Crankshafts: Using Simple, Flat<i>C</i><sub>2<i>h</i></sub>-Symmetric Molecules to Direct the Assembly of Chiral 2D Nanopatterns
Bibliographic record
Abstract
Linear D2h-symmetric bisisophthalic acids 1 and 2 and related substances have well-defined flattened structures, high affinities for graphite, and strong abilities to engage in specific intermolecular interactions. Their adsorption produces characteristic nanopatterns that reveal how 2D molecular organization can be controlled by reliable interadsorbate interactions such as hydrogen bonds when properly oriented by molecular geometry. In addition, the behavior of these compounds shows how large-scale organization can be obstructed by programming molecules to associate strongly according to competing motifs that have similar stability and can coexist smoothly without creating significant defects. Analogous new bisisophthalic acids 3a and 4a have similar associative properties, and their unique C2h-symmetric crankshaft geometry gives them the added ability to probe the poorly understood effect of chirality on molecular organization. Their adsorption shows how nanopatterns composed predictably of a single enantiomer can be obtained by depositing molecules that can respect established rules of association only by accepting neighbors of the same configuration. In addition, an analysis of the adsorption of crankshaft compounds 3a and 4a and their derivatives by STM reveals directly on the molecular level how kinetics and thermodynamics compete to control the crystallization of chiral compounds. In such ways, detailed studies of the adsorption of properly designed compounds on surfaces are proving to be a powerful way to discover and test rules that broadly govern molecular organization in both 2D and 3D.
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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".