Efficient Passivation of Ag Nanowires with 11‐Mercaptoundecanoic Acid Probed Using In Situ Total‐Internal‐Reflection Surface‐Enhanced Raman Scattering Spectroscopy
Bibliographic record
Abstract
Abstract A promising passivation strategy of one class of Ag nanostructures of high interest for optoelectronics, namely Ag nanowires (AgNWs), with 11‐mercaptoundecanoic acid (MUA) is described. Combining XPS, electron microscopy and original in‐situ total‐internal‐reflection (TIR) surface‐enhanced Raman scattering (SERS) spectroscopic measurements, the substitution of polyvinylpyrrolidone (PVP) molecules on as‐synthesized PVP‐coated AgNWs by their MUA counterparts is clearly demonstrated. The relevance of the SERS approach to examine the anchoring of the MUA onto the silver surface of specific AgNWs with pentagonal cross‐sections is supported by FDTD simulations. In particular, the TIR‐SERS technique reveals that the PVP‐MUA substitution process can be divided into two main steps, namely (i) the desorption of PVP molecules associated with the anchoring of MUA molecules via the formation of Ag−OOC and Ag−S bonds (accomplished within 40 min), and (ii) the subsequent reorientation of MUA molecules to form only Ag−S bonds (finished in 90 min). The resulting passivation of AgNWs with MUA significantly improves their resistance to corrosion, which is crucial for future commercial applications.
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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".