Silicon nanowire field-effect transistor biosensors with bowtie antenna
Bibliographic record
Abstract
• High-quality, liquid GAA silicon NW FET biosensors with a gold bowtie antenna are fabricated using a SOI wafer and studied. • Electrical and noise properties of novel NW FETs were investigated under 940 nm LED optical excitation in different solutions. • A two-level signal (TLS) that is useful for biosensing was successfully activated by light excitation. • The TLS demonstrates a linear dependence of its amplitude in relation to intensity. • TLS studies in MgCl2 solutions indicate that the FET devices allowing the biosensor sensitivity to be about 300% enhanced. In this study, we fabricated high-quality, liquid gate-all-around silicon nanowire (NW) field-effect transistor (FET) biosensors with a gold bowtie antenna using a silicon-on-insulator (SOI) wafer. The electrical and noise properties of these novel NW FETs were investigated under 940 nm light-emitting diode (LED) optical excitation in different solutions. A two-level signal (TLS) that is useful for biosensing was successfully activated at the light excitation only. The detection of repeatable fluctuations in current, manifested as minor peaks in the I–V curves under infrared illumination, confirms the activation of a TLS in the biosensors. The TLS demonstrates a linear dependence of its amplitude in relation to intensity. Moreover, we performed TLS studies in MgCl 2 solutions of different concentrations. The results indicate that the FET devices incorporating a gold antenna have considerable potential for the excitation of TLS, thus allowing the sensitivity of the biosensors to be about 300 % enhanced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".