfactants is systematically studied in thin films using grazing incidence X-ray scattering and transmission electron microscopy, focusing on the effects of molecular nanoparticle (MNP) functionalities and molecular architectures on nanostructure formation. Two MNPs with different surface functionalities, i.e., hydrophilic carboxylic acid functionalized [60]fullerene (AC60) and omniphobic fluorinated polyhedral oligomeric silsesquioxane (FPOSS), are utilized as the head portions of the giant surfactants. By covalently tethering these functional MNPs onto the end point or junction point of polystyrene-block-poly(ethylene oxide) (PS-b-PEO) diblock copolymer, linear and star-like giant surfactants with different molecular architectures are constructed. With fixed length of the PEO block, changing the molecular weight of the PS block leads to the formation of various ordered phases and phase transitions. Due to the distinct affinity, the AC60-based and FPOSS-based giant surfactants form two- or three-component morphologies, respectively. A stretching parameter for the PS block is introduced to characterize the PS chain conformation in the different morphologies. The highly diverse self-assembled nanostructures with high etch resistance between components in small dimensions obtained from the giant surfactant thin films suggest that these macromolecules could provide a promising and robust platform for nanolithography 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 | 0.000 |
| 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".