Block Copolymer Mimetic Self-Assembly of Inorganic Nanoparticles
Bibliographic record
Abstract
Emerging strategies for assembling inorganic nanoparticles into ensembles with multiscale organization are establishing a new paradigm for the synthesis of devices and functional materials with applications ranging from drug delivery to photonics. In this work, the solution self-assembly of amphiphilic ionic block copolymers into morphologically tunable aggregates provides the inspiration and design strategy for nanoparticle building blocks with the essential chemical and conformational features of ionic block copolymer chains in aqueous media. We produce inorganic nanoparticles with surface-tethered mixed brushes of hydrophobic and chargeable hydrophilic chains which self-assemble in polar solvent mixtures into unprecedented hierarchical superstructures analogous to known ionic block copolymer aggregates but with complex organizations of nanoparticles in three dimensions. Electrostatic repulsion between hydrophilic chains forces nonequilibrium pathways to variable kinetic structures with internal lamellar organization of nanoparticles; however, decreasing electrostatic interactions through salt or acid addition allows tunable equilibrium assemblies, including supermicelles and bilayer vesicles of nanoparticles, to be formed. The application of ionic block copolymer assembly principles and mechanisms opens a new chemical toolbox for the organization of nanoparticles into functional assemblies.
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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) | 0.001 | 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".