Templated Fabrication of Fiber-Basket Polymersomes via Crystallization-Driven Block Copolymer Self-Assembly
Bibliographic record
Abstract
Immobilizing uniform nanostructures on a mesoscale substrate is a promising approach to prepare nanometer to micrometer sized materials with new functionalities. The hierarchical structures formed depend on both the nature of the substrate and the components deposited. In this paper, we describe the use of colloidal polystyrene microbeads as a sacrificial template to create a nanofibrous network coating consisting of elongated block copolymer micelles. This network has a secondary structure very different from that of conformal coatings obtained by other methods. In addition, the fibers of the network could be elongated by crystallization-driven self-assembly. The network was locked in place by cross-linking the micelles through in situ generation of small Pt nanoparticles. Subsequent removal of the sacrificial template gave an open vesicular structure. To demonstrate further transformation of the membrane, we showed that the cross-linked micelles could also be used to embed silver nanoparticles. The sacrificial template contained known amounts of Tb and Tm ions, allowing us to estimate via atomic mass spectrometry that 85% of the template surface was covered with micelle seeds. This approach to fabricating hierarchical coating structures expands the generality and scope of template-assisted synthesis to build advanced hierarchical materials with precise morphological control.
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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 | 0.001 |
| 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".