Electrochemistry of Au-SAM-Protein Stacks
Bibliographic record
Abstract
Several biosensing techniques like SPR and QCM use glass as the substrate, bearing a thin Au layer which serves as the sensing surface. Other substrates of interest include Si due to processing knowledge and the potential integration of electronics, and Cytop which has a refractive index close to that of water. Au on glass is highly crystalline, with a large 111 character, especially after annealing, whereas on Si and on Cytop, Au is non-textured ("polycrystalline"). These structural differences result in distinct voltammetric behavior, both faradaic and capacitive, of SAM- and protein-coated Au on different substrate types. A contact angle and double-layer capacitance examination of protein adsorption on SAM-coated Au on these substrates, and electrochemical desorption of the resulting molecular stacks, indicates that the usual assumption that hydrophobic SAMs promote adsorption and hydrophilic ones prevent adsorption is not universally true, and depends both on the protein type and the substrate. Thus, caution is advised against applying results obtained on one substrate to another. On the other hand, systems utilizing bioconjugation seem to be much less sensitive to Au type, and thus the underlying substrate. Electrochemical desorption of proteins from Au does occur but with limited efficiency.
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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".