Derivatization of Optically Transparent Materials with Diazonium Reagents for Spectroscopy of Buried Interfaces
Bibliographic record
Abstract
This paper presents a method to derivatize a wide variety of substrate materials that are frequently used in spectroscopic characterizations with molecular layers through the reduction of aromatic diazonium reagents. The method relies on an ultrathin (5 nm) layer of a reactive metal (e.g., Ti or Al) deposited as a primer that subsequently mediates the reduction of aromatic diazonium reagents from acetonitrile solution. Following surface modification, the Ti can be oxidized to provide a passivated support surface. Raman, Infrared, UV-vis and X-ray photoelectron spectroscopic techniques are used to characterize the molecular layers on the metal primer surface. When a Ti primer layer is derivatized via diazonium reduction, the molecule is shown to be present on the ultrathin Ti layer on Au, Al, quartz, Si/SiO(x), glass, and polyethylene surfaces. For molecules bound to a Ti primer, the molecular layer was found to be stable to sonication in acetone or acetonitrile, a 1 h exposure to boiling water, and a 30 min exposure to 0.1 M acid or base. The approach also permits spectroscopic characterization of buried thin-film molecular layers on optically transparent substrates after deposition of thick top metal contacts.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".