Interfacial capacitance immunosensing using interdigitated electrodes: the effect of insulation/immobilization chemistry
Bibliographic record
Abstract
With the aim of improving the reproducibility of capacitive immunosensors, we performed a comparative study of four different insulating/immobilization chemistries. Each chemistry targeted different areas of an interdigitated electrode including an alkyl thiol monolayer on the electrode surface, an amino silane monolayer on the gaps between electrodes, and conformal coatings via passive adsorption of the probe and a spin-coated layer of poly(methyl methacrylate) (PMMA). We analyzed the dielectric properties of these chemistries by comparing their capacitive behavior through equivalent circuit modeling and correlate the observed behavior with their surface characteristics by using atomic force microscopy and finite element modeling. We found that surface binding events occurring in the interdigitated electrode gaps play a major role in the overall change in capacitance. This was confirmed via finite element modeling showing an increased electric field intensity in the electrode gaps by 14%, compared to directly above the electrodes. Among the investigated surface chemistries, PMMA conformal coating produced a smooth surface (Rq roughness = 0.21 ± 0.02 nm) providing the most reproducible and stable capacitance change (15.6 ± 0.4%) in response to specific antigen-antibody binding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".