Alkyl chain length effects on double-deck assembly at a liquid/solid interface
Bibliographic record
Abstract
Controlled double-deck packing is an appealing means to expand upon conventional 2D self-assembly which is critical in crystal engineering, yet it is rare and poorly understood. Herein, we report the first systematic study of double-deck assembly in a series of alkylated aminoquinone derivatives at the liquid-solid interface. The competition between the fraction of alkyl chains adsorbed on the surface and the optimal conformation of the alkyl chains near the head group leads to a stepwise structural transformation ranging from complete double-deck packing to complete monolayer packing. Alkyl chains on the bottom or top layer of the double-deck assemblies were selectively visualized by carefully tuning the scanning tunneling microscopy settings. A method to easily identify mirror image domains was discovered based on the coincidence of domain boundaries with a graphite main axis. The effect of molecular symmetry and metal complexation on the formation of the double-deck assembly was also explored. Based on 2D crystal engineering principles, this bottom-up double-deck assembly can potentially provide an essential toehold for constructing precise 3D hierarchical structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".