Amphiphilic Inorganic Nanoparticles with Mixed Polymer Brush Layers of Variable Composition: Bridging the Paradigms of Block Copolymer and Nanoparticle Self-Assembly
Bibliographic record
Abstract
The application of molecular self-assembly principles to nanoscopic building blocks can inform new pathways to hierarchical nanomaterials. For instance, amphiphilic brush nanoparticles (ABNPs) are inorganic nanoparticles functionalized with mixed polymer brushes of hydrophobic and hydrophilic chains that possess several structural and chemical features similar to those of amphiphilic block copolymers (ABCs), including spatial segregation of covalently connected hydrophilic and hydrophobic regions, anisotropic interactions, and conformational flexibility. However, the phase behavior of ABNPs with respect to hydrophobic fraction, concentration, and salt content has not been established to date, precluding direct comparison with ABC self-assembly. In this study, we produce and characterize a series of ABNPs with similar cadmium sulfide core sizes and brush densities but with different polystyrene/poly(methacrylic acid) brush compositions using a diblock copolymer mixed micelle approach. Self-assembly of the resulting ABNPs in THF/water mixtures yields hybrid spheres, cylinders, and vesicles with morphological transitions following trends with respect to hydrophobic fraction, initial concentration, and salt content similar to those previously established for ABCs. The resulting ABNP phase behavior demonstrates that microphase separation principles established in the 1990s for the solution self-assembly of macromolecular ABCs can provide vital experimental guidelines for the controlled self-organization of nanoscopic amphiphiles.
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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".