Selective<i>in situ</i>potential-assisted SAM formation on multi electrode arrays
Bibliographic record
Abstract
Abstract The selective modification of individual components in a biosensor array is challenging. To address this challenge, we present a generalizable approach to selectively modify and characterize individual gold surfaces in an array, in an in situ manner. This is achieved by taking advantage of the potential dependent adsorption/desorption of surface-modified organic molecules. Control of the applied potential of the individual sensors in an array where each acts as a working electrode provides differential derivatization of the sensor surfaces. To demonstrate this concept, two different self-assembled monolayer (SAM)-forming electrochemically addressable ω -ferrocenyl alkanethiols (C 11 ) are chemisorbed onto independent but spatially adjacent gold electrodes. The ferrocene alkanethiol does not chemisorb onto the surface when the applied potential is cathodic relative to the adsorption potential and the electrode remains underivatized. However, applying potentials that are modestly positive relative to the adsorption potential leads to extensive coverage within 10 min. The resulting SAM remains in a stable state while held at potentials <200 mV above the adsorption potential. In this state, the chemisorbed SAM does not significantly desorb nor do new ferrocenylalkythiols adsorb. Using three set applied potentials provides for controlled submonolayer alkylthiol marker coverage of each independent gold electrode. These three applied potentials are dependent upon the specifics of the respective adsorbate. Characterization of the ferrocene-modified electrodes via cyclic voltammetry demonstrates that each specific ferrocene marker is exclusively adsorbed to the desired target electrode.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".