Structure and Properties of Nano- and Meso-scale Materials Prepared by Designed Self-Aasembly of Nanoparticles
Bibliographic record
Abstract
The work presented in this thesis explores the homo- and co-assembly process, structural characteristics and properties of nano- and meso-scale, one- and three-dimensional structures composed of nanoscale building blocks. In chapter 3, we developed a method for terminating the colloidal polymerization process of gold nanorods by post-assembly photocrosslinking of polymer ligands. By functionalizing gold nanorods with a hydrophobic polymer containing pendant double bonds, we were able to compartmentalize a hydrophobic photoinitiator. We could then suppress the association ability of the polymer ligands by covalently crosslinking them using photoirradiation. The averaged aggregation number of the polymers could be predicted from the kinetics of molecular step-growth polymerization. Furthermore, we found that following crosslinking, the inert-rod distance reduced, and the nanorod co-linearity increased, both allowing for better electromagnetic coupling between the nanorods. In chapter 4, we further explored the resemblance between colloidal and molecular polymerization reactions by applying strategies from molecular copolymerization to the co-assembly of gold nanorods with different dimensions into random and block copolymer structures (plasmonic copolymers). The approach was extended to the co-assembly of random copolymers of gold and palladium nanorods. We showed that the formation of random copolymers is achieved regardless of the composition, size, or concentration if both co-monomers are present at the beginning of the copolymerization. Block copolymers were prepared by combining one pre-polymer with the second monomer, or two pre-polymers. A kinetic model validated and further expanded the kinetic theories developed for molecular copolymerization reactions. Chapters 5 and 6 explore a bottom-up method to produce composite materials from spherical nanoparticles with different core-composition, surface-characteristics, and concentrations and cellulose nanocrystals. A relationship was established between the physical properties of the spherical particles and the characteristics of the co-assembled composite by extensive structural and optical characterization of mixed suspensions and composite solid films.
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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".