Formation of Highly Ordered Self-Assembled Monolayers on Two-Dimensional Materials via Noncovalent Functionalization
Bibliographic record
Abstract
Functionalized two-dimensional materials (2DMs) are attracting much attention due to their promising applications in nanoscale devices. Producing continuous and homogeneous surface assemblies with a high degree of order has been challenging. In this work, we demonstrate that by noncovalently self-assembling molecular platforms on 2DMs, high-quality and highly ordered monolayers can be generated. The high degree of order and uniformity of the self-assembled monolayer layers were confirmed by a variety of analytic techniques including time-of-flight secondary ion mass spectrometry, scanning tunnelling microscopy, X-ray photoelectron spectroscopy, and atomic force microscopy. Furthermore, by selectively enhancing the molecular vibrations of the molecular platform, via a combination of graphene-enhanced Raman spectroscopy (GERS) and surface-enhanced Raman spectroscopy (SERS), we were able to determine the orientation of self-assembled molecular platforms with respect to the surface normal. The selective enhancement of the vibrational modes occurs by taking advantage of the distance dependence of the Raman enhancement either by the graphene surface (GERS) or the silver nanoparticules (SERS) that are located on top of the self-assembled monolayer.
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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".