Chip-Scale Electrochemical Differentiation of SAM-Coated Gold Features Using a Probe Array
Bibliographic record
Abstract
A method for chemically differentiating the surface of a set of small, closely-spaced, lithographically-defined Au features on a die, from another set of similar features intimately inter-dispersed, is described. The key enabler of the method is a standard electronics probe array adapted to carry out electrochemistry on the features. The probe array is used first to verify the electrical integrity of features and the quality of electrical contacts by measuring electrical resistance, then, in the presence of the electrolyte, simultaneously maintain one potential on one set of Au features and another potential on the other set in order to carry out desired electrochemical reactions. The technique was demonstrated on dies bearing 40 electrically isolated Au features (based on 5 μm wide stripes) accessed via 64 contact pads each 100 × 100 μm 2 in area. The array had 64 probes, of which 16 were maintained at a desorbing potential (−1.6 V vs. Ag/AgCl) and 48 at a stability potential (−0.3 V). The surface compositions were analyzed with time-of-flight secondary ion mass spectrometry by imaging ion fragments characteristic to the thiols forming SAMs, thereby validating the process.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".