New Method to Measure Packing Densities of Self-Assembled Thiolipid Monolayers
Bibliographic record
Abstract
For a monolayer of 2,3-di-phytanyl-sn-glycerol-1-tetraethylene glycol-D,L-a-lipoic acid ester lipid (DPTL) self-assembled (SAM) at a gold electrode surface we propose a new method to determine the charge number per adsorbed molecule and the packing density (area per molecule) in the monolayer. The method relies on chronocoulometry to measure the charge density at the SAM covered gold electrode surface. Two series of measurements have to be performed. In the first series, charge densities are measured for a monolayer transferred from the air-solution to the metal-solution interface using the Langmuir-Blodgett (LB) technique. This series of measurements allows one to determine charge numbers per adsorbed DPTL molecule. The second series is performed using a gold electrode covered with a self-assembled monolayer. The charge densities obtained in this series are then used to calculate the packing density with the help of charge numbers per adsorbed DPTL determined in the first series. The area per adsorbed molecule determined by the new method was compared to the area per molecule determined by the popular reductive desorption method. The molecular area determined with the new method is about 20% larger than the area calculated from the van der Waals model, which is a physically reasonable result. In contrast, the popular reductive desorption method gives an area per molecule 20% lower than the minimum estimated based on a van der Waals model. This is a physically unreasonable result. It is also shown that the charge numbers per adsorbed molecule depend on the electrode potential and may assume values smaller than the number of electrons participating in the reductive desorption step. An explanation of the origin of the "partial charge numbers" is provided. We recommend the new method be used in future studies of thiol adsorption at metal surfaces.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".