Self-assembly of 1-D organic semiconductor nanostructures
Bibliographic record
Abstract
This review focuses on the molecular design and self-assembly of a new class of crowded aromatics that form 1-D nanostructures via hydrogen bonding and pi-pi interactions. These molecules have a permanent dipole moment that sums as the subunits self assemble into molecular stacks. The assembly of these molecular stacks can be directed with electric fields. Depending on the nature of the side-chains, molecules can obtain the face-on or edge-on orientation upon the deposition onto a surface via spin cast technique. Site-selective steady state fluorescence, time-resolved fluorescence, and various types of scanning probe microscopy measurements detail the intermolecular interactions that drive the aromatic molecules to self-assemble in solution to form well-ordered columnar stacks. These nanostructures, formed in solution, vary in their number, size, and structure depending on the functional groups, solvent, and concentration used. Thus, the substituents/side-groups and the proper choice of the solvent can be used to tune the intermolecular interactions. The 1-D stacks and their aggregates can be easily transferred by solution casting, thus allowing a simple preparation of molecular nanostructures on different surfaces.
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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 | 0.001 |
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".