Sub-terahertz silicon-based on-chip absorption spectroscopy using thin-film model for biological applications
Bibliographic record
Abstract
Spectroscopy in the sub-terahertz (sub-THz) range of frequencies has been utilized to study the picosecond dynamics and interaction of biomolecules. However, widely used free-space THz spectrometers are typically limited in their functionality due to low signal-to-noise ratio and complex setup. On-chip spectrometers can revolutionize THz spectroscopy allowing integration, compactness, and low-cost fabrication. In this paper, a low-loss silicon-based platform is proposed for on-chip sub-THz spectroscopy. Through functionalization of silicon chip and immobilization of bio-particles, we demonstrate the ability to characterize low-loss nano-scale biomolecules across the G-band (0.14-0.22 THz). We also introduce an electromagnetic thin-film model to account for the loading effect of the immobilized biomolecules, i.e. dehydrated streptavidin and immunoglobulin antibody, as two key molecules in the biosensing discipline. The proposed platform was fabricated using a single mask micro-fabrication process, and then measured by a vector network analyzer (VNA), which offers high dynamic range and high spectral resolution measurements. The proposed planar platform is general and paves the way towards low-loss, cost-effective and integrated sub-THz biosensors for the detection and characterization of biomolecules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".