Supramolecular Assemblies on Surfaces: Nanopatterning, Functionality, and Reactivity
Bibliographic record
Abstract
Understanding how molecules interact to form large-scale hierarchical structures on surfaces holds promise for building designer nanoscale constructs with defined chemical and physical properties. Here, we describe early advances in this field and highlight upcoming opportunities and challenges. Both direct intermolecular interactions and those that are mediated by coordinated metal centers or substrates are discussed. These interactions can be additive, but they can also interfere with each other, leading to new assemblies in which electrical potentials vary at distances much larger than those of typical chemical interactions. Earlier spectroscopic and surface measurements have provided partial information on such interfacial effects. In the interim, scanning probe microscopies have assumed defining roles in the field of molecular organization on surfaces, delivering deeper understanding of interactions, structures, and local potentials. Self-assembly is a key strategy to form extended structures on surfaces, advancing nanolithography into the chemical dimension and providing simultaneous control at multiple scales. In parallel, the emergence of graphene and the resulting impetus to explore 2D materials have broadened the field, as surface-confined reactions of molecular building blocks provide access to such materials as 2D polymers and graphene nanoribbons. In this Review, we describe recent advances and point out promising directions that will lead to even greater and more robust capabilities to exploit designer 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".