The Influence of Thiolate Readsorption on the Quality of Mixed Monolayers Formed through an Electrochemcial Method
Bibliographic record
Abstract
Lateral Force Microscopy (LFM) was used to probe the quality of binary mixed monolayers formed on planar polycrystalline gold through an electrochemical method. In the approach, portions of a self-assembled monolayer (SAM) composed of 2-aminoethanethiol (AET) were removed from the Au(111) surface facets by selective reductive desorption which maintained undisrupted regions of AET elsewhere on the polycrystalline surface. Monolayer voids created by this method were backfilled with 11-mercaptoundecanoic acid (MUA) and the interface characterized with ex situ LFM. This produced images with domains of high and low friction corresponding to isolated zones of MUA and AET respectively. Reverse sequence mixed monolayers were also prepared with MUA as the starting layer and rendered LFM images that mirrored the AET based layers. This demonstrates flexibility of the electrochemical method to produce heterogeneous binary SAMs, and to further probe the quality of mixed monolayers, a number of experimental conditions including desorption time, electrode configuration, and initial incubation period were studied. AET/MUA layers that produced the most enhanced LFM images were formed on a planar electrode that was vertically submerged into the electrolyte while maintaining a selective desorption potential for 5 min before backfilling with MUA. This condition allowed for the effective diffusion of AET away from the interface and created well-defined monolayer voids for backfilling. At desorption times lower than 1 min, some of the AET molecules that remained near the interface would readsorb onto the surface and interfere with the backfilling process thereby creating lower contrast LFM images. Structural features of these layers were independent of initial incubation time (10 min and 16 h); however, the contrast between domains was improved when using AET layers formed over a longer incubation period. Interestingly, the contrast was significantly reduced when mixed layers were created on electrodes set in a hanging meniscus with the electrolyte. Here, electrochemical evidence pointed to prolonged readsorption of thiolates creating less well-defined voids for backfilling, and the event was most pronounced for MUA based layers.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".