Noise considerations in field-effect biosensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Field-effect sensors used to detect and identify biological species have been proposed as alternatives to other methods such as fluorescence deoxyribonucleic acid (DNA) microarrays. Sensors fabricated using commercial complementary metal-oxide-semiconductor technology would enable low-cost and highly integrated biological detection systems. In this paper, the small-signal and noise modeling of biosensors implemented with electrolyte-insulator-semiconductor structures is studied, with emphasis on design guidelines for low-noise performance. In doing so, a modified form of the general charge sheet metal-oxide-semiconductor field-effect transistor model that better fits the electrolyte-insulator-semiconductor structure is used. It is discussed how if the reference electrode and the insulator-electrolyte generate no noise associated with charge transport, then the main noise mechanisms are the resistive losses of the electrolyte and the low-frequency noise of the field-effect transistor. It is also found that for realistic sensor geometries and high electrolyte concentrations, the noise from the field-effect transistor (FET) dominates the thermal noise from the electrolyte resistance, and the optimal biasing point for the FET for minimum noise is found to be around moderate inversion.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it