Nanoscale Patterning of Organic and Metallic Features on Semiconductors via Self-Assembly of Soft Materials
Bibliographic record
Abstract
Nanostructured materials continue to be the focus of intense research due to their promise of innumerable practical applications as well as advancing the fundamental understanding of these intriguing materials. In particular, the need for metallic and organic features of increasingly smaller size regimes has imposed stringent demands upon chemists to produce a variety of highly functional materials with reduced dimensions. The successful realization of arrayed nanosensor and nanoelectrode production, molecular electronics, ultra large scale integration (ULSI) device fabrication, and nanoelectromechanical systems (NEMS) will require unparalleled precision and control of geometry, aspect ratio, surface morphology, deposition rate, and substrate adhesion without sacrificing throughput or cost effectiveness. While much effort has been expended towards the synthesis of nanoscale structures, one of the most challenging aspects for the nanoscale materials community is the question of how to ‘wire in’ these functional elements with the real world. In this talk, we will describe recent work towards the interfacing of nanoscale patterns of organic molecular and metallic structures with semiconductor surfaces such as silicon, germanium, gallium arsenide and indium phosphide. We have developed a repertoire of chemical reactivities on semiconductor interfaces, and are now patterning them through straightforward and efficient, highly parallel patterning strategies via self-assembly of soft polymer materials. The self-assembled materials direct transport of reagents to the semiconductor so that the reaction takes place in a spatially defined manner, with precise control over the quantity of reagent delivered. Even mixtures of reagents can be ‘sorted out’ by these interfaces to produce nanoscale (∼10 nm) domains of different chemical functionalities, simultaneously. We will describe these and related approaches towards precise patterning of semiconductor surfaces, entirely via wet-chemical processes that are compatible with existing fabrication strategies.
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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) | 0.002 | 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".