Effect of Nanowire Number, Diameter, and Doping Density on Nano-FET Biosensor Sensitivity
Bibliographic record
Abstract
Semiconductive nanowire-based biosensors are capable of label-free detection of biological molecules. Nano-FET (field-effect transistor) biosensors exhibiting high sensitivities toward proteins, nucleic acids, and viruses have been demonstrated. Rational device design methodologies, particularly those based on theoretical predictions, were reported. However, few experimental studies have investigated the effect of nanowire diameter, doping density, and number on nano-FET sensitivity. In this study, we devised a fabrication process based on parallel approaches and nanomanipulation-based post-processing for constructing nano-FET biosensor devices with carefully controlled nanowire parameters (diameter, doping density, and number). We experimentally reveal the effect of these nanowire parameters on nano-FET biosensor sensitivity. The experimental findings quantitatively demonstrate that device sensitivity decreases with increasing number of nanowires (4 and 7 nanowire devices exhibited a ∼38 and ∼82% decrease in sensitivity as compared to a single-nanowire device), larger nanowire diameters (sensors with 81-100 and 101-120 nm nanowire diameters exhibited a ∼16 and ∼37% decrease in sensitivity compared to devices with nanowire diameters of 60-80 nm), and higher nanowire doping densities (∼69% decrease in sensitivity due to an increase in nanowire doping density from 10(17) to 10(19) atoms·cm(-3)). These results provide insight into the importance of controlling nanowire properties for maximizing sensitivity and minimizing performance variation across devices when designing and manufacturing nano-FET biosensors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".