Measuring and Remediating Nonspecific Modifications of Gold Surfaces Using a Coupled in Situ Electrochemical Fluorescence Microscopic Methodology
Bibliographic record
Abstract
In surface-based biosensors, the nonspecific or undesired adsorption of the probe is an important characteristic that is typically difficult to measure and therefore to control or eliminate. A methodology for measuring and then minimizing or eliminating this problem on gold surfaces, readily applicable to many common surface modifications is presented. Combining electrochemical perturbation and fluorescence microscopy, we show that the potential at which the adsorbed species is removed can be used as an estimate of the strength of the adsorbate-surface interaction. This desorption potential can be easily measured through an increase in fluorescence intensity as the potential is manipulated. Furthermore, this method can be used to evaluate strategies for preventing or removing nonspecific adsorption. This is demonstrated for a wide variety of surface modifications, from strongly chemisorbed monolayers such as thiol self-assembled monolayers (SAMs) to physisorbed monolayers as well as for complex surface structures like peptide and DNA mixed-component SAMs. The use of a coadsorption strategy or small magnitude potential-step cycles was shown to significantly decrease the amount of nonspecifically or noncovalently bound probe, creating better defined surfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".