Giant surfactants provide a versatile platform for sub-10-nm nanostructure engineering
Bibliographic record
Abstract
The engineering of structures across different length scales is central to the design of novel materials with controlled macroscopic properties. Herein, we introduce a unique class of self-assembling materials, which are built upon shape- and volume-persistent molecular nanoparticles and other structural motifs, such as polymers, and can be viewed as a size-amplified version of the corresponding small-molecule counterparts. Among them, "giant surfactants" with precise molecular structures have been synthesized by "clicking" compact and polar molecular nanoparticles to flexible polymer tails of various composition and architecture at specific sites. Capturing the structural features of small-molecule surfactants but possessing much larger sizes, giant surfactants bridge the gap between small-molecule surfactants and block copolymers and demonstrate a duality of both materials in terms of their self-assembly behaviors. The controlled structural variations of these giant surfactants through precision synthesis further reveal that their self-assemblies are remarkably sensitive to primary chemical structures, leading to highly diverse, thermodynamically stable nanostructures with feature sizes around 10 nm or smaller in the bulk, thin-film, and solution states, as dictated by the collective physical interactions and geometric constraints. The results suggest that this class of materials provides a versatile platform for engineering nanostructures with sub-10-nm feature sizes. These findings are not only scientifically intriguing in understanding the chemical and physical principles of the self-assembly, but also technologically relevant, such as in nanopatterning technology and microelectronics.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".