Polymer-Tethered Nanoparticles: From Surface Engineering to Directional Self-Assembly
Bibliographic record
Abstract
Current interest in nanoparticle ensembles is motivated by their collective synergetic properties that are distinct from or better than those of individual nanoparticles and their bulk counterparts. These new advanced optical, electronic, magnetic, and catalytic properties can find applications in advanced nanomaterials and functional devices, if control is achieved over nanoparticle organization. Self-assembly offers a cost-efficient approach to produce ensembles of nanoparticles with well-defined and predictable structures. Nanoparticles functionalized with polymer molecules are promising building blocks for self-assembled nanostructures, due to the comparable dimensions of macromolecules and nanoparticles, the ability to synthesize polymers with various compositions, degrees of polymerization, and structures, and the ability of polymers to self-assemble in their own right. Moreover, polymer ligands can endow additional functionalities to nanoparticle assemblies, thus broadening the range of their applications.In this Account, we describe recent progress of our research groups in the development of new strategies for the self-assembly of nanoparticles tethered to macromolecules. At the beginning of our journey, we developed a new approach to patchy nanoparticles and their self-assembly. In a thermodynamically driven strategy, we used poor solvency conditions to induce homopolymer surface segregation in pinned micelles (patches). Patchy nanoparticles underwent self-assembly in a well-defined and controlled manner. Following this work, we overcame the limitation of low yield of the generation of patchy nanoparticles, by using block copolymer ligands. For block copolymer-capped nanoparticles, patch formation and self-assembly were "staged" by using distinct stimuli for each process. We expanded this work to the generation of patchy nanoparticles via dynamic exchange of block copolymer molecules between the nanoparticle surface and micelles in the solution. The scope of our work was further extended to a series of strategies that utilized the change in the configuration of block copolymer ligands during nanoparticle interactions. To this end, we explored the amphiphilicity of block copolymer-tethered nanoparticles and complementary interactions between reactive block copolymer ligands. Both approaches enabled exquisite control over directional and self-limiting self-assembly of complex hierarchical nanostructures. Next, we focused on the self-assembly of chiral nanostructures. To enable this goal, we attached chiral molecules to the surface of nanoparticles and organized these hybrid building blocks in ensembles with excellent chiroptical properties. In summary, our work enables surface engineering of polymer-capped nanoparticles and their controllable and predictable self-assembly. Future research in the field of nanoparticle self-assembly will include the development of effective characterization techniques, the synthesis of new functional polymers, and the development of environmentally responsive self-assembly of polymer-capped nanoparticles for the fabrication of nanomaterials with tailored functionalities.
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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".