Directed Self-Assembly of Functionalized Silica Nanoparticles on Molecular Printboards through Multivalent Supramolecular Interactions
Bibliographic record
Abstract
Silica nanoparticles functionalized with beta-cyclodextrin (CD) host molecules (5) have been prepared by reacting carboxylic active ester-terminated silica nanoparticles (4) with CD heptamine. Silica nanoparticles functionalized with glucosamine (6), having similar surface properties as 5 but lacking the host-guest recognition motif, were used to perform blank experiments. The CD-functionalized silica nanoparticles 5 were determined by TEM to be 55 +/- 6 nm in size. They exhibited pH-dependent aggregation, which is explained by the presence of free amino and carboxylic acid groups on the particle surface, which was corroborated by zeta potential measurements. The functionalization with CD was further confirmed by host-guest studies in solution and at CD-functionalized silicon substrates. The addition of an adamantyl-terminated dendrimer, capable of multivalent host-guest binding with CD, led to strong aggregation of the CD particles 5, but not of the glucosamine-functionalized 6. Furthermore, 5 gave strong adsorption to CD monolayers on silicon onto which adamantyl-terminated dendrimers were adsorbed, whereas 6 did not. The good discrimination between dendrimer-covered and uncovered areas of the CD monolayer substrates allowed the directed self-assembly of the silica particles 5 onto dendrimer-patterned areas created by microcontact printing.
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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".