Quantifying the Selective Modification of Au(111) Facets via Electrochemical and Electroless Treatments for Manipulating Gold Nanorod Surface Composition
Bibliographic record
Abstract
Abstract Manipulating the composition of a mixed alkylthiol self-assembled monolayer (SAM) modified gold surface using both electrochemical and electroless methods is demonstrated. Through the use of fluorophore labeled thiolated DNA and in situ fluorescence microscopy with a gold single crystal bead electrode, a procedure was developed to study and quantify the selective desorption of an alkylthiolate SAM. This method enabled a self-consistent measurement of the removal of the SAM from the 111 surface compared to the 100 surface region at various potentials. A 20-fold increase in the electrochemical removal and replacement of the SAM from the 111 surface over the 100 surface was realized at −0.8 V/AgAgCl. A related procedure was developed for the solution-based electroless removal of the SAM using NaBH4 achieving a similar selectivity at the same potential. Unfortunately, in the electroless process fine control over the reducing potential was difficult to achieve. In addition, working in the presence of O2 complicates the solution potential measurement due to depolarization by the reduction of O2, resulting in a less clear relationship between selectivity and measured solution potential. Interestingly, the electrochemical method was not disturbed by the presence of O2. In preparation for work with Au nanorods, electrochemical measurements were performed in electrolyte that included 1 mM CTAB and was found to not interfere with this method. Preliminary results are promising for using this methodology for treatment of acid-terminated alkylthiol modified Au nanorods.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".