Nanoscale Patterning of Organic Monolayers by Catalytic Stamp Lithography: Scope and Limitations
Bibliographic record
Abstract
Developing a method to pattern organic molecules, particularly on the sub-100-nm scale, is of wide interest in current nanoscience for a broad range of technological applications. Because of the efficiency and simplicity of soft lithography, here we describe in detail the process for synthesizing and applying catalytic stamp lithography, a process that can easily produce sub-100-nm patterns on surfaces; in this work, the approach is demonstrated on silicon. Catalytic stamps were fabricated through a two-step procedure in which the nanoscale pattern of catalysts is produced via a self-assembled block-copolymer-templated synthesis of metallic nanostructures on SiO(x)/Si supports, followed by the production of the poly(dimethylsiloxane) (PDMS) stamp on top of the as-patterned metals. Simply peeling off the as-formed PDMS stamp removes the metallic nanostructures, leading to the functional stamp. Two different patterns, pseudohexagonal and linear Pt nanoarrays, were produced from a single block copolymer, PS(125000)-b-P2VP(58500), by controlling the morphology of thin-film templates through tetrahydrofuran vapor annealing. When terminal alkenes, alkynes, or aldehydes with different functionalities were used as molecular inks, these Pt nanopatterns on catalytic stamps were translated into corresponding molecular arrays on Si(111)-H and Si(100)-H(x) surfaces because catalytic hydrosilylation took place exclusively underneath patterned Pt nanostructures. With this straightforward approach, the resolution limit of conventional microcontact printing (approximately 100 nm) could be downsized to a sub-20-nm scale, while maintaining the advantages of stamp-based patterning (i.e., large-area, high-throughput capabilities and low cost).
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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 | 0.003 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".