Plasma within Templates: Molding Flexible Nanocrystal Solids into Multifunctional Architectures
Bibliographic record
Abstract
Recently, there has been a great deal of progress in the synthesis of colloidal nanocrystals with tailored physical and chemical properties through control of size and shape, chemical composition, and surface functionalization (Alivisatos, A. P. Science 1996, 271 (5251), 933−937; Burda, C.; Chen, X. B.; Narayanan, R.; El-Sayed, M. A. Chem. Rev. 2005, 105 (4), 1025−1102). Nanocrystals are thus ideal building blocks for hierarchical self-assembly of topologically complex, multifunctional architectures with properties tuneable at each level of the hierarchy. Here, we present a method, which combines template-assisted self-assembly of nanocrystals with plasma polymerization (Cademartiri, L.; von Freymann, G.; Arsenault, A. C.; Bertolotti, J.; Wiersma, D. S.; Kitaev, V.; Ozin, G. A. Small 2005, 1 (12), 1184−1187) to realize hierarchical architectures that both retain the properties of the nanocrystals and offer multifunctionality. As examples, we describe CoFe 2 O 4 −PbS mixed nanocrystal nanorods, which photoluminesce, and align and move in unison with an applied magnetic field and PbS nanocrystal inverse opals, which emit and Bragg diffract near-infrared light. Such hierarchical nanocrystal architectures can be envisioned to spawn new nanotechnologies that integrate multiple functionalities into a single construct.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".