Self-Assembled Structures of Giant Surfactants Exhibit a Remarkable Sensitivity on Chemical Compositions and Topologies for Tailoring Sub-10 nm Nanostructures
Bibliographic record
Abstract
We report a remarkable sensitivity of self-assembled structures of giant surfactants on their chemical compositions and molecular topology, which facilitate the engineering of various nanophase-separated structures with sub-10 nm feature sizes. Two classes of giant surfactants composed of various functionalized polyhedral oligomeric silsesquioxane (POSS) heads tethered with one or two polystyrene (PS) tails were efficiently prepared from common precursors of vinyl-substituted POSS–PS conjugates via one-step “thiol–ene” postpolymerization functionalization. With identical molecular weights of the PS tails, the resulting giant surfactants exhibited distinct highly ordered phases, as evidenced by small-angle X-ray scattering and transmission electron microscopy observations. Moreover, comparison between the topological isomers revealed that the self-assembled structures are also highly sensitive to molecular topology. Introduction of two PS tails with half-length not only shifted the boundaries between different ordered phases but also altered the packing configurations of the functional POSS cages, leading to further reduced feature sizes of the self-assembled nanodomains. Interestingly, a lower order–disorder transition temperature was also observed in the fluorinated F 13 POSS tethered with two PS 17 tails, compared to its topological isomer composed of F 13 POSS tethered with one PS 35 tail, indicating that the topological effect also existed in phase transition behaviors. These results provide insights to rationally design and precisely tailor self-assembled structures by controlling both primary chemical compositions and molecular topology in POSS-based giant surfactants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".